The Taihang Mountains are an important ecological barrier in China, and their ecosystems have good carbon sink capacity. Studying the spatial-temporal variation characteristics and driving factors of carbon storage in the Taihang Mountains ecosystem provides decision-making for the construction of “dual carbon” projects and the improvement of ecological environment quality in this region. This paper takes the area in the Taihang Mountains as the research area, based on the land use and carbon density data of 2005, 2010, 2015, and 2019 of the Taihang Mountains, calculates the carbon storage in the region with the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, explores the main factors affecting the spatial differentiation of carbon storage in this region, and analyzes their driving mechanisms by Geodetector. The results show that: (1) From 2005 to 2019, the land use of the Taihang Mountains changed somewhat. The area of forest and construction land increased slightly, while the area of farmland and grassland decreased. (2) The current carbon storage in the Taihang Mountains ranges from 1472.91 × 106 t to 1478.17 × 106 t (t is the abbreviation of ton), and shows a decreasing trend, which is due to the decrease in forest and the increase in construction land. (3) Slope and Normalized Difference Vegetation Index (NDVI) are the main driving factors affecting the spatial variation of carbon storage in the Taihang Mountains ecosystem. Temperature, precipitation, and population density are the secondary factors affecting the spatial variation of carbon storage. (4) The synergy between the driving factors is more potent than the individual factor, which is the most evident between NDVI and slope. This means some areas may have more abundant carbon storage under the combined effect of slope and NDVI.
Many scholars are skeptical about the poverty reduction effect and the ecological effect of poverty alleviation resettlement (PAR). This study evaluates the spatial and temporal evolution of the ecological environment quality (EEQ) to analyze the effectiveness of ecological restoration from PAR. Based on cloud computing using the Google Earth Engine platform, remote-sensing data were obtained and reconstructed from 2000 to 2020. The remote-sensing ecological index (RSEI) was used to analyze the spatial and temporal evolution of EEQ. The results show that the RSEI of the study area increased by 13.07% after the implementation of PAR, and the rate of increase was higher than that in the period before PAR; the Pu’an and Qinglong areas improved most obviously, in terms of the fragile ecological environment and the prominent contradiction between peasants and land. The residual trends method indicated that the contribution rate of improvement in RSEI due to PAR was 70.56%, 88.38%, and 82.96% in 2017, 2018, and 2020, respectively. An increase in RSEI was more obvious in the area with a greater relocated population and a higher corresponding coupling coordination level. PAR has a promoting effect on EEQ improvement but does not have ecological restoration benefits in every region. It is not satisfactory in terms of the degeneration of the LST indicator and the ecological impact of human wells.
The implementation of China’s ex situ poverty alleviation and relocation project has alleviated the further deterioration of the ecological environment in the relocation area. It can create favorable conditions for the management of ecological problems such as the natural restoration of rocky desertification and soil erosion. Panzhou City, Guizhou Province, is one of the key areas for the implementation of ex situ poverty alleviation and relocation projects in the 13th Five-Year Plan for China’s National Economic and Social Development. The typical ecological problem of karst rocky desertification is an important factor hindering the sustainable development of local society, economy, and ecology. Based on the five-phase remote sensing images and relocated population data, the dynamic change rate, transition matrix, and coupling coordination degree model are utilized to analyze the spatiotemporal changes in rocky desertification in Panzhou City. Meanwhile, the cellular automata (CA) Markov model is used to simulate its future scenarios of rocky desertification. The results show that (i) over the past 20 years, the vegetation coverage in Panzhou has generally increased. The implementation of the ex situ poverty alleviation and relocation project has significantly promoted the reduction of the area and degree of rocky desertification. After relocation (2015–2020), the positive improvement rate of rocky desertification accelerated. (ii) After relocation, the potential rocky desertification (PRD), light rocky desertification (LRD), medium rocky desertification (MRD), severe rocky desertification (SRD), and extreme severe rocky desertification (ESRD) showed a trend of transition to the no rocky desertification (NRD). The improvement effect of rocky desertification is remarkable, and the main contribution is from the PRD and LRD. (iii) The greater the relocation intensity is, the more obvious the improvement effect of the rocky desertification area is, and the higher the corresponding coupling coordination level is. The coupling coordination between LRD and relocation intensity is the highest. (iiii) The forecast results show that by 2025 and 2035, rocky desertification in Panzhou will continue to improve.
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