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      Land Cover Changes and Their Driving Mechanisms in Central Asia from 2001 to 2017 Supported by Google Earth Engine

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      Remote Sensing
      MDPI AG

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          Abstract

          Limited research has been published on land changes and their driving mechanisms in Central Asia, but this area is an important ecologically sensitive area. Supported by Google Earth Engine (GEE), this study used Landsat satellite imagery and selected the random forest algorithm to perform land classification and obtain the annual land cover datasets of Central Asia from 2001 to 2017. Based on the temporal datasets, the distributions and dynamic trends of land cover were summarized, and the key factors driving land changes were analyzed. The results show that (1) the obtained land datasets are reliable and highly accurate, with an overall accuracy of 0.90 ± 0.01. (2) Grassland and bareland are the two most prominent land cover types, with area proportions of 45.0% and 32.9% in 2017, respectively. Over the past 17 years, bareland has displayed an overall reduction, decreasing by 2.6% overall. Natural vegetation (grassland, forest, and shrubland), cultivated land, water bodies and wetlands have displayed increasing trends at different rates. (3) The amount of precipitation and degree of drought are the driving factors that affect natural vegetation. The changes in cultivated land are mainly affected by precipitation and anthropogenic drivers. The effects of increasing urban populations and expanding industrial development are the factors driving the expansion of urban regions. The advantages and uncertainties arising from the land mapping and change detection method and the complexity of the driving mechanisms are also discussed.

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          Most cited references42

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          Google Earth Engine: Planetary-scale geospatial analysis for everyone

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            High-resolution global maps of 21st-century forest cover change.

            Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.
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              Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

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                Author and article information

                Contributors
                Journal
                Remote Sensing
                Remote Sensing
                MDPI AG
                2072-4292
                March 2019
                March 06 2019
                : 11
                : 5
                : 554
                Article
                10.3390/rs11050554
                5b10a21a-f19b-47dd-a69d-6a8e011a4094
                © 2019

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

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