SPATIO-TEMPORAL ANALYSIS OF NATURAL HUMAN HABITABILITY ENVIRONMENT ALONG THE COASTAL TALUKS OF TAMIL NADU, INDIA

In this present world, due to the increasing adverse effect of anthropological activities on the natural environment causes a large scale environmental degradation which directly reduces the suitable natural environment for human habitation. As a consequence, in recent years, human realised the need for a favourable natural environment which is adoptable for habitation. In this present study, some of the following five criterions such as Land Surface Temperature (LST), vegetation coverage, impervious surface, wetness and water condition derived from the remotely sensed data were used to evaluate the Natural Human habitation Environment Suitability Index (NHESI) along the coastal taluks of Tamil Nadu. Landsat-7 (ETM+) images and Landsat-8 (OLI/TIRS) images with a spatial resolution of 30m have been used to derive the evaluation factors of NHESI for the year of 2000 and 2018. Multi Criteria Evaluation (MCE) based Analytical Hierarchical Process (AHP) and fuzzy linear membership has been used in this study to evaluate the weighs and ratings of each criterion and its classes. The best NHESI is seen in 2000 where a total area of about 13902.9 km2 comes under the habitable region, against an area of 7726.9 km2 in 2018. The study area is further classified into moderately habitable, marginally habitable and uninhabitable regions. This study clearly indicates the degradation of the natural environmental conditions for human habitation. This kind of habitability study will help the researchers, decision makers and government agencies in creating awareness and adopting policies in the spatial planning of human land utilization for habitability

Prioritization of Erosion Prone Micro-Watersheds Using Morphometric Analysis coupled with Multi-Criteria Decision Making

Soil erosion is a serious environmental threat amongst the prevailing major natural hazards which affects the livelihood of millions of people around the world. The deterioration of nutrient-rich topsoil can affect the sustainability of agriculture and various ecosystems by decreasing soil productivity. Conservation measures should be implemented in those regions which are critical to soil erosion. The identification of areas susceptible to soil erosion through prioritization of watershed can help in proper planning and implementation of suitable conservational measures. Therefore, in this study, the prioritization of 23 micro-watersheds present in the Dnyanganga watershed of Tapti River basin is carried out based on morphometric parameters and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). TanDEM-X 90m openly accessible DEM generated from SAR interferometry, obtained through DLR, is used for determining the morphometric parameters. These parameters are grouped into linear, areal and relief aspects. Initially, the relative weights of various morphometric parameters used in TOPSIS were determined using Saaty’s Analytical Hierarchy Process (AHP). Thereafter, the MCDM package in R software was utilized to implement TOPSIS. The micro-watersheds were classified into very high (0.459–0.357), high (0.326–0.240), moderate (0.213–0.098), and low (0.096–0.088) prioritization levels based on the TOPSIS highest closeness (Ci+) to ideal solution. It is evident from the results that micro-watersheds (MW10, MW18, MW19, MW2, MW11, and MW17) are highly susceptible to soil erosion and thus, conservation measures can be carried out in these micro-watersheds with the priority to ensure the sustainability of future agriculture by preventing excessive soil loss through erosion

Phenological Monitoring of Paddy Crop Using Time Series MODIS Data

Rice is an important staple food crop worldwide, especially in India. Accurate and timely prediction of rice phenology plays a significant role in the management of water resources, administrative planning, and food security. In addition to conventional methods, remotely sensed time series data can provide the necessary estimation of rice phenological stages over a large region. Thus, the present study utilizes the 16-day composite Enhanced Vegetation Index (EVI) product with a spatial resolution of 250 m from the Moderate Resolution Imaging Spectroradiometer (MODIS) to monitor the rice phenological stages over Karur district of Tamil Nadu, India, using the Google Earth Engine (GEE) platform. The rice fields in the study area were classified using the machine learning algorithm in GEE. The ground truth was obtained from the paddy fields during crop production which was used for classifying the paddy grown area. After the classification of paddy fields, local maxima, and local minima present in each pixel of time series, the EVI product was used to determine the paddy growing stages in the study area. The results show that in the initial stage the pixel value of EVI in the paddy field shows local minima (0.23), whereas local maxima (0.41) were obtained during the peak vegetative stage. The results derived from the present study using MODIS data were cross-validated using the field data