Indian Institute of Remote Sensing

About: Indian Institute of Remote Sensing is a government organization based out in Dehra Dūn, India. It is known for research contribution in the topics: Land cover & Normalized Difference Vegetation Index. The organization has 756 authors who have published 1355 publications receiving 16915 citations. The organization is also known as: Indian Photo-interpretation Institute.

Species Mapping Using Citizen Science Approach Through IBIN Portal: Use Case in Foothills of Himalaya

Abstract: In this era of rapid global change, biodiversity monitoring and improving species repository to meet requirements toward conservation is costly affairs and needs a practical solution to identify and locate species with habitats. The integrated approach of citizen science and information technologies has proven to be effective solution for geographical and taxonomical data collection with public engagement, covering local to national scale. As a first step, a mobile app is designed and developed for the IBIN (Indian Bioresource Information Network), a digitized collection of the biological resources of India that serves as a common platform to access spatial and non-spatial information on biorecources can host their data through this single and intuitive platform with full privileges and authenticity. IBIN mobile app can be seen as an efficient and rapid solution to record data on species, utilizing GPS and camera features of mobile devices. Present paper deals with the citizen science approaches, its contribution in biodiversity field, outlines the design and development of IBIN mobile app and its first case study carried out at foothills of Himalaya.

Evaluation of grasslands and spectral reflectance relationship to its biomass in Kanha National Park (M.P.), India

Abstract: Grasslands play an important role in a National Park environment. Its evaluation through remote sensing satellite data has been carried out in the present study. In context of Kanha National Park low grasslands are having significant importance to support increasing grassier her‐bivore population. Low grasslands occur both at hill tops and valley flat area. Field spectral measurements have been carried out in both the low grassland sites to estimate biomass (above ground). The present study reveals that there exist strong correlation between dry weight of green biomass and vegetation index (IR/R) at both the sites.

Compositional mapping and the evolutionary history of Mare Tranquillitatis

Abstract: This study signifies the compositional variability of Mare Tranquillitatis basalt and the Irregular Mare patches (IMPs) – the youngest volcanic feature on the Moon, using hyperspectral data from Moon Mineralogy Mapper (M3) for the first time. Along with composition, the topographic and morphological mapping has been done to understand the possible evolutionary history of this mare. Total 22 spectral units has been identified based on Integrated Band Depth (IBD) parameter technique. Number of reflectance spectra were collected from the fresh craters of each spectral unit and quantitative mineralogical abundances estimated using band parameters like band centre, band strength and band area. The result shows abundances of olivine and pyroxene mixture bearing material in the mare basalt. The compositional map shows smaller spectral units in the western-low lying half and larger spatial distribution of spectral unit in the eastern half depicts probable large-scale volcanic eruption in the eastern part that may have flowed to longer distances from the Cauchy shield to the central mare. This study marks 61 new domes in the Cauchy shield area and also depicts possible formation and evolutionary history of the Mare Tranquillitatis.

Growing stock estimation through stratified sampling on satellite remote sensing data

Abstract: Aerial photographs are being extensively used for forest surveys i.e. forest cover type mapping, assessment of growing stock, estimation of area, vegetation studies, etc.

Moist deciduous forest identification using MODIS temporal indices data

Abstract: The present research aims to extract moist deciduous forest (MDF) from Moderate Resolution Imaging Spectroradiometer (MODIS) temporal data by using the fuzzy c-means (FCM)-based noise clustering (NC) soft classification approach. Seven-date temporal MODIS data were used to identify MDF, and temporal Advanced Wide Field Sensor (AWiFS) data were used as reference data for testing. Different types of spectral indices were used to generate the temporal data set combinations for both MODIS and AWiFS. The NC resolution parameter delta was optimized to achieve the best output. It was found that for both AWiFS and MODIS data, optimum NC outputs were obtained when reached close to 105. For assessment of the accuracy, NC classified outputs were optimized using the entropy approach. The optimized data set of AWiFS was then used for assessing the accuracy of the optimized data set of MODIS using fuzzy error matrix (FERM), composite operators (MIN-MIN, MIN-PROD, and MIN-LEAST), and a sub-pixel confusion-uncertainty ma...