Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Quantitative analysis of desertification driving mechanisms in the Shiyang River Basin: Examining interactive effects of key factors through the Geographic Detector Model

Version 1 : Received: 10 April 2023 / Approved: 11 April 2023 / Online: 11 April 2023 (05:15:47 CEST)

A peer-reviewed article of this Preprint also exists.

Ngabire, M.; Wang, T.; Liao, J.; Sahbeni, G. Quantitative Analysis of Desertification-Driving Mechanisms in the Shiyang River Basin: Examining Interactive Effects of Key Factors through the Geographic Detector Model. Remote Sens. 2023, 15, 2960. Ngabire, M.; Wang, T.; Liao, J.; Sahbeni, G. Quantitative Analysis of Desertification-Driving Mechanisms in the Shiyang River Basin: Examining Interactive Effects of Key Factors through the Geographic Detector Model. Remote Sens. 2023, 15, 2960.

Abstract

Desertification is a global environmental and socio-economical issue threatening humanity's survival and development. The Shiyang River Basin ecosystem is vulnerable and prone to desertification. In addition, establishing the quantitative analysis of desertification driving factors and understanding their relative contribution, separately or combined, is still an unresolved problem. The present study applied geographic information system (GIS) techniques and a geographic detector model to quantify desertification spatial extent and driving mechanisms. This research utilized Fractional Vegetation Cover (FVC) to elucidate desertification spatial heterogeneity. The 30 years Coefficient of Variation (CV) of the Normalized Difference Vegetation Index (NDVI) was a dependent variable and indicator of ecosystem terrestrial conditions; Elevation, near-surface air temperature, precipitation, wind velocity, land cover change, soil salinity, road buffers, waterway buffers, and soil types were independent variables. The results showed that 89.41% of the total area is under desertification risk, where 20.99% is extremely desertified, 34.45% is severely desertified, 12.05% is moderately, and 21.92% is slightly desertified. The results from the Geodetector model showed that Power Determinant (PD) values ranged between 0.004 and 0.270. Elevation and soil types had the highest contributing factors with PD values of 0.270 and 0.227, whereas precipitation, soil salinity, the buffer of the waterway, and wind velocity played a moderate role with PD values of 0.146, 0.117, 0.107, and 0.071. Near-surface air temperature, road buffer, and land cover dynamics exhibited lower impact with PD values of 0.028, 0.013, and 0.004. In most cases, investigating the interaction between driving factors resulted in a mutual or non-linear enhancement. There was an apparent linear and mutual enhancement between elevation and soil salinity, precipitation, and soil types with values of 0.3513, 0.3232, and 0.3204, respectively. In addition, there was a mutual enhancement between soil salinity and soil types with a value of 0.2962. On the other hand, a non-linear enhancement was observed between Elevation and near-surface air temperature (0.3116), Elevation and Land cover dynamics (0.2759), soil types and near-surface air temperature (0.2687), land cover dynamics and soil types (0.234), precipitation and near-surface air temperature (0.2248), precipitation and wind velocity (0.2248), and between land cover dynamics and precipitation (0.223). This research revealed irrefutable evidence that environmental factors might be the primary drivers of ecosystem disturbance, provided the basis for the environmental footprint of desertification mechanism, and might be a cornerstone for future policy on ecological restoration sustainability in the Shiyang River Basin.

Keywords

Desertification; Geographical Detector Model; Google Earth Engine; Driving factors; The Shiyang River Basin

Subject

Environmental and Earth Sciences, Remote Sensing

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