Remote sensing, Monitoring
The goal of this work was to determine the current land cover of a concession in Ethiopia.
The sustainable management of forest concessions requires detailed information about the current land cover and the distribution of the land cover classes like a closed forest, bamboo, grassland and bare soil. Knowing the properties and spatial distribution will allow a precise planning of management measures.
For this land cover classification, we have selected Sentinel 2 satellite imagery. Its great advantage is a global weekly revisiting. This enables us to potentially create cloud-free composites on a monthly base even in regions with a high cloud occurrence. The free accessible data enables a cost-efficient generation of time series, which are a valuable source for a better understanding of the land cover during wet and dry seasons.
Carrying out multi-band analysis combined with ground truth data collected with a mobile app, we have been able to train the classification to distinguish various ground cover types.
The result of the classification process: distribution of three different classes of land cover.