Analysis

Spatial Analytics: Being able to visualize, manipulate and analyze spatial data is paramount to forestry management (see article). Many if not all aspects of forest management – forest inventory, forest stands location, harvest schedule, etc … - depend on spatial relationships. There are a number of popular software that have been developed for undertaking GIS, remote sensing or other spatial analysis. Spatial tools for forestry management are included in commercial or open source Geographic Information System (GIS). Commercial GIS software has license restrictions, does not publish the underlying code and charges a licensing fee, but is often better streamlined. Open source GIS software publishes the underlying code, has less restriction on licensing and is free to use for commercial purposes, but is often less streamlined. An illustrative (but non-exhaustive) list of some popular open source and commercial software that can support forest management (e.g. spatial analysis, visualization, integration of GPS and other field data, etc.) include:

Quantum (QGIS) GIS is a free open source program licensed under GNU Public license, QGIS is user friendly and provides a lot of documentation for beginners, it is also one if not the most widely used open source GIS software. QGIS is regularly updated and the code is maintained by a large group of developers. Furthermore, other programs on the list can be added to the QGIS toolset. QGIS can read most vector and raster file formats as well as all the common geodatabase and web map services. QGIS composer can produce maps from various layers.
System for Automated Geoscientific Analyses (SAGA) GIS is a free open source program licensed under GNU Public license. SAGA GIS is a spatial modeling GIS program for intermediate to advanced users in spatial modeling. Users should note that SAGA GIS could be used as part of QGIS or R. SAGA GIS’s strength is in spatial statistics analysis, namely spatial sampling, interpolation and modeling. All SAGA tools can be used to predict forest fire as well as forest health risk indicators.
Geographic Resources Analysis Support System (GRASS) GIS is a free open source program licensed under GNU Public license. GRASS GIS is for advanced users, preferably with experience in GIS and image processing. GRASS GIS is best used for raster analysis. GRASS computes various raster analyses such as Leaf Area Index (LAI), Normalized Difference Vegetation Index (NDVI) or forest fragmentation analysis.
R + spatial packages (sp, rgrass7, raster, rgdal, spdep, rgeos) is a free open source program licensed under GNU Public license. R is a statistical scripting environment and includes many geo statistical analysis packages. R has a steep learning curve and is for advanced users with experience in GIS, spatial data analysis and python or another language. Albeit extremely versatile, R strength lies in script automation and statistical analysis. Shiny provides web-based support for R.
Google Earth Engine is a radically new approach to spatial analysis, combining a growing catalog of satellite imagery and geospatial datasets with cloud computing and collaborative platforms.
IDRISI is a popular image processing software with a suite of associated tools primarily for raster data analysis.
Environmental Systems Research Institute (ESRI) is an industry leader for commercial GIS systems (e.g. ArcGIS), used in over 350,000 organizations around the world. There are also a number of growing applications in forestry.
Intergraph and Hexagon have a range of commercial geospatial software (e.g. ERDAS IMAGINE, GEOMEDIA) that are popular for remote sensing and GIS use.
Harris has a number of products such as ENVI that are very popular for remote sensing analysis.
Explore here to get more of a flavor for the large number of firms and open-sources and commercial spatial analytical products that could be useful in forestry.

Forest-Related Analytics: There are also a number of innovative ways in which data can be analyzed to support decisions. These are increasingly becoming tools that customize or interface other existing platforms and are expected to also be increasingly be automated and cloud-based in the future. This will allow complex computations based on large datasets to be carried out on the cloud from any simple device connected to the internet. Some example of tools that illustrate forest-related analytics include:

Machine Learning is a rapidly-evolving artificial intelligence discipline that helps systems to learn without being explicitly programmed – i.e., which can adapt the programs when exposed to new data. Amusingly in the context of this e-book, one of the popular algorithms used for machine learning is called Random Forest that are collections of decision trees.
USHAHIDI is an innovative, real-time crowdsourcing platform collecting inputs from several sources.
MOABI is a collaborative crowdsourcing mapping system that allows users to collate, analyze, and discuss ecosystem sustainability data.
Carnegie Landsat Analysis System (CLASLite) is a highly automated software to identify deforestation and forest degradation from satellite imagery.
Silvia Terra Products (Canopy, Plot Reduce, Plot Hound) are software programs that combine big data, cloud computing, and other modern ICT tools to support forest inventory and analysis.
Global Allometric Tree tool was the first international web platform to share data and equations for assessing volume, biomass, and carbon stock of trees and forests.
US Forest Service Tools are examples of the types of software and online tools used for various aspects of forest management – from forest health to forest inventory and planning.
Collect Earth is a tool that combines the power for Google Earth, Bing Maps, and Google Earth Engine to facilitate data collection (E.g. to support multi-phase National Forest Inventories) – the data is also exportable to Saiku to facilitate data analysis.
Slope Project: This integrates remote sensing and on-field surveys to support analysis to characterize forest resources.