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.

SST and OLR relationship during Indian summer monsoon: a coupled climate modelling perspective

Abstract: The study mainly investigates sea surface temperature (SST) and outgoing longwave radiation (OLR) relationships in coupled climate model. To support the analysis, high-level cloud and OLR relationship is also investigated. High-level cloud and OLR relationship depicts significant negative correlation over the entire monsoon regime. Coupled climate model is able to produce the same. SST and OLR relationship in observation also depicts significant negative relationship, in particular, over the Equatorial Eastern Indian Ocean (EIO) region. Climate Forecast System version 2 (CFSv2) is able to portray the negative relationship over EIO region; however, it is underestimated as compared to observation. Significant negative correlations elucidate that local SSTs regulate the convection and further it initiates Bjerknes feedback in the central Indian Ocean. It connotes that SST anomalies during monsoon period tend to be determined by oceanic forcing. The heat content of the coastal Bay of Bengal shows highest response to EIO SST by a lag of 1 month. It suggests that the coastal region of the Bay of Bengal is marked by coastally trapped Kelvin waves, which might have come from EIO at a time lag of 1 month. Sea surface height anomalies, depth at 20 °C isotherms and depth at 26 isotherms also supports the above hypothesis. Composite analysis based on EIO index and coupled climate model sensitivity experiments also suggest that the coastal Bay of Bengal region is marked by coastally trapped Kelvin waves, which are propagated from EIO at a time lag of 1 month. Thus, SST and OLR relationship pinpoints that the Bay of Bengal OLR (convection) is governed by local ocean–atmospheric coupling, which is influenced by the delayed response from EIO brought forward through oceanic planetary waves at a lag of 1 month. These results have utmost predictive value for seasonal and extended range forecasting. Thus, OLR and SST relationship can constitute a pivotal role in investigating the atmosphere–ocean interaction.

Estimating net primary productivity in tropical forest plantations in India using satellite-driven ecosystem model

Abstract: Net Primary Productivity (NPP) is a significant biophysical vegetation variable to understand the spatio-temporal distribution of carbon, source-sink nature of the ecosystem, and the carbon cycle. Remote sensing, by virtue of its synoptic coverage, spatial and temporal resolutions, and low cost, makes an ideal tool for studying the NPP on local to global scales. This study was carried out in a forest plantation area located in the Lesser Himalaya (Lower Shivaliks) foothills with sub-tropical humid climate. The study aimed to (i) estimate the spatio-temporal patterns of NPP during 2009 and 2010 using CASA model and (ii) study the effects of climate variables on the NPP using GLM approach. Satellite-derived Inputs (LAI, rainfall etc.) were brought to a common resolution of 250 m to match the scale. Seasonal dynamics of NPP captured the pattern of fPAR, showing maximum NPP during September and October (wet season) and minimum during December and January (dry season).The total annual NPP varied from ...

A novel approach to use semantic segmentation based deep learning networks to classify multi-temporal SAR data

Abstract: The use of SAR data for land cover mapping provides many advantages over land cover classification achieved using optical remote sensing data. However, the classification of SAR data has always bee...

Utilizing the Potential of World View 2 for Discriminating Urban and Vegetation Features Using Object Based Classification Techniques

Abstract: With the availability of very high resolution multispectral imagery, it is possible to identify small features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult task. This paper demonstrates the potential of 8 bands capability of World View 2 satellite for better automated feature extraction and discrimination studies. Multiresolution segmentation and object based classification techniques were then applied for discrimination of urban and vegetation features in a part of Dehradun, Uttarakhand, India. The study demonstrates that scale, colour, shape, compactness and smoothness have a significant influence on the quality of image objects achieved, which in turn governs the classified result. The object oriented analysis is a valid approach for analyzing high spatial and spectral resolution images. World View 2 imagery with its rich spatial and spectral information content has very high potential for discrimination of the less varied varieties of vegetation.

Understanding the Linkages between Climate Change and Forest

Abstract: The present study reviews the application of various regional climate models and remote sensing techniques to understand and define impacts of climate change on the forest resources with specific reference to India. It illustrates the potentials and limitations of regional climate models, vegetation models and remote sensing techniques like normalized difference vegetation index time-series analysis, change detection method and phenological attributes in assessing and monitoring the impacts of climate change on vegetation. The study recommends that regional climate models and remote sensing techniques need to be integrated in tandem for understanding the present and future impacts of climate change on forest ecosystems. This could help to improve the accuracy and prediction, which can contribute to planning effective adaptation strategies in the forestry sector.