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      Changes and Driving Forces of Urban–Agricultural–Ecological Space in the Yangtze River Economic Belt from 2000 to 2020

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      Land
      MDPI AG

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          Abstract

          Optimizing the urban–agricultural–ecological space in the Yangtze River Economic Belt is integral to China’s sustainable land development and protection. Based on land use data from 2000 to 2020, this study identified the urban-agricultural-ecological space in the Yangtze River Economic Belt. It analyzed its changes and driving forces using the land use transfer matrix, the Dagum Gini coefficient, and GeoDetector. The results show that urban space has increased significantly over the past 20 years, agricultural space has decreased dramatically, and ecological space has remained stable. The transformation of agricultural space into urban space was the dominant type of space transformation, followed by a mutual transformation between agricultural and ecological spaces. Each transformation type exhibited significant spatial inequality within and between regions. Socioeconomic and natural conditions significantly impacted the spatial transformation, and all factors have an apparently interactive reinforcing effect. The research has enhanced the identification accuracy of urban–agricultural–ecological spaces, precisely illustrating the changes and driving forces of the land spatial pattern in the Yangtze River Economic Belt over the last two decades. It holds vital theoretical and practical implications for the optimization of China’s land spatial pattern.

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          Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset

          CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5° latitude by 0.5° longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901–2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically.
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            Mapping farmland abandonment and recultivation across Europe using MODIS NDVI time series

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              Trends in urban land expansion, density, and land transitions from 1970 to 2010: a global synthesis

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                Author and article information

                Contributors
                Journal
                Land
                Land
                MDPI AG
                2073-445X
                May 2023
                May 05 2023
                : 12
                : 5
                : 1014
                Article
                10.3390/land12051014
                3dfaa2d5-0f89-4c90-acc1-5bb146d53479
                © 2023

                https://creativecommons.org/licenses/by/4.0/

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