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

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.

Precision restoration: a necessary approach to foster forest recovery in the 21st century

“…Bobrowski et al 2018); canopy water content and photosynthetic activity (e.g. Vila‐Viçosa et al 2020); vegetation structure (Betbeder et al 2017); or soil moisture, texture, and salinity (Kim et al 2020). Finally, artificial intelligence, in particular deep learning, can boost these tasks through the exploitation of petabytes of remote sensing data, such as in the detection and counting of seedlings or adult crowns (Buters et al 2019; Albuquerque et al 2020), the identification of tree species (Egli & Höpke 2020), and the detection of scattered trees (Brandt et al 2020; Guirado et al 2021) that could serve for postdisturbance regeneration.…”

Reviving the Aral Sea: A Hydro‐Eco‐Social Perspective

“… Map showing the suitability for vegetation establishment in the exposed lakebeds of the Aral Sea. NDVI was calculated as described in the Materials and Methods section; normalized multiband drought index (NMDI), soil salinity index (SSI), and topsoil grain size index (TGSI) were derived from Landsat‐4/5/7/8 16‐day images from 2016 to 2020 (https://www.usgs.gov/core-science-systems/nli/landsat/); the derivation of classification for NMDI, SSI, and TGSI, and the identification of suitability for vegetation establishment were carried out following the methodology in the literature (Kim et al., 2020). Values in the legend inset mean ± SD; values in the inset panels are Min‐Max.…”

“…The first of these involves mitigation of the severe consequences of soil salinization by implementing measures to reduce soil salinity, increase soil water content, and enhance enzymatic activities. This is accomplished by establishing vegetation on the exposed lakebed in the South Aral Sea, aimed at rehabilitating the area (An et al., 2019; Kim et al., 2020; Schachtsiek et al., 2014). However, it is important to note that the areas identified as “suitable” and “very suitable” for such a rehabilitation project cover only 13.6% and 1.4%, respectively, of the exposed lakebed within a benchmark study area of 9,600 km 2 along the eastern side of the South Aral Sea (Kim et al., 2020).…”

Spatial assessment of land degradation using MEDALUS focusing on potential afforestation and reforestation areas in Ethiopia

“…Desertification and land degradation were assessed using several indices based on their causes and effects, and an integrated assessment of these indices was applied, such as focusing on various vegetation indices, productivity, climatic variance, and decision trees for managing the symptoms (Becerril-Piña et al, 2016;Kim et al, 2020;Kosmas et al, 1997Kosmas et al, , 1999Negaresh et al, 2016;Xiao et al, 2006;Zhang & Huisingh, 2018). The MEDALUS approach is an effective tool for geo-spatially assessing desertification and understanding complex concourses by aggregating the soil status, climate, management, and vegetation as quality indices (Ferrara et al, 2020;Kosmas et al, 2014).…”