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.

Simulation of SAR backscatter for forest vegetation

Abstract: Synthetic Aperture Radar (SAR) is one of the most recent imaging technology to study the forest parameters. The invincible characteristics of microwave acquisition in cloudy regions and night imaging makes it a powerful tool to study dense forest regions. A coherent combination of radar polarimetry and interferometry (PolInSAR) enhances the accuracy of retrieved biophysical parameters. This paper attempts to address the issue of estimation of forest structural information caused due to instability of radar platforms through simulation of SAR image. The Terai Central Forest region situated at Haldwani area in Uttarakhand state of India was chosen as the study area. The system characteristics of PolInSAR dataset of Radarsat-2 SAR sensor was used for simulation process. Geometric and system specifications like platform altitude, center frequency, mean incidence angle, azimuth and range resolution were taken from metadata. From the field data it was observed that average tree height and forest stand density were 25 m and 300 stems/ha respectively. The obtained simulated results were compared with the sensor acquired master and slave intensity images. It was analyzed that for co-polarized horizontal component (HH), the mean values of simulated and real master image had a difference of 0.3645 with standard deviation of 0.63. Cross-polarized (HV) channel showed better results with mean difference of 0.06 and standard deviation of 0.1 while co-polarized vertical component (VV) did not show similar values. In case of HV polarization, mean variation between simulated and real slave images was found to be the least. Since cross-polarized channel is more sensitive to vegetation feature therefore better simulated results were obtained for this channel. Further the simulated images were processed using PolInSAR inversion modelling approach using three different techniques DEM differencing, Coherence Amplitude Inversion and Random Volume over Ground Inversion. DEM differencing technique calculates tree height by generating Digital Elevation Models (DEM) from interferograms in different polarizations and differences in DEM estimates the vegetation height. In CAI technique the phase of coherence is ignored and volume scattering is mainly considered for estimating height. The RVoG model considers both vegetation layer and ground interactions. In this model, the vertical distribution of scatterers do not change with the change in polarization. It was found that with vertical wavenumber values between 0.2113 to .2249 rad/m for mean incidence angle 34.226 degrees the range of tree height achieved by Coherence Amplitude Inversion and RVoG was better among the three inversion techniques.

Urban land use change detection using sequential aerial photographs and spot image case study: Chiangmai, Thailand

Abstract: In most of the developing countries, cities are expanding due to urbanization. To cope-up with problem, planners/decision makers need up-to-date physical data base for proper planning and management of the cities. Such data base can be generated quickly through aerial/satellite remote sensing techniques. In this ‘Case Study’ of Chiangmai, Thailand, sequential aerial photographs and SPOT image were used for urban landuse change studies. A comparison was also made for the growth studies of SPOT and aerial photographs. The analysis of study revealed that maximum agriculture land was converted to urban landuse. Urban growth of the city found to be 3.5 times less in case of SPOT results compared to aerial photographs. Urban landuse change was observed all around the city.

Estimation of instantaneous evapotranspiration using remote sensing based energy balance technique over parts of north india

Abstract: . Evapotranspiration (ET) is an essential element of the hydrological cycle and plays a significant role in regional and global climate through the hydrological circulation. Estimation and monitoring of actual crop evapotranspiration (ET) or consumptive water use over large-area holds the key for better water management and regional drought preparedness. In the present study, the remote sensing based energy balance (RS-EB) approach has been used to estimate the spatial variation of instantaneous evapotranspiration (ETinst). The (ETinst) is evaluated as the residual value after computing net radiation, soil heat flux and sensible heat flux using multispectral remote sensing data from Landsat-8 for the post-monsoon and summer season of 2016–2017 over the parts of North India. Cloud free temporal remote sensing data of October 12, 2016; November, 13, 2016; March 05, 2017 and May 24, 2017 were used as primary data for this study. The study showed that normalized difference vegetation index and LST are closely related and serve as a proxy for qualitative representation of (ETinst).

Tropical rain forest mapping and community analysis of South Andaman Islands (India)

Abstract: Present study deals with the vegetation type mapping, structure and composition analysis of the tropical forests, spread over 1,294 km2 area in South Andaman Islands. Seventeen vegetation classes spreading over 89.92% forested area of the islands were mapped with the overall accuracy of 88.89%. Evergreen, semi-evergreen and mangrove forests were reasonably well distributed forests, while moist deciduous and littoral evergreen were narrowly restricted. The stocking was quite variable across the forest types. 60.04% of forested area was under medium to high canopy density. Secondary and degraded forest types were mapped. Information on floristic composition, structure and diversity of various forest types were obtained from 84 field sample plots. An inventory of 423 species of plants from 101 families included 155 trees, 84 shrubs, 150 herbs and 84 climbers. Tree density and mean basal area ranged from 517 to 900 stems ha−1 and 36.15 to 53.58 m2 ha−1 respectively. Evergreen forests accounted for highest diversity followed almost equally by semi-evergreen and moist deciduous forests.

Effect of Spectral Shape of Calibration Source on Retrieved Ocean Radiance and Reflectance

Abstract: Radiometric calibration of ocean colour sensor establishes a quantitative relationship between the sensor output and the incoming spectral radiance or reflectance. Calibration coefficients, generated using precise sources in laboratory, are validated after launch, using Sun/Moon. Vicarious calibration uses ocean targets. The calibration coefficients are used to retrieve ocean spectral radiance/reflectance. Sensor provides the convolved output of input spectrum and its own spectral response function. The shapes of input spectra are different during calibration and ocean measurement. Also, sensor response function varies with sensor. The effect of above variations, on the accuracy of calibration coefficients and retrieved radiance/reflectance, are presented in this paper for two ocean colour sensors. Calibration coefficient and retrieved spectral radiance and reflectance are found varying upto 2.8% and 3% respectively depending on the shapes of spectra of calibration source, sensor response and ocean radiance. This study shows that spectral shape effects need to be accounted for accurate retrievals.