The first successful sand-control was achieved in the Mu Us Desert by local people in the 1950–1960s, and their experience and approach have been extended to the whole Ordos and Northern China since then. The objective of this paper is to assess comprehensively the effectiveness of sand-control in 15 counties in and around Mu Us using multitemporal satellite images and socioeconomic data. After atmospheric correction, Landsat TM and OLI images were harnessed for land cover classification based on the ground-truth data and for derivation of the GDVI (generalized difference vegetation index) to extract the biophysical changes of the managed desert and desertification. Climatic, socioeconomic, environmental and spatial factors were selected for coupling analysis by multiple linear and logistic regression models to reveal the driving forces of desertification and their spatial determinants. The results show that from 1991 to 2020, 8712 km2 or 63% of the desert has been converted into pastures and shrublands with a greenness increase of 0.3509 in GDVI; the effectiveness of sand-control is favored by the rational agropastoral activities and policies; though desertification occurs locally, it is associated with both climatic and socioeconomic factors, such as wind speed, precipitation, water availability, distance to roads and animal husbandry.
To better implement the Strategy of Rural Revitalization, it is essential to characterize the rural settlements and understand their roles in the socio-environmental interactive system. This paper is hence aimed at achieving such a study using different spatial analysis such as kernel density and spatial autocorrelation (SA) and modeling approaches, e.g., simple and multiple linear regression analyses taking Jiangxi, a province in China as an example. Remote sensing, topographic and socioeconomic data were employed for this purpose. Through these analyses, it is found that the rural settlements in the study area appear to have a spatial distribution pattern of “dense north and sparse south” as an “F” type, and are quantitatively characterized as low elevations, flat terrain, high river and road densities, rich cultivated land resources and susceptible to the impact of urban radiation with a R2 of 0.520–0.748. Based on this understanding, a new inequality evaluation indicator of rural development, i.e., socio-environmental evaluation index (SEI), was developed. Areas with SEI lower than 0.40 should be given a priority to implement the revitalization strategy in the province. This index can also be extended to study of the imbalance of rural development in other regions and countries.
Detection of land use and land cover change (LUCC) and its future projection have become a critical issue for rational management of land resources. For this purpose, land use mapping in 2010, 2015 and 2020 in Hefei were conducted by an integrated classification approach based on spring Landsat images and digital elevation model (DEM) data, and dynamic LUCC of 2010-2015 and 2015-2020 were characterized. To predict land use change, a new comprehensive hybrid model consisting of Celluar Automata (CA) and Markov chain (M), Logistic Regression (LR) and Multi-Critical Evaluation (MCE), namely Logistic-MCE-CA-Markov (LMCM) model, was proposed to avoid the disadvantages of the previous models such as CA-Markov (CM), Logistic-CA-Markov (LCM) and MCE-CA-Markov (MCM). This new hybrid model LMCM used the fully standardized logistic regression coefficients as importance of the driving factors to represent their impact weight on each land use type. The CM, LCM, MCM and LMCM models were applied to estimate the land use pattern of 2020 based on the states of 2010 and 2015 of the study area, and we noted that the LMCM model performed better than other three versus the classified map of 2020 with a higher accuracy, that is, 1.72-5.4%, 2.14-6.63% and 2.78-9.33% higher than CM, LCM and MCM models respectively. We believed hence that the newly proposed LMCM hybrid model was capable of achieving more reliable prediction of LUCC and was employed to predict the land use and land cover (LULC) situation of 2025 within four scenarios, i.e., business as usual (BAU), economic development (ED), ecological protection (EP), and comprehensive development (CD). The results show that the LUCC modeling using the LMCM model with ED or CD scenario would be pertinent for a socioeconomic development in the study area and the approaches may be extended for such study in other regions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.