Simplified and High Accessibility Approach for the Rapid Assessment of Deforestation in Developing Countries: A Case of Timor-Leste

Forests are essential for sustaining ecosystems, regulating the climate, and providing economic benefits to human society. However, activities such as commercial practices, fuelwood collection, land use changes have resulted in severe forest degradation deforestation. Timor-Leste, a small island nation, faces environmental sustainability challenges due changes, limited infrastructure, agricultural practices. This study proposes simplified highly accessible approach assess deforestation (SHAD) nationally using non-human resources experts, software, hardware facilities. To developing countries, we utilize open-source software (Dryad), employ U-Net deep learning algorithm, data generated from Google Earth Engine platform construct time-series cover classification model Timor-Leste. In addition, map label satellite imagery training inputs, our demonstrates satisfactory performance classifying cover. Next, classify Timor-Leste 2016 2021, verified that achieved high accuracy ranging 0.79 0.89. Thereafter, produced by comparing two maps. The estimated rate was 1.9% annually with primary concentration northwestern municipalities of dense population activities. potential SHAD nationwide, particularly countries scientific experts infrastructure. We anticipate will support development management strategies ecosystem sustainability, climate adaptation, conservation various fields.