An Innovative Approach for the Assessment and Monitoring of Land Degradation and Desertication in Semi-Arid Regions Using Remote Sensing and GIS Techniques

Land degradation (LD) and desertication is a serious ecological, environmental, and social-economic threat in the world, and there is a demanding need to develop accountable and reproducible techniques to assess it at different scales. In this study to assess LD and desertication with the help of Remote Sensing (RS) and Geographical Information System (GIS) in the study region for the period of past 29 years i.e., from 1990 to 2019. The severity of LD and desertication was assessed quantitatively by collecting twelve soil samples in the study region, and analyzing the eleven soil Physico-chemical parameters and these values have made correlated with Digital Number (DN) values with LANDSAT 8 satellite image. The land cover analysis of LANDSAT imagery revealed that the water body slightly increased from 0.29% in 1990 to 0.46% in 2019, and built-up-land increased from 2.87% in 1990 to 5.31% in 2019. Vegetation is decreased from 52.03% in 1990 to 28.57%. Fallow land, degraded land, and desertied lands are increased at alarming rates, respectively 13.71% to 26.35, 18.57% to 22.31%, and 12.53% to 17.00%. It is also established that the multi-temporal analysis of change detection data can provide a sophisticated measure of ecosystem health and variation, and that, over the last 29 years, considerable progress has been made in the respective research.


Introduction
Deserti cation refers to land degradation (LD) in drylands, although over hundred de nitions of deserti cation have been developed emphasizing different processes contributing to deserti cation, the United Nations Convention to Combat Deserti cation (UNCED) provides the most current, authoritative de nition "land degradation in arid, semi-arid and dry subhumid areas resulting from various factors including climatic variations and human activities" (UNCED 1992). LD is a multifaceted phenomenon, it decreases soil fertility especially in semi-arid regions, and it leads to deserti cation occurrence.
LD causes different aspects of natural resource depletion such as degradation of soil, waterbody, and vegetation. Furthermore, LD has a negative in uence on biophysical and socio-economic processes that society has de ned as important components in various spatial and temporal scales (Kassas, 1999;Lal and Stewart, 1990 . Land Use and Land Cover (LULC) are often blended. This is somewhat logical for the reason that both terms are scarcely related and to more or less scope even overlap. In this normal state, land cover systematizes a faultless arrival of the environmental equilibrium among parent rock, climatic ailment, soil, and vegetation. landcover eminent into various categories i.e., area of vegetation, bare soil, rock outcrops, wet and water bodies, etc. in simple terms land cover is the result of observation. Land use denotes also to land cover but concerning its socio-economic persistence and global use (Harshika. A. K, et al 2012). This is in pure contrast with land cover as stated to above which is most expressive and deals with physical observations. Land use may differ in nature and intensity with both purposes it helps and with physical observations (A. M. Talha & Dewan M.; et al 2009 &2014). Land use varies from land cover for the reason that of the intentional role of people to familiarize the natural land cover to their assistance. Geospatial technologies, including GPS (Global Positioning System), Satellite imageries, and GIS hold great promising for improving the quality and quantity of information on degradation trends over large areas as well as provide for more effective management of that information. Furthermore, it is believed that dryland degradation can be slowed and reversed if areas undergoing deserti cation can be identi ed and properly managed (Kumar. B. P et al., 2020). The current research has also shown that RS and GIS can investigate temporal variations in deserti cation and land degradation, analyze changes amongst land cover features, develop baseline deserti cation maps, and also monitor deserti cation (Kumar. S et al., 2014).
In addition to LD, deserti cation assessment and land use land cover dynamics, this exploration also comprises the study of spatial variability in soil parameters (Raina et al.,1999;Ahmad, N., & Pandey, P. 2018) like pH, EC, Soil organic matter(SOM), Nitrogen (N), Phosphorus (P), Potassium (K), Iron (Fe), Zinc (Zn), Copper (Cu), and Manganese (Mn), and Sulfur using the digital soil mapping Inverse Distance Weighted (IDW) method in irrigated and non-irrigated soils of Bommanahal Mandal of Anantapur district, Andhra Pradesh state, India. The results obtained by both approaches have been correlated to get a better picture of the extent of degraded lands and deserti ed lands in the semi-arid region in southern India.

Study area
Bommanahal region of Anantapur district of Andhra Pradesh, India, with an area of 305.86 Km 2 . This region is located in between the "longitude of 76˚ 52 and 77˚ 08 and latitude of 14˚ 48 and 15˚ 04 (Fig. 1). The climate of the study region varies from semi-arid to sub-humid and being positioned in the rain shadow area of Western Ghats, in the interior of Deccan plateau, having low rainfall of about 520 mm is lowest in the state and recognized as second driest part in the country next to Jaisalmer, and is one of the chronic drought-affected regions in the country". This region experiences a tropical climate, in summer seasons continuous from March to May, the temperature varies from 24 to 46˚C.
Lithologically the current study region having two distinct and well-marked groups of older groups of metamorphic rocks belongs to the Archean age and younger groups of sedimentary rocks belong to the Proterozoic age ( Figure.   Because of high-speed winds causing by the southwestern monsoon season (34 to 38 km/h) between June to August, the sand and dunes present on the side of the river banks get migrated to the agricultural elds and the land has been degraded and nally deserti ed.

Land use / Land cover (LULC)
False Colour Composite (FCC) of multi-temporal satellite imageries of the years 1990, 2000, 2010, and 2019 were collected and layer stacked in ERDAS software, with a 1:50000 scale. Besides, the unsupervised classi cation method is adoptable in the ERDAS, in that the main digital value for each input band could be signi ed as a spectral re ectance pro le and spectral variability in class. Afterward, the classi cation has been nished each class should be observed and allocated a name it may also be necessary to merge several classes into a single category. To nish, the LULC map was prepared with supervised classi cation using the maximum likelihood classi cation algorithm (MLC). The objective is to encompass or extrapolate info on land cover types for an acknowledged area of the image to the unidenti ed areas of the whole image.

Physico -Chemical analysis of Soil
Eleven chemical parameters -"pH, EC, Soil organic matter (SOM), Nitrogen (N), Phosphorus (P), Potassium (K), Iron (Fe), Zinc (Zn), Copper (Cu), and Manganese (Mn) and sulfur (S)", were analyzed for 12 soil samples in the study region as represented in Table 1 with locations and parameters values. Figure 2c represents the location of soil samples collected in the study region. The chemical parameters pH, EC, SOM, N, P, K, Zn, Mn, Fe, Cu, S, and B of the soils samples collected from the sampling sites in the study region are given in Table 1. The pH was determined following the procedure of IS:2720, part  in which a pH meter (MT-103 Delux) was used to record the pH in an extract of soli or supernatant liquid (ICAR, 2010; Indian Standard, 1987). The EC was carried out in EC conductivity meter (Indian Standard, 2000). An extract of soil water suspension was prepared and ltered using lter paper to avoid any interference before recording the conductivity with EC meter. The parameters of Zn, Mn, Fe, and Cu were determined by the titration method, in that soil added to DTPA into 1:2 ratios and then the sample has been placed into Atomic Absorption Spectra Photometer (Mathew, 2014      respectively. Coming to the deserti ed lands, active geomorphic changes take place in this study region. the wind is the geomorphological agent in the transportation or enhancement of dunes in the study part. Dune migration leads to an ecosystem imbalance in the study part. This leads the study part to be faced with deserti cation conditions and nally to be deserti ed. The yellow color is given to the deserti ed lands category in the supervised image. In the year 1990 it is noticed as  Table 3 labels the change detection in the total area (km 2 and percentage) covered by different LULC between the years 1990 and 2019 procured from LANDSAT satellite data. The area under waterbody is slightly increased to 0.52 km 2 (0.17 %), and the Built-up land is extended to 7.43 km 2 (2.42 %). Vegetation is decreased to 71.76 km 2 (23.46 %). Fallow land, Degraded lands, and Deserti ed lands showed increasing in trend to 38.67 km 2 (12.64%), 11.46 km 2 (3.74%), and 13.68 km 2 (4.47%) (Fig 5b).

Soil Parameters analysis
Spatial erraticism of speci c soil properties in the study region was predicted by digital soil mapping, these maps were produced in digital format in a rapid, e cient, operative, and little cost method. LD and deserti cation were shown as the spatial distribution of all the eleven in quanti able terms via IDW interpolation methods using ArcGIS software-based statistical analysis tools (Sheng, 2010). Grounded on the pH values, a soil map for sampling sites was composed of the software ArcGIS 10.4 (Fig 6a). similarly, based on EC, SOM, N, P, K, Zn, Mn, Fe, Cu, and S values, soil maps were self-possessed as shown in Fig. 6b, c, d, e, f, g, h, I, j, and k, correspondingly, portraying the severity of LD and deserti cation in terms of soil parameters.
Areas like Nemakallu, Unthakallu, Uddehal, Bommanahal, Kuruvalli, of the North-Western (NW) part of the selected part exhibited a huge proportion of soils that were alkali or sodic, South-Eastern (SE) areas like Govindavada, Honnuru were calcareous or saline in nature, Bollanaguddam, Kalludevanahalli, Kuruvalli areas of north-eastern parts (NE) shows slightly to saline in nature. There is no acidic sample were traced in the study region (Fig 6a). Grounded on the EC standards, it has detected from the map (Fig .6b) that in the selected part, the maximum study part occupied by somewhat a little saline, and around of the south-western (SW) regions like Elanji and Kodaganahalli remained abstemiously saline. Founded on SOM ranges, it has detected from the map area (Fig .6c) that in the study, the maximum study area was high SOM likely 0.45-0.53.
Founded on the NPK values, it is observed from the maps ( g .6d, 6e, 6f) that in the study area, de ciency of N is traced in NW and NE parts, de ciency of P is traced in SW part and moderate values in EW part, and lower in K values throughout the study region. Based on Zn values, it has observed from the map (Fig .6g) lower in the NE part and higher in the SW part. Founded on Mn values, it has observed from the map (Fig .6h) moderate throughout the region and NE part having variation in their values to moderate to high, based on Fe values, it has observed from the map (Fig. 6i), NE-SW Part of the study regions shows high iron content in the soils and remaining area having moderate values, based on Cu values, it has observed from the map (Fig  .6j), lower values in the NE part and moderate in the center of the study region. Based on S values, it has observed from the map (Fig .6k), SE, and SW parts having low values and NW part having moderate and center of the study region showing high values.

Correlation between Physico-chemical parameters
To determine the relationship between the soil salinity and DN values for the total pixels of the different soils, an effort was completed to intercalate the soil salinity data. To interpolate the available soil salinity data, for the 11 soil samples have been observed with different soil parameter observations at the topsoil derived from soil mapping were digitized and then rasterized. The rasterized map was then interpolated using ArcGIS software which performs IDW based on the values of soil parameters. The correlation examination has been given in Table 3. "Based on the results attained from the soil analysis, an attempt was made to establish correlation for band 3 (green), band 5 (NIR), and band 7 (SWIR) of the LANDSAT  Rendering to our outcomes, the correlation between coe cient amongst the soil salinity and related DN values using LANDSAT data was supportive in calculating the signi cant relation between satellite data and soil salinity. The salt-affected soils in semi-arid regions show a high re ectance, especially when a salt crust (whitish color) is formed. For the assessment of LD and deserti cation, the correlation between DN's and Soil parameters by concocting soil maps from remotely sensed data.

Conclusions
In this study, we make an effort to assess and map the land degradation, deserti cation changes, and soil characteristics in the semi-arid area of the ATP district of AP, India between the years 1990 to 2019 using the combined technique of RS and GIS modeling. We found that there is a positive sign that the increase in water bodies is noticed0.52 Km 2 or 52 hectares. Built-up areas lengthened around 7.43 Km 2 or 743 hectares between 1990 to 2019. The land under vegetation is decreased from 159.14 Km 2 to 87.38 km 2 , which means 7176 hectares of vegetation land is decreased. Fallow land is increased from 41.93 km 2 to 80.60 km 2 , which means 3867 hectares of land is uncultivated in this region, this leads to degradation increase in the land. Degraded land is also increased from 56.79 km 2 to 68.25 km 2 , the resultant changes are 11.46 km 2 or 1146 hectares. LD leads to deserti cation in the study region, it is increased in alarming rates from 38.31 km 2 to 51.99 km 2 , in other words, 13.68 km 2 or 1318 hectares of agricultural lands converted into deserts. Because of the deserti cation conditions, most people stop their agricultural forming and get migrated to other cities for their livelihood.
The soil Physico-chemical analysis provides to be very useful in assessing the degree of soil-salinization, most of the regions had moderate to saline type. Correlation studies examining the response amongst the spectral response of the soil and physio-chemical parameters revealed that band 5 (NIR) was better indicators of soil. Hence, the usage of multispectral remotely sensed data in a GIS environment to assess the amount of LD and deserti cation can assistance in the direction of the renovation of the degradation of land resources and their conservation. The current work is very useful for the identi cation of hotspots of degradation, and taking the action plans to control the deserti cation at global scales. The RS and GIS provide pointers on tools to monitor, approximation, appraise achieve, and supervisory factor the environmental imperils to save life and society.