干旱区生态环境知识资源中心(Arid): Land cover change detection in the Aralkum with multi-source satellite datasets

Arid

DOI	10.1080/15481603.2021.2009232
	Land cover change detection in the Aralkum with multi-source satellite datasets
	Low, Fabian; Dimov, Dimo; Kenjabaev, Shavkat; Zaitov, Sherzod; Stulina, Galina; Dukhovny, Viktor
通讯作者	Low, F (corresponding author), Dept Sci Res SIC ICWC, Tashkent, Uzbekistan.
来源期刊	GISCIENCE & REMOTE SENSING
ISSN	1548-1603
EISSN	1943-7226
出版年	2022
英文摘要	The Aral Sea, once the fourth largest freshwater lake on Earth, has lost circa 90% of its original water surface in 1960. Maps of different land cover categories provide a suitable baseline to plan and implement effective measures to combat ongoing desertification, such as reforestation of dried out Aral Sea soils. In this study, we used satellite-based remote sensing data and applied a machine learning method (Random Forest) to map land cover in the Aralkum in 2020. We tested different satellite data from optical (Landsat-8, Sentinel-2) and Radar instruments (Sentinel-1) and trained a random forest model for classifying different combinations of these data sets into ten distinct land cover classes. We further calculated per-pixel uncertainty based on posterior classification probability scores. An accuracy assessment, based on in-situ data, revealed that the average overall accuracy of land cover maps was 86.8%. Fusing optical and radar instruments achieved the highest overall accuracy (88.8%, with lower/higher 95% confidence interval values of 87.6%/89.9%, and a Kappa value of 0.865. Classification uncertainty was lower in more homogeneous landscapes (i.e. large expanses of a single land cover class like water or shrubland). Only around 9% of the study area was still water in 2020, while 32% was covered by saline soils with high erosion risk. Several potential applications of this land cover map in the Aralkum exist - spanning many areas of environmental impact assessment, policy, and planning and management or afforestation. This methodological framework can similarly provide a useful template for more broadly assessing large-scale, land dynamics at high-resolution in the entire Aralkum and surrounding areas.
英文关键词	Aral sea google earth engine ground truth data land cover remote sensing random forest
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000725333100001
WOS关键词	SOIL-SALINITY ; CENTRAL-ASIA ; TIME-SERIES ; CLASSIFICATION ; ACCURACY ; DUST ; UNCERTAINTY ; DIFFERENCE ; DERIVATION ; SMOTE
WOS类目	Geography, Physical ; Remote Sensing
WOS研究方向	Physical Geography ; Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/374686
作者单位	[Low, Fabian; Dimov, Dimo; Kenjabaev, Shavkat; Zaitov, Sherzod; Stulina, Galina; Dukhovny, Viktor] Dept Sci Res SIC ICWC, Tashkent, Uzbekistan
推荐引用方式 GB/T 7714	Low, Fabian,Dimov, Dimo,Kenjabaev, Shavkat,et al. Land cover change detection in the Aralkum with multi-source satellite datasets[J],2022.
APA	Low, Fabian,Dimov, Dimo,Kenjabaev, Shavkat,Zaitov, Sherzod,Stulina, Galina,&Dukhovny, Viktor.(2022).Land cover change detection in the Aralkum with multi-source satellite datasets.GISCIENCE & REMOTE SENSING.
MLA	Low, Fabian,et al."Land cover change detection in the Aralkum with multi-source satellite datasets".GISCIENCE & REMOTE SENSING (2022).