Tarun Kumar De

Bio: Tarun Kumar De is an academic researcher from University of Calcutta. The author has contributed to research in topics: Mangrove & Shore. The author has an hindex of 4, co-authored 9 publications receiving 55 citations.

Assessment of changes in land use, land cover, and land surface temperature in the mangrove forest of Sundarbans, northeast coast of India

Abstract: This paper investigates the impacts of changing land use–land cover on the land surface temperature (LST) and normalized differential vegetation index (NDVI) distribution in the Indian part of the Sundarbans Biosphere Reserve by utilizing remote sensing and geographical information system. Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and Operational Land Imager images of the year 2000, 2010 and 2017, respectively, were used to assess the essential indicators for regional environmental health employing appropriate calibrations and corrections. It was observed that there has been a marked reduction in the areas of plantation, mangrove swamp, mangrove forests and agricultural land since 2000. In contrast, an increase in sand beach, waterlogged areas, mudflat, river, and agriculture area was observed. The mean NDVI values for mangrove forests and plantation have decreased from 0.441 to 0.229 and 0.266 to 0.195, respectively, while river, aquaculture, agricultural and open scrubs classes had higher values. The rate of increase in surface LST was highest over settlements, followed by sand beaches, mudflats, aquaculture, mangrove forest, river, plantations, waterlogged areas and agricultural field. LST showed a negative correlation with NDVI values probably due to the high rate of evapo-transpiration activities of the mangrove vegetations. All these above facts distinctly substantiates that there is an increase in open patches/non-vegetated cover and that the ecosystem is under constant stress.

A review of the application of multispectral remote sensing in the study of mangrove ecosystems with special emphasis on image processing techniques

Abstract: Much progress has been made since the first published report on application of remote sensing in mangrove mapping nearly 40 years ago. Remote sensing is now a widely used proxy in mangrove ecosystem related research mostly due to increased affordability of data and ease of access. This work reviews previous works on mangrove forests using remote sensing techniques with an aim to identify the best combinations of sensors, methods of image processing and vegetation indices for future studies. It was observed that higher accuracies were directly related to sensor resolution and expertise of the authors. From the imageries used, Quickbird had the highest accuracies (~ 100%) whereas aerial photos had ~ 93% accuracy and IRS LISS recorded (76–97%). Accuracies for Landsat images classified using hybrid approaches were up to 88% while AVIRIS and ASTER, gave up to 99% accuracies. Sentinel images also produced very high accuracy levels. Non parametric methods using machine learning techniques produced the highest levels of accuracy (up to 99%). But not all datasets are freely available and the cost of procuring datasets varies from country to country. This suggests that remote sensing of mangrove vegetation pose some serious challenges that require careful considerations in order to obtain better results. An in-depth understanding of the factors affecting the interaction between electro-magnetic radiation and mangrove vegetations in the environment, selection of images with appropriate spatial and spectral resolution and use of proper processing techniques for extracting spectral information is a must. This review paper thus, can be considered as a convenient asset to the researchers involved in assessing the mangrove environment by remote sensing technique.

Spatio-temporal modelling of shoreline migration in Sagar Island, West Bengal, India

Abstract: Sagar Island is a very popular pilgrimage destination located in the western part of Indian Sundarbans. This study employs Digital Shoreline Analysis System (DSAS) extension tool in ArcGIS platform to study and analyse the shoreline dynamics of Sagar Island by utilizing satellite images extending 40 years (1975–2015). 44 transects with 100 m spacing were laid and divided into six littoral cells (LCs). End Point Rate (EPR) and Linear Regression (LR) models were utilized to analyze the shoreline change patterns and also for predicting the future shoreline positions. It was observed that almost the entire southern portion of Sagar Island is susceptible to high rate of shoreline erosion. Most of the erosion occurred in Dhablat (LC 4 (a)) in the south eastern part at a rate of 11.695 ± 2.1 m/year. The mean shoreline change rate was also high in LC5 (±23.525 m/year). However, the overall shoreline change rate for the island was 4.94 m/year and uncertainty of total shoreline change rate was ±4.4 m/year. The study shows the usefulness of DSAS as a scientific tool for shoreline change studies and highlights state of erosion in the study area.

Land use/land cover modeling of Sagar Island, India using remote sensing and GIS techniques

Abstract: Image classification is an important process of land use and land cover mapping. For effective image classification, quite a few aspects have to be considered including the accessibility of quality of satellite imagery, ground control points, a accurate classification method and the skill and proficiency of the user in the processes involved. This study classifies and maps the land use/land cover (LULC) of the Sagar Island using two images (1975 and 2015) and additionally verifies the precision of the classification method used. The study has been divided into two sections (1) Landuse/Land cover (LULC) classification and (2) accuracy assessment. Unsupervised classification was performed using Non-Parametric Rule and change detection was done for the 40 years study period. It was observed that 7.60% of mangrove vegetation’s were converted to cropland. Similarly, 40.26% of agricultural (mono-crop) land was converted to agriculture land, 1.48% of mud flat was converted to mangrove swamps, 1.87% wetland area converted to aquaculture land, and 22.54% agricultural (mono-crop) land converted to the settlement with homestead orchard respectively. Other LULC conversions are agricultural (mono-crop) land to cropland (40.26%), mud flat to shallow water (1.36%), wetlands to cropland (0.055%). The study had an overall classification accuracy of 79.53% and Kappa coefficient (K) 0.7465. This overall classification accuracy of the LULC maps is quite significant in terms of their potential use for land use change modeling of Sagar Island.

Remote Sensing And Image Interpretation

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Introductory Digital Image Processing

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The effect of urban planning on urban formations determining bioclimatic comfort area’s effect using satellitia imagines on air quality: a case study of Bursa city

Abstract: Biocomfort is limit values ​​in which people feel comfortable in environmental conditions such as wind, humidity, temperature, and precipitation. Bioclimatic comfort areas are used in urban planning, rural planning, tourism purposes, urban area’s residential area, and landscape architecture. Bioclimatic comfort maps were made according to PET (Physiological Equivalent Temperature) index from meteorological station; also, data were obtained from satellite images to determine the bioclimatic comfort structure of the urban area in ​​Bursa. For this data, average wind, temperature, and relative humidity values ​​were converted to Geographic Information Systems (GIS) using ArcView GIS ™ 10 software and create thermal perception maps. The study integrates remote sensing (RS) and GIS to analyze land cover and use change in Bursa defining bioclimatic comfort. Utilizing Landsat (7 ETM and 8 OLITIRS) satellite data and Digital Elevation Model (DEM), the change map was analyzed and produced in 2002–2016. The objective was to understand the suitability of Change Vector Analysis (CVA) and elevation parameters in the region. The results indicate the appropriateness of change in vector analysis. With the help of a bioclimatic comfort conditions, map taking into account the temperature, humidity, and rainfall data and bioclimatic comfort zone for the 12-year period perceived for 30 years. The result maps of the study may serve as the basis for sustainable urban planning studies for Bursa.