Description: The Carnegie Landsat Analysis System – Lite (CLASLite) is a software package designed for highly automated identification of deforestation and forest degradation from remotely sensed satellite imagery. CLASlite converts satellite imagery from its original (raw) format, through calibration, pre-processing, atmospheric correction, and cloud masking steps, Monte Carlo Spectral Mixture Analysis, and expert classification to derive high-resolution output images. Developed by Greg Asner and his team at the Carnegie Institution, CLASLite incorporates state-of-the-art research in remote sensing into a simple, user-friendly yet powerful tool for non-profit institutions and governments in need of technologies for forest monitoring and environmental planning.

CLASLite’s automated satellite mapping approach identifies the most important components of tropical forest structure - fractional cover of vegetation canopies, dead vegetation, and bare surfaces - which further determines ecosystem composition, structure, and biomass. The program uses Automated Monte Carlo Unmixing to monitor forests with error tracking. Several stakeholders have used this software to map deforestation, carbon stocks, mining activity, and for mapping tree species (bamboo types etc.).

Schematic: The core features of TerrSet modeling tools are the same set of features much needed by forestry sector decision making process involving land use planning, better biodiversity and habitat knowledge, assessment of emissions and devising carbon payments.

How can the ICT product/innovation benefit the project?

CLASLite is primarily used for forest mapping in tropical locations, and its special algorithms may be particularly useful for countries such as Indonesia, the DRC, Brazil, Colombia, Malaysia and others containing tropical forests.