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.

Forest encroachment mapping in Baratang Island, India, using maximum likelihood and support vector machine classifiers

Abstract: Maximum likelihood classifier (MLC) and support vector machines (SVMs) are commonly used supervised classification methods in remote sensing applications. MLC is a parametric method, whereas SVM is a nonparametric method. In an environmental application, a hybrid scheme is designed to identify forest encroachment (FE) pockets by classifying medium-resolution remote sensing images with SVM, incorporating knowledge-base and GPS readings in the geographical information system. The classification scheme has enabled us to identify small scattered noncontiguous FE pockets supported by ground truthing. On Baratang Island, the detected FE area from the classified thematic map for the year 2003 was ∼202 ha, and for the year 2013, the encroachment was ∼206 ha. While some of the older FE pockets were vacated, new FE pockets appeared in the area. Furthermore, comparisons of different classification results in terms of Z-statistics indicate that linear SVM is superior to MLC, whereas linear and nonlinear SVM are not significantly different. Accuracy assessment shows that SVM-based classification results have higher accuracy than MLC-based results. Statistical accuracy in terms of kappa values achieved for the linear SVM-classified thematic maps for the years 2003 and 2013 is 0.98 and 1.0, respectively.

Changing landscape and ecotourism development in a large dam site: a case study of Tehri dam, India

Abstract: This paper presents an analysis of the historical change in the local landscape of Tehri dam, the highest earth and land filled dam in India, and surroundings focusing on changes that transpired du...

Mapping of thermal power plant emitted atmospheric carbon dioxide concentration using AVIRIS-NG data and atmospheric radiative transfer model simulations

Abstract: Our study demonstrates the potential of Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) hyperspectral data and radiative transfer modeling to retrieve the atmospheric carbon dioxide (CO2) concentration from point source emission of one of India’s major coal-fed super thermal power plants at Kota, Rajasthan, India. AVIRIS-NG data with synchronous in situ measurements were collected as a part of the first ISRO-NASA joint airborne hyperspectral science campaign in India. The method utilized in our study includes theoretical simulations of at-sensor radiance in AVIRIS-NG spectral bands through the atmospheric radiative transfer model MODTRAN. Simulations were performed for the specific scene-sensor-atmospheric conditions pertaining to AVIRIS-NG overpass over Kota site. To eliminate the interfering effect of atmospheric water vapor with CO2 concentrations, simulations pertaining to variable water vapor values varying from 0.25 to 4.5 g / cm2 were also carried out, which in turn were utilized to normalize the water vapor effects. Based upon the simulation results pertaining to the specific absorption bands of CO2 in the shortwave infrared spectral range of AVIRIS-NG radiance data, a theoretical model was established between continuum interpolated band ratio (CIBR) and CO2 concentration. The CIBR − CO2 model coefficients for each of the water vapor subranges were then applied to AVIRIS-NG L1 radiance data to obtain the CO2 concentration map of the study area. A distinct CO2 plume could be detected from the coal-fed Kota Super Thermal Power Station with the CO2 concentration (XCO2) of the order of more than 500 ppmv near the power plant stacks. A range of XCO2 from 400 to 550 ppmv was observed within the scene. Our study has provided promising results in mapping the atmospheric CO2 content from the point source using the high-resolution airborne hyperspectral AVIRIS-NG data.

Role of SAR Data in Water Body Mapping and Reservoir Sedimentation Assessment

Abstract: . Reservoir sedimentation is the major problem, due to it every year the reservoir capacity is lost to considerable amount. Surveying for assessment of the reservoir by conventional approach is time and money consuming. Geospatial technology provides ample opportunity in this field through the availability of high resolution satellite data from sensors such as Sentinel, Indian Remote Sensing Satellite, Landsat, and SPOT have been used to calculate the water spread area of the reservoir. However, due to presence of cloud in most of the optical data during onset of monsoon, the water spread at the lowest reservoir level could not be mapped. In turn the revised capacity or sedimentation is generally assessed between either below full reservoir level (FRL) or above maximum draw down level (MDDL). Nowadays, the microwave synthetic aperture radar (SAR) data at reasonable spatial resolution is available freely in public domain. Moreover, microwave data has capability to penetrate cloud and the information below cloud can easily be retrieved. To overcome the issues related to optical data, in the present study, the reservoir sedimentation for Ghataprabha reservoir was estimated using SAR data. Sentinel-1A data was used to delineate the water spread area for the water year of 2016–17. The original live storage capacity (1974) was estimated to be 1434.14 Mm3 at FRL 662.940 m by the authorities using the hydrographic survey during the commissioning of the reservoir in the year 1974. The live storage capacity was found out to be 1366.14 Mm3 at FRL, however, as per original elevation-area-capacity curve the live capacity is around 1262.404 Mm3 at 660.50 m. Estimated live storage capacity from Remote sensing approach (2016–17) was assesses as 1182.5 Mm3 at 660.51 m. The storage capacity has reduced from 1262.40 Mm3 (1974) to 1182.51 Mm3 i.e. around 171.732 Mm3. As per present analysis the rate of sedimentation is around 4 Mm3/yr. It was realized that using the SAR microwave data, the revised capacity of the reservoir from its near MDDL to FRL could be assessed through remote sensing approach.

Forest vegetation - landuse change detection and impact assessment in part of western Himilaya

Abstract: Himalayan mountain ranges, which represent nearly 18% of the total area of Indian sub-continent, play an important role for the maintenance of environmental set up of the country. The exploding population pressure has created the adverse changes and subsequent impact in the total eco-system of this region. In the present study, aerial photographs of the year 1960 and Landsat Thematic Maper False Colour Composite image of the year 1985 were interpreted for detecting the changes on forest vegetation and landuse categories identified on both of them. These categories consist of Oak, Deodar, Pine, Miscellaneous, oak- Deodar, Oak-Pine, degraded forest, scrub/shrub, agriculture, habitation and lime stone quarries. The dynamics of changes within forest vegetation /landuse categories has been assessed by creating the database of the maps and subsequent analysis under GIS domain. The ground realities of changes and impact of those changes have been verified and ascertained respectively through field observations and site specific interviews. The study revealed a total change of 27 % out of a total area of 64.12 Km 2 during the year 1960-85. The changes have mainly taken place in the form of its depletion/degradation of forest vegetation and expansion of settlements. It is significant to note that most of the changes (70% out of total change) have occurred in the Oak forest area and among all the types of changes, forest degradation is the highest one. The impact of changes has been severe for the existing agro-ecosystem, as the productivity of agricultural crops has gone down considerably with the passage of time.