Meiling Huang , Yusuyunjiang Mamitimin , Abudukeyimu Abulizi , Rebiya Yimaer , Bahejiayinaer Tiemuerbieke , Han Chen , Tongtong Tao , Yunfei Ma
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引用次数: 0
Abstract
Precise forecasting of land use modifications and carbon storage (CS) alterations is essential for effective regulatory measures and ecological quality enhancement. However, there are limited studies on land use dynamics and its impact on CS in the arid regions of Northwest China. Therefore, this study explores land use and CS changes in the Tulufan-Hami Basin from 2000 to 2050. The SD-FLUS and InVEST models were employed to simulate land use patterns and assess CS under three scenarios (SSP126-EP, SSP245-ND and SSP585-ED). The results of our study indicate that the area of cropland and built-up land both increased dramatically from 2000 to 2020, expanding by 347 km2 and 505 km2 respectively. CS initially rose by 0.74 × 106t from 2000 to 2010 but then declined by 1.37 × 106t from 2010 to 2020. Construction expansion and grassland degradation drove the decline. By 2050, the SSP126-EP scenario predicts an increase in CS of 3.64 × 106t compared to 2020. However, both the SSP245-ND and SSP585-ED scenarios show significant decreases, with a decline of 0.55 × 106t and 1.87 × 106t respectively. These findings provide a foundation for global ecological preservation and CS enhancement in arid regions.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.