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.

Spatial Analysis and Mapping of Malaria Risk in Dehradun City India: A Geospatial Technology-Based Decision-Making Tool for Planning and Management

Abstract: Land-use change emerged as one of the most rational component to the global environmental change, potentially has significant consequences on human health in relation to mosquito-borne blood diseases like malaria. Land-use change can influence mosquito habitat, and therefore the distribution and abundance of vectors and land use mediates human–mosquito interactions, including biting rate. Based on a conceptual model linking the landscape, human, animal and mosquitoes, this study focuses on the impacts of changes in land use on malaria in Dehradun city of India. Health center wise data on malaria and land-use change data were prepared. Results of the different components of the study were integrated in the geographic information system (GIS) environment and linking land use to disease. The impacts of a number of possible scenarios for land-use changes in the region were delineated and also a risk map of the study area was prepared. Results indicated that land-use changes have a detectable impact on malaria. This impact varies according to the land use land cover (LULC) condition as well as the socio-economic condition but can be counteracted by the adoption of preventive measures.

Space-borne sun-induced fluorescence: an advanced probe to monitor seasonality of dry and moist tropical forest sites.

Abstract: Space-borne sun-induced fluorescence (SIF) is the latest breakthrough in remote sensing of physiological response of plants. We studied the seasonality of sal ( Shorea robusta ) forest canopies analysing space-borne SIF and reflectance data collected over moist and dry sites in central India. Results indicate that the monthly response of OCO-2 SIF, MODIS NDVI and GPP differs significantly across the wet and dry forest sites. SIF explained higher seasonal variations and was also better correlated to rainfall across sites compared to NDVI.

PolInSAR tomography for vertical profile retrieval of forest vegetation using spaceborne SAR data

Abstract: Forest height plays a crucial role to investigate the bio-physical parameters of forest and the terrestrial carbon. PolInSAR based inversion modeling has been successfully implemented on airborne and space-borne SAR data. SAR tomography, which is an extension of cross-track interferometric processing is a recent approach to separate scatterers in cross range direction, thus generates its vertical profile. This study highlighted the potential of tomographic processing of fully polarimetric Radarsat-2 SAR system to retrieve backscatter power at different height levels. Teak forest in Haldwani forest division of Uttarakhand state of India was chosen as the test site. Since SAR tomography is a spectral estimation problem, Fourier transform and beamforming based spectral estimations were applied on the dataset to obtain their vertical profiles. Fourier severely suffered from high side lobes which was drastically reduced by implementing beam-forming by taking into account the multi-looking effect at the expense of radiometric accuracy. Backscattered power values were found to be different at different height levels of the forest vegetation. Vertical profile for Fourier as well as beam-forming were also retrieved.

Fuzzy-based approach to incorporate spatial constraints in possibilistic c-means algorithm for remotely sensed imagery

Abstract: This paper presents a robust possibilistic c-means with constraints (PCM-S) algorithms in a supervised way for remotely sensed imagery. The PCM-S overcome the disadvantages of PCM, by incorporating local information through spatial constraints to control the effect of neighbouring terms. PCM-S has been deployed by adding spatial constraints in order to provide robustness to noise and outliers. Neighbourhood labelling has been done in PCM-S by introducing local window (NR) and regulariser parameter (ηi). Experiments have been conducted on Formosat-2 satellite imagery of Haridwar area in which classified results of PCM and PCM-S is optimised using mean membership difference (MMD) method and performance of classifiers are analysed using root mean square (RMSE) method. Experiments performed on 1% salt and pepper noisy image and original image show that PCM-S classifier is effective in minimising noisy pixels which produces least RMSE than PCM.