Author
Arijit Roy
Other affiliations: National Remote Sensing Centre, Indian Space Research Organisation, Banaras Hindu University
Bio: Arijit Roy is an academic researcher from Indian Institute of Remote Sensing. The author has contributed to research in topics: Land cover & Biodiversity. The author has an hindex of 14, co-authored 59 publications receiving 783 citations. Previous affiliations of Arijit Roy include National Remote Sensing Centre & Indian Space Research Organisation.
Papers published on a yearly basis
Papers
More filters
••
University of Hyderabad1, Indian Space Research Organisation2, The Energy and Resources Institute3, Centre for Development of Advanced Computing4, Remote Sensing Center5, Indian Institute of Technology Kharagpur6, University of Illinois at Urbana–Champaign7, Banaras Hindu University8, CEPT University9, Anna University10, International Institute of Information Technology, Hyderabad11, Central University, India12, North Eastern Regional Institute of Science and Technology13
TL;DR: The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares to conclude that this dataset has captured the maximum cumulative patch diversity frequency, indicating the detailed representation that can be attributed to the on- screen visual interpretation technique.
Abstract: India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study.
186 citations
••
University of Hyderabad1, Indian Institute of Technology Kharagpur2, International Centre for Integrated Mountain Development3, Indian Institute of Remote Sensing4, Remote Sensing Center5, TERI University6, Banaras Hindu University7, University of Twente8, International Water Management Institute9, Centre for Development of Advanced Computing10, International Center for Agricultural Research in the Dry Areas11, Wildlife Institute of India12, Annamalai University13, Berhampur University14, United Nations University15, Indian Institutes of Information Technology16, University of Agricultural Sciences, Dharwad17, World Agroforestry Centre18, University of Kashmir19, National Botanical Research Institute20, Assam University21, Kerala Forest Research Institute22, North Orissa University23, Botanical Survey of India24, University of Calcutta25, Lincoln University (Pennsylvania)26, Pondicherry University27, Mohanlal Sukhadia University28, University of Jammu29, Council of Scientific and Industrial Research30
TL;DR: This vegetation type map is the most comprehensive one developed for India so far and was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil.
140 citations
••
TL;DR: In this article, the authors used GIS based network analysis to assess the accessibility of urban green spaces at hierarchical levels by applying different network distance to each hierarchy of UGS in a dense and complex urban setting in a developing region.
118 citations
••
TL;DR: In this article, fire danger models are used for the prediction of fire danger in western Himalayan forests, which is an annual phenomenon in more than 50% in the forests of Uttarakhand state.
Abstract: Forest fire is one of the major causes of degradation in western Himalaya, and is an annual phenomenon in more than 50% in the forests of Uttarakhand state. Fire danger models are useful for the fi...
46 citations
Cited by
More filters
30 Apr 1984
TL;DR: A review of the literature on optimal foraging can be found in this article, with a focus on the theoretical developments and the data that permit tests of the predictions, and the authors conclude that the simple models so far formulated are supported by available data and that they are optimistic about the value both now and in the future.
Abstract: Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.
2,709 citations
••
TL;DR: This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.
Abstract: With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.
2,284 citations
01 Jan 2016
TL;DR: The remote sensing and image interpretation is universally compatible with any devices to read and is available in the digital library an online access to it is set as public so you can get it instantly.
Abstract: Thank you very much for downloading remote sensing and image interpretation. As you may know, people have look hundreds times for their favorite novels like this remote sensing and image interpretation, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some malicious virus inside their computer. remote sensing and image interpretation is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the remote sensing and image interpretation is universally compatible with any devices to read.
1,802 citations