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.
Highlights
The Xinjiang Uygur Autonomous Region of China, Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan, located in the heart of the Eurasian continent, is a typical arid and semiarid region with fragile environments that are sensitive to climate change and human activities
Several global land cover products based on remote sensing data have been released, such as the International Geosphere Biosphere Programme (IGBP) Data and Information System global land cover data set (DISCover) [3], the global land cover classification for the year 2000 (GLC2000) dataset [4], the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover (MCD12) dataset [5], and the Global Land Cover Characteristics Data Base (GLCC) [6]
These products are based on satellite imagery; the resolution and classification accuracy are not high and cannot meet the needs of environmental monitoring, agricultural planning and ecological governance [7]
Summary
The Xinjiang Uygur Autonomous Region of China, Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan, located in the heart of the Eurasian continent, is a typical arid and semiarid region with fragile environments that are sensitive to climate change and human activities. One is to dynamically monitor the spatial pattern of land cover in Central Asia, and the other is to evaluate the driving mechanisms of climate and human activities in terms of land cover Such tasks are important for exploring the responses of typical arid and semiarid regions to global climate change [1,2]. Several global land cover products based on remote sensing data have been released, such as the International Geosphere Biosphere Programme (IGBP) Data and Information System global land cover data set (DISCover) [3], the global land cover classification for the year 2000 (GLC2000) dataset [4], the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover (MCD12) dataset [5], and the Global Land Cover Characteristics Data Base (GLCC) [6] These products are based on satellite imagery; the resolution and classification accuracy are not high (the most common spatial resolution is 1 km, and the nominal overall accuracy ranges from 60–80%) and cannot meet the needs of environmental monitoring, agricultural planning and ecological governance [7]. Land mapping research requires high spatial resolution imagery, improved classification and change detection algorithms, and powerful platforms [9]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
