A huge variety of methods and software packages or programming languages exist for each task within Remote Sensing data analysis.
Based on discussions with colleagues from the remote sensing, biodiversity and conservation community we decided to provide only information which is based on OpenSource software to allow everybody to redo the analysis without any legal restrictions.
The amount of OpenSource solutions to analyse and modify spatial data sets is continuously increasing as well as its capabilities. Sometimes the offerend functionalities are exceeding those provided by proprietary software packages but sometimes it is still easier and faster to do it within a proprietary program. We try to ease the learning curve using OpenSource software with spatial data sets.
Useful software packages:
- QGIS.org – an easy to use GIS with limited yet useful functionalities. Especially the growing amount of add-ons are providing very useful functions
- GRASS GIS – a very large GIS and remote sensing program. Very powerful and good for large data analysis.
- R – most powerful and up-to-date statistical software, also with raster data analysis functionality, some useful packages:
- raster package for raster data manipulation
- RStoolbox for remote sensing data analysis
- ropensci.org packages for data retrieval
- Bfast for time-series analysis: bfast.r-forge.r-project.org
- SAGA-GIS.org – a more complicated analysis program for spatial data, but very interesting functions
- GMT – The Generic Mapping Tool produces high level maps but is difficult to learn
- InterImage – An open source knowledge based framework for automatic image interpretation
- GeoDMA – Geographic Data Mining Analyst is a toolbox for integrating remote sensing imagery analysis methods with data mining techniques
- SPRING – a GIS and remote sensing image processing system with an object-oriented data model
- MARXAN – a conservation planning software tool
Various plugins or packages are available for specific tasks, such as an GBIF interface for R.
for R the following packages exist:
a longer list of R packages which retrieve data from online repositories is available here