No you do not. You can call the tools contained within WhiteboxTools completely independent from the Whitebox GAT user interface using a Remote Procedure Call (RPC) approach. In fact, you can interact with the tools using Python scripting or directly, using a terminal application (command prompt). See Interacting With WhiteboxTools From the Command Prompt for further details.
Eventually most of the tools in Whitebox GAT will be ported over to WhiteboxTools and all new tools will be added to this library as well. Naturally, this will take time. The order by which tools are ported is partly a function of ease of porting, existing infrastructure (i.e. raster and LiDAR tools will be ported first since their is currently no support in the library for vector I/O), and interest. If you are interested in making a tool a higher priority for porting or if you have an request for a tool to be added that is not part of Whitebox GAT, i.e. a new feature, then please submit an issue to the GitHub repository. The request will be marked with the enhancement label.
WhiteboxTools was developed with the open-source GIS Whitebox GAT in mind. That said, the tools can be accessed independently and so long as you abide by the terms of the MIT license, there is no reason why other software and GIS projects cannot use WhiteboxTools as well. In fact, this was one of the motivating factors for creating the library in the first place. Feel free to use WhiteboxTools as the geospatial analysis engine in your open-source software project.
WhiteboxTools is developed using the Rust programming language, which supports a wide variety of platforms including MS Windows, MacOS, and Linux operating systems and common chip architectures. Interestingly, Rust also supports mobile platforms, and WhiteboxTools should therefore be capable of targeting (although no testing has been completed in this regard to date). Nearly all development and testing of the software is currently carried out on MacOS and we cannot guarantee a bug-free performance on other platforms. In particularly, MS Windows is the most different from the other platforms and is therefore the most likely to encounter platform-specific bugs. If you encounter bugs in the software, please consider reporting an issue using the GitHub support for issue-tracking.
The answer to this question depends strongly on the type of analysis and data that you intend to process. However, generally we find performance to be optimal with a recommended minimum of 8-16GB of memory (RAM), a modern multi-core processor (e.g. 64-bit i5 or i7), and an solid-state-drive (SSD). It is likely that WhiteboxTools will have satisfactory performance on lower-spec systems if smaller datasets are being processed. Because WhiteboxTools reads entire raster datasets into system memory (for optimal performance, and in recognition that modern systems have increasingly larger amounts of fast RAM), this tends to be the limiting factor for the upper-end of data size successfully processed by the library. 64-bit operating systems are recommended and extensive testing has not been carried out on 32-bit OSs. See "What platforms does WhiteboxTools support?" for further details on supported platforms.
Pre-compiled binaries for WhiteboxTools can be downloaded from the Geomorphometry and Hydrogeomatics Research Group software web site for various supported operating systems. If you need binaries for other operating systems/system architectures, you will need to compile the executable from source files. See Installation for details.
I spent a long time evaluating potential programming language for future development efforts for the Whitebox GAT project. My most important criterion for a language was that it compile to native code, rather than target the Java virtual machine (JVM). I have been keen to move Whitebox GAT away from Java because of some of the challenges that supporting the JVM has included for many Whitebox users. The language should be fast and productive--Java is already quite fast, but if I am going to change development languages, I would like a performance boost. Furthermore, given that many, though not all, of the algorithms used for geospatial analysis scale well with concurrent (parallel) implementations, I favoured languages that offered easy and safe concurrent programming. Although many would consider C/C++ for this work, I was looking for a modern and safe language. Fortunately, we are living through a renaissance period in programming language development and there are many newer languages that fit the bill nicely. Over the past two years, I considered each of Go, Rust, D, Nim, and Crystal for Whitebox development and ultimately decided on Rust. [See GoSpatial and lidario.]
Each of the languages I examined has its own advantages of disadvantages, so why Rust? It's a combination of factors that made it a compelling option for this project. Compared with many on the list, Rust is a mature language with a vibrant user community. Like C/C++, it's a high-performance and low-level language that allows for complete control of the system. However, Rust is also one of the safest languages, meaning that I can be confident that WhiteboxTools will not contain common bugs, such as memory use-after-release, memory leaks and race conditions within concurrent code. Importantly, and quite uniquely, this safety is achieved in the Rust language without the use of a garbage collector (automatic memory management). Garbage collectors can be great, but they do generally come with a certain efficiency trade-off that Rust does not have. The other main advantage of Rust's approach to memory management is that it allows for a level of interaction with scripting languages (e.g. Python) that is quite difficult to do in garbage collected languages. Although WhiteboxTools is currently set up to use an automation approach to interacting with Python code that calls it, I like the fact that I have the option to create a WhiteboxTools shared library.
Not everything with Rust is perfect however. It is still a very young language and there are many pieces still missing from its ecosystem. Furthermore, it is not the easiest language to learn, particularly for people who are inexperienced with programming. This may limit my ability to attract other programers to the Whitebox project, which would be unfortunate. However, overall, Rust was the best option for this particular application.
No, you would only need Rust installed if you were compiling the WhiteboxTools codebase from source files.
Whitebox GAT is frequently praised for its consistent design and ease of use. Like Whitebox GAT, WhiteboxTools follows the convention of one tool for one function. For example, in WhiteboxTools assigning the links in a stream channel network their Horton, Strahler, Shreve, or Hack stream ordering numbers requires running separate tools (i.e. HortonStreamOrder, StrahlerStreamOrder, ShreveStreamMagnitude, and HackStreamOrder). By contrast, in GRASS GIS1 and ArcGIS single tools (i.e. the r.stream.order and Stream Order tools respectively) can be configured to output different channel ordering schemes. The WhiteboxTools design is intended to simplify the user experience and to make it easier to find the right tool for a task. With more specific tool names that are reflective of their specific purposes, users are not as reliant on reading help documentation to identify the tool for the task at hand. Similarly, it is not uncommon for tools in other GIS to have multiple outputs. For example, in GRASS GIS the r.slope.aspect tool can be configured to output slope, aspect, profile curvature, plan curvature, and several other common terrain surface derivatives. Based on the one tool for one function design approach of WhiteboxTools, multiple outputs are indicative that a tool should be split into different, more specific tools. Are you more likely to go to a tool named r.slope.aspect or TangentialCurvature when you want to create a tangential curvature raster from a DEM? If you're new to the software and are unfamiliar with it, probably the later is more obvious. The WhiteboxTools design approach also has the added benefit of simplifying the documentation for tools. The one downside to this design approach, however, is that it results (or will result) in a large number of tools, often with signifcant overlap in function.
1 NOTE: It is not my intent to criticize GRASS GIS, as I deeply respect the work that the GRASS developers have contributed. Rather, I am contrasting the consequences of WhiteboxTools' design philosophy to that of other GIS.