Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin

Abstract

The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia. It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin. In this study, the arid remote sensing ecological index (ARSEI) for large-scale arid areas was developed, which coupled the information of the greenness index, the salinity index, the humidity index, the heat index, and the land degradation index of arid areas. The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019. The results show that the greenness index, the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin, while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment. The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement, followed by deterioration, and finally further improvement. The spatial variation of these changes was significant. From 2000 to 2019, grassland and wasteland (saline alkali land and sandy land) in the central and western parts of the basin had the worst ecological environment quality. The areas with poor ecological environment quality are mainly distributed in rivers, wetlands, and cultivated land around lakes. During the period from 2000 to 2019, except for the surrounding areas of the Aral Sea, the ecological environment quality in other areas of the Aral Sea Basin has been improved in general. The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were-0.593 and 0.524, respectively. Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin. However, human activities had a greater impact. The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas; it is very suitable for the study of the eco-environmental quality in arid areas.

Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

“…The combined use of geospatial technologies and AHP in zoning LDV areas enhances the decision-making process and provides better accuracy on a regional to local scale [3,16]. Previous case studies demonstrated that the integration of geospatial technologies and AHP proved successful in assessing LDV in many desert areas worldwide, such as in China [12], Iraq [32], and the Aral Sea basin in Central Asia [33].…”

Assessment of Urban Ecological Quality and Spatial Heterogeneity Based on Remote Sensing: A Case Study of the Rapid Urbanization of Wuhan City

“…In this study, I wet was chosen as the humidity index [28], which is expressed as land surface moisture and is generated from Landsat TM, ETM+ and OLI image reflectance using Equations ( 1)-( 3) [10,23,29]:…”

“…Using the RSEI will help in studying the interactions between human activities and natural ecology, and the resulting knowledge of theory, concepts and methods is expected to benefit local governments [20]. In recent years, the RSEI has been applied in ecological quality monitoring in 35 cities of China [19,21,22], Eurasia [23] and America [21,23]. The RSEI and the results of principal component analysis (PCA) have been combined to develop an ecological index [19,24].…”

“…The IRSEI for the four years is classified into five groups employing the ArcGIS software (Esri, USA). Referring to previous studies [22][23][24]29,41], these groups are labeled "Excellent, Good, Moderate, Fair, and Poor" and they facilitate comparisons across the study area (Table 4). Global spatial autocorrelation (SA) measures the average correlation, spatial distribution pattern and significance of all of the objects in the entire study area.…”

Organic Carbon Burial in the Aral Sea of Central Asia

“…More in-depth studies have been carried out on the shrinkage of the Aral Sea [22][23][24], the microbiome of the Aral Sea [25][26][27], and the ecological environment of the basin [28][29][30][31][32]. The research on lake sediments has become more abundant, e.g., environmental reconstructions of the salt water, geological structure, etc.…”