Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020

Desertification is one of the most serious ecological and environmental problems in arid regions. Low-cost, wide-ranging, and high-precision methods are essential for the formulation of appropriate strategies for quantitatively monitoring desertification. In this study, based on Google Earth Engine...

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Autores principales:	Xiaoyu Meng, Xin Gao, Sen Li, Shengyu Li, Jiaqiang Lei
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	GEE Machine learning methods Desertification dynamics Gravity center change Intensity analysis Desertification maps Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/69d41b3480704e15a8e975d570450c15
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id	oai:doaj.org-article:69d41b3480704e15a8e975d570450c15
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spelling	oai:doaj.org-article:69d41b3480704e15a8e975d570450c152021-12-01T04:55:36ZMonitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 20201470-160X10.1016/j.ecolind.2021.107908https://doaj.org/article/69d41b3480704e15a8e975d570450c152021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21005732https://doaj.org/toc/1470-160XDesertification is one of the most serious ecological and environmental problems in arid regions. Low-cost, wide-ranging, and high-precision methods are essential for the formulation of appropriate strategies for quantitatively monitoring desertification. In this study, based on Google Earth Engine and Landsat images, six machine learning methods were used to monitor desertification dynamics in 1990–2020 in Mongolia. The spatiotemporal distributions and changes in desertification at different stages were analyzed using gravity center change and intensity analysis models. Subsequently, we quantitatively investigated the factors driving desertification in Mongolia. The results indicate that the maximum entropy method can obtain the most accurate assessment of the degree of desertification in comparison with the other five methods, with an accuracy of 96%. In 1990–2005, the area of desertified land increased significantly, afterward, a decreasing trend was observed. Lightly and moderately desertified lands had the highest change intensities and were most sensitive to environmental factors. Although the desertification dynamics are under the influence of both natural and anthropogenic factors, precipitation plays a dominant role in Mongolia. This study provides a comprehensive analysis of the desertification status and trends in Mongolia, and presents desertification maps that can be used to formulate preventive measures and guide desertification prevention and control.Xiaoyu MengXin GaoSen LiShengyu LiJiaqiang LeiElsevierarticleGEEMachine learning methodsDesertification dynamicsGravity center changeIntensity analysisDesertification mapsEcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 107908- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	GEE Machine learning methods Desertification dynamics Gravity center change Intensity analysis Desertification maps Ecology QH540-549.5
spellingShingle	GEE Machine learning methods Desertification dynamics Gravity center change Intensity analysis Desertification maps Ecology QH540-549.5 Xiaoyu Meng Xin Gao Sen Li Shengyu Li Jiaqiang Lei Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020
description	Desertification is one of the most serious ecological and environmental problems in arid regions. Low-cost, wide-ranging, and high-precision methods are essential for the formulation of appropriate strategies for quantitatively monitoring desertification. In this study, based on Google Earth Engine and Landsat images, six machine learning methods were used to monitor desertification dynamics in 1990–2020 in Mongolia. The spatiotemporal distributions and changes in desertification at different stages were analyzed using gravity center change and intensity analysis models. Subsequently, we quantitatively investigated the factors driving desertification in Mongolia. The results indicate that the maximum entropy method can obtain the most accurate assessment of the degree of desertification in comparison with the other five methods, with an accuracy of 96%. In 1990–2005, the area of desertified land increased significantly, afterward, a decreasing trend was observed. Lightly and moderately desertified lands had the highest change intensities and were most sensitive to environmental factors. Although the desertification dynamics are under the influence of both natural and anthropogenic factors, precipitation plays a dominant role in Mongolia. This study provides a comprehensive analysis of the desertification status and trends in Mongolia, and presents desertification maps that can be used to formulate preventive measures and guide desertification prevention and control.
format	article
author	Xiaoyu Meng Xin Gao Sen Li Shengyu Li Jiaqiang Lei
author_facet	Xiaoyu Meng Xin Gao Sen Li Shengyu Li Jiaqiang Lei
author_sort	Xiaoyu Meng
title	Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020
title_short	Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020
title_full	Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020
title_fullStr	Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020
title_full_unstemmed	Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020
title_sort	monitoring desertification in mongolia based on landsat images and google earth engine from 1990 to 2020
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/69d41b3480704e15a8e975d570450c15
work_keys_str_mv	AT xiaoyumeng monitoringdesertificationinmongoliabasedonlandsatimagesandgoogleearthenginefrom1990to2020 AT xingao monitoringdesertificationinmongoliabasedonlandsatimagesandgoogleearthenginefrom1990to2020 AT senli monitoringdesertificationinmongoliabasedonlandsatimagesandgoogleearthenginefrom1990to2020 AT shengyuli monitoringdesertificationinmongoliabasedonlandsatimagesandgoogleearthenginefrom1990to2020 AT jiaqianglei monitoringdesertificationinmongoliabasedonlandsatimagesandgoogleearthenginefrom1990to2020
_version_	1718405665195556864

Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020

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