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Article

Dynamic Simulation of Land Use/Cover Change and Assessment of Forest Ecosystem Carbon Storage under Climate Change Scenarios in Guangdong Province, China

1
College of Forestry, Nanjing Forestry University, Nanjing 210037, China
2
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3
Anhui Province Key Laboratory of Physical Geographical Environment, Chuzhou 239000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(10), 2330; https://doi.org/10.3390/rs14102330
Submission received: 13 April 2022 / Revised: 8 May 2022 / Accepted: 9 May 2022 / Published: 11 May 2022
(This article belongs to the Special Issue Monitoring Forest Carbon Sequestration with Remote Sensing)

Abstract

Exploring the spatial distribution of land use/cover change (LUCC) and ecosystem carbon storage under future climate change scenarios can provide the scientific basis for optimizing land resource redistribution and formulating policies for sustainable socioeconomic development. We proposed a framework that integrates the patch-generating land use simulation (PLUS) model and integrated valuation of ecosystem services and tradeoffs (InVEST) model to assess the spatiotemporal dynamic changes in LUCC and ecosystem carbon storage in Guangdong based on shared socioeconomic pathways and representative concentration pathways (SSP-RCP) scenarios provided by the Coupled Model Intercomparison Project 6 (CMIP6). The future simulation results showed that the distribution patterns of LUCC were similar under SSP126 and SSP245 scenarios, but the artificial surface expanded more rapidly, and the increase in forest land slowed down under the SPP245 scenario. Conversely, under the SSP585 scenario, the sharply expanded artificial surface resulted in a continuous decrease in forest land. Under the three scenarios, population, elevation, temperature, and distance to water were the highest contributing driving factors for the growth of cultivated land, forest land, grassland, and artificial surface, respectively. By 2060, the carbon storage in terrestrial ecosystems increased from 240.89 Tg in 2020 to 247.16 Tg and 243.54 Tg under SSP126 and SSP245 scenarios, respectively, of which forest ecosystem carbon storage increased by 17.65 Tg and 15.34 Tg, respectively; while it decreased to 226.54 Tg under the SSP585 scenario, and the decreased carbon storage due to forest destruction accounted for 81.05% of the total decreased carbon storage. Overall, an important recommendation from this study is that ecosystem carbon storage can be increased by controlling population and economic growth, and balancing urban expansion and ecological conservation, as well as increasing forest land area.
Keywords: carbon storage; climate change; land use/cover change; scenario simulation; PLUS model; InVEST model carbon storage; climate change; land use/cover change; scenario simulation; PLUS model; InVEST model
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MDPI and ACS Style

Tian, L.; Tao, Y.; Fu, W.; Li, T.; Ren, F.; Li, M. Dynamic Simulation of Land Use/Cover Change and Assessment of Forest Ecosystem Carbon Storage under Climate Change Scenarios in Guangdong Province, China. Remote Sens. 2022, 14, 2330. https://doi.org/10.3390/rs14102330

AMA Style

Tian L, Tao Y, Fu W, Li T, Ren F, Li M. Dynamic Simulation of Land Use/Cover Change and Assessment of Forest Ecosystem Carbon Storage under Climate Change Scenarios in Guangdong Province, China. Remote Sensing. 2022; 14(10):2330. https://doi.org/10.3390/rs14102330

Chicago/Turabian Style

Tian, Lei, Yu Tao, Wenxue Fu, Tao Li, Fang Ren, and Mingyang Li. 2022. "Dynamic Simulation of Land Use/Cover Change and Assessment of Forest Ecosystem Carbon Storage under Climate Change Scenarios in Guangdong Province, China" Remote Sensing 14, no. 10: 2330. https://doi.org/10.3390/rs14102330

APA Style

Tian, L., Tao, Y., Fu, W., Li, T., Ren, F., & Li, M. (2022). Dynamic Simulation of Land Use/Cover Change and Assessment of Forest Ecosystem Carbon Storage under Climate Change Scenarios in Guangdong Province, China. Remote Sensing, 14(10), 2330. https://doi.org/10.3390/rs14102330

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