Large Earth Simulator For Testing Low Altitude IR Earth Sensor

The launch of the first Indian Remote Sensing Satellite in 1988 ushered in a new era of utilization of satellite data and technology in natural resources monitoring, coastal area management, disaster mitigation, weather and climate change studies. India with its very long coast line, areal extent, resources and population would have been a huge challenge to the coastal researchers and administrators if not for the Indian Remote Sensing satellite data that provided the much needed temporal, thematic information for effective mapping, monitoring and management of the coastal areas. The country’s large and diverse coastal regions are economically important and ecologically rich but also extremely vulnerable and highly prone to natural and anthropogenic hazards. Through the last three decades, the Indian remote sensing programme has contributed immensely to our understanding of the coast and ocean processes, coastal ecosystems, habitats, marine pollution and the vulnerability of the coast to natural hazards. IRS-1C with its improved spatial resolutions, additional spectral band, improved repetitively ushered in a new era of research specifically in the management of coastal areas and resources. Availability of spatial data at various resolutions, low cost, advances in technology resulted in new applications for management coastal areas. IRS-1C also can be aptly termed as the precursor for thematic satellite series such as OCEANSAT, RESOURCESAT and CARTOSAT, thereby setting the environs for thematic research in coastal areas. Integrated utilization of remote sensing data, numerical models, GIS and GPS has paved the way for the development of operation systems for management of coastal areas, thereby realizing the vision of Dr. Vikram Sarabhai, the father of Indian Space Programme, of building a self-reliant and indigenous Space Programme for the betterment of quality of life of the common people.

This paper reports the bandpass solar exoatmospheric irradiance and Rayleigh optical thickness for six categories of sensors, namely, the linear imaging self scanning sensor (LISS)-I, -II, -III, the wide field sensor (WiFS), the panchromatic (PAN), and the ocean color monitor (OCM) which have been flown on Indian Remote Sensing Satellites (IRS)-1B, -1C, -1D, and -P4 platforms, based on laboratory measurements of relative spectral response (RSR) of each band. These estimates have been compared with similar contemporary international sensors.

Spherical and oblate Earth conical shadow models for LEO satellites: Applications and comparisons with real time data and STK to IRS satellites

Identification of homogeneous regions in a satellite image is essentially the clustering of pixels in intensity space. Importantly remote sensing image like satellite images contain varieties of land cover types. Some of the covers are significantly large areas whereas some are relatively smaller regions. Therefore, automatically detecting such wide varying areas is a challenging task. Hence, this fact motivated us to propose an improved clustering technique viz. Ensemble based Differential Evolution for Fuzzy Clustering (EDEFC). For this purpose, very recently developed three variants of differential evolution (DE) are used in order to perform the clustering with different set of solutions. As a result, better clustering solution yields from the ensemble of DEs by exhaustive exploration of search space. The proposed EDEFC technique is applied on two numeric remote sensing datasets and Indian Remote Sensing (IRS) satellite image of Kolkata. The results of the EDEFC are shown quantitatively and visually by comparing with eight other clustering techniques. Moreover, the statistical significance test has also been performed in order to judge the superiority of EDEFC.

Land use/land cover (LULC) change occurs due to natural and anthropogenic causes. In developing countries, rapid industrialization and urbanization imposes a major threat to natural environment. Present study was carried out to monitor the LULC changes due to urbanization in a rapidly changing river basin, India. The purpose of choosing the river basin was to analyze past changes and predict possible consequences within a defined natural boundary. Multi-temporal images acquired from Landsat and Indian Remote Sensing (IRS) satellites between 1992–2009 as well as a digital elevation model was used to generate historical and current LULC pattern in the basin. An object based image analysis technique was employed for precise classification of multi-temporal images followed by GIS-based change detection studies. The study reveals that the built up area has increased significantly and added 288 km2 between 1992–2009. Increase in built up area is attributed to decrease in wastelands and agricultural land. The expansion of built up area along major transportation networks, specifically after the year 2000 shows the rapid rate of urbanization in the basin.

Urban settlements in developing countries are, at present, growing five times as fast as those in developed countries. This paper presents the urban expansion and land use/land cover changes in the fast urbanizing coastal area of the Dakshina Kannada district in Karnataka state, South India, during the years 1983-2008 as a case study. Six Indian Remote Sensing (IRS) satellite images were used in the present work. Supervised classification was carried out using maximum likelihood algorithm. The overall accuracy of the classification varied from 79% to 86.6%, and the kappa statistics varied from 0.761 to 0.850. The results indicate that the urban/built-up area in the study area has almost tripled during the study period. During the same time, the population has increased by 215%. The major driving forces for the urbanization were the enhanced economic activity due to the port and industrialization in the area. The urban/built-up area is projected to increase to 381 km 2 and the population in the study area is expected to reach 2.68 million by the year 2028. Urban growth prediction helps urban planners and policymakers provide better infrastructure services to a huge number of new urban residents.

Recognising the immense potential of communications satellites for establishing connectivity to far-flung and remote areas and for using television broadcasting for mass education, India undertook experimental projects to demonstrate the need for having a domestic communications satellite that paved the way for establishing Indian National Satellite (INSAT) system. Starting with multipurpose communications satellites INSAT-1 series combining telecommunications, TV broadcast and meteorological services procured from abroad, ISRO embarked on design,development of more advanced INSAT -2, 3 and 4 series of satellites indigenously.The telecommunications, TV broadcast, radio networking and meteorological services were operationalised with INSAT-1 system in 1983. While providing uninterrupted services to the country in the above areas, ISRO undertook several innovative experiments using communications satellites. Staring with using INSAT satellites for training and developmental communications, societal applications such as tele-education and tele-medicine facilities were established. Use of INSAT satellites for disaster management support has also been established. A new concept namely Village Resource Centres (VRC) combining the services of INSAT satellites and Indian Remote Sensing (IRS) Satellites for providing holistic services at village level has also been operationalised.

An efficient nonparametric, hierarchical, symbolic agglomerative clustering procedure based on the mutual nearest neighbourhood concept is proposed for classifying remotely sensed multispectral data. The procedure utilized a data reduction technique and an innovative symbolic concept to minimize the memory and computational time requirements. A new non-metric similarity measure and a novel method of formulation of composite symbolic objects are proposed to enrich the performance of the algorithm. A Mean Difference Index (MDI) concept for identifying the optimal number of classes was used. Experiments were conducted on IRS (Indian Remote Sensing) satellite data to authenticate the efficacy of the procedure.

This paper reports the development of a new shaped beam antenna for remote sensing satellite data transmission applications at S-band. For medium altitude, near earth satellites such as those used in remote sensing applications in sun-synchronous mode, data transmission link can be optimised for the whole pass of the satellite by making the antenna pattern profile vary so as to compensate for the path loss profile - i.e., the pattern profile becomes an inversion of the path loss profile. Such an antenna pattern, having all other parameters fixed, provides a uniform data level throughout the pass. The present development is for a similar application for an Indian Remote Sensing Satellite and this development provides for 6.5 dBi gain with higher polarisation purity using a shaped reflector and helical feeding system.

In this paper, we have analysed active and passive earth observation data for identifying the different geomorphic sub-provinces associated with coastal and fluvial geomorphic processes in the south-western part of Bengal Basin. For this purpose, variations in the spectral response of these sub-provinces in IRS P6 LISS III data are enhanced using principal component (PC) and independent component (IC) methods to spatially delineate the geomorphic sub-provinces and associated Quaternary sediments. It has been observed that the false colour composite derived using IC (first, second and third ICs) are effective in enhancing fluvial sub-provinces, while IC band 4, band 3 and band 2 are suitable to delineate coastal sub-provinces. On the other hand, PC composite derived using band 4, band 3 and band 1 are suitable to delineate different Quaternary sediments deposited within the spatial extent of geomorphic sub-provinces. Fused image enhanced products of IRS (Indian Remote Sensing Satellite) LISS III band and ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array L-band Synthetic Aperture Radar) polarisation bands are used to supplement and complement the variations observed for each geomorphic province in optical data as radar interactions are sensitive to variation in moisture and surface cover. Although there is a strong synergy between the spatial extent of geomorphic sub-provinces and the Quaternary units in terms of spatial disposition, different Quaternary units are also found within the same geomorphic province. Therefore, we have segmented each geomorphic sub-provinces based on the spatial extent of the different Quaternary deposits to identify few geoenvironmental (termed here as geomorpho-environmental) zones. In this regard, eleven zones are identified. Each zone has been characterised based on geomorphic stability, potentiality for agriculture practice, scope for urban development and reclamation. These geoenvironmental zones may provide important input to planners.

Indian Remote Sensing satellite (IRS)-1B, Linear Imaging Self Scanner (LISS)-II spectral digital data was analysed to determine the feasibility of quantifying the concentration of suspended solids in the surface water of inland water body, Dal lake, in Srinagar, India. The water samples collected in concurrent with IRS-1B overpass, were analysed to determine the concentration of suspended solids. The results indicate that a positive functional relationship exist between the concentration of suspended solids and the visible wave length bands 1 and 3 and near infrared band 4. It has been observed that as the concentration of suspended solids increase, the spectral response also increases. It is concluded that IRS LISS-H data can be effectively used to quantify suspended sediment concentration in the Dal lake surface water.

One of the problems associated with synthetic aperture radar (SAR) data analysis is the presence of random noise or speckle SAR data, being achromatic in nature, which offers very limited scope for the detection and delineation of various terrain features. ERS-1 SAR data for the coastal region of West Bengal, India were processed (a) to suppress the random noise using various filters, (b) to generate the intensity, hue and saturation (IHS) transform from temporal SAR data, and (c) to study the synergism of SAR data with optical sensor data. The results indicate that the Gamma MSP filter with a 5 5 pixel kernel size has been the most efficient in suppressing the noise and concurrently improving the image contrast. The IHS transform of temporal SAR data made it easier to discriminate between various wetland categories. This was also the case with hybrid image generated by the Indian Remote Sensing Satellite (IRS-1B) Linear Imaging and Self-scanning Sensor (LISS-II) data when compared to SAR data alone.

Map information for ( 1937-38 and 1965-66) Indian Remote Sensing Satellite (IRS) data of October 1988 and 1990, Landsat-TM data of 1987 and airborne SAR data ( 1988) were used in the identification of river course, geomor-phological features and palaeo channels over part of the Godavari river, India. The change in river course and growth and erosion of the island/ sand bar were measured. The palaeocourse of the river at test sites has been reconstructed. The measured cut-off ratio indicates that the chute cut-off of the river course near Gannavaram is very high. Flood affected areas in the regions could be assessed by this method.

Water depth estimation using optical remote sensing offers a reliable and efficient means of mapping coastal zones. Here, we aim to find a suitable model for fast and practical bathymetry of an estuary using Indian Remote Sensing Satellite (IRS) Linear Imaging Self Scanning Sensor (LISS-3) images. The study examines three different models; (1) least square regression model, (2) spectral band-ratio method and (3) multi-tidal bathymetry model. The findings are supported with in situ observed depth values and statistical estimates. Although the least square regression model has provided best results with root mean square error (RMSE) of 0.4 m, it requires a large number of observed data points for absolute depth estimation. Spectral band-ratio and multi-tidal model provides results with RMSEs 2.1 and 0.9 m, respectively. The present investigation demonstrates that multi-date imagery exploitation at disparate tide levels is the best estimation technique for recursive shallow water bathymetry where in situ observation is not possible.

Remote sensing quantification and change detection of natural resources over Delhi