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Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images

Jiwon KimDepartment of Environmental Science and Ecological Engineering Korea University Seoul 02841 Republic of KoreaCholho SongOJEong Resilience Institute (OJERI) Korea University Seoul 02841 Republic of KoreaSujong LeeDepartment of Environmental Science and Ecological Engineering Korea University Seoul 02841 Republic of KoreaHyun‐Woo JoDepartment of Environmental Science and Ecological Engineering Korea University Seoul 02841 Republic of KoreaEunbeen ParkDepartment of Environmental Science and Ecological Engineering Korea University Seoul 02841 Republic of KoreaHangnan YuCollege of Geography and Ocean Sciences Yanbian University Hunchun 133300 ChinaSungeun ChaDepartment of Environmental Science and Ecological Engineering Korea University Seoul 02841 Republic of KoreaJiae AnDepartment of Environmental Science and Ecological Engineering Korea University Seoul 02841 Republic of KoreaYowhan SonDivision of Environmental Science and Ecological Engineering Korea University Seoul Republic of KoreaAsia KhamzinaDivision of Environmental Science and Ecological Engineering Korea University Seoul Republic of KoreaWoo‐Kyun LeeDivision of Environmental Science and Ecological Engineering Korea University Seoul Republic of Korea
2020en
ABI

Аннотация

Abstract The Aral Sea was one of the largest lakes in the world, but almost 60,000 km 2 of the waterbody has dried up due to water withdrawal for irrigation. Afforestation on the desiccated seafloor could be important in preventing soil flation, dust storms, and negative impact on human health. In this study, we aimed to delineate potential vegetation establishment areas on the dried Aral Sea bed using remote‐sensed data in support of the decision‐making related to afforestation. Various indices such as normalized difference vegetation index (NDVI), topsoil grain size index (TGSI), soil salinity index (SSI), and normalized multiband drought index (NMDI) were calculated from the LANDSAT‐8 OLI satellite imagery. As an indicator of vegetation existence, NDVI was classified into three groups and set as a base for classifying other indices by performing statistical analyses. Based on the decision tree method, indices were combined and the potential vegetation establishment area was detected. Higher NDVI was identified in the southeast than the west of the study area. The results of statistical analyses showed that TGSI had a positive correlation with NDVI, while SSI and NMDI had a negative correlation. Overall, the potential vegetation area comprised 7,295.21 km 2 (61.34%) of the 'unsuitable' area, 2,818.64 km 2 (23.7%) of the 'intermediate' area, 1,612.15 km 2 (13.56%) of the 'suitable' area, and 166.42 km 2 (1.4%) of the 'very suitable' area. The developed map enables to identify dried seafloor area suitable for vegetation establishment thus contributing to planning the land rehabilitation efforts and preventing further land degradation.

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