Deforestation and Technology: Towards Intelligent Forest Management
Monitoring tropical forests: scientific and technological approaches
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This study analyzes the drivers of deforestation in the Tumba-Lediima Nature Reserve, using historical data on forest cover from 2010 to 2022. Its main objective is to understand anthropogenic activities and develop a computerized system to detect deforestation, in order to guarantee the reserve's sustainability. Landsat 7 and 8 satellite images were acquired for 2010 and 2022, processed and classified to generate two land-use maps. A future projection was made with 2022 as the reference year. A machine learning model was developed using Python, exploiting a database of images simulating defor...
This study analyzes the drivers of deforestation in the Tumba-Lediima Nature Reserve, using historical data on forest cover from 2010 to 2022. Its main objective is to understand anthropogenic activities and develop a computerized system to detect deforestation, in order to guarantee the reserve's sustainability. Landsat 7 and 8 satellite images were acquired for 2010 and 2022, processed and classified to generate two land-use maps. A future projection was made with 2022 as the reference year. A machine learning model was developed using Python, exploiting a database of images simulating deforestation.the results include a detailed analysis of deforestation, accurate maps of forest cover and an assessment of human impacts on ecosystems. The maps show a reduction in forest cover between 2010 and 2022, with losses expected by 2050. A survey of 192 neighbouring households reveals heavy involvement in agriculture and tree felling, compounded by population growth.
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