Spatiotemporal change detection of carbon storage and sequestration in an arid ecosystem by integrating Google Earth Engine and InVEST (the Jiroft plain, Iran)

Abstract Our study uses regional-scale maps to quantify carbon storage and sequestration from different land use types evaluate the effects of future scenarios. We developed an integrated modeling approach assess spatiotemporal impacts use/cover change (LUCC) on provision value during historical period (2000–2019) predicted scenarios (2019–2046) in Jiroft plain, Iran. several analytic tools for our analysis, which was comprised Google Earth Engine (GEE), Cellular Automata Markov Chain (CA-MC) model, Intensity Analysis (IAA), Integrated Valuation Ecosystem Services Tradeoffs (InVEST) model. results demonstrate that: (1) agriculture urban expansion led a considerable decrease storage, mainly due rapid deforestation 2000–2019; (2) if trend continues under business as usual (BAU) scenario, it will lead social costs loss stored plain (2,624,113 Mg) with annual average −475,547 Mg; (3) downward could potentially be reversed by employing environmentally sound planning (ESP) scenario that is estimated save 3,705,491 Mg gain + 605,830 Mg. The design provide useful guide policymakers local governments help understand potential outcomes various development strategies, ultimately more effective ecosystem management.