Estimating afforestation related forest cover change using data fusion and machine learning

Le résumé fourni par la source

Abstract The rapid increase in population and changes in land use have led to the issue of climate change, which is threatening the overall human well-being in general, and particularly the forest resources. Recognizing the rapid decline in the forest cover and in adherence to the Bonn Challenge, Pakistan has initiated the Billion Tree Afforestation Project (BTAP) to restore forests. Hence, there is a need to analyze the spatio-temporal dynamics of forest cover to assess the efficacy of BTAP. The objectives of this study were: (1) to develop machine learning methods that combine Sentinel-1 and Sentinel-2 data to characterize forest cover; and (2) to characterize the forest cover dynamics in the study area during the study period. In the study area, the land cover was classified using two machine learning models: random forests (RF) and support vector machines (SVM). We then used the models to create forest cover maps for the period of 2016 to 2022. Based on the classifications of land cover, the study area was classified into forest and non-forest classes. Finally, the spatiotemporal distribution of the changes induced by afforestation was generated. The results demonstrate an increase of 3.7% in forest cover in the study area during the study period. The increase in forest cover was more prominent in the northern and central regions as compared to that of the southern region. In terms of species, the increase in broadleaved forests was more prominent. The results show that RF produces superior results as compared to the SVM, with overall accuracy and kappa coefficient of 94%–97% & 0.93–0.96 respectively. The overall accuracy and Kappa coefficient of the SVM model range from 92%–94% & 0.91–0.95. The techniques used in this study are cost-effective for accurately monitoring changes in forest cover.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.