2018
DOI: 10.1002/ldr.2997
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Post‐socialist cropland changes and abandonment in Mongolia

Abstract: Large-scale cropland changes have significant implications for global and national food supply as well as degradation in land resources. We examined cropland dynamics at the national scale in Mongolia over the last three decades using Google Earth Engine cloud computing and 11,360 Landsat satellite images. Our overarching goal was to develop the first comprehensive, high-resolution maps of Mongolia's cropland extent for two nominal years: 1990 at the peak of the socialist era production and 2014 after major so… Show more

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Cited by 13 publications
(10 citation statements)
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“…It is necessary for the government to make a more targeted and adaptive response to cropland abandonment considering the different mechanisms in peri‐urban and rural regions (Li et al, 2018; Sankey et al, 2018). High‐quality cropland abandonment should definitely be reduced.…”
Section: Discussionmentioning
confidence: 99%
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“…It is necessary for the government to make a more targeted and adaptive response to cropland abandonment considering the different mechanisms in peri‐urban and rural regions (Li et al, 2018; Sankey et al, 2018). High‐quality cropland abandonment should definitely be reduced.…”
Section: Discussionmentioning
confidence: 99%
“…Significant environmental and economic consequences (i.e., soil erosion and reduced crop yield) have been observed after cropland abandonment (Deng, Zeng, Xu, Wei, & Qi, 2019; Vidal‐Macua et al, 2018). Regardless of the causes of cropland abandonment, the concerns related to land degradation remain (Sankey, Massey, Yadav, Congalton, & Tilton, 2018). Therefore, it is important to understand the spatiotemporal patterns of historical cropland abandonment and potential future trends for both food security and sustainable environmental management (Castro, Pedroso, Lautenbach, Villanueva, & Vicens, 2019; Hatna & Bakker, 2011).…”
Section: Introductionmentioning
confidence: 99%
“…The computational efficiency of GEE combined with the complete archives of 40 years of pre-processed Landsat data and the GRID-MET surface meteorological dataset allowed the entire workflow and data merging within the same consistent environment. Several other studies have similarly produced Landsat-based analysis at regional and continental scales using GEE (Azzari and Lobell, 2017;Dong et al, 2015;Massey et al, 2018;Patel et al, 2015;Sankey et al, 2018;Simonetti et al, 2015;Padarian et al, 2015). We first evaluated long-term trends in annual total precipitation across our study region and observed that much of the study region (up to 50% of the region in some seasons) was already experiencing a significantly decreasing trend in annual total precipitation prior to treatments.…”
Section: Discussionmentioning
confidence: 99%
“…However, by building a large and dense time‐series images from satellite data, we might detect gradual trends in forest health, vigor, and canopy moisture, and thus resiliency. Google Earth Engine (GEE) provides a unique opportunity to synthesize such trends with its large archives of Landsat satellite images and high‐performance computing capabilities (Gorelick et al, 2017; Sankey et al, 2018). GEE offers complex analysis capabilities for image classification, change detection, time‐series analysis, and vector‐based extraction of image statistics (Erickson, 2014; Hancher, 2013; Moore & Hansen, 2011).…”
Section: Introductionmentioning
confidence: 99%
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