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Soil erosion is a natural occurrence in landforms but is a major threat that causes essential soil nutrients loss and it is hazardous to agriculture practices as depletion of soil makes the land less productive. The major factors influencing soil erosion are rainfall erosivity factor (R), soil erodibility factor (K), length and steepness factor (LS), cover management factor (C) and support practice factor (P). In most of the earlier research studies, the estimation of soil erosion has been done with Revised Universal Soil Loss Equation (RUSLE) but accuracy in analysis of C and P factors has not been considered. The main objective of the study is to explore the influence of different LULC classifier in determining the rate of erosion using RUSLE model. This study utilizes five classifiers such as Maximum Likelihood Classifier (MLC), Random Trees Classifier (RTC), Support Vector Machine Classifier (SVM) and Artificial Neural Network (ANN) and Mahalanobis Distance (MD) to generate the C and P factor. The Accuracy assessment, a statistical method is used to validate the classified Land Use / Land Cover (LULC). The obtained results show that the respective annual mean soil losses based on MLC, MD, ANN, SVM, RTC are 1.95, 2.08, 1.71, 2.19 and 1.85 tons−1 ha−1 yr−1. The estimated annual mean soil loss by using different classifiers is validated using a statistical method Receiver operating curve / Area under Curve. The accuracy level of RTC is 0.800 of Area Under the Curve (AUC) which is higher compared to conventional classifiers like MLC, MDC, ANN, SVM with the values 0.793, 0.707, 0.693, 0.793 of AUC, respectively. RTC, a Machine learning-based classifier, is observed to be more accurate than the conventional classifiers. The insights obtained from the present study will be very useful for land use planning and management and to undertake soil and water conservation measures. [ABSTRACT FROM AUTHOR]

Land-use/land-cover (LU/LC) change is an important component of global environmental change. The need to understand LU/LC change is essential for regional development and land use management towards sustainable development. To understand LU/LC change, the different LU/LC categories and their spatial as well as temporal variability in Tiruchirappalli city has been studied over a period of eight years (1998–2006), using the analysis of Indian Remote Sensing Satellite (IRS) images. In this, an attempt was made to adopt Markov model for obtaining and understanding LU/LC dynamics. Model performance was evaluated between the empirical LU/LC map extracted from CARTOSAT-1 PAN image and the simulated LU/LC map obtained from the Markov model. The future landscape distribution in the year 2014 and 2022 was derived using a Markov model. This result shows that Markov model and geospatial technology together are able to effectively capture the spatio-temporal trend in the landscape pattern associated with urbanization in this region. [ABSTRACT FROM PUBLISHER]

Land use/land cover mapping serves as a basic inventory of land resources throughout the world. Detecting and analyzing the quantitative changes along the earth's surface has become necessary and advantageous because it can result in proper planning, which would ultimately result in improvement in infrastructure development, and economic and industrial growth. This paper examines the changes in land use/land cover types in Tiruchirappalli city, Tamil Nadu from 1998-2006, using remote sensing technology and Geographic Information Systems (GIS). The spatial and temporal changes of land use/land cover were analyzed through visual interpretation and image classification of IRS remote sensing data collected for years 1998, 2002 and 2006. The results of the study indicated an overall growth in built-up land by 80% over a span of eight years, with an incredible boost (58%) in development from 1998-2002. The area under agricultural wet land was found to have decreased from 3,776 ha to 2,689 ha, with an increase in agricultural dry land from 814 ha to 1,058 ha during this period, suggesting the wastage of land due to excessive use of fertilizers. [ABSTRACT FROM AUTHOR]