1. Introduction
Land is a vital and precious resource to produce food, fiber, fuel, and other ecosystem services for the survival of humans and animals [
1,
2]. However, the constant pace of degradation and deterioration due to persistent human-induced disturbances and climatic irregularities [
3] places livelihood and sustainable progress under acute threat [
4]. Land degradation is a major environmental problem all around the world and influences human society and its livelihoods. Globally, the life of around 3.2 billion people totally depends on degraded lands, and around one-third of the world’s lands are affected by land degradation [
5,
6]. In recent years, land degradation has been considered a pivotal factor in environmental issues and has attracted the attention of all stakeholders [
7]. The United Nations General Assembly adopted Sustainable Development Goal 15.3 in September 2015, which focuses on achieving land degradation neutrality (LDN) by implementing the best management practices that reduce the loss of healthy land and maintain or improve the productivity of the land [
8,
9]. Land degradation can be defined as a spatio-temporal deterioration of physico-chemical and biological properties of land, making it unsuitable for human society, and a deterioration of the soil ecosystem, influencing agricultural production and ecological instability [
10,
11].
Around 24% of the world’s total geographic area (approximately 3500 Mha) is severely affected by land degradation [
11,
12]. Around 20% of cropland, 10% of grassland, and 30% of forests are under the process of land degradation throughout the world [
13]. In India, around 36.7% of total geographical area (TGA) (120.7 Mha) is under different types of land degradation such as soil erosion, soil acidity, soil salinity and alkalinity, and waterlogging [
14], and soil salinity and alkalinity alone affect 6.73 Mha in different arid, semi-arid, and sub-humid areas [
15]. According to the Indian Space Research Organization (ISRO), land degradation accounts for around 29.32% of the TGA of India. It covers 96.4 Mha of agricultural, forest, and non-forest land spread across the country [
16]. India joined the Bonn Challenge and the United Nations Decade on Ecosystem Restoration 2021–2030 to maximize ecological and economic advantages from the restoration of degraded ecosystems, pledging to rehabilitate 26 Mha of degraded land by 2030 [
17].
The problem of land degradation is especially severe in arid and semi-arid areas of the country, such as the state of Rajasthan. Land degradation affects 67% of Rajasthan’s land, where wind erosion contributes to the maximum percentage (44.2%), and water erosion (11.2%), vegetal degradation (6.25%), and salinization (1.07%) are the next most common forms of degradation. Chambal ravines in the state of Rajasthan are perhaps among the worst physically degraded lands, as cultivated fertile lands were engulfed by ravines and rendered unsuitable for agricultural activities [
18]. The Chambal ravines are very typical as they are deep to very deep (>20 m) and are devoid of any kind of vegetation, with ravines and gullies being the typical forms of degradation [
19]. For the development of effective strategies to minimize and lessen the effects of land degradation, it is a prerequisite to understand the process of land degradation, including the causes and its consequences for major functions of the ecosystem and the proper identification of the affected area and the regions at high risk.
Modeling and assessing the vulnerability of land degradation play a pivotal role in land degradation neutrality planning and prioritization processes and in fulfilling targets for restoration. Assessment of land degradation requires various information such as climate, soil properties, topography, land use, etc. Several techniques are being adopted in monitoring and evaluating the area, rate, and type of land degradation. A survey using satellite images overcomes the time-consuming and expensive traditional survey, particularly in areas tough to assess [
20]. Geospatial techniques such as remote sensing (RS) and geographic information system (GIS) play an important role in the assessment and monitoring of land degradation vulnerability. Satellite imageries with precise spatial and spectral resolution are excellent resources for detecting, mapping, and monitoring various degradation kinds and issues in a rapid, consistent, reliable, and cost-effective manner [
21,
22,
23,
24,
25].
The integrated use of geospatial techniques with the multi-criterion decision analysis (MCDA) method is the most feasible option to assess and map land degradation vulnerability. This MCDA technique has numerous applications in multiple areas such as groundwater potential mapping, crop suitability zonation, and land degradation vulnerable mapping. It is mostly used to solve complex problems by breaking them up into sections, then solving and integrating each section to obtain the ultimate results. The AHP, which was first developed by Saaty (1980), is the most widely used multi-criterion decision method for the mapping of vulnerable zones [
26,
27]. Decisions may be made using this strategy based on judgements, hierarchical structure, and accurate perception, all of which have a dominant influence on the final decision [
27,
28]. The AHP approach is a widely recognized, basic, and well-structured decision-making technique. Few research findings have been generated by other researchers [
12,
13,
29] with respect to the assessment and mapping of land degradation vulnerability zones (LDVZ) based on AHP and GIS modeling approaches and their validation with Google Earth imageries. Considering the importance of land degradation vulnerability assessment through remote sensing and GIS and AHP approaches, the present study was carried out in the semi-arid region of Rajasthan, western India. In the present study area, water erosion is the most important cause of land degradation due to favorable erosion geology, vegetal degradation, and the perennial Chambal River. Despite this fact, so far, no studies have been carried out in this area to assess and prepare a land degradation susceptibility map. The core objectives of the study are to (i) characterize the terrain, climatic, vegetative, and pedological variables of the watershed and (ii) identify the most vulnerable areas to land degradation using remote sensing and geospatial techniques. Furthermore, the research provides important information for long-term land use management and development.
4. Discussion
Land degradation is generally considered one of world’s most serious environmental issues. In India, the western state of Rajasthan is a part of the Thar Desert, where degradation is a severe issue. The Chambal River valley in the state of Rajasthan is one of the severely affected regions in the country, where gully erosion/ravines have major physical and economic implications [
18,
19,
36]. For sustainable agricultural planning and development, the identification of vulnerable hotspots to soil/water erosion is the need of the hour. Therefore, the present research was carried out to identify hot spots of land degradation in a small watershed using an AHP- and GIS-based modeling approach. Previous research has found that only a few variables play an important role in the assessment of land degradation [
13,
37]. In the present investigation, LULC, NDVI, TWI, MRVBF, LST, MAR, soil texture, and SOC were considered for the mapping of land degradation vulnerable zones. LULC and NDVI were taken as the most influential layers for land degradation vulnerability. Land use/land cover implies man-made and natural modification of the land surface, and it is a major cause of land degradation [
38,
39]. The NDVI has long been recognized as a useful measure for determining the greenness of flora and it is well accepted in science that a decrease in NDVI is a sign of land degradation and is closely linked to climatic conditions [
40,
41]. The most basic soil physical property, on the other hand, is soil texture, which impacts hydraulic properties and surface soil loss [
42].
LST is an important parameter in the semi-arid region as it is directly linked with soil moisture availability and indirectly linked with the flora conditions of the study area [
43,
44]. A rise in the LST might result in a reduction in vegetative greenness and an increase in land degradation. Increased rainfall during the monsoon season increases the risk of topsoil loss by higher water velocity, which causes more soil erosion [
37]. SOC is a universal biomarker of soil degradation since its decrease may have severe consequences for soil-derived ecosystem services [
45]. Similarly, TWI is one of the most important terrain parameters and plays a significant role in assessing land degradation vulnerability. A higher TWI value is associated with good vegetation cover, and vice versa [
46]. As a result, vegetation cover promotes infiltration, reduces surface runoff, and thus greatly delays the incidence of soil erosion [
47]. Therefore, thematic layers were given weights based on their importance. The AHP model assigned the weightage of each factor, i.e., LULC (0.38), NDVI (0.23), soil texture (0.15), LST (0.08), TWI (0.06), MAR (0.05), SOC (0.03), and MRVBF (0.02). The higher the index value, the more exposed the area is to land degradation, whereas the lower the value, the less vulnerable it is. Consistency ratios for each thematic layer and subclasses of each thematic layer were calculated before the integration of thematic layers. The computed CR value was less than 0.1, which shows that all the parameters’ assumptions about their impact on soil erosion are valid.
Research findings showed that five land degradation vulnerability zones (LDVZ) namely, very low, low, moderate, high, and very high, were identified in the study area. Very low, low, moderate, high, and very high classes covered 27%, 29%, 20%, 11%, and 12% of the area of the watershed, respectively. Parmar et al. (2021) [
48] also conducted a study to assess land degradation vulnerability using the geospatial technique in the Kutch district of Gujarat, India, and the results revealed that 67% of the land area has high vulnerability to land degradation, and 27% of the area falls under the moderate class. Similarly, an assessment of potential land degradation using the geospatial technique and multi-influencing factor technique was carried by Senapati et al. (2020) [
49] in the Akarsa Watershed, West Bengal, and they also classified the study area into five land degraded zones. The analysis revealed that the very low to low categories of land degradation vulnerability covered almost half of the area of the watershed, and this portion of the study area is associated with good vegetative coverage with open forest, very low vegetative degradation, adequate rainfall (843.21–846 mm), and well-drained soils deep in nature with clayey texture.
All the environmental covariables are linked with each other, e.g., adequate rainfall positively correlates with NDVI and LULC and a good amount of LULC links with optimum SOC content and better soil health, which directly relate to a lower chance of land degradation [
50]. The moderate class of LDVZ was related to very less vegetative coverage in the scrub/shrub class of LU/LC, with normal rainfall (<843.20 mm) and rock outcrops. This class also represented a low to medium MRVBF value with low TWI. High and very high classes of LDVZ covered about 607.95 hectares (23.54%) of the watershed. These two classes showed high to very high severity of land degradation, such as ravines and gullies. This section of the watershed had no or very little vegetation cover, higher rainfall (>846 mm), high valley bottom flatness, higher LST, and clay to fine loamy texture soils with low soil organic carbon. In this research, the AHP- and GIS-based modeling approach showed its potential for the assessment of vulnerability to land degradation by compiling different parameters. Validation of LDVZ was carried out with the help of Google Earth images of high resolution and the results were very well in agreement with the AHP–GIS model-based approach. Similar work has been conducted by several researchers [
12,
13,
29,
51,
52,
53,
54] with respect to assessment and mapping of LDVZ based on an AHP and GIS modeling approach and their validation with Google Earth imageries.
This study has identified areas that are more prone to land degradation, which can help prioritize and implement soil water conservation practices to reduce the consequences of degradation. Farmers should be encouraged to grow cover crops and crop rotation practices to maintain soil quality over time. Farmers should maintain crop residue and biomass over soil surface after harvesting to avoid exposing the topsoil. Furthermore, the findings of this research may be useful in developing better soil and water management policies. Although this study was carried out at the watershed level, it should be replicated to the sub-district or district level. The pedological parameters used in this study are available at coarse resolution, which caused some challenge and gaps in the results. Future research should concentrate on high-resolution satellite and soil survey data to delineate degradation zones with higher accuracy.
5. Conclusions
In the study, LULC, NDVI, soil texture, LST, MAR, TWI, SOC, and MRVBF were considered major contributing factors in the identification of land degradation vulnerability zones through the GIS- and AHP-based model. The AHP- and GIS-based modeling shows that about 607.95 hectares of the total study area are in the high and very high categories of LDV, and 530.01 hectares are in the moderate LDV category. Validation of moderate, high, and very high LDV classes using high-resolution Google Earth imagery demonstrates that the degree of land degradation features of Google Earth imagery of the selected study area was in agreement with the AHP–GIS model-based approach. This study demonstrates the potential of high-resolution satellite data and the robustness of GIS-based spatial modeling in obtaining accurate, reliable, and cost-effective results for the assessment of land degradation in semi-arid ecosystems. The prevalence and severity of LDV were determined using AHP- and GIS-based modeling, which will be extremely useful in recommending soil conservation and management measures that are suited for each site, particularly in highly and extremely vulnerable regions, for long-term land resources management. These data were derived from satellite data that could cause some challenges and gaps in the results. Therefore, macro- and micro-scale observations are required to account for the high environmental variability and to distinguish between the influences of anthropogenic actions and climate variability on land degradation processes.