GRASS GIS has contained remote sensing tools for decades, catering to the needs of users since the first generations of Landsat satellites. In this talk we will present the current efforts of integrating modern data sources and modern approaches into GRASS GIS. Tools exist for pre-processing recent very high resolution images, object-based image analysis (OBIA), Lidar data handling, etc. At the same time, efforts have gone into ensuring the scalability of tools for huge data sets. The presentation will provide a short brief on the general state of GRASS GIS development, go on to an overview of the remote sensing tools available, to end with a use case on how to use GRASS GIS for time series processing in a high performance cluster/grid computing environment.
GRASS GIS has been existing for over 30 years by now and provides a very large and diverse set of state-of-the-art tools for the analysis of spatial data. Less known by many, remote sensing tools have been part of it almost from the beginning. GRASS GIS provides a series of imagery analysis tools for pre-processing (radiometric correction, cloud detection, pansharpening, etc), creating derived indices (vegetation indices, texture analysis, principal components, fourier transform, etc), classifying (management of training zones, different classifiers, validation tools), and producing other derived products such as evapotranspiration and energy balance models. Next to these tools for satellite images, other tools exist for the handling of aerial photography for creation of orthophotos, and for the import and analysis of Lidar data.
In addition to these tools, efforts have gone into integrating current state-of-the-art methods such as object-based image analysis and machine learning. A complete toolchain exists to segment images using different algorithms, to create superpixels, to collect statistics characterizing the resulting objects, and to apply machine learning algorithms for classification. New modules also include unsupervised segmentation parameter optimization and active learning. Options for pixel-based classification have also been enlarged to a host of machine learning algorithms.
A specific aspect of treating the rapidly increasing amounts of satellite data is the scalability of tools. GRASS GIS has a long tradition of computational efficiency and work is continuously ongoing to increase both computational speed and the handling of huge datasets. Most relevant tools provide the choice to treat data either completely in memory, if enough RAM is available, or with a disk-based tiling scheme that allows treating data much larger than available memory resources would otherwise allow. Through its modular structure, GRASS GIS also allows to easily parallelize certain operations, thus opening the door to the use of cluster/grid computing environments.
The presentation will provide a brief, general introduction to the state of GRASS GIS development, focusing on the recent release of version 7.4. It will then provide an overview of the different elements in the remote sensing toolbox of GRASS GIS. It will end with an explanation of how these tools can be used in grid/cluster computing environments, demonstrated through an example of processing large time series of satellite data.
