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Identifying soil erosion-prone zones in an ungauged river basin is crucial for devising and implementing timely soil protection measures to mitigate soil degradation and protect soil quality. Soil erosion damages the fragile ecosystem, decreases soil fertility, and reduces reservoir water storage, thereby impacting food production. The prime motive of the current research work is to assess and categorize on the basis of priority the sub-watersheds (SWs) susceptible to substantial soil erosion in the Ponnaniyar River basin (an ungauged river basin) based on the morphometric parameters that impact soil erosion. To achieve this research objective, four multi-criteria decision-making (MCDM) approaches based on the outranking approach and synthesis method are adopted to facilitate the decision-making process by considering an integrated and balanced assessment of multiple complex parameters for devising effective soil conservation measures to minimize soil erosion. Cartosat-1 digital elevation model (DEM) is employed to extract eighteen morphometric parameters under linear, shape, areal, relief and hypsometric aspects. The priority of SWs obtained by different MCDM techniques is evaluated using percentage of variation and intensity of variation. The outcomes show that the MABAC method is effective in prioritizing SWs with the least percentage of variation (59.61%) and intensity of variation (4.397). It is also found to be the best method for integration with the RSS method for determining SW priority with a root sum of squares value of 43. SW1 is identified to be highly vulnerable to soil erosion with a grade average value of 1.00 followed by SW2 (3.00), SW3 (3.25) and SW13 (5.00), requiring immediate implementation of watershed planning and management measures to control the extent of soil erosion and safeguard soil resources. [ABSTRACT FROM AUTHOR]

The identification of areas prone to soil erosion in ungauged river basins is crucial for timely preventive measures, as erosion causes significant damage by lowering soil productivity and filling reservoirs with sedimentation. This study proposes a novel approach to prioritize sub-watersheds (SWs) in Ponnaniyar river basin. It utilizes different combinations of five objective-based weighting methods and seven Multi-criteria Decision Making (MCDM) techniques under outranking and synthesis methods with soil loss, morphometry, land use/land cover (LULC), and topography parameters. The results obtained from different hybrid models are validated using metrics like percentage and intensity of change. The findings reveal that MW-PROMETHEE (53.85%) and CRITIC-WASPAS (8.31) perform best in prioritizing areas based on morphometry, while CRITIC-TOPSIS (48.35% and 7.58) is more effective in prioritizing areas based on land use/land cover (LULC) and topography. The grade average method is used to integrate the rankings from 71 models: 35 based on morphometry, 35 based on LULC, and 1 based on the RUSLE model. The analysis identifies SW2 with a grade value of 4.34 as severely affected by soil erosion, followed by SW11 (5.45), SW5 (5.56), and SW9 (5.68), all falling within the very high priority level. This study recommends implementing appropriate water harvesting structures, which might be helpful in mitigating soil degradation, promoting soil conservation, and ensuring sustainable agricultural productivity. [ABSTRACT FROM AUTHOR]

Soil erosion is a significant problem in the agriculture sector and the environment globally. Susceptible soil erosion zones must be identified and erosion rates evaluated to decrease land degradation problems and increase crop productivity by protecting soil fertility. Therefore, a research study has been carried out in the Ponnaniyar River basin, an ungauged tributary of the Cauvery basin in India, primarily used for agriculture. The main purpose of this study is to assess soil erosion (SE) and sediment yield (SY) for the future in an ungauged basin by utilizing the projected land use/land cover (LULC) map of the study area. Additionally, Landsat 8 satellite dataset was only used for the classification and prediction of LULC to eliminate the variation between the resolution, bands and its wavelength of different satellites datasets. To achieve the goals of this study, three phases were followed. First, the LULC of the study area was classified using a Random Trees Classifier (RTC), a machine learning technique, followed by the projection of land cover using a Cellular Automata-based Artificial Neural Network (CA-ANN) model. The driving factors for this model include digital elevation model (DEM), slope, distance to roads, settlements, and water bodies. The accuracy level of the projected LULC map was determined by comparing it with the classified LULC map of the study area, and the results showed an overall accuracy (OA) of 85.35 percentage and a kappa coefficient (K) of 0.74, respectively. Second, the projected LULC map was used in the land management factor (C) and conversation practice factor (P) of the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion. The model was integrated with the sediment delivery ratio (SDR) to estimate sediment yield within the study area. The accuracy of the generated erosion map based on the classified and projected LULC for the year 2022 was determined using the receiver operating characteristic curve (ROC) curve, and it was found to be in satisfactory agreement. Finally, for effective soil and water conservation measures, the basin was divided into 13 sub-watersheds (SWs) using terrain analysis in geographical information system (GIS). The SWs were prioritized based on the mean soil loss in the 4-year interval from 2014 to 2030 and integrated using the weighted average method to determine the final prioritization. From these findings, SW 11, SW 9, SW 12, and SW 1 are extremely affected by soil erosion, and immediate implementation of water harvesting structures is required for soil conservation. Also, this research might be useful for decision-makers and policymakers in land management. [ABSTRACT FROM AUTHOR]

Understanding the LULC change is essential to adopt the new development activities in the particular study area. The study reveals the LULC change for four different decades (1990, 2000, 2011, and 2022) of the Sulur-Singanallur region of the Coimbatore district. In this study, the Landsat satellite data of four decades were used to assess the regional LULC changes and identified the area under Built-up land increased from 6 to 25.77% and Waterbodies decreased by 5.48% (6–0.52%). Similarly, the current scenario of agriculture has decreased as Crop lands by 12.77% (30–17.23%). At the same time, the fallow land has risen and fall from starting to the ending years. Hence, the CA-ANN method has been adopted using the Modules for Land Use Change Evaluation (MOLUSCE) plugin of QGIS software. The spatial entities such as DEM, slope, distance to river, and distance to road were utilized as drivers to predict the future LULC of 2030, 2040, and 2050. The study's findings reveal that the crop land has decreased to 10.90%, 8.89% and 8.87%, respectively. Similarly, the waterbodies will be decreased by upto 1%, and the prediction findings portrays the increasing trend was observed in Built-up area and the better kappa statistics results identified as 0.81, 0.76, and 0.76 for 2030, 2040, and 2050. The observation of post classification shows that MLC is one of the best suited method and producing realistic result in the study for the change detection analysis. Overall, the outcome of the study showcased the continuous increase Built-up land were declined the natural land environment. If it continues, the agriculture ecosystem will be affected, and minimizes the cropping pattern and further anthropogenic influences will also reduce the agricultural activities in future. This modelling study results serves the benefit to land managers, stakeholders, farmers, and researchers to take possible measures immediately to manage the land sustainably. [ABSTRACT FROM AUTHOR]

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]

Image transformation techniques such as Principal Component Analysis, Minimum Noise Fraction and Independent Component Analysis were used for extracting information from the satellite image in various fields, but its application in landform mapping seems to be very less. The objective of the study is to demonstrate the viability of less explored image transformation techniques for delineating coastal dune complex and associated features using European satellite Sentinel 2 multispectral data. Based on the difference in lithology and vegetation cover the study sites has been chosen to extract coastal dune complex features such as dune ridges, dune with or without vegetation, swale with or without vegetation and water. Overall results of ICA shows enhanced visualization followed by PCA in bringing up the fine variations, identifying micro landforms within the dune complex from the optimum band combinations. Thus, sentinel data coupled with advanced image processing technique like ICA could identify maximum information about the boundaries and micro landform within the coastal sand dune complex in a cost-effective manner. However, in areas with dense vegetation cover none of these techniques has given good results in identifying the boundary or in identifying the micro landforms. [ABSTRACT FROM AUTHOR]

The changes that occur in various forms of land classes are known as land-use and land-cover changes (LULC). Such changes have a substantial impact on the contours of an urban basin and consequently have an effect on the surface runoff of the rainfall that occurs in the area. The runoff features obviously deteriorate because of the decrease in initial abstraction, and an increase in imperviousness causes enhanced runoff. The particular challenges in the urban basin are specificity calculation and quantification of surface runoff. Geospatial techniques are adopted along with the Soil Conservation Service-curve number (SCS-CN) technique to reliably predict and accurately measure runoff. The GIS is used to prepare the transformed layers of land classes from remotely sensed data. In this explorative study, LULC change and its impact on the urban Koraiyar basin, Tiruchirappalli city, South India, is studied by using SCS-CN with the aid of GIS techniques. As the basin that passes is located in a developing city, rapid changes in LULC are observed in and around the periphery of the basin. The LULC changes and their impact on surface runoff are analyzed using GIS with multi-dated Landsat satellite images for the years 1986–2016 at intervals of every 10 years. The supervised classification algorithm is used to develop LULC maps. From the study, it is observed that there is a continuous increase in settlement area of 1.04% from 1986 to 2016, especially in the northern part of the basin. A Markov model analysis is done from the historically developed LULC maps to predict the anticipated future LULC changes for the years 2026 and 2036 along with an estimation of the surface runoff. The predicted maps show that there is likely to be an increase in settlement area of 1.04% and decrease in 12.29% of agricultural land. The various thematic layers like slope, soil and curve number (CN) maps are also prepared using GIS. The composite CNs are generated for various land classes in the basin from 1986 to 2036. The increase in CN from 72 to 74.76 and their influence on runoff are studied. Finally, the study attempts to reliably estimate the present and future LULC status and its effect on surface runoff in the Koraiyar river basin. [ABSTRACT FROM AUTHOR]

Flooding in urban basins is a major natural catastrophe that leads to many causalities of life and property. The semi-urbanized Koraiyar River basin in Tamil Nadu has important cities like Tiruchirappalli and many towns located in it. The basin unfailingly experiences a flood event in almost every decade. It is anticipated that the basin will undergo rapid unplanned urbanization in the years to come. Such fast and erratic urban developments will only increase the risk of urban floods ultimately resulting in loss of human lives and extensive damages to property and infrastructure. The effects of urbanization can be quantified in the form of land use land cover (LULC) changes. The LULC change and its impacts on urban runoff are studied for the continuous 30-year present time period of (1986–2016) to reliably predict the anticipated impact in the future time period of (2026–2036). The analysis of land cover patterns over the years shows that urbanization is more prevalent in the northern part of the basin of the chosen study area when compared with the other regions. The extreme rainfall events that occurred in the past, and the probable future LULC changes, as well as their influence on urban runoff, are studied together in the current study. In order to minimize flood damages due to these changing land use conditions, certain preventive and protective measures have to be adopted at the earliest. There are some inevitable limitations while applying traditional measures in flood modeling studies. This investigative work considers a case study on the ungauged Koraiyar floodplains. The spatial scale risk assessment is assessed by coupling geographic information systems, remote sensing, hydrologic, and hydraulic modeling, to estimate the flood hazard probabilities in the Koraiyar basin. The maximum flood flow is generated from the Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS), the hydrologic model adopted in the present study. The maximum flood flow is given as input to the Hydrologic Engineering Centre-River Analysis System (HEC-RAS), an effective hydraulic model that generates water depth and flood spread area in the basin. The flood depth and hazard maps are derived for 2, 5, 10, 50, and 100-year return periods. From the analysis, it is observed that the minimum flood depth is less than 1.2 m to a maximum of 4.7 m for the 100-year return period of past to predicted future years. The simulated results show that the maximum flood depth of 4.7 m with flood hazard area of 4.32% is identified as high hazard zones from the years 1986–2036, located in the center of the basin in Tiruchirappalli city. The very high hazard flood-affected zone in the Koraiyar basin during this period is about 198.85 km2. It is noticed that the very low hazard zone occupies more area in the basin for the present and future simulations of flood hazard maps. The results show that the increase in peak runoff and runoff volume is marginally varied. [ABSTRACT FROM AUTHOR]

This paper gives an overview of a major application of modern geospatial tools such as remote sensing, GIS and GPS, i.e., 3S technology in estimating travel demand along Indian roads by considering the study area, Tiruchirappalli urban city in Tamil Nadu. In the study, an attempt was made to estimate travel demand based on the current land-use classification as the pattern of travel depends on the type of land use activity in a zone. IRS high resolution image Cartosat-1 of year 2009 was used to extract the land-use information required. The travel demand model developed was validated with the field obtained OD-matrix to understand the accuracy of the method considered. [ABSTRACT FROM AUTHOR]

The increase in population as well as migration from rural areas has resulted in drastic land use/land cover changes in the city of Tiruchirappalli. Unfortunately, these changes have not been quantified appropriately. This paper gives an overview of the study done to quantify the spread of urban activity from years 1998 to 2009 along the city using remote sensing/GIS technology. The results showed an unruly 41 % increase in urban activity, with a proportionate decrease in agricultural land during the period of study. An attempt was also made to project the built-up land for the next 20 years based on the available trend of urban changes and rate of population. [ABSTRACT FROM AUTHOR]

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broad overview of how GPS can be used to identify the active fault plane along which earthquakes occur in the seismic prone Western Maharashtra Peninsular shield of India is given. Indian Institute of Technology, Bombay in collaboration with Indian Institute of Geomagnetism, Mumbai was actively working in the field of crustal deformation monitoring along Western Maharashtra from year 2004-2006. A regional GPS network had been established for this purpose, periodically observed between May 2004 to May 2006, and analysed. To understand the pattern of seismicity along Western Maharashtra region, the GPS-estimated coordinates and displacements were used as an indicator to estimate and identify regions of high strain rates, the reliability of which was confirmed with real seismic data collected. These strain rates were used as a means to identify the fault plane along which the earthquakes occurred during the period of observation. [ABSTRACT FROM AUTHOR]

Rainfall-runoff modeling plays a crucial role in determining the regular water balance. Modifications in land use and land cover (LULC) significantly impact on the hydrological response of watersheds. The study aims to analyze the effect of land use change on river runoff with the use of hydrological modeling in the Wadi Ouahrane watershed in northwestern Algeria. The study was conducted for the period from 1987 to 2017. According to the LULC change study, cultivated land and built-up areas have increased, whereas forest and grassland areas have decreased. Sensitivity evaluation has shown that the CN (curve number) is the most important factor affecting the watershed hydrology. The Nash–Sutcliffe (NSE) and efficiency values for the Wadi Ouahrane watershed were 0.76–0.82 and 0.86–0.91 for the calibration period and 0.72–0.74 and 0.81–0.83 for the validation one, respectively. The assessment of the HEC-HMS response to the LULC change showed that the peak discharge for 2017 increased by 68% relative to the 1987 peak discharge. This research has improved the knowledge of the relationship between land use change and hydrological regimes in the Wadi Ouahrane watershed. [ABSTRACT FROM AUTHOR]