Forest Mask

Forest masks are a basic tool in remote sensing, meant for forest research, aiding in the assessment and monitoring of forested areas worldwide. They provide valuable data on forest cover from remote sensing imagery, aiding in understanding forest dynamics across spatial and temporal scales. These masks are binary classification maps distinguishing between forested and non-forested regions, created using image processing techniques and classification algorithms applied to remote sensing data like multi-spectral and hyper-spectral imagery. Initially based on spectral signatures, newer methods employ machine learning algorithms such as Random Forests, Support Vector Machines, and Convolutional Neural Networks for improved accuracy. Incorporating temporal dynamics is essential due to seasonal changes, achieved through time series analysis and multi-temporal satellite imagery. Forest masks find applications in carbon sequestration, habitat monitoring, and more. The integration of various data sources and advanced techniques enhances their accuracy and applicability in environmental research. As technology advances, refining forest mask methods will bolster global forest ecosystem monitoring and management, supporting sustainable development and conservation efforts.

Solution

Differentiation between forest stand area and non forest areas.

MMU: 0.5 ha

Reporting: Area in ha

Solution

Further Development