CLASlite software

Monitoring tropical deforestation and forest degradation with satellites can be an everyday activity for people who support environmental conservation, forest management, and resource policy development. Through extensive observation of user needs, CLASlite was developed to assist with high-resolution mapping and monitoring of forests using satellite imagery. The latest CLASlite v3.3 supports images from nine satellites (Landsat 8, 7, 5, 4, SPOT 4 , 5, ALI, ASTER, Sentinel-2 support and Google Earth Engine ).

The CLASlite project was originally funded by the Morgan Family Foundation for use by governments, nongovernmental organizations, and academic institutions. That funding ended in 2018. CLASlite is now managed by i-Cultiver, Inc. and is supported by licensing fees. Now CLASlite is available for industry use as well.

Additional Information: English or Spanish

To get more involved with CLASlite

Join the CLASlite Google Group. Connect with other CLASlite users and receive occasional updates. To request to join the CLASlite Group, send an email to info@i-cultiver.com with “Request to join CLASlite Group” in the Subject line of your email.

Contribute to the CLASlite Community. Share your CLASlite Success Story with the CLASlite Community . You can get a CLASlite Success Story Certificate and we may post your success story to share with the CLASlite Community.

About

The power of CLASlite rests in its unique ability to convert seemingly green “carpets” of dense tropical forest cover found in the basic satellite images into highly detailed maps that can be readily searched for deforestation, logging and other forest disturbance events.

Areas where clearing, logging and other forest disturbances have recently occurred are accentuated and easily identified by CLASlite’s algorithms. Both deforestation and secondary forest regrowth can also be tracked by the user of CLASlite.

What is CLASlite?

CLASlite is a highly automated system for converting 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.

Testimonial

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