这样的错误 local class member functions must be defined within the class这样的程序

okop 2003-09-14 09:42:18
这样的错误 local class member functions must be defined within the class这样的程序
#include<iostream.h>

void main(void)
{
class man
{
public:
void setx(int x);
void sety(int y);
int result(void);
private:
int r;
int h;

};
void man::setx(int x)
{
r=x;
}
void man::sety(int y)
{
h=y;
}
int man::result(void)
{
return r*h;
}
man objman;
objman.setx(10);
objman.sety(10);
cout<<objman.result();

}

请问一下,怎么样才能把函数在类外定义啊?
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okop 2003-09-14
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vc6和tc3都是这个错误
akiko 2003-09-14
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怎么在main里定义啊??改成这样
#include<iostream.h>
class man
{
public:
void setx(int x);
void sety(int y);
int result(void);
private:
int r;
int h;

};
void man::setx(int x)
{
r=x;
}
void man::sety(int y)
{
h=y;
}
int man::result(void)
{
return r*h;
}

void main(void)
{

man objman;
objman.setx(10);
objman.sety(10);
cout<<objman.result();

}
gezihou 2003-09-14
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你使用的是什么编译的
Table of Contents Header Files The #define Guard Header File Dependencies Inline Functions The -inl.h Files Function Parameter Ordering Names and Order of Includes Scoping Namespaces Nested Classes Nonmember, Static Member, and Global Functions Local Variables Static and Global Variables Classes Doing Work in Constructors Default Constructors Explicit Constructors Copy Constructors Structs vs. Classes Inheritance Multiple Inheritance Interfaces Operator Overloading Access Control Declaration Order Write Short Functions Google-Specific Magic Smart Pointers cpplint Other C++ Features Reference Arguments Function Overloading Default Arguments Variable-Length Arrays and alloca() Friends Exceptions Run-Time Type Information (RTTI) Casting Streams Preincrement and Predecrement Use of const Integer Types 64-bit Portability Preprocessor Macros 0 and NULL sizeof Boost C++0x Naming General Naming Rules File Names Type Names Variable Names Constant Names Function Names Namespace Names Enumerator Names Macro Names Exceptions to Naming Rules Comments Comment Style File Comments Class Comments Function Comments Variable Comments Implementation Comments Punctuation, Spelling and Grammar TODO Comments Deprecation Comments Formatting Line Length Non-ASCII Characters Spaces vs. Tabs Function Declarations and Definitions Function Calls Conditionals Loops and Switch Statements Pointer and Reference Expressions Boolean Expressions Return Values Variable and Array Initialization Preprocessor Directives Class Format Constructor Initializer Lists Namespace Formatting Horizontal Whitespace Vertical Whitespace Exceptions to the Rules Existing Non-conformant Code Windows Code Important Note Displaying Hidden Details in this Guide link ▶This style guide contains many details that are initially hidden from view. They are marked by the triangle icon, which you see here on your left. Click it now. You should see "Hooray" appear below. Hooray! Now you know you can expand points to get more details. Alternatively, there's an "expand all" at the top of this document. Background C++ is the main development language used by many of Google's open-source projects. As every C++ programmer knows, the language has many powerful features, but this power brings with it complexity, which in turn can make code more bug-prone and harder to read and maintain. The goal of this guide is to manage this complexity by describing in detail the dos and don'ts of writing C++ code. These rules exist to keep the code base manageable while still allowing coders to use C++ language features productively. Style, also known as readability, is what we call the conventions that govern our C++ code. The term Style is a bit of a misnomer, since these conventions cover far more than just source file formatting. One way in which we keep the code base manageable is by enforcing consistency. It is very important that any programmer be able to look at another's code and quickly understand it. Maintaining a uniform style and following conventions means that we can more easily use "pattern-matching" to infer what various symbols are and what invariants are true about them. Creating common, required idioms and patterns makes code much easier to understand. In some cases there might be good arguments for changing certain style rules, but we nonetheless keep things as they are in order to preserve consistency. Another issue this guide addresses is that of C++ feature bloat. C++ is a huge language with many advanced features. In some cases we constrain, or even ban, use of certain features. We do this to keep code simple and to avoid the various common errors and problems that these features can cause. This guide lists these features and explains why their use is restricted. Open-source projects developed by Google conform to the requirements in this guide. Note that this guide is not a C++ tutorial: we assume that the reader is familiar with the language. Header Files In general, every .cc file should have an associated .h file. There are some common exceptions, such as unittests and small .cc files containing just a main() function. Correct use of header files can make a huge difference to the readability, size and performance of your code. The following rules will guide you through the various pitfalls of using header files. The #define Guard link ▶All header files should have #define guards to prevent multiple inclusion. The format of the symbol name should be ___H_. To guarantee uniqueness, they should be based on the full path in a project's source tree. For example, the file foo/src/bar/baz.h in project foo should have the following guard: #ifndef FOO_BAR_BAZ_H_ #define FOO_BAR_BAZ_H_ ... #endif // FOO_BAR_BAZ_H_ Header File Dependencies link ▶Don't use an #include when a forward declaration would suffice. When you include a header file you introduce a dependency that will cause your code to be recompiled whenever the header file changes. If your header file includes other header files, any change to those files will cause any code that includes your header to be recompiled. Therefore, we prefer to minimize includes, particularly includes of header files in other header files. You can significantly minimize the number of header files you need to include in your own header files by using forward declarations. For example, if your header file uses the File class in ways that do not require access to the declaration of the File class, your header file can just forward declare class File; instead of having to #include "file/base/file.h". How can we use a class Foo in a header file without access to its definition? We can declare data members of type Foo* or Foo&. We can declare (but not define) functions with arguments, and/or return values, of type Foo. (One exception is if an argument Foo or const Foo& has a non-explicit, one-argument constructor, in which case we need the full definition to support automatic type conversion.) We can declare static data members of type Foo. This is because static data members are defined outside the class definition. On the other hand, you must include the header file for Foo if your class subclasses Foo or has a data member of type Foo. Sometimes it makes sense to have pointer (or better, scoped_ptr) members instead of object members. However, this complicates code readability and imposes a performance penalty, so avoid doing this transformation if the only purpose is to minimize includes in header files. Of course, .cc files typically do require the definitions of the classes they use, and usually have to include several header files. Note: If you use a symbol Foo in your source file, you should bring in a definition for Foo yourself, either via an #include or via a forward declaration. Do not depend on the symbol being brought in transitively via headers not directly included. One exception is if Foo is used in myfile.cc, it's ok to #include (or forward-declare) Foo in myfile.h, instead of myfile.cc. Inline Functions link ▶Define functions inline only when they are small, say, 10 lines or less. Definition: You can declare functions in a way that allows the compiler to expand them inline rather than calling them through the usual function call mechanism. Pros: Inlining a function can generate more efficient object code, as long as the inlined function is small. Feel free to inline accessors and mutators, and other short, performance-critical functions. Cons: Overuse of inlining can actually make programs slower. Depending on a function's size, inlining it can cause the code size to increase or decrease. Inlining a very small accessor function will usually decrease code size while inlining a very large function can dramatically increase code size. On modern processors smaller code usually runs faster due to better use of the instruction cache. Decision: A decent rule of thumb is to not inline a function if it is more than 10 lines long. Beware of destructors, which are often longer than they appear because of implicit member- and base-destructor calls! Another useful rule of thumb: it's typically not cost effective to inline functions with loops or switch statements (unless, in the common case, the loop or switch statement is never executed). It is important to know that functions are not always inlined even if they are declared as such; for example, virtual and recursive functions are not normally inlined. Usually recursive functions should not be inline. The main reason for making a virtual function inline is to place its definition in the class, either for convenience or to document its behavior, e.g., for accessors and mutators. The -inl.h Files link ▶You may use file names with a -inl.h suffix to define complex inline functions when needed. The definition of an inline function needs to be in a header file, so that the compiler has the definition available for inlining at the call sites. However, implementation code properly belongs in .cc files, and we do not like to have much actual code in .h files unless there is a readability or performance advantage. If an inline function definition is short, with very little, if any, logic in it, you should put the code in your .h file. For example, accessors and mutators should certainly be inside a class definition. More complex inline functions may also be put in a .h file for the convenience of the implementer and callers, though if this makes the .h file too unwieldy you can instead put that code in a separate -inl.h file. This separates the implementation from the class definition, while still allowing the implementation to be included where necessary. Another use of -inl.h files is for definitions of function templates. This can be used to keep your template definitions easy to read. Do not forget that a -inl.h file requires a #define guard just like any other header file. Function Parameter Ordering link ▶When defining a function, parameter order is: inputs, then outputs. Parameters to C/C++ functions are either input to the function, output from the function, or both. Input parameters are usually values or const references, while output and input/output parameters will be non-const pointers. When ordering function parameters, put all input-only parameters before any output parameters. In particular, do not add new parameters to the end of the function just because they are new; place new input-only parameters before the output parameters. This is not a hard-and-fast rule. Parameters that are both input and output (often classes/structs) muddy the waters, and, as always, consistency with related functions may require you to bend the rule. Names and Order of Includes link ▶Use standard order for readability and to avoid hidden dependencies: C library, C++ library, other libraries' .h, your project's .h. All of a project's header files should be listed as descentants of the project's source directory without use of UNIX directory shortcuts . (the current directory) or .. (the parent directory). For example, google-awesome-project/src/base/logging.h should be included as #include "base/logging.h" In dir/foo.cc, whose main purpose is to implement or test the stuff in dir2/foo2.h, order your includes as follows: dir2/foo2.h (preferred location — see details below). C system files. C++ system files. Other libraries' .h files. Your project's .h files. The preferred ordering reduces hidden dependencies. We want every header file to be compilable on its own. The easiest way to achieve this is to make sure that every one of them is the first .h file #included in some .cc. dir/foo.cc and dir2/foo2.h are often in the same directory (e.g. base/basictypes_test.cc and base/basictypes.h), but can be in different directories too. Within each section it is nice to order the includes alphabetically. For example, the includes in google-awesome-project/src/foo/internal/fooserver.cc might look like this: #include "foo/public/fooserver.h" // Preferred location. #include #include #include #include #include "base/basictypes.h" #include "base/commandlineflags.h" #include "foo/public/bar.h" Scoping Namespaces link ▶Unnamed namespaces in .cc files are encouraged. With named namespaces, choose the name based on the project, and possibly its path. Do not use a using-directive. Definition: Namespaces subdivide the global scope into distinct, named scopes, and so are useful for preventing name collisions in the global scope. Pros: Namespaces provide a (hierarchical) axis of naming, in addition to the (also hierarchical) name axis provided by classes. For example, if two different projects have a class Foo in the global scope, these symbols may collide at compile time or at runtime. If each project places their code in a namespace, project1::Foo and project2::Foo are now distinct symbols that do not collide. Cons: Namespaces can be confusing, because they provide an additional (hierarchical) axis of naming, in addition to the (also hierarchical) name axis provided by classes. Use of unnamed spaces in header files can easily cause violations of the C++ One Definition Rule (ODR). Decision: Use namespaces according to the policy described below. Unnamed Namespaces Unnamed namespaces are allowed and even encouraged in .cc files, to avoid runtime naming conflicts: namespace { // This is in a .cc file. // The content of a namespace is not indented enum { kUnused, kEOF, kError }; // Commonly used tokens. bool AtEof() { return pos_ == kEOF; } // Uses our namespace's EOF. } // namespace However, file-scope declarations that are associated with a particular class may be declared in that class as types, static data members or static member functions rather than as members of an unnamed namespace. Terminate the unnamed namespace as shown, with a comment // namespace. Do not use unnamed namespaces in .h files. Named Namespaces Named namespaces should be used as follows: Namespaces wrap the entire source file after includes, gflags definitions/declarations, and forward declarations of classes from other namespaces: // In the .h file namespace mynamespace { // All declarations are within the namespace scope. // Notice the lack of indentation. class MyClass { public: ... void Foo(); }; } // namespace mynamespace // In the .cc file namespace mynamespace { // Definition of functions is within scope of the namespace. void MyClass::Foo() { ... } } // namespace mynamespace The typical .cc file might have more complex detail, including the need to reference classes in other namespaces. #include "a.h" DEFINE_bool(someflag, false, "dummy flag"); class C; // Forward declaration of class C in the global namespace. namespace a { class A; } // Forward declaration of a::A. namespace b { ...code for b... // Code goes against the left margin. } // namespace b Do not declare anything in namespace std, not even forward declarations of standard library classes. Declaring entities in namespace std is undefined behavior, i.e., not portable. To declare entities from the standard library, include the appropriate header file. You may not use a using-directive to make all names from a namespace available. // Forbidden -- This pollutes the namespace. using namespace foo; You may use a using-declaration anywhere in a .cc file, and in functions, methods or classes in .h files. // OK in .cc files. // Must be in a function, method or class in .h files. using ::foo::bar; Namespace aliases are allowed anywhere in a .cc file, anywhere inside the named namespace that wraps an entire .h file, and in functions and methods. // Shorten access to some commonly used names in .cc files. namespace fbz = ::foo::bar::baz; // Shorten access to some commonly used names (in a .h file). namespace librarian { // The following alias is available to all files including // this header (in namespace librarian): // alias names should therefore be chosen consistently // within a project. namespace pd_s = ::pipeline_diagnostics::sidetable; inline void my_inline_function() { // namespace alias local to a function (or method). namespace fbz = ::foo::bar::baz; ... } } // namespace librarian Note that an alias in a .h file is visible to everyone #including that file, so public headers (those available outside a project) and headers transitively #included by them, should avoid defining aliases, as part of the general goal of keeping public APIs as small as possible. Nested Classes link ▶Although you may use public nested classes when they are part of an interface, consider a namespace to keep declarations out of the global scope. Definition: A class can define another class within it; this is also called a member class. class Foo { private: // Bar is a member class, nested within Foo. class Bar { ... }; }; Pros: This is useful when the nested (or member) class is only used by the enclosing class; making it a member puts it in the enclosing class scope rather than polluting the outer scope with the class name. Nested classes can be forward declared within the enclosing class and then defined in the .cc file to avoid including the nested class definition in the enclosing class declaration, since the nested class definition is usually only relevant to the implementation. Cons: Nested classes can be forward-declared only within the definition of the enclosing class. Thus, any header file manipulating a Foo::Bar* pointer will have to include the full class declaration for Foo. Decision: Do not make nested classes public unless they are actually part of the interface, e.g., a class that holds a set of options for some method. Nonmember, Static Member, and Global Functions link ▶Prefer nonmember functions within a namespace or static member functions to global functions; use completely global functions rarely. Pros: Nonmember and static member functions can be useful in some situations. Putting nonmember functions in a namespace avoids polluting the global namespace. Cons: Nonmember and static member functions may make more sense as members of a new class, especially if they access external resources or have significant dependencies. Decision: Sometimes it is useful, or even necessary, to define a function not bound to a class instance. Such a function can be either a static member or a nonmember function. Nonmember functions should not depend on external variables, and should nearly always exist in a namespace. Rather than creating classes only to group static member functions which do not share static data, use namespaces instead. Functions defined in the same compilation unit as production classes may introduce unnecessary coupling and link-time dependencies when directly called from other compilation units; static member functions are particularly susceptible to this. Consider extracting a new class, or placing the functions in a namespace possibly in a separate library. If you must define a nonmember function and it is only needed in its .cc file, use an unnamed namespace or static linkage (eg static int Foo() {...}) to limit its scope. Local Variables link ▶Place a function's variables in the narrowest scope possible, and initialize variables in the declaration. C++ allows you to declare variables anywhere in a function. We encourage you to declare them in as local a scope as possible, and as close to the first use as possible. This makes it easier for the reader to find the declaration and see what type the variable is and what it was initialized to. In particular, initialization should be used instead of declaration and assignment, e.g. int i; i = f(); // Bad -- initialization separate from declaration. int j = g(); // Good -- declaration has initialization. Note that gcc implements for (int i = 0; i < 10; ++i) correctly (the scope of i is only the scope of the for loop), so you can then reuse i in another for loop in the same scope. It also correctly scopes declarations in if and while statements, e.g. while (const char* p = strchr(str, '/')) str = p + 1; There is one caveat: if the variable is an object, its constructor is invoked every time it enters scope and is created, and its destructor is invoked every time it goes out of scope. // Inefficient implementation: for (int i = 0; i < 1000000; ++i) { Foo f; // My ctor and dtor get called 1000000 times each. f.DoSomething(i); } It may be more efficient to declare such a variable used in a loop outside that loop: Foo f; // My ctor and dtor get called once each. for (int i = 0; i < 1000000; ++i) { f.DoSomething(i); } Static and Global Variables link ▶Static or global variables of class type are forbidden: they cause hard-to-find bugs due to indeterminate order of construction and destruction. Objects with static storage duration, including global variables, static variables, static class member variables, and function static variables, must be Plain Old Data (POD): only ints, chars, floats, or pointers, or arrays/structs of POD. The order in which class constructors and initializers for static variables are called is only partially specified in C++ and can even change from build to build, which can cause bugs that are difficult to find. Therefore in addition to banning globals of class type, we do not allow static POD variables to be initialized with the result of a function, unless that function (such as getenv(), or getpid()) does not itself depend on any other globals. Likewise, the order in which destructors are called is defined to be the reverse of the order in which the constructors were called. Since constructor order is indeterminate, so is destructor order. For example, at program-end time a static variable might have been destroyed, but code still running -- perhaps in another thread -- tries to access it and fails. Or the destructor for a static 'string' variable might be run prior to the destructor for another variable that contains a reference to that string. As a result we only allow static variables to contain POD data. This rule completely disallows vector (use C arrays instead), or string (use const char []). If you need a static or global variable of a class type, consider initializing a pointer (which will never be freed), from either your main() function or from pthread_once(). Note that this must be a raw pointer, not a "smart" pointer, since the smart pointer's destructor will have the order-of-destructor issue that we are trying to avoid. Classes Classes are the fundamental unit of code in C++. Naturally, we use them extensively. This section lists the main dos and don'ts you should follow when writing a class. Doing Work in Constructors link ▶In general, constructors should merely set member variables to their initial values. Any complex initialization should go in an explicit Init() method. Definition: It is possible to perform initialization in the body of the constructor. Pros: Convenience in typing. No need to worry about whether the class has been initialized or not. Cons: The problems with doing work in constructors are: There is no easy way for constructors to signal errors, short of using exceptions (which are forbidden). If the work fails, we now have an object whose initialization code failed, so it may be an indeterminate state. If the work calls virtual functions, these calls will not get dispatched to the subclass implementations. Future modification to your class can quietly introduce this problem even if your class is not currently subclassed, causing much confusion. If someone creates a global variable of this type (which is against the rules, but still), the constructor code will be called before main(), possibly breaking some implicit assumptions in the constructor code. For instance, gflags will not yet have been initialized. Decision: If your object requires non-trivial initialization, consider having an explicit Init() method. In particular, constructors should not call virtual functions, attempt to raise errors, access potentially uninitialized global variables, etc. Default Constructors link ▶You must define a default constructor if your class defines member variables and has no other constructors. Otherwise the compiler will do it for you, badly. Definition: The default constructor is called when we new a class object with no arguments. It is always called when calling new[] (for arrays). Pros: Initializing structures by default, to hold "impossible" values, makes debugging much easier. Cons: Extra work for you, the code writer. Decision: If your class defines member variables and has no other constructors you must define a default constructor (one that takes no arguments). It should preferably initialize the object in such a way that its internal state is consistent and valid. The reason for this is that if you have no other constructors and do not define a default constructor, the compiler will generate one for you. This compiler generated constructor may not initialize your object sensibly. If your class inherits from an existing class but you add no new member variables, you are not required to have a default constructor. Explicit Constructors link ▶Use the C++ keyword explicit for constructors with one argument. Definition: Normally, if a constructor takes one argument, it can be used as a conversion. For instance, if you define Foo::Foo(string name) and then pass a string to a function that expects a Foo, the constructor will be called to convert the string into a Foo and will pass the Foo to your function for you. This can be convenient but is also a source of trouble when things get converted and new objects created without you meaning them to. Declaring a constructor explicit prevents it from being invoked implicitly as a conversion. Pros: Avoids undesirable conversions. Cons: None. Decision: We require all single argument constructors to be explicit. Always put explicit in front of one-argument constructors in the class definition: explicit Foo(string name); The exception is copy constructors, which, in the rare cases when we allow them, should probably not be explicit. Classes that are intended to be transparent wrappers around other classes are also exceptions. Such exceptions should be clearly marked with comments. Copy Constructors link ▶Provide a copy constructor and assignment operator only when necessary. Otherwise, disable them with DISALLOW_COPY_AND_ASSIGN. Definition: The copy constructor and assignment operator are used to create copies of objects. The copy constructor is implicitly invoked by the compiler in some situations, e.g. passing objects by value. Pros: Copy constructors make it easy to copy objects. STL containers require that all contents be copyable and assignable. Copy constructors can be more efficient than CopyFrom()-style workarounds because they combine construction with copying, the compiler can elide them in some contexts, and they make it easier to avoid heap allocation. Cons: Implicit copying of objects in C++ is a rich source of bugs and of performance problems. It also reduces readability, as it becomes hard to track which objects are being passed around by value as opposed to by reference, and therefore where changes to an object are reflected. Decision: Few classes need to be copyable. Most should have neither a copy constructor nor an assignment operator. In many situations, a pointer or reference will work just as well as a copied value, with better performance. For example, you can pass function parameters by reference or pointer instead of by value, and you can store pointers rather than objects in an STL container. If your class needs to be copyable, prefer providing a copy method, such as CopyFrom() or Clone(), rather than a copy constructor, because such methods cannot be invoked implicitly. If a copy method is insufficient in your situation (e.g. for performance reasons, or because your class needs to be stored by value in an STL container), provide both a copy constructor and assignment operator. If your class does not need a copy constructor or assignment operator, you must explicitly disable them. To do so, add dummy declarations for the copy constructor and assignment operator in the private: section of your class, but do not provide any corresponding definition (so that any attempt to use them results in a link error). For convenience, a DISALLOW_COPY_AND_ASSIGN macro can be used: // A macro to disallow the copy constructor and operator= functions // This should be used in the private: declarations for a class #define DISALLOW_COPY_AND_ASSIGN(TypeName) \ TypeName(const TypeName&); \ void operator=(const TypeName&) Then, in class Foo: class Foo { public: Foo(int f); ~Foo(); private: DISALLOW_COPY_AND_ASSIGN(Foo); }; Structs vs. Classes link ▶Use a struct only for passive objects that carry data; everything else is a class. The struct and class keywords behave almost identically in C++. We add our own semantic meanings to each keyword, so you should use the appropriate keyword for the data-type you're defining. structs should be used for passive objects that carry data, and may have associated constants, but lack any functionality other than access/setting the data members. The accessing/setting of fields is done by directly accessing the fields rather than through method invocations. Methods should not provide behavior but should only be used to set up the data members, e.g., constructor, destructor, Initialize(), Reset(), Validate(). If more functionality is required, a class is more appropriate. If in doubt, make it a class. For consistency with STL, you can use struct instead of class for functors and traits. Note that member variables in structs and classes have different naming rules. Inheritance link ▶Composition is often more appropriate than inheritance. When using inheritance, make it public. Definition: When a sub-class inherits from a base class, it includes the definitions of all the data and operations that the parent base class defines. In practice, inheritance is used in two major ways in C++: implementation inheritance, in which actual code is inherited by the child, and interface inheritance, in which only method names are inherited. Pros: Implementation inheritance reduces code size by re-using the base class code as it specializes an existing type. Because inheritance is a compile-time declaration, you and the compiler can understand the operation and detect errors. Interface inheritance can be used to programmatically enforce that a class expose a particular API. Again, the compiler can detect errors, in this case, when a class does not define a necessary method of the API. Cons: For implementation inheritance, because the code implementing a sub-class is spread between the base and the sub-class, it can be more difficult to understand an implementation. The sub-class cannot override functions that are not virtual, so the sub-class cannot change implementation. The base class may also define some data members, so that specifies physical layout of the base class. Decision: All inheritance should be public. If you want to do private inheritance, you should be including an instance of the base class as a member instead. Do not overuse implementation inheritance. Composition is often more appropriate. Try to restrict use of inheritance to the "is-a" case: Bar subclasses Foo if it can reasonably be said that Bar "is a kind of" Foo. Make your destructor virtual if necessary. If your class has virtual methods, its destructor should be virtual. Limit the use of protected to those member functions that might need to be accessed from subclasses. Note that data members should be private. When redefining an inherited virtual function, explicitly declare it virtual in the declaration of the derived class. Rationale: If virtual is omitted, the reader has to check all ancestors of the class in question to determine if the function is virtual or not. Multiple Inheritance link ▶Only very rarely is multiple implementation inheritance actually useful. We allow multiple inheritance only when at most one of the base classes has an implementation; all other base classes must be pure interface classes tagged with the Interface suffix. Definition: Multiple inheritance allows a sub-class to have more than one base class. We distinguish between base classes that are pure interfaces and those that have an implementation. Pros: Multiple implementation inheritance may let you re-use even more code than single inheritance (see Inheritance). Cons: Only very rarely is multiple implementation inheritance actually useful. When multiple implementation inheritance seems like the solution, you can usually find a different, more explicit, and cleaner solution. Decision: Multiple inheritance is allowed only when all superclasses, with the possible exception of the first one, are pure interfaces. In order to ensure that they remain pure interfaces, they must end with the Interface suffix. Note: There is an exception to this rule on Windows. Interfaces link ▶Classes that satisfy certain conditions are allowed, but not required, to end with an Interface suffix. Definition: A class is a pure interface if it meets the following requirements: It has only public pure virtual ("= 0") methods and static methods (but see below for destructor). It may not have non-static data members. It need not have any constructors defined. If a constructor is provided, it must take no arguments and it must be protected. If it is a subclass, it may only be derived from classes that satisfy these conditions and are tagged with the Interface suffix. An interface class can never be directly instantiated because of the pure virtual method(s) it declares. To make sure all implementations of the interface can be destroyed correctly, they must also declare a virtual destructor (in an exception to the first rule, this should not be pure). See Stroustrup, The C++ Programming Language, 3rd edition, section 12.4 for details. Pros: Tagging a class with the Interface suffix lets others know that they must not add implemented methods or non static data members. This is particularly important in the case of multiple inheritance. Additionally, the interface concept is already well-understood by Java programmers. Cons: The Interface suffix lengthens the class name, which can make it harder to read and understand. Also, the interface property may be considered an implementation detail that shouldn't be exposed to clients. Decision: A class may end with Interface only if it meets the above requirements. We do not require the converse, however: classes that meet the above requirements are not required to end with Interface. Operator Overloading link ▶Do not overload operators except in rare, special circumstances. Definition: A class can define that operators such as + and / operate on the class as if it were a built-in type. Pros: Can make code appear more intuitive because a class will behave in the same way as built-in types (such as int). Overloaded operators are more playful names for functions that are less-colorfully named, such as Equals() or Add(). For some template functions to work correctly, you may need to define operators. Cons: While operator overloading can make code more intuitive, it has several drawbacks: It can fool our intuition into thinking that expensive operations are cheap, built-in operations. It is much harder to find the call sites for overloaded operators. Searching for Equals() is much easier than searching for relevant invocations of ==. Some operators work on pointers too, making it easy to introduce bugs. Foo + 4 may do one thing, while &Foo + 4 does something totally different. The compiler does not complain for either of these, making this very hard to debug. Overloading also has surprising ramifications. For instance, if a class overloads unary operator&, it cannot safely be forward-declared. Decision: In general, do not overload operators. The assignment operator (operator=), in particular, is insidious and should be avoided. You can define functions like Equals() and CopyFrom() if you need them. Likewise, avoid the dangerous unary operator& at all costs, if there's any possibility the class might be forward-declared. However, there may be rare cases where you need to overload an operator to interoperate with templates or "standard" C++ classes (such as operator<<(ostream&, const T&) for logging). These are acceptable if fully justified, but you should try to avoid these whenever possible. In particular, do not overload operator== or operator< just so that your class can be used as a key in an STL container; instead, you should create equality and comparison functor types when declaring the container. Some of the STL algorithms do require you to overload operator==, and you may do so in these cases, provided you document why. See also Copy Constructors and Function Overloading. Access Control link ▶Make data members private, and provide access to them through accessor functions as needed (for technical reasons, we allow data members of a test fixture class to be protected when using Google Test). Typically a variable would be called foo_ and the accessor function foo(). You may also want a mutator function set_foo(). Exception: static const data members (typically called kFoo) need not be private. The definitions of accessors are usually inlined in the header file. See also Inheritance and Function Names. Declaration Order link ▶Use the specified order of declarations within a class: public: before private:, methods before data members (variables), etc. Your class definition should start with its public: section, followed by its protected: section and then its private: section. If any of these sections are empty, omit them. Within each section, the declarations generally should be in the following order: Typedefs and Enums Constants (static const data members) Constructors Destructor Methods, including static methods Data Members (except static const data members) Friend declarations should always be in the private section, and the DISALLOW_COPY_AND_ASSIGN macro invocation should be at the end of the private: section. It should be the last thing in the class. See Copy Constructors. Method definitions in the corresponding .cc file should be the same as the declaration order, as much as possible. Do not put large method definitions inline in the class definition. Usually, only trivial or performance-critical, and very short, methods may be defined inline. See Inline Functions for more details. Write Short Functions link ▶Prefer small and focused functions. We recognize that long functions are sometimes appropriate, so no hard limit is placed on functions length. If a function exceeds about 40 lines, think about whether it can be broken up without harming the structure of the program. Even if your long function works perfectly now, someone modifying it in a few months may add new behavior. This could result in bugs that are hard to find. Keeping your functions short and simple makes it easier for other people to read and modify your code. You could find long and complicated functions when working with some code. Do not be intimidated by modifying existing code: if working with such a function proves to be difficult, you find that errors are hard to debug, or you want to use a piece of it in several different contexts, consider breaking up the function into smaller and more manageable pieces. Google-Specific Magic There are various tricks and utilities that we use to make C++ code more robust, and various ways we use C++ that may differ from what you see elsewhere. Smart Pointers link ▶If you actually need pointer semantics, scoped_ptr is great. You should only use std::tr1::shared_ptr under very specific conditions, such as when objects need to be held by STL containers. You should never use auto_ptr. "Smart" pointers are objects that act like pointers but have added semantics. When a scoped_ptr is destroyed, for instance, it deletes the object it's pointing to. shared_ptr is the same way, but implements reference-counting so only the last pointer to an object deletes it. Generally speaking, we prefer that we design code with clear object ownership. The clearest object ownership is obtained by using an object directly as a field or local variable, without using pointers at all. On the other extreme, by their very definition, reference counted pointers are owned by nobody. The problem with this design is that it is easy to create circular references or other strange conditions that cause an object to never be deleted. It is also slow to perform atomic operations every time a value is copied or assigned. Although they are not recommended, reference counted pointers are sometimes the simplest and most elegant way to solve a problem. cpplint link ▶Use cpplint.py to detect style errors. cpplint.py is a tool that reads a source file and identifies many style errors. It is not perfect, and has both false positives and false negatives, but it is still a valuable tool. False positives can be ignored by putting // NOLINT at the end of the line. Some projects have instructions on how to run cpplint.py from their project tools. If the project you are contributing to does not, you can download cpplint.py separately. Other C++ Features Reference Arguments link ▶All parameters passed by reference must be labeled const. Definition: In C, if a function needs to modify a variable, the parameter must use a pointer, eg int foo(int *pval). In C++, the function can alternatively declare a reference parameter: int foo(int &val). Pros: Defining a parameter as reference avoids ugly code like (*pval)++. Necessary for some applications like copy constructors. Makes it clear, unlike with pointers, that NULL is not a possible value. Cons: References can be confusing, as they have value syntax but pointer semantics. Decision: Within function parameter lists all references must be const: void Foo(const string &in, string *out); In fact it is a very strong convention in Google code that input arguments are values or const references while output arguments are pointers. Input parameters may be const pointers, but we never allow non-const reference parameters. One case when you might want an input parameter to be a const pointer is if you want to emphasize that the argument is not copied, so it must exist for the lifetime of the object; it is usually best to document this in comments as well. STL adapters such as bind2nd and mem_fun do not permit reference parameters, so you must declare functions with pointer parameters in these cases, too. Function Overloading link ▶Use overloaded functions (including constructors) only if a reader looking at a call site can get a good idea of what is happening without having to first figure out exactly which overload is being called. Definition: You may write a function that takes a const string& and overload it with another that takes const char*. class MyClass { public: void Analyze(const string &text); void Analyze(const char *text, size_t textlen); }; Pros: Overloading can make code more intuitive by allowing an identically-named function to take different arguments. It may be necessary for templatized code, and it can be convenient for Visitors. Cons: If a function is overloaded by the argument types alone, a reader may have to understand C++'s complex matching rules in order to tell what's going on. Also many people are confused by the semantics of inheritance if a derived class overrides only some of the variants of a function. Decision: If you want to overload a function, consider qualifying the name with some information about the arguments, e.g., AppendString(), AppendInt() rather than just Append(). Default Arguments link ▶We do not allow default function parameters, except in a few uncommon situations explained below. Pros: Often you have a function that uses lots of default values, but occasionally you want to override the defaults. Default parameters allow an easy way to do this without having to define many functions for the rare exceptions. Cons: People often figure out how to use an API by looking at existing code that uses it. Default parameters are more difficult to maintain because copy-and-paste from previous code may not reveal all the parameters. Copy-and-pasting of code segments can cause major problems when the default arguments are not appropriate for the new code. Decision: Except as described below, we require all arguments to be explicitly specified, to force programmers to consider the API and the values they are passing for each argument rather than silently accepting defaults they may not be aware of. One specific exception is when default arguments are used to simulate variable-length argument lists. // Support up to 4 params by using a default empty AlphaNum. string StrCat(const AlphaNum &a, const AlphaNum &b = gEmptyAlphaNum, const AlphaNum &c = gEmptyAlphaNum, const AlphaNum &d = gEmptyAlphaNum); Variable-Length Arrays and alloca() link ▶We do not allow variable-length arrays or alloca(). Pros: Variable-length arrays have natural-looking syntax. Both variable-length arrays and alloca() are very efficient. Cons: Variable-length arrays and alloca are not part of Standard C++. More importantly, they allocate a data-dependent amount of stack space that can trigger difficult-to-find memory overwriting bugs: "It ran fine on my machine, but dies mysteriously in production". Decision: Use a safe allocator instead, such as scoped_ptr/scoped_array. Friends link ▶We allow use of friend classes and functions, within reason. Friends should usually be defined in the same file so that the reader does not have to look in another file to find uses of the private members of a class. A common use of friend is to have a FooBuilder class be a friend of Foo so that it can construct the inner state of Foo correctly, without exposing this state to the world. In some cases it may be useful to make a unittest class a friend of the class it tests. Friends extend, but do not break, the encapsulation boundary of a class. In some cases this is better than making a member public when you want to give only one other class access to it. However, most classes should interact with other classes solely through their public members. Exceptions link ▶We do not use C++ exceptions. Pros: Exceptions allow higher levels of an application to decide how to handle "can't happen" failures in deeply nested functions, without the obscuring and error-prone bookkeeping of error codes. Exceptions are used by most other modern languages. Using them in C++ would make it more consistent with Python, Java, and the C++ that others are familiar with. Some third-party C++ libraries use exceptions, and turning them off internally makes it harder to integrate with those libraries. Exceptions are the only way for a constructor to fail. We can simulate this with a factory function or an Init() method, but these require heap allocation or a new "invalid" state, respectively. Exceptions are really handy in testing frameworks. Cons: When you add a throw statement to an existing function, you must examine all of its transitive callers. Either they must make at least the basic exception safety guarantee, or they must never catch the exception and be happy with the program terminating as a result. For instance, if f() calls g() calls h(), and h throws an exception that f catches, g has to be careful or it may not clean up properly. More generally, exceptions make the control flow of programs difficult to evaluate by looking at code: functions may return in places you don't expect. This causes maintainability and debugging difficulties. You can minimize this cost via some rules on how and where exceptions can be used, but at the cost of more that a developer needs to know and understand. Exception safety requires both RAII and different coding practices. Lots of supporting machinery is needed to make writing correct exception-safe code easy. Further, to avoid requiring readers to understand the entire call graph, exception-safe code must isolate logic that writes to persistent state into a "commit" phase. This will have both benefits and costs (perhaps where you're forced to obfuscate code to isolate the commit). Allowing exceptions would force us to always pay those costs even when they're not worth it. Turning on exceptions adds data to each binary produced, increasing compile time (probably slightly) and possibly increasing address space pressure. The availability of exceptions may encourage developers to throw them when they are not appropriate or recover from them when it's not safe to do so. For example, invalid user input should not cause exceptions to be thrown. We would need to make the style guide even longer to document these restrictions! Decision: On their face, the benefits of using exceptions outweigh the costs, especially in new projects. However, for existing code, the introduction of exceptions has implications on all dependent code. If exceptions can be propagated beyond a new project, it also becomes problematic to integrate the new project into existing exception-free code. Because most existing C++ code at Google is not prepared to deal with exceptions, it is comparatively difficult to adopt new code that generates exceptions. Given that Google's existing code is not exception-tolerant, the costs of using exceptions are somewhat greater than the costs in a new project. The conversion process would be slow and error-prone. We don't believe that the available alternatives to exceptions, such as error codes and assertions, introduce a significant burden. Our advice against using exceptions is not predicated on philosophical or moral grounds, but practical ones. Because we'd like to use our open-source projects at Google and it's difficult to do so if those projects use exceptions, we need to advise against exceptions in Google open-source projects as well. Things would probably be different if we had to do it all over again from scratch. There is an exception to this rule (no pun intended) for Windows code. Run-Time Type Information (RTTI) link ▶We do not use Run Time Type Information (RTTI). Definition: RTTI allows a programmer to query the C++ class of an object at run time. Pros: It is useful in some unittests. For example, it is useful in tests of factory classes where the test has to verify that a newly created object has the expected dynamic type. In rare circumstances, it is useful even outside of tests. Cons: A query of type during run-time typically means a design problem. If you need to know the type of an object at runtime, that is often an indication that you should reconsider the design of your class. Decision: Do not use RTTI, except in unittests. If you find yourself in need of writing code that behaves differently based on the class of an object, consider one of the alternatives to querying the type. Virtual methods are the preferred way of executing different code paths depending on a specific subclass type. This puts the work within the object itself. If the work belongs outside the object and instead in some processing code, consider a double-dispatch solution, such as the Visitor design pattern. This allows a facility outside the object itself to determine the type of class using the built-in type system. If you think you truly cannot use those ideas, you may use RTTI. But think twice about it. :-) Then think twice again. Do not hand-implement an RTTI-like workaround. The arguments against RTTI apply just as much to workarounds like class hierarchies with type tags. Casting link ▶Use C++ casts like static_cast(). Do not use other cast formats like int y = (int)x; or int y = int(x);. Definition: C++ introduced a different cast system from C that distinguishes the types of cast operations. Pros: The problem with C casts is the ambiguity of the operation; sometimes you are doing a conversion (e.g., (int)3.5) and sometimes you are doing a cast (e.g., (int)"hello"); C++ casts avoid this. Additionally C++ casts are more visible when searching for them. Cons: The syntax is nasty. Decision: Do not use C-style casts. Instead, use these C++-style casts. Use static_cast as the equivalent of a C-style cast that does value conversion, or when you need to explicitly up-cast a pointer from a class to its superclass. Use const_cast to remove the const qualifier (see const). Use reinterpret_cast to do unsafe conversions of pointer types to and from integer and other pointer types. Use this only if you know what you are doing and you understand the aliasing issues. Do not use dynamic_cast except in test code. If you need to know type information at runtime in this way outside of a unittest, you probably have a design flaw. Streams link ▶Use streams only for logging. Definition: Streams are a replacement for printf() and scanf(). Pros: With streams, you do not need to know the type of the object you are printing. You do not have problems with format strings not matching the argument list. (Though with gcc, you do not have that problem with printf either.) Streams have automatic constructors and destructors that open and close the relevant files. Cons: Streams make it difficult to do functionality like pread(). Some formatting (particularly the common format string idiom %.*s) is difficult if not impossible to do efficiently using streams without using printf-like hacks. Streams do not support operator reordering (the %1s directive), which is helpful for internationalization. Decision: Do not use streams, except where required by a logging interface. Use printf-like routines instead. There are various pros and cons to using streams, but in this case, as in many other cases, consistency trumps the debate. Do not use streams in your code. Extended Discussion There has been debate on this issue, so this explains the reasoning in greater depth. Recall the Only One Way guiding principle: we want to make sure that whenever we do a certain type of I/O, the code looks the same in all those places. Because of this, we do not want to allow users to decide between using streams or using printf plus Read/Write/etc. Instead, we should settle on one or the other. We made an exception for logging because it is a pretty specialized application, and for historical reasons. Proponents of streams have argued that streams are the obvious choice of the two, but the issue is not actually so clear. For every advantage of streams they point out, there is an equivalent disadvantage. The biggest advantage is that you do not need to know the type of the object to be printing. This is a fair point. But, there is a downside: you can easily use the wrong type, and the compiler will not warn you. It is easy to make this kind of mistake without knowing when using streams. cout << this; // Prints the address cout << *this; // Prints the contents The compiler does not generate an error because << has been overloaded. We discourage overloading for just this reason. Some say printf formatting is ugly and hard to read, but streams are often no better. Consider the following two fragments, both with the same typo. Which is easier to discover? cerr << "Error connecting to '" hostname.first << ":" hostname.second << ": " hostname.first, foo->bar()->hostname.second, strerror(errno)); And so on and so forth for any issue you might bring up. (You could argue, "Things would be better with the right wrappers," but if it is true for one scheme, is it not also true for the other? Also, remember the goal is to make the language smaller, not add yet more machinery that someone has to learn.) Either path would yield different advantages and disadvantages, and there is not a clearly superior solution. The simplicity doctrine mandates we settle on one of them though, and the majority decision was on printf + read/write. Preincrement and Predecrement link ▶Use prefix form (++i) of the increment and decrement operators with iterators and other template objects. Definition: When a variable is incremented (++i or i++) or decremented (--i or i--) and the value of the expression is not used, one must decide whether to preincrement (decrement) or postincrement (decrement). Pros: When the return value is ignored, the "pre" form (++i) is never less efficient than the "post" form (i++), and is often more efficient. This is because post-increment (or decrement) requires a copy of i to be made, which is the value of the expression. If i is an iterator or other non-scalar type, copying i could be expensive. Since the two types of increment behave the same when the value is ignored, why not just always pre-increment? Cons: The tradition developed, in C, of using post-increment when the expression value is not used, especially in for loops. Some find post-increment easier to read, since the "subject" (i) precedes the "verb" (++), just like in English. Decision: For simple scalar (non-object) values there is no reason to prefer one form and we allow either. For iterators and other template types, use pre-increment. Use of const link ▶We strongly recommend that you use const whenever it makes sense to do so. Definition: Declared variables and parameters can be preceded by the keyword const to indicate the variables are not changed (e.g., const int foo). Class functions can have the const qualifier to indicate the function does not change the state of the class member variables (e.g., class Foo { int Bar(char c) const; };). Pros: Easier for people to understand how variables are being used. Allows the compiler to do better type checking, and, conceivably, generate better code. Helps people convince themselves of program correctness because they know the functions they call are limited in how they can modify your variables. Helps people know what functions are safe to use without locks in multi-threaded programs. Cons: const is viral: if you pass a const variable to a function, that function must have const in its prototype (or the variable will need a const_cast). This can be a particular problem when calling library functions. Decision: const variables, data members, methods and arguments add a level of compile-time type checking; it is better to detect errors as soon as possible. Therefore we strongly recommend that you use const whenever it makes sense to do so: If a function does not modify an argument passed by reference or by pointer, that argument should be const. Declare methods to be const whenever possible. Accessors should almost always be const. Other methods should be const if they do not modify any data members, do not call any non-const methods, and do not return a non-const pointer or non-const reference to a data member. Consider making data members const whenever they do not need to be modified after construction. However, do not go crazy with const. Something like const int * const * const x; is likely overkill, even if it accurately describes how const x is. Focus on what's really useful to know: in this case, const int** x is probably sufficient. The mutable keyword is allowed but is unsafe when used with threads, so thread safety should be carefully considered first. Where to put the const Some people favor the form int const *foo to const int* foo. They argue that this is more readable because it's more consistent: it keeps the rule that const always follows the object it's describing. However, this consistency argument doesn't apply in this case, because the "don't go crazy" dictum eliminates most of the uses you'd have to be consistent with. Putting the const first is arguably more readable, since it follows English in putting the "adjective" (const) before the "noun" (int). That said, while we encourage putting const first, we do not require it. But be consistent with the code around you! Integer Types link ▶Of the built-in C++ integer types, the only one used is int. If a program needs a variable of a different size, use a precise-width integer type from , such as int16_t. Definition: C++ does not specify the sizes of its integer types. Typically people assume that short is 16 bits, int is 32 bits, long is 32 bits and long long is 64 bits. Pros: Uniformity of declaration. Cons: The sizes of integral types in C++ can vary based on compiler and architecture. Decision: defines types like int16_t, uint32_t, int64_t, etc. You should always use those in preference to short, unsigned long long and the like, when you need a guarantee on the size of an integer. Of the C integer types, only int should be used. When appropriate, you are welcome to use standard types like size_t and ptrdiff_t. We use int very often, for integers we know are not going to be too big, e.g., loop counters. Use plain old int for such things. You should assume that an int is at least 32 bits, but don't assume that it has more than 32 bits. If you need a 64-bit integer type, use int64_t or uint64_t. For integers we know can be "big", use int64_t. You should not use the unsigned integer types such as uint32_t, unless the quantity you are representing is really a bit pattern rather than a number, or unless you need defined twos-complement overflow. In particular, do not use unsigned types to say a number will never be negative. Instead, use assertions for this. On Unsigned Integers Some people, including some textbook authors, recommend using unsigned types to represent numbers that are never negative. This is intended as a form of self-documentation. However, in C, the advantages of such documentation are outweighed by the real bugs it can introduce. Consider: for (unsigned int i = foo.Length()-1; i >= 0; --i) ... This code will never terminate! Sometimes gcc will notice this bug and warn you, but often it will not. Equally bad bugs can occur when comparing signed and unsigned variables. Basically, C's type-promotion scheme causes unsigned types to behave differently than one might expect. So, document that a variable is non-negative using assertions. Don't use an unsigned type. 64-bit Portability link ▶Code should be 64-bit and 32-bit friendly. Bear in mind problems of printing, comparisons, and structure alignment. printf() specifiers for some types are not cleanly portable between 32-bit and 64-bit systems. C99 defines some portable format specifiers. Unfortunately, MSVC 7.1 does not understand some of these specifiers and the standard is missing a few, so we have to define our own ugly versions in some cases (in the style of the standard include file inttypes.h): // printf macros for size_t, in the style of inttypes.h #ifdef _LP64 #define __PRIS_PREFIX "z" #else #define __PRIS_PREFIX #endif // Use these macros after a % in a printf format string // to get correct 32/64 bit behavior, like this: // size_t size = records.size(); // printf("%"PRIuS"\n", size); #define PRIdS __PRIS_PREFIX "d" #define PRIxS __PRIS_PREFIX "x" #define PRIuS __PRIS_PREFIX "u" #define PRIXS __PRIS_PREFIX "X" #define PRIoS __PRIS_PREFIX "o" Type DO NOT use DO use Notes void * (or any pointer) %lx %p int64_t %qd, %lld %"PRId64" uint64_t %qu, %llu, %llx %"PRIu64", %"PRIx64" size_t %u %"PRIuS", %"PRIxS" C99 specifies %zu ptrdiff_t %d %"PRIdS" C99 specifies %zd Note that the PRI* macros expand to independent strings which are concatenated by the compiler. Hence if you are using a non-constant format string, you need to insert the value of the macro into the format, rather than the name. It is still possible, as usual, to include length specifiers, etc., after the % when using the PRI* macros. So, e.g. printf("x = %30"PRIuS"\n", x) would expand on 32-bit Linux to printf("x = %30" "u" "\n", x), which the compiler will treat as printf("x = %30u\n", x). Remember that sizeof(void *) != sizeof(int). Use intptr_t if you want a pointer-sized integer. You may need to be careful with structure alignments, particularly for structures being stored on disk. Any class/structure with a int64_t/uint64_t member will by default end up being 8-byte aligned on a 64-bit system. If you have such structures being shared on disk between 32-bit and 64-bit code, you will need to ensure that they are packed the same on both architectures. Most compilers offer a way to alter structure alignment. For gcc, you can use __attribute__((packed)). MSVC offers #pragma pack() and __declspec(align()). Use the LL or ULL suffixes a
Welcome to Turbo C++ Version 3.0 -------------------------------- This README file contains important information about Turbo C++. For the latest information about Turbo C++ and its accompanying programs and manuals, read this file in its entirety. TABLE OF CONTENTS ----------------- 1. How to Get Help 2. Installation 3. Features 4. Important Information 5. Testing Your Expanded Memory 6. Corrections to the On-line Help 1. HOW TO GET HELP ------------------- If you have any problems, please read this file, the HELPME!.DOC and other files in your DOC subdirectory, and the Turbo C++ manuals first. If you still have a question and need assistance, help is available from the following sources: 1. Type GO BPROGB on the CompuServe bulletin board system for instant access to the Borland forums with their libraries of technical information and answers to common questions. If you are not a member of CompuServe, see the enclosed special offer, and write for full details on how to receive a free IntroPak containing a $15 credit toward your first month's on-line charges. 2. Check with your local software dealer or users' group. 3. Borland's TECHFAX service. Call (800) 822-4269 for a FAX catalog of entries. 4. If you have an urgent problem that cannot wait and you have sent in the license agreement that came with the package, you may call the Borland Technical Support Department at (408) 438-5300. Please have the following information ready before calling: a. Product name and serial number on your original distribution disk. Please have your serial number ready or we will be unable to process your call. b. Product version number. The version number for Turbo C++ can be displayed by pressing Alt-H/A. c. Computer brand, model, and the brands and model numbers of any additional hardware. d. Operating system and version number. (The version number can be determined by typing VER at the DOS prompt.) e. Contents of your AUTOEXEC.BAT file. f. Contents of your CONFIG.SYS file. 2. INSTALLATION ---------------- You MUST use the INSTALL program to install Turbo C++. The files on the distribution disks are all archived and have to be properly assembled. You cannot do this by hand! IMPORTANT! If you want to create backup copies of your disks, make sure that you put the backup on the same type of disk as the source. If you're backing up the 5 1/4 inch 1.2 Mb disk set, use only blank 5 1/4 inch 1.2 Mb disks for backup, etc. The installation will not work correctly if you do not use the same media type for the backup disks. To start the installation, change your current drive to the one that has the install program on it and type INSTALL. You will be given instructions in a box at the bottom of the screen for each prompt. For example, if you will be installing from drive A:, type: A: INSTALL - This INSTALL handles the installation of both the compiler and tools in one operation, and allows several new configuration options. - After installation, make sure you insert \TC\BIN - or whatever you selected as your BIN directory - into your DOS path so the executable files can be found. - Note: The list of files is contained in a separate file called FILELIST.DOC, which will appear in the target directory you specify during installation. - After your initial installation, you can run INSTALL again to add elements you omitted the first time. Just select the items you want to add in the INSTALL options screen. Because some things you may want to save could be overwritten, review the following items to make sure you don't lose important information: 1. Selecting CMD (the Command-line compiler) causes an overwrite of any existing turboc.cfg & tlink.cfg file with path information provided in that INSTALL session. Any switches other than -L (library path) and -I (include path) will not be preserved. 2. Selecting IDE will reset the include and library paths to those provided in that INSTALL session. 3. By selecting any one of the following, the help file paths and choices for THELP.CFG will reflect the current session's installation choices: a. CMD - command-line compiler b. IDE - integrated environment 4. Alterations to headers or startup files will be overwritten if any library models are selected. In general, any selection you make of something installed earlier will cause an overwrite of the earlier version without prompting. You should read the rest of this README file to get further information about this release before you do the installation. 3. FEATURES ------------ Turbo C++ 3.0 includes big speed and capacity gains. Here are some important features found in this version: - DPMI services for increased capacity - C++ 2.1 support, including the new nested class specifications, and support of C++ 3.0 templates. - Support for pre-compiled headers for substantial time savings during subsequent recompiles. - Color syntax highlighting - Unlimited Undo/Redo replacing previous 'restore line' capability - Added library functions for compatibility with other runtime libraries, and addition of support for long double parameters in math functions. (Please refer to On-line Help for details.) - New MAKE features. (Please see the MAKE chapter in the User's Guide for details.) - Added BGI (Borland Graphics Interface) fonts and support. (See "New BGI fonts" below.) - A resident DPMI kernel program, DPMIRES.EXE. (See "DPMI" below.) - THELP now allows you to switch between help files without unloading and reloading. (Please see UTIL.DOC for details.) NEW BGI FONTS ------------- Several new fonts have been added to the Borland Graphics Interface: Name Value Description ------------------------------------------- SCRIPT_FONT 5 Stroked script font SIMPLEX_FONT 6 Stroked simplex font TRIP_SCR_FONT 7 Stroked triplex script font COMPLEX_FONT 8 Stroked complex font EURO_FONT 9 Stroked European font BOLD_FONT 10 Stroked bold font The fonts in the BGI now support the full ASCII character set. DPMI ---- TC.EXE, TCC.EXE, and TLINK.EXE are now hosted under DPMI. These files support protected-mode compilation and replace the files of the same name in Turbo C++ Second Edition. Turbo C++ Second Edition should continue to be used in instances where real-mode compilation is desired. If you encounter a "machine not in database" message while attempting to run the compiler, run the DPMIINST program to add your machine configuration to the DPMI server database. This version includes a resident DPMI host program, DPMIRES.EXE, that allows you to preload the server before invoking TC, TCC, or any other DPMI-hosted executables. If you want to run such hosted EXEs in a Windows Standard Mode DOS window, you should run DPMIRES.EXE before loading Windows. To do this, enter the following commands at DOS: set DPMIMEM=MAXMEM 2000 dpmires win /s If you want to limit the amount of extended memory used by the DPMI-hosted executables, an environment variable called DPMIMEM can be set to do so. For instance, the command set DPMIMEM=MAXMEM 2000 reserves about 2 Mb of memory for DPMIRES. The number after MAXMEM can be adjusted, but cannot be lower than 1000. The hosted executables cannot spawn each other when SHARE is loaded. For instance, if you run MAKE on a file which in turn calls MAKE again, you will get a sharing violation. In this specific case, you can call the real mode version, MAKER, within the given makefile, and a sharing violation won't occur. 4. IMPORTANT INFORMATION ------------------------- - When using Brief with THELP, make sure to use Brief's -p switch to ensure that the thelp window will be visible. - We recommend that you use the following mouse drivers with this product: Microsoft Mouse version 7.04 or later; Logitech Mouse version 5.01 or later; Genius Mouse version 9.06 or later. - If you get a "floating point formats not linked" message at runtime, put the following somewhere in your source files: extern void _floatconvert(); #pragma extref _floatconvert This will force inclusion of floating point formats, which may not be linked to reduce executable size. COMPILER - The default extension for source files to the command-line compiler is .CPP; that is, if you enter TCC -c test the compiler will search for test.cpp, and give an error if a file of that name cannot be found. If you want to have the command-line compiler assume a .c extension and C language source, use the command-line option -P-c. For more information, see "The command-line compiler" in the User's Guide. - Note that the Generate COMDEFs choice under Options|Compiler|Advanced Code Generation and the -Fc command- line option are only supported in the C language. Linker errors will result if you attempt to use a communal variable in C++. - The macros min() and max() are not defined when stdlib.h is compiled as C++ (to allow their use in 3rd party libraries, etc.). - Note that SYMDEB creates .SYM files for use in debugging; Turbo C++ creates .SYM files for pre-compiled headers. They are not compatible and collisions should be avoided by setting the name of the pre-compiled header file (using - H=filename). - There is now full support of distance modifiers (near and far) used for class member pointers. Here are two sample declarations and their meanings: void (A::* far var) (); this is a far variable 'var' of type 'void (A::*)()'; void (far A::* var) (); this is a 'default distance' variable 'var' of type 'void (far A::*)()' - If you use C++ templates, and use a separate TLINK command line rather than letting TCC invoke TLINK, you should make sure that you turn on case-sensitive links with the /c switch. - Incorrect code will be generated if you have a statement of the type "A op B" where either A or B is an enum and the other operand is a long, and "op" is one of the following operators: += -= *= /= | ^ The same problem applies when the operands are a non-integer enum and an int. Cast the enum to long or int respectively to solve the problem. IDE - When debugging a mouse application the Options|Debugger|Display Swapping option should be set to "Always" for best results. - In the IDE, the mouse cursor is turned off during compilation for performance improvements. - To run or debug an overlaid application in the IDE when DOS SHARE is loaded, the .EXE file must first be marked as read-only. Otherwise, unload SHARE. - Pressing Control-Break twice while running or stepping a program from the IDE may cause unexpected results. In particular, avoid pressing Control-Break twice in response to any function requiring input (scanf, getch, etc.). To break out of a program during such interaction, press Control-Break and enter a valid input string. Control will be returned to the IDE. EXAMPLE PROGRAMS - When you are running any example programs that come with .PRJ files, if you didn't use the standard directories when you installed Turbo C++ you will have to change the .PRJ file to reflect your actual directory setup. Do this from inside Turbo C++ with Alt-O/D. LINKING C++ WITH C - Linking C++ modules with C modules requires the use of a linkage specification. Prototypes for C functions within C++ modules must be in one of the following forms: extern "C" declaration extern "C" { declarations } For example, if a C module contains these functions: char *SCopy(char*, char*); void ClearScreen(void) they must be declared in a C++ module in one of the following ways: extern "C" char *SCopy(char*, char*); extern "C" void ClearScreen(void); or extern "C" { char *SCopy(char*, char*); void ClearScreen(void); } Failure to do so will result in "Undefined symbol" errors during link. For further examples, see the standard header files. CLASS LIBRARY - Two versions of the class libraries are provided; one that includes debug information and one that does not. Small versions of each are provided, and project files are provided to build other models. Note that the non-debug versions are used by default. If you would like to use the debug version, copy it to the non-debug file. For instance, in the CLASSLIB\LIB directory, copy TCLASDBS.LIB to TCLASSS.LIB for the small model version. - In some places the User's Guide incorrectly refers to the online documentation for the Container Class Libraries as CONTAIN.DOC. The correct file name is CLASSLIB.DOC, located in the ..\DOC directory. 5. TESTING YOUR EXPANDED MEMORY: EMSTEST.COM --------------------------------------------- Included with Turbo C++ is a program to test your Expanded Memory hardware and software. If you have problems using Turbo C++ with your EMS, type EMSTEST at the DOS prompt and follow the instructions. 6. CORRECTIONS TO THE ON-LINE HELP ----------------------------------- The information for alloca is not available in on-line help. The correct help screen should read as follows: ------------------------------------------------------------------ Function: alloca Allocates temporary stack space Syntax: #include void *alloca(size_t size); Remarks: alloca allocates bytes on the stack. The allocated space is automatically freed up when the calling function exits. Return value: o On success (if enough stack space is available), returns a pointer to the allocated stack area. o On error, returns null. Argument size is the number of bytes allocated on the stack. Because alloca modifies the stack pointer, do no place calls to alloca in an expression that is an argument to a function. NOTE: If the calling function does not contain any references to local variables in the stack, the stack won't be resotored correctly when the function exits and your program will crash. To ensure that the stack is restored correctly, use this code in your calling function: char *p; char dummy[1]; dummy[0] := 0;; ... p = alloca(nbytes); Because alloca is not defined in ANSI C, you should use malloc instead. See also: malloc ------------------------------------------------------------------
Welcome to Turbo C++ Version 3.0 -------------------------------- This README file contains important information about Turbo C++. For the latest information about Turbo C++ and its accompanying programs and manuals, read this file in its entirety. TABLE OF CONTENTS ----------------- 1. How to Get Help 2. Installation 3. Features 4. Important Information 5. Testing Your Expanded Memory 6. Corrections to the On-line Help 1. HOW TO GET HELP ------------------- If you have any problems, please read this file, the HELPME!.DOC and other files in your DOC subdirectory, and the Turbo C++ manuals first. If you still have a question and need assistance, help is available from the following sources: 1. Type GO BPROGB on the CompuServe bulletin board system for instant access to the Borland forums with their libraries of technical information and answers to common questions. If you are not a member of CompuServe, see the enclosed special offer, and write for full details on how to receive a free IntroPak containing a $15 credit toward your first month's on-line charges. 2. Check with your local software dealer or users' group. 3. Borland's TECHFAX service. Call (800) 822-4269 for a FAX catalog of entries. 4. If you have an urgent problem that cannot wait and you have sent in the license agreement that came with the package, you may call the Borland Technical Support Department at (408) 438-5300. Please have the following information ready before calling: a. Product name and serial number on your original distribution disk. Please have your serial number ready or we will be unable to process your call. b. Product version number. The version number for Turbo C++ can be displayed by pressing Alt-H/A. c. Computer brand, model, and the brands and model numbers of any additional hardware. d. Operating system and version number. (The version number can be determined by typing VER at the DOS prompt.) e. Contents of your AUTOEXEC.BAT file. f. Contents of your CONFIG.SYS file. 2. INSTALLATION ---------------- You MUST use the INSTALL program to install Turbo C++. The files on the distribution disks are all archived and have to be properly assembled. You cannot do this by hand! IMPORTANT! If you want to create backup copies of your disks, make sure that you put the backup on the same type of disk as the source. If you're backing up the 5 1/4 inch 1.2 Mb disk set, use only blank 5 1/4 inch 1.2 Mb disks for backup, etc. The installation will not work correctly if you do not use the same media type for the backup disks. To start the installation, change your current drive to the one that has the install program on it and type INSTALL. You will be given instructions in a box at the bottom of the screen for each prompt. For example, if you will be installing from drive A:, type: A: INSTALL - This INSTALL handles the installation of both the compiler and tools in one operation, and allows several new configuration options. - After installation, make sure you insert \TC\BIN - or whatever you selected as your BIN directory - into your DOS path so the executable files can be found. - Note: The list of files is contained in a separate file called FILELIST.DOC, which will appear in the target directory you specify during installation. - After your initial installation, you can run INSTALL again to add elements you omitted the first time. Just select the items you want to add in the INSTALL options screen. Because some things you may want to save could be overwritten, review the following items to make sure you don't lose important information: 1. Selecting CMD (the Command-line compiler) causes an overwrite of any existing turboc.cfg & tlink.cfg file with path information provided in that INSTALL session. Any switches other than -L (library path) and -I (include path) will not be preserved. 2. Selecting IDE will reset the include and library paths to those provided in that INSTALL session. 3. By selecting any one of the following, the help file paths and choices for THELP.CFG will reflect the current session's installation choices: a. CMD - command-line compiler b. IDE - integrated environment 4. Alterations to headers or startup files will be overwritten if any library models are selected. In general, any selection you make of something installed earlier will cause an overwrite of the earlier version without prompting. You should read the rest of this README file to get further information about this release before you do the installation. 3. FEATURES ------------ Turbo C++ 3.0 includes big speed and capacity gains. Here are some important features found in this version: - DPMI services for increased capacity - C++ 2.1 support, including the new nested class specifications, and support of C++ 3.0 templates. - Support for pre-compiled headers for substantial time savings during subsequent recompiles. - Color syntax highlighting - Unlimited Undo/Redo replacing previous 'restore line' capability - Added library functions for compatibility with other runtime libraries, and addition of support for long double parameters in math functions. (Please refer to On-line Help for details.) - New MAKE features. (Please see the MAKE chapter in the User's Guide for details.) - Added BGI (Borland Graphics Interface) fonts and support. (See "New BGI fonts" below.) - A resident DPMI kernel program, DPMIRES.EXE. (See "DPMI" below.) - THELP now allows you to switch between help files without unloading and reloading. (Please see UTIL.DOC for details.) NEW BGI FONTS ------------- Several new fonts have been added to the Borland Graphics Interface: Name Value Description ------------------------------------------- SCRIPT_FONT 5 Stroked script font SIMPLEX_FONT 6 Stroked simplex font TRIP_SCR_FONT 7 Stroked triplex script font COMPLEX_FONT 8 Stroked complex font EURO_FONT 9 Stroked European font BOLD_FONT 10 Stroked bold font The fonts in the BGI now support the full ASCII character set. DPMI ---- TC.EXE, TCC.EXE, and TLINK.EXE are now hosted under DPMI. These files support protected-mode compilation and replace the files of the same name in Turbo C++ Second Edition. Turbo C++ Second Edition should continue to be used in instances where real-mode compilation is desired. If you encounter a "machine not in database" message while attempting to run the compiler, run the DPMIINST program to add your machine configuration to the DPMI server database. This version includes a resident DPMI host program, DPMIRES.EXE, that allows you to preload the server before invoking TC, TCC, or any other DPMI-hosted executables. If you want to run such hosted EXEs in a Windows Standard Mode DOS window, you should run DPMIRES.EXE before loading Windows. To do this, enter the following commands at DOS: set DPMIMEM=MAXMEM 2000 dpmires win /s If you want to limit the amount of extended memory used by the DPMI-hosted executables, an environment variable called DPMIMEM can be set to do so. For instance, the command set DPMIMEM=MAXMEM 2000 reserves about 2 Mb of memory for DPMIRES. The number after MAXMEM can be adjusted, but cannot be lower than 1000. The hosted executables cannot spawn each other when SHARE is loaded. For instance, if you run MAKE on a file which in turn calls MAKE again, you will get a sharing violation. In this specific case, you can call the real mode version, MAKER, within the given makefile, and a sharing violation won't occur. 4. IMPORTANT INFORMATION ------------------------- - When using Brief with THELP, make sure to use Brief's -p switch to ensure that the thelp window will be visible. - We recommend that you use the following mouse drivers with this product: Microsoft Mouse version 7.04 or later; Logitech Mouse version 5.01 or later; Genius Mouse version 9.06 or later. - If you get a "floating point formats not linked" message at runtime, put the following somewhere in your source files: extern void _floatconvert(); #pragma extref _floatconvert This will force inclusion of floating point formats, which may not be linked to reduce executable size. COMPILER - The default extension for source files to the command-line compiler is .CPP; that is, if you enter TCC -c test the compiler will search for test.cpp, and give an error if a file of that name cannot be found. If you want to have the command-line compiler assume a .c extension and C language source, use the command-line option -P-c. For more information, see "The command-line compiler" in the User's Guide. - Note that the Generate COMDEFs choice under Options|Compiler|Advanced Code Generation and the -Fc command- line option are only supported in the C language. Linker errors will result if you attempt to use a communal variable in C++. - The macros min() and max() are not defined when stdlib.h is compiled as C++ (to allow their use in 3rd party libraries, etc.). - Note that SYMDEB creates .SYM files for use in debugging; Turbo C++ creates .SYM files for pre-compiled headers. They are not compatible and collisions should be avoided by setting the name of the pre-compiled header file (using - H=filename). - There is now full support of distance modifiers (near and far) used for class member pointers. Here are two sample declarations and their meanings: void (A::* far var) (); this is a far variable 'var' of type 'void (A::*)()'; void (far A::* var) (); this is a 'default distance' variable 'var' of type 'void (far A::*)()' - If you use C++ templates, and use a separate TLINK command line rather than letting TCC invoke TLINK, you should make sure that you turn on case-sensitive links with the /c switch. - Incorrect code will be generated if you have a statement of the type "A op B" where either A or B is an enum and the other operand is a long, and "op" is one of the following operators: += -= *= /= | ^ The same problem applies when the operands are a non-integer enum and an int. Cast the enum to long or int respectively to solve the problem. IDE - When debugging a mouse application the Options|Debugger|Display Swapping option should be set to "Always" for best results. - In the IDE, the mouse cursor is turned off during compilation for performance improvements. - To run or debug an overlaid application in the IDE when DOS SHARE is loaded, the .EXE file must first be marked as read-only. Otherwise, unload SHARE. - Pressing Control-Break twice while running or stepping a program from the IDE may cause unexpected results. In particular, avoid pressing Control-Break twice in response to any function requiring input (scanf, getch, etc.). To break out of a program during such interaction, press Control-Break and enter a valid input string. Control will be returned to the IDE. EXAMPLE PROGRAMS - When you are running any example programs that come with .PRJ files, if you didn't use the standard directories when you installed Turbo C++ you will have to change the .PRJ file to reflect your actual directory setup. Do this from inside Turbo C++ with Alt-O/D. LINKING C++ WITH C - Linking C++ modules with C modules requires the use of a linkage specification. Prototypes for C functions within C++ modules must be in one of the following forms: extern "C" declaration extern "C" { declarations } For example, if a C module contains these functions: char *SCopy(char*, char*); void ClearScreen(void) they must be declared in a C++ module in one of the following ways: extern "C" char *SCopy(char*, char*); extern "C" void ClearScreen(void); or extern "C" { char *SCopy(char*, char*); void ClearScreen(void); } Failure to do so will result in "Undefined symbol" errors during link. For further examples, see the standard header files. CLASS LIBRARY - Two versions of the class libraries are provided; one that includes debug information and one that does not. Small versions of each are provided, and project files are provided to build other models. Note that the non-debug versions are used by default. If you would like to use the debug version, copy it to the non-debug file. For instance, in the CLASSLIB\LIB directory, copy TCLASDBS.LIB to TCLASSS.LIB for the small model version. - In some places the User's Guide incorrectly refers to the online documentation for the Container Class Libraries as CONTAIN.DOC. The correct file name is CLASSLIB.DOC, located in the ..\DOC directory. 5. TESTING YOUR EXPANDED MEMORY: EMSTEST.COM --------------------------------------------- Included with Turbo C++ is a program to test your Expanded Memory hardware and software. If you have problems using Turbo C++ with your EMS, type EMSTEST at the DOS prompt and follow the instructions. 6. CORRECTIONS TO THE ON-LINE HELP ----------------------------------- The information for alloca is not available in on-line help. The correct help screen should read as follows: ------------------------------------------------------------------ Function: alloca Allocates temporary stack space Syntax: #include void *alloca(size_t size); Remarks: alloca allocates bytes on the stack. The allocated space is automatically freed up when the calling function exits. Return value: o On success (if enough stack space is available), returns a pointer to the allocated stack area. o On error, returns null. Argument size is the number of bytes allocated on the stack. Because alloca modifies the stack pointer, do no place calls to alloca in an expression that is an argument to a function. NOTE: If the calling function does not contain any references to local variables in the stack, the stack won't be resotored correctly when the function exits and your program will crash. To ensure that the stack is restored correctly, use this code in your calling function: char *p; char dummy[1]; dummy[0] := 0;; ... p = alloca(nbytes); Because alloca is not defined in ANSI C, you should use malloc instead. See also: malloc ------------------------------------------------------------------
关于雷达方面的知识! EFFECTIVENESS OF EXTRACTING WATER SURFACE SLOPES FROM LIDAR DATA WITHIN THE ACTIVE CHANNEL: SANDY RIVER, OREGON, USA by JOHN THOMAS ENGLISH A THESIS Presented to the Department of Geography and the Graduate School of the University of Oregon in partial fulfillment of the requirements for the degree of Master of Science March 2009 11 "Effectiveness of Extracting Water Surface Slopes from LiDAR Data within the Active Channel: Sandy River, Oregon, USA," a thesis prepared by John Thomas English in partial fulfillment of the requirements for the Master of Science degree in the Department of Geography. This thesis has been approved and accepted by: Date Committee in Charge: W. Andrew Marcus, Chair Patricia F. McDowell Accepted by: Dean of the Graduate School © 2009 John Thomas English 111 IV An Abstract of the Thesis of John Thomas English in the Department of Geography for the degree of to be taken Master of Science March 2009 Title: EFFECTIVENESS OF EXTRACTING WATER SURFACE SLOPES FROM LIDAR DATA WITHIN THE ACTIVE CHANNEL: SANDY RIVER, OREGON, USA Approved: _ W. Andrew Marcus This paper examines the capability ofLiDAR data to accurately map river water surface slopes in three reaches of the Sandy River, Oregon, USA. LiDAR data were compared with field measurements to evaluate accuracies and determine how water surface roughness and point density affect LiDAR measurements. Results show that LiDAR derived water surface slopes were accurate to within 0.0047,0.0025, and 0.0014 slope, with adjusted R2 values of 0.35, 0.47, and 0.76 for horizontal intervals of 5, 10, and 20m, respectively. Additionally, results show LiDAR provides greater data density where water surfaces are broken. This study provides conclusive evidence supporting use ofLiDAR to measure water surface slopes of channels with accuracies similar to field based approaches. CURRICULUM VITAE NAME OF AUTHOR: John Thomas English PLACE OF BIRTH: Eugene, Oregon DATE OF BIRTH: January 1st, 1980 GRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED: University of Oregon, Eugene, Oregon Southern Oregon University, Ashland, Oregon DEGREES AWARDED: Master of Science, Geography, March 2009, University of Oregon Bachelor of Science, Geography, 2001, Southern Oregon University AREAS OF SPECIAL INTEREST: Fluvial Geomorphology Remote Sensing PROFESSIONAL EXPERIENCE: LiDAR Database Coordinator, Oregon Department of Geology & Mineral Industries, June 2008 - present. LiDAR & Remote Sensing Specialist, Sky Research Inc., 2003 - 2008 GRANTS, AWARDS AND HONORS: Gamma Theta Upsilon Geographic Society Member, 2006 Gradutate Teaching Fellowship, Social Science Instructional Laboratory, 20062007 v VI ACKNOWLEDGMENTS I wish to express special thanks to Professors W.A. Marcus and Patricia McDowell for their assistance in the preparation of this manuscript. In addition, special thanks are due to Mr. Paul Blanton who assisted with field data collection for this project. I also thank the members ofmy family who have been encouraging and supportive during the entirety of my graduate schooling. I wish to thank my parents Thomas and Nancy English for always being proud of me. Special thanks to my son Finn for always making me smile. Lastly, special thanks to my wife Kathryn for her unwavering support, love, and encouragement. Dedicated to my mother Bonita Claire English (1950-2004). Vll V111 TABLE OF CONTENTS Chapter Page I. INTRODUCTION 1 II. BACKGROlTND 5 Water Surface Slope 5 LiDAR Measurements of Active Channel Features 7 III. STUDY AREA 10 IV. METHODS 22 Overview 22 LiDAR Data and Image Acquisition 23 Field Data Acquisition 24 LiDAR Processing 25 Calculation of Water Surface Slopes 27 Evaluating LiDAR Slope Accuracies and Controls 33 V. RESULTS 35 Comparison of Absolute Elevations from Field and LiDAR Data in Reach 1 35 Slope Comparisons 41 Surface Roughness Analysis 46 VI. DiSCUSSiON 51 VII. CONCLUSION 57 APPENDIX: ARCGIS VBA SCRIPT CODE 58 REFERENCES 106 IX LIST OF FIGURES Figure Page 1. Return Factor vs. LiDAR Scan Angle 2 2. Angle of Incidence 3 3. Wave Action Relationship to LiDAR Echo 3 4. Site Map 11 5. Annual Hydrograph of Sandy River 13 6. Oregon GAP Vegetation within Study Area 15 7. Photo of Himalayan Blackberry on Sandy River 16 8. Reach 1 Site Area Map with photo 18 9. Reach 2 Site Area Map 20 10. Reach 3 Site Area Map 21 11. LiDAR Point Filtering Processing Step 26 12. Field DEM Interpolated using Kriging 29 13. Reach 1 LiDAR Cross Sections and Sample Point Location 31 14. Differences Between LiDAR and Field Based Elevations 37 15. Regression ofLiDAR and Field Cross section Elevations 38 16. Comparison of LiDAR and Field Longitudinal Profiles (5, 10,20 meters) 40 17. Regression ofField and LiDAR Based Slopes (5, 10,20 meters) 42 18. Differences Between LiDAR and Field Based Slopes (5, 10,20 meters) 44 19. Relationship of Water Surfaces to LiDAR Point Density 47 20. Marmot Dam: Orthophotographyand Colorized Slope Model 50 21. LiDAR Point Density versus Interpolation 53 LIST OF TABLES T~k p~ 1. Reported Accuracies of 2006 and 2007 LiDAR 24 2. Results of LiDAR and Field Elevation Comparison 38 3. Results ofLiDAR and Field Slope Comparison (5, 10,20 meters) 45 4. Results of Reach 1 Slope Comparison 46 5. Water Surface Roughness Results for Reach 1,2, and 3 48 6. Results of Reach 1 Water Surface Roughness Comparison 49 7. Subset of Reach 3 Water Surface Roughness Analysis Near Marmot Dam 50 x 1 CHAPTER I INTRODUCTION LiDAR (Light Detection and Ranging) has become a common tool for mapping and documenting floodplain environments by supplying individual point elevations and accurate Digital Terrain Models (DTM) (Bowen & Waltermire, 2002; Gilvear et aI., 2004; Glenn et aI., 2005; Magid et aI., 2005; Thoma, 2005; Smith et aI., 2006; Gangodagamage et aI., 2007). Active channel characteristics that have been extracted using LiDAR include bank profiles, longitudinal profiles (Magid et aI., 2005; Cavalli et aI., 2007) and transverse profiles of gullies under forest canopies (James et aI., 2007). To date, however, no one has tested if LiDAR returns from water surfaces can be used to measure local water surface slopes within the active channel. Much of the reason that researchers have not attempted to measure water surface slopes with LiDAR is because most LiDAR pulses are absorbed or not returned from the water surface. However, where the angle of incidence is close to nadir (i.e. the LiDAR pulse is fired near perpendicular to water surface plane), light is reflected and provides elevations off the water surface (Figure 1, Maslov et aI., 2000). Where LiDAR pulses glance the water surface at angles of incidence greater than 53 degrees, a LiDAR pulse is 2 more often lost to refraction (Figure 2) (Jenkins, 1957). In broken water surface conditions the water surface plane is angled, which produces perpendicular angles of incidence allowing for greater chance of return (Maslov et al. 2000). Su et al. (2007) documented this concept by examining LiDAR returns off disturbed surfaces in a controlled lab setting (Figure 3). LiDAR returns off the water surface potentially provide accurate surface elevations that can be used to calculate surface slopes. 1.0 08 ~ 0.6 o t5 ~ E .2 ~ 04 02 00 000 __d=2° d=10 ° --d=200 --d=300 d=40o d=50o I I 2000 4000 60.00 sensing angle, degree I 8000 Figure 1. Return Factor vs. LiDAR Scan Angle. Figure shows relationship between water surface return and scan angle. Return Factor versus sensing angle at different levels of the waving d (d = scan angle). Figure shows the relationship of scan angle of LiDAR to return from a water surface. Return factor is greatest at low scan angles relative to the nadir region of scan. (Maslov, D. V. et. al. (2000). A Shore-based LiDAR for Coastal Seawater Monitoring. Proceedings ofEARSeL-SIGWorkshop, Figure 1, pg. 47). 3 reflected\\ :.;/ incident 1 I 1 . '\ I lAIR \ •••••••• ••••••••••••• •••••• ••••••••••••••••••••• • •• eo ••••••••••• o •••••••••••• _0 •••••••••• 0 ••• .•.•.•.•.•.•00 ,••••• ' 0•••• 0 ••••••••••• 0 ••I' .•.•.•.•.•.,................. .".0 ••••••••••••• , •••••••••••• , ••••••••••0••••. .....................................~ . ••••••••••••••••••••••••••••••••••••• • •••••••••••••••••••••••••• 0 •••••••••••••••••••• 0 ••••• 0 •• ~~~)}))}))})))))))))\..)}))?()))))))))))))))))j((~j< Figure 2. Angle of Incidence. Figure displays concept of reflection and refraction of light according to angle of incidence. The intensity of light is greater as the angle of incidence approaches nadir. (Jenkins, F.A., White, RE. "Fundamentals of Optics". McGraw-Hili, 1957, Chapter 25) 09 08 0.7 0.6 0.5 0.4 0.3 0.2 0.1 r - 0.\ O,j/6Y3- -500 17.5 35 52.5 70 horizonral scanning dislancC(lllm) 0.9 0.8 0.7 06 0.5 0.4 0.3 0.2 0.1 a b Figure 3. Wave Action Relationship to LiDAR Echo. "LiDAR measurements of wake profiles generated by propeller at 6000 rpm (a) and 8000 rpm (b). Su's work definitively showed LiDAR's ability to measure water surfaces, and the relationship of wave action to capability of echo. From Su (2007) figure 5, p.844 . This study examines whether LiDAR can accurately measure water surface elevations and slopes. In order to address this topic, I assess the vertical accuracy of LiDAR and the effects of water surface roughness on LiDAR within the active channel. Findings shed light on the utility of LiDAR for measuring water surface slopes in different stream environments and methodological constraints to using LiDAR for this purpose. 4 5 CHAPTER II BACKGROlJND Water Surface Slope Water surface slope is a significant component to many equations for modeling hydraulics, sediment transport, and fluvial geomorphic processes (Knighton, 1999, Sing & Zang, in press). Traditional methods for measuring water surface slope include both direct and indirect methods. Direct water surface slope measurements typically use a device such as a total station or theodolite in combination with a stadia rod or drop line to measure water surface elevations (Harrelson, et ai., 1994, Western et ai., 1997). Inaccuracies in measurements stem from surface turbulence that makes it difficult to precisely locate the water surface, especially in fast water where flows pile up against the measuring device (Halwas, 2002). Direct survey methods often require a field team to occupy several known points throughout a reach. This is a time consuming process, especially if one wanted to document water surface slope along large portions of a river. This method can be dangerous in deep or fast water. 6 Indirect methods of water surface slope measurement consist of acquiring approximate water surface elevations using strand lines, water marks, secondary data sources such as contours from topographic maps, or hydraulic modeling to back calculate the water depth (USACE, 1993; Western et aI., 1997). Variable quality of data and modeling errors can lead to inaccuracies using these methods. The use of strand lines and water marks may not necessarily represent the peak flows or the water surface. Contours may be calculated or interpolated from survey points taken outside the channel area. The most commonly used hydraulic models are based on reconstruction of I-dimensional flow within the channel and do not account for channel variability between cross section locations. LiDAR water surface returns have a great deal of promise for improving measurement of water surfaces in several significant ways. LiDAR measurements eliminate hazards associated with surveyors being in the water. LiDAR also captures an immense amount of elevation data over a very short period of time, with hundreds of thousands of pulses collected within a few seconds for a single swath. Within this mass of pulses, hundreds or thousands of measurements off the water's surface may be collected depending on the nature of surface roughness, with broken water surfaces increasing the likelihood of measurements (Figure 3). In addition, most terrestrial LiDAR surveys collect data by flying multiple overlapping flight lines, thus increasing the number of returns in off nadir overlapping areas and the potential for returns from water surfaces. 7 The accuracy of high quality LiDAR measurements is comparable to field techniques. The relative variability of quality LiDAR vertical measurements typically ranges between 0.03-0.05 meters (Leica, 2007), where relative variability is the total range of vertical error within an individual scan on surface of consistent elevation. Lastly, LiDAR has the ability to collect water surface elevations over large stretches of river within a single flight of a few hours. LiDAR Measurements of Active Channel Features Recent studies evaluating the utility of LiDAR in the active channel environment have documented the effectiveness of using LiDAR DTMs to extract bank profiles. Magid et al. (2005) examined long term changes of longitudinal profiles along the Colorado River in the Grand Canyon. The study used historical survey data from 1923 and differenced topographic elevations with LiDAR data flown in 2000. LiDAR with three meter spot spacing was used to estimate water surface profiles based on the LiDAR elevations nearest to the known channel. Cavalli et al. (2007) extracted longitudinal profiles of the exposed bed of the Rio Cordon, Italy using 0.5 meter LiDAR DEM cells. This study successfully attributed LiDAR DEM roughness within the channel to instream habitats. Bowen and Waltermire (2002) found that LiDAR elevations within the floodplain were less accurate than advertised by vendors and sensor manufacturers. Dense vegetation within the riparian area prevented LiDAR pulses from reaching the 8 ground surface resulting in accuracies ranging 1-2 meters. Accuracies within unvegetated areas and flat surfaces met vendor specifications (l5-20cm). James et al. (2007) used LiDAR at 3 meter spot spacing to map transverse profiles of gullies under forest canopies. Results from this study showed that gully morphologies were underestimated by LiDAR data, possibly due to low density point spacing and biased filtering of the bare earth model. Today, point densities of 4-8 points/m2 are common and would likely alleviate some of the troubles found in this study. Additional studies have used LiDAR to extract geomorphic data from channel areas. Schumann et al. (2008) compared a variety of remotely sensed elevation models for floodplain mapping. The study used 2 meter LiDAR DEMs as topographic base data for floodplain modeling, and found that modeled flood stages based on the LiDAR DEM were accurate to within 0.35m. Ruesser and Bierman (2007) used high resolution LiDAR data to calculate erosion fluxes between strath terraces based on elevation. Gangodagamage et al. (2007) used LiDAR to extract river corridor width series, which help to quantify processes involved in valley formation. This study used a fixed water surface elevation and did not attempt to demonstrate the accuracy of LiDAR derived water surfaces. Green LiDAR also has been used to examine riverine environments. Green LiDAR functions much like terrestrial LiDAR (which uses an infrared laser) except that green LiDAR systems use green light that has the ability to penetrate the water surface and measure the elevation of the channel bed. Green LiDAR is far less common than terrestrial LiDAR and the majority of studies have been centered on studies of ocean shorelines. Wang and Philpot (2007) assessed attenuation parameters for measuring bathymetry in near shore shallow water, concluding that quality bathymetric models can be achieved through a number of post-processing steps. Hilldale and Raft (2007) assessed the accuracy and precision of bathymetric LiDAR and concluded that although the resulting models were informative, bathymetric LiDAR was less precise than traditional survey methods. In general, it is often difficult to assess the accuracy of bathymetric LiDAR given issues related to access of the channel bed at time of flight. 9 10 CHAPTER III STUDY AREA The study area is the Sandy River, Oregon, which flows from the western slopes ofMount Hood northwest to the Columbia River (Figure 4). Recent LiDAR data and aerial photography capture the variety of water surface characteristics in the Sandy River, which range from shooting flow to wide pool-riffle formations. The recent removal of the large run-of-river Marmot Dam upstream of the analysis sites has also generated interest in the river's hydraulics and geomorphology. 11 545000 ,·......,c' 550000 556000 560000 Washington, I 565000 -. Portland Sandy River .Eugene Oregon 570000 ooo '~" ooo ~ ooo~ • Gresham (""IIIII/hill /flIt'r Oregon Clack. fna County Marmot Dam IHillshaded area represents 2006 LiDAR extent. Ol1hophotography was collected only along the Sandy River channel within the LiDAR extent. 10 KiiomElt:IS t---+---+-~I--+--+----t-+--+---+----jl 545000 550000 555000 560000 565000 570000 Figure 4. Site Map. Site area map showing location of analysis reaches within the 2006 and 2007 LiDAR coverage areas. Olihophotography was also collected for the 2006 study, but was collected only along the Sandy River channel. 12 Floodplain longitudinal slopes along the Sandy River average 0.02 and reach a maximum of 0.04. The Sandy River has closely spaced pool-riffles and rapids in the upper reaches, transitioning to longer sequenced pool-riffle morphology in the middle and lower reaches. The Sandy River bed is dominated by sand. Cobbles and small boulders are present mostly in areas of riffles and rapids. Much of the channel is incised with steep slopes along the channel boundaries. The flow regime is typical of Pacific Northwest streams, with peak flows in the winter months ofNovember through February and in late spring with snowmelt runoff (Figure 5). Low flows occur between late September and early October. The average peak annual flow at the Sandy River station below Bull Run River (USGS 14142500) is 106cms. Average annual low flow for the same gauge is 13.9cms. 13 USGS 14142500 SRNDY RIVER BL~ BULL RUN RIVER, NR BULL RUN, OR 200 k.===_~~~=~~~=.......==",,=~-........==~ ~....J Jan 01Feb Ollar 01Rpr O:t1ay 01Jun 01Jul 01Rug OJSep 010ct 01Nov O:IJec 01 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 \ 11 ~I\\ ,1\ 1\ j\ 1"J'fn I\. I, ) \ , ,;' ) I I" 'I'•., I I' I' ] 30000 ~~-~----~-------------~-------, o ~ 20000 ~ 8'-. 10000 ~ Ql Ql ~ U '001 ~ ::::J U, Ql to 1000 to .= u Co? '001 Cl )- .....J. a: Cl Hedian daily statistic <59 years) Daily nean discharge --- Estinated daily nean discharge Period of approved data Period of provisional data Figure 5, Annual Hydrograph of Sandy River. US Geological Survey gaging station annual hydrograph of Sandy River, Oregon at Bull Run River. Data from http://waterdata.usgs.gov/or/nwis/annual/ Vegetation is mostly a mixture of Douglas fir and western red hemlock (Figure 6). Other vegetation includes palustrine forest found in the upper portions of the study area, and agricultural lands found in the middle and lower portions. Douglas fir and western red hemlock make up 87% of vegetated areas, palustrine forest 5%, and agricultural lands 5%, the remaining 3% is open water associated with the channel and reservoirs (Oregon GAP Analysis Program, 2002). The city of Troutdale, OR abuts the lower reaches of the Sandy River. Along this stretch of river Himalayan blackberry, an invasive species, dominates the western banks (Figure 7). The presence of Himalayan blackberry is significant because LiDAR has trouble penetrating through the dense clusters of vines. When this blackberry is close to the water's edge it is difficult to accurately define the channel boundary. 14 15 545000 550000 555000 560000 565000 570000 Reach 3 10 !' 0° 200 MetersO 0 ~~~~~~I O~~~OOO~ Figure 9. Reach 2 Site Area Map. Site map of Reach 2. Reach 2 contains 359 cross sections derived from LiDAR and 3,456 sample points. Inset map shows cross section sample locations derived from LiDAR and smooth/rough water surface delineations used in analysis. 21 Reach 3 is located 40.7km upstream from the mouth of the Sandy and is 2,815 meters in length (Figure 10). The widest portion of this section at approximate banle full is 88 meters. The upstream extent of the channel includes the supercritical flow of Marmot Dam. The channel is incised and relatively straight with a sinuosity of 1.08. Fine sands dominate the channel bed with some boulders likely present from mass wasting along valley walls. As with Reach 2, Douglas fir dominates bank vegetation along. 200 40) Inset mAp displays UDAR point I densily alol1g willl cross seellon Sanlpleing dala LiDAR cross section SAmple locations were used to eX1mcl poinl density values. 503 fOC I 000 '.1..Hrs 1-.,...--,.-+--=1..,=-,---4I--+-1---11 . Reach 3 Figure 10. Reach 3 Site Area Map. Site map of Reach 3. Inset map shows point LiDAR water surface points. Reach 3 contains 550 cross sections and 3,348 sample points. Visual examination of this map allows one to see how point density varies within the active channel. 22 CHAPTER IV METHODS Overview LiDAR data and orthophotography were collected in 2006 and additional LiDAR data were collected over the same area in 2007. Field measurements were obtained five days after the 2007 LiDAR flight in order to compare field measurements of water surface slope to LiDAR-based measurements. Time of flight field measurements of water surface elevations were not obtained for the 2006 flight, but the coincident collection of LiDAR data and orthophotos provide a basis for evaluating variability of LiDAR-based slopes over different channel types as identified from aerial photos. Following sections provide more detail regarding these methods. 23 LiDAR Data and Image Acquisition All LiDAR data were collected using a Leica ALS50 Phase II LiDAR system mounted on a Cessna Caravan C208 (see Table 1 for LiDAR acquisition specifications). The 2006 LiDAR data were collected October 2211d and encompassed 13,780 hectares of high resolution (2':4 points/m2 ) LiDAR data from the mouth of the Sandy River to Marmot Dam. Fifteen centimeter ground resolution orthophotography was collected September 26th , 2006 along the riparian corridor of the Sandy River from its mouth to just above the former site ofMarmot dam (Figure 4). The 2007 LiDAR were collected on October 8th and covered the same extent as the 2006 flight, but did not include orthophotography. Data included filtered XYZ ASCII point data, LiDAR DEMs as ESRI formatted grids at 0.5 meter cell size. Data were collected at 2':8 points per m2 providing a data set with significantly higher point density than the 2006 LiDAR data. The 2006 LiDAR data were collected in one continuous flight. 2006 orthophotography was collected using an RC30 camera system. Data were delivered in RGB geoTIFF format. LiDAR data were calibrated by the contractor to correct for IMU position errors (pitch, roll, heading, and mirror scale). Quality control points were collected along roads and other permanent flat features for absolute vertical correction of data. Horizontal accuracy ofLiDAR data is governed by flying height above ground with horizontal accuracy being equal to 1I3300th of flight altitude (meters) (Leica, 2007). 24 Table 1. Reported Accuracies of 2006 and 2007 LiDAR. Reported Accuracies and conditions for 2006 and 2007 LiDAR data. (Watershed Sciences PGE LiDAR Delivery Report, 2006, Watershed Sciences DOGAMI LiDAR Delivery Report, 2007). Relative Accuracy is a measure of flight line offsets resulting from sensor calibration. 2006 LiDAR 2007 LiDAR Flying height above ground level meters (AGL) 1100 1000 Absolute Vertical Accuracy in meters 0.063 0.034 Relative Accuracy in meters (calibration) 0.058 0.054 Horizontal Accuracy (l/3300th * AGL) meters 0.37 0.33 Discharge @ time of flight (cms) 13.05 20.8 - 21.8 LiDAR data collection over the Reach 1 field survey location was obtained in a single flight on October 8, 2007 between 1:30 and 6:00 pm. During the LiDAR flight, ground quality control data were collected along roads and other permanent flat surfaces within the collection area. These data were used to adjust for absolute vertical accuracy. Field Data Acquisition A river survey crew was dispatched at the soonest possible date (October 13, 2007) after the 2007 flight to collect ground truth data within the Reach 1. The initial aim was to survey water surface elevations at cross sections of the channel, but the survey was limited to near shore measurements due to high velocity conditions. We collected 187 measurements of bed elevation and depth one to fifteen meters from banks along both sides of the channel (Figure 8a) using standard total station longitudinal profile 25 survey methods (Harrelson, 1994). Seventy-six and 98 measurements were collected along the east and west banks, respectively, at intervals of approximately 1 to 2 meters. Thirteen additional measurements were collected along the east bank at approximately ten meter intervals. Depth measurements were added to bed elevations to derive water surface elevations. Discharge during the survey ranged between 22.5 and 22.7 cms during the survey of the east bank and remained steady at 22.5 cms during the survey of the west bank (USGS station 14142500). LiDAR Processing The goal ofLiDAR processing for this project was to classify LiDAR point data within the active channel as water and output this subset data for further analysis. The LiDAR imagery was first clipped to the active channel using a boundary digitized from the 2006 high resolution orthophotography. LiDAR point data were then reclassified to remove bars, banks, and overhanging vegetation (Figure 11). 26 Figure 11. LiDAR Point Filtering Processing Step. LiDAR processing steps. Top image shows entire LiDAR point cloud clipped to active channel boundary. Lower image shows the final processed LiDAR points representing only those points that reflect off the water surface. All bars and overhanging vegetation have been removed as well. 27 Water points were classified using the ground classification algorithm in Terrascan© (Soininen, 2005) to separate water surface returns from those off of vegetation or other surfaces elevated above the ground. The classification routine uses a proprietary mathematical model to accomplish this task. Once the ground classification was finished, classified points were visually inspected to add or remove false positives and remove in-channel features such as bar islands. A total of 11,593 of 1,854,219 LiDAR points were classified as water. Points classified as water were output as comma delimited x,y,z ASCII text files (XYZ), then converted to a 0.5 meter linearly interpolated ESRI formatted grid using ESRI geoprocessing model script. Calculation of Water Surface Slopes Water surface slopes were calculated using the rise over run dimensionless slope equation where the rise is the vertical difference between upstream and downstream water surface elevations and run is the longitudinal distance between elevation locations. LiDAR data is typically used in grid format. For this reason grid data were used for calculation of water surface slopes. We used linear interpolation to grid the LiDAR point data as this is the standard method used by the LiDAR contractor. In order to compare the LiDAR and field data it was also necessary to interpolate field 28 measurements to create a water surface for the entire stream. The field data-based DEM was created using kriging interpolation within ArcGIS Desktop Spatial Analyst (Figure 12). No quantitative analysis was performed to evaluate the interpolation method of the field-based water surface. The kriging interpolation was chosen because it producex the smoothest water surface based on visual inspection when compared to linear and natural neighbor interpolations, which generated irregular fluctuations that were unrealistic for a water surface. The kriged surface provided a water surface elevation model for comparative analysis with LiDAR. 29 Figure 12. Field DEM Interpolated using Kriging. Field DEM interpolated from field survey points using kriging method found in ArcGIS Spatial Analyst. DEM has been hiIlshaded to show surface characteristics. The very small differences in water surface elevations generate only slight variations in the hillshadeing. To compare LiDAR and field-based water surface slopes, water surface elevations from the LiDAR and field-based DEMS were extracted at the same locations along Reach I. To accomplish this, 37 cross sections were manually constructed at approximately Sm spacings (Figure 13). Cross sections comparisons were used rather than point-to-point comparisons between streamside field and LiDAR data points because the cross sections provide water surface slopes that are more representative of the entire channel. The Sm interval spacing was considered to be a sufficient for fine resolution slope extraction. Because cross section center points were used to calculate the longitudinal distance and because the stream was sinuous, the projection of the cross sections from the center line to the banks led to stream side distances between cross sections that differed from Sm. 30 31 Smooth 125 Meters I 100 I 75 I 50 I 25 I Cross Sections Cross Section Data Roughness Delineation Cross Section Sample Locations _ Rough oI ~ each 1 Figure 13. Reach 1 LiDAR Cross Sections and Sample Point Locations. Reach I LiDAR-derived cross section sample locations and areas of smooth and rough water surface delineations. 37 cross section and 444 sample points lie within Reach 1. 32 Cross sections were extracted using a custom ArcObjects VBA script (Appendix A). This script extracted 1 cell nearest neighbor elevations along the transverse cross sections at 5 meter intervals creating 444 cross section sample locations (Figure 13). Cross section averages were calculated using field-based and LiDAR-based elevation water surface grids. The average cross sectional elevation value for field and LiDAR data were then exported to Excel files, merged with longitudinal distance between cross section, and used to calculate field survey-based and LiDAR-based slopes between cross sections. Reaches 2 and 3, for which only LiDAR data were available, were sampled using the same cross sectional approach used in Reach 1. The data extracted from these reaches were used to characterize how LiDAR-based elevations, slopes and point densities interact with varying water surface roughness. Within Reach 2, 359 cross sections were drawn and elevations were sampled every five meters along each cross section creating 3,456 cross section sample locations (Figure 9). Reach 3 contained 550 cross sections and 3,348 cross section sample locations (Figure 10). Slopes were calculated between each cross section. 33 Evaluating LiDAR Slope Accuracies and Controls The accuracy of elevation data is the major control on slope accuracy, so a comparative analysis was performed using field survey and LiDAR elevations. First, field-based and LiDAR slopes were calculated at distance intervals of five, ten and twenty meters using average cross section elevations to test the sensitivity of the slopes to vertical inaccuracies in the LiDAR data. The field and LiDAR elevations were differenced using the same points used to create average cross section elevations. Differences were plotted in the form of histogram and cumulative frequency plot after transforming them into absolute values. Descriptive statistics were calculated to examine the range, minimum, maximum, and mean offset between data sets. Finally LiDAR and field-based values were compared using regression analysis. This study also examined the effects of water surface roughness on LiDAR elevation measurements, LiDAR point density, and LiDAR derived water surface slopes. Each reach was divided into smooth and rough sections based on visual analysis of the orthophoto data. One-meter resolution slope rasters were created from the LiDAR water surface grids using ArcGIS Spatial Analyst. One meter resolution point density grids were created from LiDAR point data (ArcGIS Spatial Analyst). Using the cross section sample points, values for water surface type, elevation, slope, and point density were extracted within each reach. Point sample data were transferred to tabular format, and average values were generated for each cross section. These tables were used to calculate 34 descriptive statistics associated with water surfaces such as elevation variance, average slope variance, average point density, and average slope. It is assumed in this study that smooth water surfaces are associated with pools and thus ought to have relatively low slopes. Conversely rough water surfaces are assumed to be representative of riffles and rapids, and thus ought to have relatively steeper slopes. Reach 1 contains field data, so slopes from LiDAR and field data were compared with respect to water surface conditions as determined from the aerial photos. 35 CHAPTER V RESULTS Results of this study encompass three analyses. Elevation analysis describes the statistical difference between LiDAR and field-based water surface elevations for Reach 1. Slope analysis compares LiDAR derived and field-based slopes calculated at 5, 10, and 20m longitudinal distances. These analyses aim to quantify both slope accuracy and slope sensitivity. Lastly, water surface analysis examines the relationship between LiDAR measured water surface slopes, point density, and water surface roughness. Comparison of Absolute Elevations from Field and LiDAR Data in Reach 1 The difference between water surface elevations from LiDAR affects the numerator within the rise over run equation, which in tum affects slope. This elevation analysis evaluation quantifies differences between field and LiDAR data. LiDAR-based cross section elevations were differenced from field-based cross section elevations. Difference values were examined through statistical analysis. 36 In terms of absolute elevations relative to sea level, the majority of LiDAR-based water surface elevations were lower than field-based elevations, although the LiDAR elevations were higher in the upper portion ofReach 1. Differences ranged between -0.04 and 0.05m with a mean absolute difference between field and LiDAR elevations of 0.02m (Figure 14 and Table 2). The range of differences is within the expected relative accuracies of LiDAR claimed by the LiDAR provider. Elevations for field and LiDAR data are significantly correlated with an R2 of 0.94 (Figure 15). The negative offset was expected given that discharge at time of LiDAR acquisition was lower than discharge at time of field data acquisition. Discharge during field acquisition ranged between 22.5 and 22.7 cfs, while discharge during LiDAR acquisition was between 20.8 and 21.8cfs. The portion of Reach 1 where LiDAR water surface measurements were higher than field measurements may be related to difference in discharge or change in bed configuration. Overall results showed that LiDAR data and field-based water surface measurements are comparable. 37 Distribution of Elevation Differences Between Field and LiDAR Water Surfaces 10 9 8 7 >. 6 u r:: ell 5 :l C'" ~ 4 u.. 3 2 0+---+ -0.05 -0.04 -0.03 -0.02 -0.01 0 0.01 0.02 0.03 0.04 0.05 More Elevation Difference, Field - L1DAR (m) Figure 14. Differences Between LiDAR and Field Based Elevations. Elevation difference statistics between cross sections derived from field and LiDAR elevation data. Positive differences indicate that field-based elevations were higher than LiDAR; negative differences indicate LiDAR elevations were higher. Values on x axis represent minimum difference within range. For example, the 0.01 category includes values ranging from 0.01 to 0.0199. y-1.18x-1.03 .... R2 =0.94 ""..,; I •• ./... ./ .- ./ • ./ • ./. /""I ./iI ../. _._~. -? , 38 Table 2. Results of LiDAR and Field Elevation Comparison. Descriptive and regression statistics for absolute difference lField - LiDARI values between cross section elevations. All units in meters. Sample size is 37. Mean 0.028 Median 0.030 Standard Deviation 0.013 Kurtosis -0.640 Skewness -0.484 Range of difference 0.093 Minimum difference 0.002 Absolute maximum difference 0.047 Confidence Level(95.0%) (m) 0.004 Elevation Comparison of Field and LiDAR Water Surface Elevations 5.72 5.70 ~_ 5.68 g 5.66 :0:; I1l 5.64 > iii 5.62 ell 5.60 () ~ 5.58 ~ 5.56 ~ 5.54 1\1 5.52 ~ IX 5.50 Cll 5.55 • • ~ • • w 5.50 • • • • • • • • • 5.45 5.40 0 20 40 60 80 100 120 140 160 180 Longitudinal Distance Down Stream (m) B 20 meter Longitudinal Profile Comparison 5.75 5.70 • ,. 20 Cll 5.55 •• Q) W 5.50 •• • , 5.45 . 5.40 0 20 40 60 80 100 120 140 160 180 Longitudinal Distance Down Stream (m) C Figure 16. Comparison of LiDAR and Field Longitudinal Profiles (5, 10, 20 meters). Longitudinal profiles of a) 5 meter, b) 10 meter, and c) 20 meter cross section elevations. 41 Slope Comparisons Slope in this study is calculated as the dimensionless ratio of rise over run. As noted in the Methods section, slopes were calculated over three different horizontal intervals to test the sensitivity of the LiDAR's internal relative accuracy. Differences in Sm LiDAR and field-based slopes derived from cross sections reveal substantial scatter (Figure l7a), although they clearly covary. Ten meter interval slopes show a stronger relationship (Figure 17b), while slopes based on cross sections spaced 20 m apart have the strongest relationship (Figure l7c). The slope associated with regression of field and LiDAR elevation data is not approximately 1 as one might expect. This is because LiDAR elevations are higher than field elevations at the upstream end of the reach, and lower at the downstream end. 42 5m Slope Comparison -c: ~ -0:: Q) (/l ~ ~.01 Q) C. .2 en 0:: « 0 ::i A -c: ~ 0:: --Q) (/l i2 -0.01 Q) C. 0 en 0:: « 0 ::i B 0.004 = 0.58x - 0.001 R2 = 0.38 ~.008 -0.008 Field Slope (Rise/Run) 10 meter Slope Comparison 0.004 y = 0.63x - 0.001 R2 = 0.51 -0.008 -0.008 Field Slope (Rise/Run) 20 meter Slope Comparison • 0.004 0.002 0.004 C :::l -0:: Q) (/l i2 ~.01 -Q) c. o Ci5 0:: « o~ 0.004 =0.66x - 0.001 R2 = 0.80 ~.008 ~.006 -0.008 Field Slope (Rise/Run) 0.002 0.004 C Figure 17. Regression of Field and LiDAR Based Slopes (5,10,20 meters). Scatter plots showing comparisons between slope values calculated at distance intervals of a) 5 meters, b) 10 meters, and c) 20 meters. 43 Figure 18 shows how the range of differences between LiDAR and field-based water surface slopes decrease as longitudinal distance increases. Five meter slope differences ranged between -0.004 and 0.004 (Figure 18a). Ten meter slope differences ranged between -0.002 and 0.003 (Figure 18b). Twenty meter slope differences ranged between 0 and 0.002 (Figure 18c). 44 Differences of Slope at 5m Between Field and LiDAR 10 » 8 0c Ql 6 :J 0" 4 .Q..l u. 2 0 SIll>< SIl"> SIll\- ~<::J <;:><::J <;:><::J SIl" ~ SIl" SIll\- SIl"> SIll>< ~/l, r;:,<::J ~'::; ~'::; ~'::; ~'::; ~o Slope Difference (Field-LiDAR) A Differences of Slope at 10m Between Field and L1DAR 7 6 ~ 5 lii 4 :J 0" 3 ~ u. 2 1 o +---+--~--;..J SIll>< ~<::J Slope Difference (Field-LiDAR) B Differences of Slope at 20m Between Field and LiDAR 4 ~~I\- ~~" ~ ~~" ~~I\- ~~"> ~~I>< o"/l, <;:>.~. ~.~.~.~. ~ Slope Difference (Field-LiDAR) o +---+--+--+--t- SIll>< SIl"> <;:><::J <;:><::J ~ 3 c Ql :J 2 0" ~ U. C Figure 18. Differences Between LiDAR and Field Based Slopes (5, 10,20 meters). Histogram charts showing difference values between field and LiDAR derived slopes at a) 5 meter slope distances, b) 10 meter slope distances, and c) 20 meter slope distances. 45 The mean difference between slopes decreases from 0.0017 to 0.0007 as slope distance interval is increased. Maximum slope difference and standard deviation of offsets decrease from 0.001 to 0.0005 and 0.0047 to 0.0014 respectively. Regression analysis of these data show a significant relationship for all three comparisons, and adjusted R2 increased from 0.357 to 0.763 with slope distance interval (Table 3). Table 3. Results of LiDAR and Field Slope Comparison (5, 10,20 meters). Descriptive and regression statistics for offsets between field and LiDAR derived slope values (Field minus LiDAR). Slope values are dimensionless rise / run. All data is significant at 0.01. Distance Interval 5m 10m 20m Mean 0.0017 0.0012 0.0007 Standard Deviation 0.0010 0.0007 0.0005 Range of Difference 0.0080 0.0047 0.0024 Minimum difference 0.0000 0.0000 0.0001 Maximum difference 0.0047 0.0026 0.0015 Count 36 16 8 Adjusted R squared 0.36 0.47 0.76 Water surface slope for the entire length of Reach 1 (l59.32m) was compared and yielded a difference of 0.0005. This difference is smaller (by 0.0002) than the difference between 20 meter slope (Table 4). Slope was calculated by differencing the most upstream and downstream cross sections and dividing by total length of reach. Differences between LiDAR and field-based slopes may represent real change due to the five day lag between data sets and difference in discharge. 46 Table 4. Results of Reach 1 Slope Comparison. Comparison of slopes calculated using the farthest upstream and downstream cross section elevation values. Slope values have dimensionless units stemming from rise over run. Upper Lower Reach Elevation (m) Elevation (m) Len2th (m) Slope Field 5.652 5.491 159.32 -0.0010 LiDAR 5.697 5.455 159.32 -0.0015 Surface Roughness Analysis Water surface condition was characterized as smooth or rough based on 2006 aerial photography (Figure 19). Surface roughness was examined to understand its effect on LiDAR data within the active channel, as well as LiDAR's ability to potentially capture difference in water surface turbulence. Table 5 shows statistics with relation to water surface condition for all three reaches. 47 Figure 19. Relationship of Water Surfaces to LiDAR Point Density. 2006 aerial photos were used to delineate rough and smooth water surfaces. Image on left shows a transition between rough water surface (seen as white water) and smooth water surface (seen as upstream pool). Image on right shows LiDAR point density in points per square meter. In all reaches point density, variance of elevations, and water surface slopes were significantly higher in rough surface conditions. These results indicate that LiDAR point density is directly related to the roughness of a water surface and that is capturing the rough water characteristics one would expect in areas where turbulence generates surface waves. 48 Table 5. Water Surface Roughness Results for Reach 1,2, and 3. Water surface statistical output for rough and smooth water surface of Reaches 1, 2, and 3. Results within table represent average values for each Reach. Slope values have dimensionless units from rise over run equation derived from ESRI generated slope grid. Point density values based on points/m2 • Elevation variance in meters. Reach 1 Reach 2 Reach 3 Rou~h water No. of Sample Points 153 1981 1968 Avg Slope -0.013 -0.011 -0.007 Point Density (pts/mL ) 1.195 1.002 1.217 Elevation Variance (m) 0.003 0.018 0.041 Smooth water No. of Sample Points 290 1474 1378 Avg Slope 0.0075 -0.0006 -0.0033 Point Density (pts/mL ) 0.149 0.550 0.480 Elevation Variance (m) 0.001 0.0077 0.024 Within Reach 1, cross section elevations were separated into rough and smooth water conditions and slopes were calculated using field and LiDAR data sets (Table 6). Again, results showed that rough water surfaces have greater slopes than smooth water surfaces. The smooth water surface of Reach 1 yielded a larger discrepancy between field and LiDAR derived slopes compared to rough water surface. This is because small differences between LiDAR and field elevations generate larger proportional error in the rise / run equation when total elevation differences between upstream and downstream are small. 49 Table 6. Results of Reach 1 Water Surface Roughness Comparison. Reach 1 water surface roughness slope analysis. Reach 1 was divided into smooth and rough water surfaces based upon visual characteristics present in aerial photography. Slopes were calculated for each area and compared with field data to examine accuracy. Surface Reach Upper Lower Slope Type Lenl!th (m) Elevation (m) Elevation (m) Slope Difference Field Smooth 83.11 5.652 5.642 -0.0001 N/A LiDAR Smooth 83.11 5.697 5.612 -0.0010 0.0009 Field Rough 71.73 5.635 5.491 -0.0020 N/A LiDAR Rough 71.73 5.592 5.455 -0.0019 -0.0001 Prior to collections of the 2007 data, Reach 3 contained the former Marmot Dam that was dismantled on October 19th , 2007 (Figure 20). The areas at and directly below the dam are rough water surfaces. The super critical flow at the dam yielded a slope of - 0.896 (Table 7). The run below the dam contained low slope values of less than -0.002. Both the dam fall and adjacent run yielded high point densities of greater than 2 points per square meter. 50 Cross Sections o Cross Section Sample Locations L1DAR derived Slope Model Value Higll 178814133 25 50 75 100 125 150 ~.',eters I I I I I I La,·, 0003936 Figure 20. Marmot Dam: Orthophotography and Colorized Slope Model. Mannot Dam at far upstream portion of Reach 3. Image on left shows dam site in 2006 orthophotography. Image on right shows the increase in slope associated with the dam. Marmot Dam was removed Oct. 19th , 2007. Table 7. Subset of Reach 3 Water Surface Roughness Analysis Near Marmot Dam. Subset of Reach 3 immediately surrounding Marmot Dam roughness analysis containing values for Mannot Dam. The roughness results fell within expectations showing increases in slope at the dam fall and high point densities at the dam fall and immediate down stream run. Habitat Type Avg Slope Point Density Point Density Variance Dam Fall -0.896 2.284 1.003 Dam Run -0.001 2.085 5.320 51 CHAPTER VI DISCUSSION The elevation analysis portion of this study shows that LiDAR can provide water surface profiles and slopes that are comparable to field-based data. The differences between LiDAR and field based measurements can be attributed to three potential sources. The first is the relative accuracy of the LiDAR data which has been reported between O.05m and O.06m by the vendor. The second source can be associated with the accuracy of field based measurements which are similar to the relative accuracy of the LiDAR (O.03m-O.05m). Lastly, the discharge differed between field data collection and LiDAR collection by O.02cms. It is possible that much of the O.05m difference observed through most of the Reach 1 profile (Figure 16) could be attributed to the difference in discharge and changes in bed configuration, but without further evidence, the degree of difference due to error or real change cannot be identified. Even if one attributes all the difference to error in LiDAR measurements, the overall correspondence ofLiDAR and field measurement (Figure 15 and 16) indicates that LiDAR-based surveys are useful for many hydrologic applications. 52 In the upper portion of the reach, the profiles display LiDAR elevations that are higher than the field data elevations, whereas the reverse is true at the base of the reach. This could be a function of difference in discharge between datasets, change in bed configuration, or an artifact of low point density. Low density of points forces greater lengths of interpolation between LiDAR points leading to a coarse DEM (Figure 21). Overall, the analysis Reach 1 profile indicates that LiDAR was able to match the fieldbased elevation measurements within ±O.05m. 53 Rough & Smooth Wa~t:e:-r~S~u=rf;:a~c:e:s~rz~~J,;~~ Grid Interpolation in Low Point Density Figure 21. LiDAR Point Density versus Interpolation. Side by side image showing long lines of interpolation associated with smooth water surfaces (right image). Smooth water surfaces tend to have low LiDAR point density. The image on the right shows a hillshade ofthe LiDAR DEM. The DEM has been visualized using a 2 standard deviation stretch to highlight long lines of interpolation. The comparability of LiDAR and field-based slopes showed a significant trend with increasing downstream distances between cross sections. Adjusted R2 values increased from 0.36 to 0.76 and the range of difference between field and LiDAR based slopes decreased from 0.0047 to 0.00 14 as longitudinal distance increased from 5 to 20- 54 m. This suggests that the 0.05m of expected variation of LiDAR derived water surface elevation has less effect on water surface slope accuracy as distance between elevation measurements points increases. Likewise, slopes accuracies along rivers with low gradients will improve as the longitudinal distance between elevation points increases. Overall, data has shown that LiDAR can measure water surface slopes with mean difference relative to field measurements of 0.017, 0.012, and 0.007 at horizontal distances of 5, 10, and 20 meters respectively. Although the discrepancy between field and LiDAR-based slopes is greatest at 5-m intervals, the overall slopes (Fig 17) and longitudinal profiles (Fig 16) even at this distance generally correspond. The use of a 5m interval water surface slope as a basis for comparison is really a worst case example, as water surface slopes are usually measured over longer reach scale distances where the discrepancy between LiDAR and field-based measurements is lower. The continuous channel coverage and accuracies derived from LiDAR represent a new level of accuracy and precision in terms of spatial extent and resolution of water surface slope measurements. Analysis of surface roughness found that rough water surfaces had significantly higher point densities than smooth water surfaces. Rough water surfaces averaged at least 1 point/m2 , while smooth water surfaces averaged less than 1 point/2m2 • Longitudinal profiles of Reach 1 indicate the most accurate water surface measurements occur in areas of higher point density (Fig. 16). Future applications that attempt to use 55 LiDAR to measure water surface slope ought to sample DEM elevations from high point density areas of channel. Water surface analysis also showed trends relating water surface roughness and slope. Rough water surfaces for all three analysis reaches averaged larger average slope values than smooth water surfaces. This is because rough water surfaces are commonly associated with steps, riffles, and rapids. All three of these habitat types are areas have higher slopes than smooth water habitats. Smooth water surfaces are commonly associated with pools or glides, which would be areas of lower slope. Future research should examine the potential for using LiDAR to characterize stream habitats based on in-stream point density and slope. This study is not without its limitations. The field area used to test the accuracy of LiDAR is only representative of a small portion of the Sandy River. Comparisons of field and LiDAR data would be improved by having mid-channel field data. One might also question the use of field based water surface slopes as control for measuring "accuracy". Water surface slope is difficult to measure for reasons stated earlier in this paper. One might make the argument that there is no real way to truly measure LiDAR accuracy of water surface slope, and that LiDAR and field based measurements are simply comparable. In this context, LiDAR holds an advantage over field based measurements given its ability to measure large sections of river in a single day. LiDAR has a distinct advantage over traditional methods of measurement in that measurements are returned from the water surface, and consequently not subject to errors 56 associated with variability of surface turbulence piling up against the measuring device. LiDAR can also capture long stretches of channel within a few seconds reducing the influence of changes in discharge. LiDAR data in general does have its limitations. LiDAR data are only as accurate as the instrumentation and vendor capabilities. LiDAR must be corrected for calibrations and GPS drift to create a reliable data set, and not all LiDAR vendors produce the same level of quality. LiDAR data may be more accurate in some river reaches than others. The study reaches of this study contained well defined open channels, which made identifying LiDAR returns off the water surface possible. Both LiDAR data sets were collected at low flows. Flows that are too low or channels that are too narrow may limit ability to extract water surface elevations because of protruding boulders or dense vegetation that hinders accurate measurements. In some cases vegetation within and adjacent to the channel may interfere with LiDAR's ability to reach the water surface. Researchers should consider flow, channel morphology, and biota when obtaining water surface slopes from LiDAR. 57 CHAPTER VII CONCLUSION This paper examined the ability of LiDAR data to accurately measure water surface slopes. This study has shown that LiDAR data provides sufficiently accurate elevation measurements within the active channel to accurately measure water surface slopes. Measurement of water surface slope with LiDAR provides researchers a tool which is both more efficient and cost effective in comparison with traditional field-based survey methods. Additionally, analysis showed that LiDAR point density is significantly higher in rough surface conditions. Water surface elevations should be gathered from high point density areas as low point density may hinder elevation accuracy. Channel morphology, gradient, flow, and biota should be considered when extracting water surface slopes as these attributes influence water surface measurement. Further study should examine accuracy of LiDAR derived water surface slopes in channel morphologies other than those in this study. Overall, the recognition that LiDAR can accurately measure water surface slopes allows researchers an unprecedented ability to study hydraulic processes for large stretches of river. Common: APPENDIX ARCGIS VBA SCRIPT CODE 58 Public g---.pStrmLayer As ILayer ' stream centerline layer selected by user (for step 1) Public g_StrearnLength As Double ' stream centerline length (for step 1) Public g_InputDistance As Integer 'As Double 'distance entered by user (for step 1) Public g_NumSegments As Integer I number of sample points entered by user (for step 1) Public gyPointLayer As ILayer I point layer created from stream centerline (for step 1) Public g]ntShpF1Name As String I point layer pathname (for step 1) Public gyMouseCursor As IMouseCursor 'mouse cursor Public g_LinearConverson As Double I linear conversion factor Public gyDEMLayer As IRasterLayer I DEM layer (for steps 3 and 4) Public g_DEMConvertUnits As Double I DEM vertical units conversion factor (for steps 3 and 4) Public g_MaxSearchDistance As Double 'maximum search distance (for step 4) Public L NumDirections As Integer I number of directions to search in (for step 4) Public g_SampleDistance As Double 'sample distance (for step 5) Public g_SampleNumber As Double ' total sample points (for step 5) Public g_VegBeginPoint As Boolean I where to start the calucaltion (for step 5) Public g_VegCaclMethod As Boolean 'which method for Vegetation Calculation (for step 5) Public gyContribLayer As ILayer ' contributing point layer (for step 6) Public gyReceivLayer As ILayer 'receiving point layer (for step 6) Public gyOutputLayerName As String I output shapefile (for step 6) Function VerifyField(fLayer As ILayer, fldName As String) As Boolean I verify that topo fields are in the stream centerline point layer Dim pFields As IFields Dim pField As IField Dim pFeatLayer As IFeatureLayer Dim pFeatClass As IFeatureClass Set pFeatLayer = fLayer Set pFeatClass = pFeatLayer.FeatureClass Set pFields = pFeatClass.Fields For i = 0 To pFields.FieldCount - 1 Set pField = pFields.Field(i) 'MsgBox pField.Name IfpField.Name = fldName Then VerifyField = True Exit Function End If Next VerifyField = False End Function Function Ca1cPointLatLong(inPnt As IPoint, inLayer As ILayer) As IPoint , in point layer Dim pFLayer As IFeatureLayer Set pFLayer = inLayer , spatial reference environment Dim pInSpatialRef As ISpatialReference Dim pOutSpatialRef As ISpatialReference Dim pGeoTrans As IGeoTransformation Dim pInGeoDataset As IGeoDataset Set pInGeoDataset = pFLayer Dim pSpatRefFact As ISpatialReferenceFactory , get map units of shapefile spatial reference Dim pPCS As IProjectedCoordinateSystem Set pPCS = pInGeoDataset.SpatialReference 'set spatial reference environment Set pSpatRefFact = New SpatialReferenceEnvironment Set pInSpatialRef= pInGeoDataset.SpatialReference 'MsgBox pInSpatialRef.Name Set pOutSpatialRef= pSpatRefFact.CreateGeographicCoordinateSystem(esriSRGeoCS_WGS1984) Set pGeoTrans = pSpatRefFact.CreateGeoTransformation(esriSRGeoTransformation_NADI983_To_WGS1984_1) Dim pOutGeom As IGeometry2 Set Ca1cPointLatLong = New Point Set CalcPointLatLong.SpatialReference = pInSpatialRef Ca1cPointLatLong.PutCoords inPnt.X, inPnt.Y Set pOutGeom = Ca1cPointLatLong pOutGeom.ProjectEx pOutSpatialRef, esriTransformForward, pGeoTrans, 0, 0, ° 'MsgBox inPnt.X &" "& inPnt.Y & vbCrLf& Ca1cPointLatLong.X &" "& Ca1cPointLatLong.Y End Function Sub OpenGxDialogO Dim pGxdial As IGxDialog Set pGxdial = New GxDialog pGxdial.ButtonCaption = "OK" pGxdial.Title = "Create Stream Centerline Point Shapefile" pGxdial.RememberLocation = True Dim pShapeFileObj As IGxObject Dim pGxFilter As IGxObjectFilter Set pGxFilter = New GxFilterShapefiles 'e.g shp Set pGxdial.ObjectFilter = pGxFilter If pGxdial.DoModaISave(ThisDocument.Parent.hWnd) Then Dim pLocation As IGxFile Dim fn As String 59 Set pLocation = pGxdial.FinalLocation fn = pGxdial.Name End If If Not pLocation Is Nothing Then LPntShpFlName = pLocation.Path & "\" & fn frmlB.tbxShpFileName.Text = g]ntShpFlName frmlB.cmdOK.Enabled = True End If End Sub Function GetAngle(pPolyline As IPolyline, dAlong As Double) As Double Dim pi As Double pi = 4 * Atn(l) Dim dAngle As Double Dim pLine As ILine Set pLine = New Line pPolyline.QueryTangent esriNoExtension, dAlong, False, 1, pLine , convert from radians to degrees dAngle = (180 * pLine.Angle) / pi I adjust angles , ESRI defines 0 degrees as the positive X-axis, increasing counter-clockwise I Ecology references 0 degrees as North, increasing clockwise If dAngle <= 90 Then GetAngle = 90 - dAngle Else GetAngle = 360 - (dAngle - 90) End If End Function Function FeatureExists(strFeatureFileName As String) As Boolean On Error GoTo ErrHandler: Dim pWSF As IWorkspaceFactory Set pWSF = New ShapefileWorkspaceFactory Dim pFeatWS As IFeatureWorksiJace Dim pFeatDS As IFeatureClass Dim strWorkspace As String Dim strFeatDS As String strWorkspace = SplitWorkspaceName(strFeatureFileName) & "\" strFeatDS = SplitFileName(strFeatureFileName) If PWSF.IsWorkspace(strWorkspace) Then Set pFeatWS = pWSF.OpenFromFile(strWorkspace, 0) Set pFeatDS = pFeatWS.OpenFeatureClass(strFeatDS) End If 60 FeatureExists = (Not pFeatDS Is Nothing) Set pWSF =Nothing Set pFeatWS = Nothing Set pFeatDS = Nothing Exit Function ErrHandler: FeatureExists = False End Function 'Returns a Workspace given for example C: \temp\dataset returns C:\temp Function SplitWorkspaceName(sWholeName As String) As String On Error GoTo ERH Dim pos As Integer pos = InStrRev(sWholeName, "\") If pos > 0 Then SplitWorkspaceName = Mid(sWholeName, 1, pos - 1) Else Exit Function End If Exit Function ERH: MsgBox "Workspace Split" & Err.Description End Function 'Returns a filename given for example C:\temp\dataset returns dataset Function SplitFileName(sWholeName As String) As String On Error GoTo ERH Dim pos As Integer Dim sT, sName As String pos = InStrRev(sWholeName, "\") Ifpos > 0 Then sT = Mid(sWholeName, 1, pos - 1) Ifpos = Len(sWholeName) Then Exit Function End If sName = Mid(sWholeName, pos + 1, Len(sWholeName) - Len(sT)) pos = InStr(sName, ".") If pos > 0 Then SplitFileName = Mid(sName, 1, pos - 1) Else SplitFileName = sName End If End If Exit Function ERH: 61 • MsgBox "Workspace Split:" & Err.Description End Function Public Sub BusyMouse(bolBusy As Boolean) 'Subroutine to change mouse cursor If g---'pMouseCursor Is Nothing Then Set g---'pMouseCursor = New MouseCursor End If IfbolBusy Then g---'pMouseCursor.SetCursor 2 Else g---'pMouseCursor.SetCursor 0 End If End Sub Function MakeColor(lRGB As Long) As IRgbColor Set MakeColor =New RgbColor MakeColor.RGB = lRGB End Function Function MakeDecoElement(pMarkerSym As IMarkerSymbol, _ dPos As Double)_ As ISimpleLineDecorationElement Set MakeDecoElement

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