A novel geospatial machine learning approach to quantify non-linear effects of land use/land cover change (LULCC) on carbon dynamics

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Jing Kang, Bailing Zhang, Anrong Dang

Published: 01 Jan 2024, Last Modified: 13 Nov 2024Int. J. Appl. Earth Obs. Geoinformation 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Highlights•Spatial machine learning to explore nonlinear linkages between annual carbon exchange and LULCC is developed.•New perspective to integrate multi-source remote sensing datasets using GIS is proposed.•Exploring the potential of top-down carbon satellite monitoring inversion data.•Short-term CO2 rebound may be linked to carbon sink loss significantly affected by LULCC.•Deforestation exceeding 1% may require triple the effort of forest regrowth to achieve the compensation effect.