GIS-Based Vulnerability Assessment of the Semi-Arid Ecosystem to Land Degradation: Case Study of Sokoto-Rima Basin

Abstract

Land degradation is one of the most ubiquitous environmental challenges affecting the semi-arid ecosystems of the world and the Sokoto-Rima basin is not immune to this. In this study, we evaluated vulnerability of the Sokoto-Rima basin to land degradation by combining remote sensing and geographic information system technologies. An appraisal model was developed for the identified nine variables, whose weights were ascertained by the analytical hierarchy process. Using this model, we examined the spatiotemporal distribution of vulnerability to land degradation stimulated by climate change from 2002 to 2015. Largely, the basin is extremely vulnerable to land degradation with roughly 88% of the land area in 2002, 2012 and 2015 while areas with low vulnerability were just 1.52%, 1.48% and 1.51% respectively. Geographically, there exists a north-south vulnerability index dichotomy as the index increases northwards. Also, integrated vulnerability index showed that the entire basin is getting exposed to the vagaries of climate change that stimulates land degradation. Large-scale resilience projects such as greening and integrated shelter-belts and woodlots can be implemented in the long run as existing ones are inadequate to address the observed degradation.

Quantifying ecosystem service interactions to support environmental restoration in a tropical semi-arid basin

“…Furthermore, indices of cropland and ecosystem service vulnerability index (EVI) (extracted from Raji et al (2019) were integrated to strengthen further the spatial expression of the ecosystem service interactions for the present (2015) and the future (2050). Gally algorithm enshrined in R was used to statistically depict the ecosystem service interactions, while respective radar charts were generated…”

A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area

“…Regarding this issue, combatting land degradation has been an important duty since 1992 (UNCCD, 2005) and efforts continue for preventing LD. In the literature, different approaches were used for assessing, monitoring, and mapping land degradation worldwide such as the indicator approach (Reed & Dougill, 2002), utilizing different models such as MEDALUS (Sepehr et al, 2007; Ibáñez et al, 2014; Saygın et al, 2011; Abuzaid et al, 2021), field monitoring and measurement (Tongway, 1994; McGarry, 2004), GIS‐based methods (El‐Gammal et al, 2015; Raji et al, 2019), and remote sensing (RS) techniques (Fadhil, 2009; Omuto et al, 2014; Mensah et al, 2015; Dubovyk, 2017; AbdelRahman et al, 2019; Pásztor, 2021). Although new LD assessment approaches and artificial intelligence procedures can contribute significantly to the field of LD susceptibility mapping, there is not enough study in this field.…”