Assessing mangrove deforestation using pixel-based image: a machine learning approach

Mangrove is one of the most productive global forest ecosystems and unique in linking terrestrial marine environment. This study aims to clarify understand artificial intelligence (AI) adoption remote sensing mangrove forests. The performance machine learning algorithms such as random (RF), support vector (SVM), decision tree (DT), object-based nearest neighbors (NN) were used this automatically classify forests using orthophotography applying an approach examine three features (tree cover loss, above-ground carbon dioxide (CO2) emissions, biomass loss). SVM with a radial basis function was remainder images, resulting overall accuracy 96.83%. Precision recall reached 93.33 96%, respectively. RF performed better than other where there no orthophotography.