Desertification characterization using predictive soil modelling and pattern recognition

Abstract

This thesis presents a hierarchical methodology for land degradation mapping,land use land cover classification, degradation process identification and map-ping using multispectral LISS-3 images. The study aims to demonstrate the im-portance of remote-sensing images for various applications, both social and en-vironmental. The study compares the results of different algorithms for differentterrains, demonstrating that Simple Linear Iterative Clustering (SLIC) segmenta-tion with the random forest(RF) method outperforms CNN and pixel-based Sup-port Vector Machine (SVM) with an accuracy of 85% for level 1 land cover clas-sification. Vegetation degradation in forest areas is assessed in central parts ofGujarat, India, and land degradation in agricultural areas due to soil salinity isstudied, particularly in southeastern parts of Gujarat, India. ML algorithms likesupport vector machine(SVM) and RF was applied to different features to identifythe degradation process. Temporal data were used to find the severity of deserti-fication using the change in degraded areas.Further, it discusses soil degradation causing desertification and severely re-ducing potential soil productivity. The study uses machine learning algorithmsand an ANN-based model to predict soil properties like EC, pH, and OC, whichare important indicators of soil degradation. Environmental parameters are takenas covariates in prediction models, including vegetation indices, terrain indices,soil parameters, spatial attributes, and meteorological parameters of the study re-gion. Field soil sampling data of the study region obtained from Soil Health Card(SHC) for the year 2014 is incorporated in training the model. The SHC data isdivided into different ratios for training and testing the model. The SCORPANmodel is considered the base approach for the development of the ANN-basedprediction model. Moreover, the thesis also discusses the mapping of vulnera-ble areas to desertification. The study combines remote sensing and geographicinformation system (GIS) to map sensitive areas. Two different approaches wereused for vulnerability assessment: Mediterranean Desertification and Land Use(MEDALUS) approach and the fuzzy logic (FL) method. Soil, climate, land uti-lization, geography, and vegetation contribute to the land degradation of anyarea. However, man�s intervention leads to significant changes in the environ-ment, making socio-economic factors a considerable input to assess desertificationvulnerability. Indices related to these factors are generated, and both methods areused to find the severity level of the desertification vulnerability in the Panchma-hal district.Lastly, the role of climate in the process of desertification is discussed. Thestudy uses the aridity index (AI), which incorporates most of the weather datalike temperature, rainfall, humidity, wind, and solar radiation, to identify the de-sertification hot-spot using AI over the Gujarat state. The study uses weatherdata from more than 18 locations all over Gujarat for the past 20 years to calcu-late AI, and the FAO Penman-Monteith method was used to calculate PET. Thestudy generates an annual AI map for the whole of Gujarat using these valuesand compares it with a globally published AI map. It also compares the changein climate with the change in vegetation over the years using the vegetation in-dex for Gujarat. In summary, this thesis provides a comprehensive approach toland degradation mapping using degradation process identification, soil predic-tion, and climate variable using geospatial technology and machine learning. Thestudy demonstrates the importance of remote sensing images in various applica-tions, including social and environmental. The study employs different machinelearning algorithms and approaches to achieve high accuracy and identify vul-nerable areas to desertification. The study also highlights the importance of soilproperties and climate in the process of desertification.