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The proper radiometric calibration of the INSAT-3D and --3DR IMAGER is of utmost importance for effective weather forecasting and monitoring. Accurate and reliable images are crucial for predicting weather patterns, tracking storms, and assessing the impact of natural disasters. This research paper presents an evaluation of the radiometric calibration of the INSAT-3D and --3DR IMAGER using a reflectance-based method referenced to in-situ ground site measurements. Calibration campaigns were conducted at the Great Rann of Kutch (GROK) in Gujarat, India during February 2022 to ensure synchronous measurements of surface reflectance and atmospheric variables. In this study, the radiative transfer model called 6 SV (Second Simulation of a Satellite Signal in the Solar Spectrum Vector) was employed to simulate the Top-Of-Atmospheric (TOA) radiance. The calibration coef- ficients for the Visible (VIS) and Short Wave Infrared (SWIR) bands of the IMAGER were determined by comparing the TOA radiance obtained from the radiative transfer model with the Level 1B product of satellite data. The results convincingly demonstrate that the reflectance-based calibration method yields highly accurate and reliable radiometric calibration for both the VIS and SWIR bands of the INSAT-3D and --3DR IMAGER, with an uncertainty significantly smaller than the expected sensor degradation. Specifically, it was found that the VIS band of INSAT-3D underestimates the radiance value by 9.05%, while the VIS band of INSAT-3DR underestimates it by 4.88%. The study also discusses various sources of uncertainty and emphasizes the need for comprehensive uncertainty quantification to ensure the radiometric accuracy of the IMAGER data. Overall, the calibration results indicate that the absolute radiometric accuracy for the VIS and SWIR bands of the IMAGER on-board INSAT-3D and INSAT-3DR satellites is better than 4%. This research contributes to enhancing the reliability and usability of the INSAT-3D and --3DR IMAGER data for weather forecasting and monitoring applications. [ABSTRACT FROM AUTHOR]

Globally, the rapid loss of mangrove forests often creates long-lasting environmental damage alongside coastlines, mudflats, and river banks. All-weather, physical monitoring of such areas is almost impossible because of inaccessibility to swampy areas and a hostile substrate. Subsequently, conventional field surveys are relatively unavailable to monitor human encroachment in coastal areas like the Mumbai mangrove region of India. In this context, the polarimetric synthetic aperture radar (PolSAR) remote sensing tool becomes a potential candidate for mangrove conservation and management. Since the Mumbai mangrove region of India exists over the extensive land cover, the large-scale classification can articulate the continuous encroachment because of the location of human settlements or activities across this metropolitan coastal city. For this, the traditional algorithms need the essential improvisation to apply for large-scale data analysis, aiming simultaneously at getting the highest decisive and time efficiency. Here, we introduce a shallow learning model of MapReduce-based Multi-Layer Perception (MLP) algorithm to classify the hybrid compact polarimetric (CP) and fully polarimetric (FP) feature space. Even though a shallow learning model of the automated method is easily scalable, it requires a distinctive shallow learnable feature set for better land cover classification. In this effort, this paper investigates the efficacy of derived feature space compared to direct polarimetric measurements of sensors and shows that the shallow learnable feature set is more effective with both CP & FP observations. Simultaneously, the relevancy of the proposed distributed model of MLP is also justified compared to distributed extreme learning machine (DELM) algorithm and provides a practically implementable scaled-MLP algorithm of shallow learning model. Ultimately, this paper comes up with a better polarimetric signature of land types for both the CP and FP datasets, which can be used in an alternative manner as per data availability for a multi-sensor data analysis. [ABSTRACT FROM AUTHOR]

In the present paper, spatio-temporal variability in surface radiative energy fluxes has been estimated for snow/ice-covered Beas River basin, lower Western Himalaya, India using remote sensing technology and evaluated with wireless sensor network (WSN) collected data at various elevation levels. Surface energy fluxes are derived using Landsat-8 satellite images and digital elevation model (DEM) at fine spatial resolution (~30-m) during clear-sky days of the year 2016–2017. RMSE have been estimated in incoming shortwave, incoming longwave, outgoing longwave, net shortwave and net radiation flux and observed to be ~5%, ~ 6.3%, ~2.4%, ~18.9%, and ~28.3% of the respective mean values. Landsat-8 images in conjunction with DEM have shown the potential to retrieve variability in surface radiative energy fluxes at fine resolution in mountain terrain. Additionally, temporal variability in radiative radiation fluxes have also been explored on different elevation ranges and aspects slopes. The high energy fluxes have been estimated on southern slopes during analysis as compared to northern slopes in the present study. The paper appears to be the first for reporting net radiation flux for Himalayan snow cover at 30-m spatial resolution. The satellite-derived energy fluxes may be valuable in various applications associated with climatology, hydrology, wet-snow avalanche detection, land use land cover, mass balance, ecology, etc., especially in the absence of in situ data. [ABSTRACT FROM AUTHOR]

Informal settlements are now becoming an unavoidable and growing phenomenon in the global south, whose locations are inappropriate in official statistics and maps. The distinctive spectral signature of informal settlements in high-resolution optical images causes difficulties in finding automatic approaches. This paper investigates the Multi-Shape Multi-Size Morphological Profile-Guided filter (MShMSiMP-GF) approach for detecting the informal settlements from high-resolution optical images. The development of the proposed approach was inspired by the Superpixel-based Guided Filter (SGF) approach, which uses superpixels to construct the guidance image. Though the superpixel based guidance image extracts detailed contextual information (scale, size), it fails to model geometrical information (shapes, structures) in an image. In order to incorporate the geometrical information, the MShMSiMP-GF approach generates multi-shape multi-size morphological profiles with increasing radius of guided filters. Quantitative and qualitative results of the proposed approach are investigated by four different images of Madurai city, India acquired by Kompsat-2 and WorldView-2 sensors. From the classified maps, it is observed that the proposed MShMSiMP-GF approach achieves an overall accuracy of 91.37%, 90.19%, 93.46% and 99.36% for the subsets 1, 2, 3 and 4, respectively. [ABSTRACT FROM AUTHOR]

The demarcation of linear geomorphological features due to fracture systems, underlying or exposed faults, drainage lines etc. from satellite images has been one of the most widely used applications in geological studies. Synthetic Aperture Radar (SAR) data has been recognized as an effective tool for lineament extraction due to its unique sensitivity to subtle topography, soil moisture and texture. However, the typical SAR sensor parameters such as frequency, polarization and look direction plays a vital role in enhancing the performance for a specific application. Therefore, the study aims to analyse the multi-frequency (C- and L-band), multi-polarization (like and cross) and multi-look direction SAR data for geological lineaments extraction. In this paper, Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2 datasets acquired over a part of the Indo-Burma ranges of Manipur region (northeastern part of India) are considered for analysis. The lineaments extracted from SAR datasets are compared with reference to lineaments provided by the Geological Survey of India (GSI) through their portal. The study observes that the like-polarization C-band SAR data provide optimal results for this application. It is also observed that the integration of lineaments extracted from multi-look SAR data can significantly improve the performance than that derived from single-look data. Overall, the study also focuses on the spatial distribution of lineaments with respect to the local structural regime for a better understanding of the regional tectonic setup. [ABSTRACT FROM AUTHOR]

Rice is the major food grain for many countries in this world, in particular South Asian countries such as Pakistan, India, Bangladesh, Sri Lanka, Myanmar, Thailand, etc. India is one of the leading producers of rice, with agriculture contributing 14% to 15% to the Gross domestic product (GDP). Detection of paddy field requires constant monitoring of a region throughout the year. Hence, we use dual-polarized Sentinel-1 Synthetic Aperture Radar (SAR) data with 20 m resolutions for our study. In this paper, we propose a semi-supervised algorithm that detects paddy fields in various seasons. The technique is divided into two parts, unsupervised and supervised. Feature extraction layers of the Visual Geometry Group (VGG16) model are used to segregate patches into five clusters using an entropy-based loss function. Depending on the availability of the ground truth, data is sampled (stratified sampling), and only a certain amount of data is further used for the supervised section. Clusters showing higher similarity (Jaccard/Tanimoto test) with the paddy or nonpaddy classes are connected to a three-layer neural network used for the supervised method. This has been tested on two study sites using 10 data sets, five from 2018 to 2019, each. The highest accuracy reported for 2018 and 2019 is 85.61% and 84.92%, respectively. The two sites are selected from different districts of West Bengal, India. Our model has also shown higher generalization capability against the conventional-supervised models. [ABSTRACT FROM AUTHOR]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

This paper proposes different mathematical morphology-based approaches to detect the urban slums from urban buildings using Very High Resolution (VHR) satellite images. Due to the rapid growth of urbanization activities, slum regions are the most evolving regions in South India whose locations are improper in official maps and statistics. Thus, automatic location identification of urban slums provides vital information for urban planners to formulate pro-poor policies and allocate resources properly. To detect the slums automatically from VHR satellite images, Multi Shape-Multi Size-Morphological Profile (MSh-MSi-MP) technique is proposed in this work. The development of this algorithm is motivated by modelling the urban slums using the conventional Morphological Profile (MP) which fails to model different structures in an image. On the other hand, the proposed MSh-MSi-MP is built by the sequence of morphological opening and closing profiles with different shapes and different sizes of structuring elements. Though MSh-MSi-MP yields high accuracy, some open areas in informal settlements and roofline structures of formal settlements are still classified as slums. To optimize these misclassification results, Morphological Spatial Pattern Analysis (MSPA) is used as a post-processing operation. The four different very high-resolution satellite images of Madurai city, South India acquired by WorldView-2 Sensor (1.84 m) proved the ability of the proposed approach to identify urban slums from other features by generating higher classification accuracy than any other methods. [ABSTRACT FROM AUTHOR]

Dust storm events are annual phenomena observed over the Indo-Gangetic plain (IGP) during the pre-monsoon period (May–June). These dust storms affect the air quality, weather conditions and radiation budget of the region. In this paper we characterize the aerosol optical parameters associated with a rare dust storm event that hit the IGP during early April 2005. This event was considered rare as it occurred much earlier than the general occurrence of dust storms in India (May–June), and in the year 2005, the warmest year in the span of the previous hundred years. In this study we considered the optical aerosol parameters for two places in the IGP: Delhi (28.5° N, 77.2° E, 325 m asl) and the high altitude station, Manora Peak (29.4° N, 79.5° E, 1958 m asl). Of the two selected stations, Delhi represents a highly populated and polluted location whereas Manora Peak represents a cleaner location in the central Himalayan region. During this dust storm event, the aerosol optical depth (AOD) was observed to increase considerably. The increment was 2.6–4.6 times over Delhi and 1.6–3.2 times over Manora Peak at wavelengths 380 and 1020 nm, respectively, with respect to the background values, whereas the Ångström exponent (α) for both the stations remained close to zero during the event. The effect shows a considerable increase in direct dust radiative forcing in terms of a reduction in the broadband global irradiance for Delhi as well as for Manora Peak stations. The direct aerosol radiative forcing thus obtained was about 34% in the 400–1100 nm wavelength band at Manora Peak. [ABSTRACT FROM AUTHOR]

India's climate is changing gradually from one year to another. A significant increase in surface air temperature in different Indian regions was observed from 1998 onwards, which became all the more pronounced in 2009. Monthly mean maximum and minimum temperatures were higher than normal values over the whole of India, accompanied by a sharp increase in the duration and frequency of hot days and heatwave conditions. In the light of these observations the paper is aimed at investigating the spatial and temporal variation of air temperature at different altitudes and the interannual trend for different months over the Indian region for a period of 108 years from 1901 to 2008. It is observed that the variation in air temperature is not uniform at all places in India. The yearly mean air temperature for the period 1948-2008 exhibits an increasing trend at all atmospheric layers from the surface up to 10 km. The recently observed increase in surface air temperature and longer periods of heatwaves and hot days in India may be due to a higher occurrence of dust storms and forest fires and the influence of local factors, such as a sharp population growth accompanied by uncontrolled urbanization and rapid industrialization, resulting in high levels of pollution and imbalances in the regional climate. [ABSTRACT FROM AUTHOR]

A dual-frequency global positioning system (GPS) receiving set-up at Guwahati (26° 10' N, 91° 45' E), has been in operation for the last year and a half, providing total electron content (TEC) data as input for understanding pre-earthquake contributions to low-latitude atmospheric dynamics. The major China earthquake of 12 May 2008, with magnitude 8.0 and an epicentre at 31° 24' N, 103° 58' E is a rare event to facilitate extracting earthquake features on the TEC data, and hence low-latitude system perturbations. This paper begins with a brief discussion on the methods adopted in identifying TEC performance before an impending earthquake from ionospheric data, and presents results of analysis of the event of 12 May. TEC magnitudes recorded with latitude / longitude and elevation of satellites for every pass are linked with pre-earthquake TEC features and are used as inputs to identify epicentre position. The role of seismic-time refractive index variations is examined to explain the observed TEC characteristics. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to evaluate the utility of different Indian remote sensing sensors for detection, mapping and patch size estimation of Lantana camara L. (Kurri). The latter, of the family Verbinaceae, is one of the most aggressive invasive plant species and has colonized large areas of forest land in the Himalayan foothills (Shiwalik range). The State Forest Departments of India are planning to develop a suitable strategy to halt its invasion. The first step in this direction is to have accurate information on the location and spread of the plant in spatial format. The test site is part of the forest of the Rajaji National Park, Uttarakhand. Indian Remote Sensing-Linear Imaging Self-Scanning Sensor (IRS-LISS) III (multi-spectral, 23.5 m), IRS-LISS IV (multi-spectral, 5.8 m), Cartosat-1 (Panchromatic, 2.5 m) and a merged image of LISS IV and Cartosat-1 using Brovey fusion techniques were used to map Lantana camara L. Further improvement was obtained using texture analysis. The study demonstrates the potentiality of LISS IV and Cartosat-1 data for detection and mapping of Lantana camara L. The results show the feasibility of developing a semi-automated procedure to map and analyse the distribution of Lantana in forest areas. [ABSTRACT FROM AUTHOR]

In a recent paper in the International Journal of Remote Sensing, Olwig et al. [28 (2007) 3153-3169] present a comprehensive, geographic information system (GIS)-based description of the spatial distribution of damage from the 2004 Indian Ocean tsunami relative to coastal vegetation in Tamil Nadu, India, and conclude that '...mangrove forests and coastal shelterbelts provided protection from the [t]sunami.' In this comment, we demonstrate that the complex spatial pattern of damage makes their observational approach unable to substantiate this claim. Contrary to the authors' assertions, other factors known to affect tsunami inundation, such as elevation, near-shore bathymetry and patterns of land use, are shown to vary across their study site so as to create the appearance of a mitigating effect by vegetation on inundation and damage. We show that without explicitly quantifying and controlling for these confounding factors, it cannot be ascertained, even in principle, whether vegetation did ameliorate tsunami damage. We conclude with a call for analyses that can simultaneously assess the role of the discernibly manifold factors generating the pattern of inundation and damage of the 2004 tsunami. [ABSTRACT FROM AUTHOR]

Changes in electric power consumption patterns of a country over a period of time reflect on its socio-economic development and energy utilization processes. In the present study, we characterized spatial and temporal changes in electric power consumption patterns over India during 1993 to 2002, using 'night-time lights' data given by the Defense Meteorological Satellite Program-Operational Line Scan System (DMSP-OLS) over the Indian region. The OLS operates in two bands: visible (0.5-0.9 µm) and thermal (10.5-12.5 µm) and has a unique capability of picking up faint sources of visible-near infrared emissions (lights) at night on the Earth's surface including cities, towns and villages with a DN value ranging from 1 to 63. Night-time light images for cloud-free dates given by the DMSP-OLS from 1993 to 2002 were segregated into respective years and were integrated to generate one 'Stable light image' per year. Changes in light scenarios over the Indian region in the decadal time frame were studied using stable lights datasets from 1993 to 2002. Information on changes in the light scenarios was integrated with demographic data to characterize developments in major cities and states of India. Results of the study suggested an increase in population by 170 million and power consumption from 44962 million kWh to 306355 million kWh over the country during 1993-2002, which was associated with an overall increase in number of night-time lights of up to 26% in all states, indicating development in electric power consumption patterns. Correlation analysis between increase in population to the increase in night-time lights and electric power consumption showed a coefficient of determination, R 2, of 0.59 and 0.56 respectively. Increase in light intensities along the peripheries of major Indian cities was observed, which indicated increased stress on the cities and corresponding development in power consumption patterns during the decadal time frame. Certain states, however, showed a decrease in night-time lights in some areas, which are primarily attributed to the decreased economic growth trend and poverty and accounted to the scatter observed in the correlation analysis. Results are discussed in the paper. [ABSTRACT FROM AUTHOR]

In this concept study, we develop a strategy for the monitoring of nighttime aerosol particle optical properties over land using artificial lights on the earth's surface. We demonstrate the possibility of detecting significant aerosol events and deriving variations in aerosol optical depth using the operational linescan system (OLS) on defence meteorological satellite program (DMSP) platforms. Since the OLS instruments have no on-board calibration, only a qualitative study of the potential to estimate aerosol and cloud properties using city lights at night is shown in this paper. The technique is demonstrated using China and India as test regions. With the launch of the visible/infrared imager/radiometer suite (VIIRS) instrument on the national polar-orbiting operational environmental satellite system (NPOESS) in the coming decade, fully quantitative retrieval of nighttime aerosol particle optical properties from space observations using such methods will become possible. Such work will benefit aerosol forecasting, safety of navigation, aerosol data assimilation and climate studies. [ABSTRACT FROM AUTHOR]

The tectonic evolution of the Sub-Himalayan longitudinal intermontane valleys, called 'Duns', in the Kumaun region of India is not yet understood as the thick forest cover and extensive anthropogenic modifications of the landscape have made it difficult to identify the various tectonic and geomorphic features. In this paper we present a conceptual model of the tectonic evolution of Kota and Pawalgarh Duns of the region, based on interpretation of digitally processed IRS P6 LISS III imagery and analyses of Digital Terrain Models (DTMs) in a Geographic Information System (GIS), together with field investigations. The Duns were formed as the fault-bend folded hills came up in front of the southern piedmont zone of the frontal Himalaya along the Himalayan Frontal Thrust (HFT) and the Pawalgarh Fault. The landscape of the Duns is further modified as a result of movement along subsequently developed transverse faults. [ABSTRACT FROM AUTHOR]

In this paper we present the results of our analyses of multidiurnal low-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) thermal data for coal fire-related thermal anomaly detection. Results are presented for data of the Jharia coal mining region of India. We combine three relatively new approaches: first, we use low-resolution MODIS data for coal fire area analyses, which has only been undertaken by a few authors. Second, we analyse data from four different times of day (morning, afternoon, evening and predawn) and for three different bands (MODIS bands 20, 32 and a ratio thereof); and third, we use an unbiased automated algorithm for thermal anomaly extraction of local thermal anomalies. The MODIS data analysed stem from the years 2001 and 2005. In 2001, MODIS data were only been available from the platform TERRA as morning and evening data (around 1030 and 2200 h). In 2005, MODIS data were available from this platform as well as from the platform AQUA as afternoon and predawn data (around 0130 and 0200 h). Our analyses indicate that MODIS multidiurnal data, and especially bands 20, 32 and ratio bands thereof, have a high potential for the detection of coal fire zones and coal fire hot spot zones, as well as for regular thermal monitoring activities. However, the data are not suitable for a quantitative coal fire analysis concerning fire outline, fire temperature or fire classification into surface and subsurface fires. We used higher-resolution ASTER and LANDSAT data from 2005 and 2002 for general orientation and later comparison of thermal anomaly extraction results. We also used high-resolution Quickbird data for the characterization of individual anomalous thermal clusters. Comparisons demonstrate that even low-resolution thermal sensors such as MODIS can support coal fire detection and zonation into warm and very hot zones. [ABSTRACT FROM AUTHOR]

Geospatial technology is an essential component of the Environmental Impact Assessment (EIA) process, as environmental resources are directly affected by changes in the shape and extent of the proposed disturbance. With the use of spatial techniques like remote sensing, Geographical Information Systems, and Global Positioning Systems, EIA has enhanced substantial viewing, movement, query, and even map-making capabilities. However, one of the main challenges is to have access to the most up-to-date and accurate geospatial data and interpretations. With an emphasis on using geospatial data in particular, the value of the information resource is far higher than is generally available with text and numeric information. This paper focuses on discussing the application of geospatial tools in environmental monitoring and the effective analysis of the natural resources for developmental planning, policy formulation, and decision-making. Several specific relevant applications of geospatial tools to integrate EIA are presented in the context of an Indian scenario. Applications have included monitoring of natural resources (air, water, land, etc.), ground-level ozone, soil erosion, study of sea-level rise due to global warming, change-detection studies, delineation of ecologically sensitive areas using digital-image analysis and Geographic Information Systems. This study focuses on the possibility of using a proposed spatial-decision-support system to conduct EIA, which should make it possible to upload, evaluate, maintain, and report field and analytical data that have been stored in a variety of formats. [ABSTRACT FROM AUTHOR]

Monsoon rainfall distribution over the Indian sub-continent is inconsistent every year. Due to uncertainty and dependence on the monsoon onset and weather conditions, estimation of crop yield in India is difficult. In this paper, analyses of the crop yield, normalized difference vegetation index, soil moisture, surface temperature and rainfall data for 16 years (from 1984 to 1999) have been carried out. A non-linear iterative multivariate optimization approach (quasi-Newton method with least square loss function) has been used to derive an empirical piecewise linear crop yield prediction equation (with a break point). The derived empirical equation (based on 1984 to 1998 data) has been used to predict 1999 crop yield with R2>0.90. The model has been validated for the three years 1997, 1998 and 1999. A crop yield prediction equation has been obtained for each province in India (for wheat and rice) that accounts for>90% of the variance in the dataset. [ABSTRACT FROM AUTHOR]

The present paper gives an account of potential of Environment Satellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) C-band data in forest parameter retrieval and forest type classification over deciduous forests of Tadoba Andhari Tiger Reserve (TATR), central India. Ground data on phyto-sociology and Leaf Area Index (LAI) over the study area was collected in 23 sampling points (20m×20m) over the study area. Phyto-sociological data collected over the study area was used to compute plot-wise biometric parameters like basal area, volume, stem density and dominant height. ENVISAT ASAR data covering the study area, pertaining to 24 November 2005, has been geo-referenced and digital number (DN) values were converted to radar backscatter values. Regression analysis between backscatter and the retrieved biometric variables has been done to explain the relationships between SAR backscatter and forest parameters. Analysis showed a significant correlation between backscatter and biometric parameters and backscatter values typically increased with increase in basal area, volume, stem density and dominant height. The scatter observed between ASAR backscatter and stem density, basal area and dominant height suggested limitation of C-band data in estimating biometric variables in heterogeneous forest systems. Further, ASAR data was used in conjunction with Indian Remote sensing Satellite (IRS-P6) - Linear Imaging Self Scanner (LISS) III data of 16 October 2004 to classify the study area into different land use/land cover (LU/LC) classes. Various texture and adaptive filters were applied on ASAR image to reduce speckle noise and enhance image features. An attempt is made to merge ASAR image with LISS-III to enhance feature discrimination. Training sets corresponding to the ground data have been used to derive confusion matrices for the ASAR and LISS-III images. Results suggested better discrimination of vegetation types in the merged data suggesting the possible use of ASAR data in forest type discrimination. [ABSTRACT FROM AUTHOR]

This paper gives an account of day-night active forest fire monitoring conducted over the sub-tropical and moist temperate forests of the Uttaranchal State, India, during 2005 using the Defence Meteorological Satellite Program - Operational Line Scan system (DMSP-OLS) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The state experienced heavy fire episodes during May-June 2005 and daily datasets of DMSP-OLS (night-time) and selected cloud-free MODIS (daytime) datasets were used in mapping active fire locations. DMSP-OLS collects data in visible (0.5 to 0.9 µm) and thermal (10.5 to 12.5 µm) bands and detects dim sources of lighting on the earth's surface, including fires. The enhanced fire algorithm for active fire detection (version 4) was used in deriving fire products from MODIS datasets. Fire locations derived from DMSP-OLS and MODIS data were validated with limited ground data from forest department and media reports. Results of the study indicated that the state experienced heavy fire episodes, most of them occurring during night-time rather than daytime. Validation of satellite-derived fires with ground data showed a high degree of spatial correlation. [ABSTRACT FROM AUTHOR]

This paper reports the conceptualization of a remote sensing based technique to map the spatio-temporal change pattern of the coastal landforms of Gahirmatha, India, the world's biggest olive ridley sea turtle rookery. Twenty-seven Indian Remote-sensing Satellite (IRS) multispectral satellite images sampled between the period 1988-2001 are the basic input for the study. While the mapping of temporal change position of the landform centroids revealed the migrational behaviour of the landforms, the tide normalized change detection indicated the habitat surface area dynamics. The analysis revealed that the Ekakula spit of Gahirmatha estuary has undergone cyclone-induced mass erosion during its 14 years' evolutionary history, thereby destroying its nesting habitat. However, the Nasi barrier bar had shifted its position by about 6.85 km without significant change in geo-environment including surface area and hence continued to be the favourable site for turtles' mass nesting. The results have aided in understanding the impact of natural processes on the nesting habitat dynamics. [ABSTRACT FROM AUTHOR]

Seasonal snow cover is one of the important natural resources of the Himalayas. This is an important parameter in assessing the availability of water in the Himalayan Rivers, forecasting and assessing avalanches and numerous other applications. In the present paper, the normalized difference snow index (NDSI) technique is discussed to monitor snow cover using an AWiFS sensor on RESOURCESAT–1, a remote sensing satellite recently launched by India. AWiFS is a unique sensor, providing data of 56 m spatial resolution at 5‐day intervals. Satellite data and field investigations were carried out to assess the correct NDSI value representing snow. This technique is particularly useful for the Himalayan region as it can also be applied under mountain shadow conditions. This is possibly due to reflectance from diffuse radiation in shadow areas. An algorithm is developed to provide changes in the areal extent of snow at intervals of 5 and 10 days. In the 5‐daily product, snow extent will be generated scene‐wise. In this product, snow and cloud extent will be given. In the 10‐daily product, three scenes will be analysed and a basin‐wise estimate of the maximum snow extent will be available. [ABSTRACT FROM AUTHOR]

Well-documented geological data (from both field and satellite) in the Deccan Volcanic Provinces (DVP) in and around the Dalvat region, Nasik District, India has been analysed by Geographic Information System (GIS) techniques and reported in this paper so as to relate the geology and structures with recent seismicity. It has been the belief among earth scientists that the Deccan Traps in Maharashtra, India is tectonically stable as the region attained solidity long ago. However, recent activity in the study area altered this concept and it is now accepted that seismic activity is still continuing on a mild scale. As such, the need has arisen to take into consideration historical as well as recent geological data to study in detail the tectonic setup in the Deccan Traps. Using the well-known relationship between the shear zone, lineaments, and geomorphology, and incorporating these with tectonic events, an attempt has been made to explore the geology and structures in and around the Dalvat region. Field observations and signatures on remote sensing data show that there is evidence of fault traces in the form of shear zones and slickensides in the Deccan Traps near the Kosurde, Dhanoli, Chikhli, and Manchandar villages of the Nasik District. The study has further been incorporated with seismic density data. Magnitudes of 3.9 were recorded as the maximum micro-epicentres, and they fell on the shear fractures detected in the area of study. In order to identify seismically vulnerable areas, seismic hazard zonation (SHZ) mapping has been carried out. Different data layers, including structural, lithological, geomorphological, drainage, and soil have been visually interpreted, scanned, and rectified. A rose diagram of the lineaments shows trends in the NNE--SSW to NS falling on major seismic zones of the study area, showing weaker zones beneath the surface. Raster images were digitized for conversion to a vector coverage using ERDAS 8.6. and ArcGIS 8.3. The ordinal scale (qualitative) relative weighting rating technique was used to give a seismic hazard index (SHI) value to delineate various seismic hazard zones; namely very low, low, moderate, high, and very high. [ABSTRACT FROM AUTHOR]

Repeat-pass synthetic aperture radar interferometry (InSAR) appears to be an excellent tool for generating digital elevation models (DEMs) of high spatial and vertical resolution. However, in recent years, it has been noticed that the atmosphere causes errors in the elevation accuracy. The objective of this paper is to try to identify climatic regions in India where InSAR can be used for generating DEMs. A detailed study was carried out to quantify atmospheric effects on the DEMs derived from repeat-pass InSAR. Three test sites were selected for the analysis: one over Mumbai, which is a part of the western ghats region with hilly terrain, lakes and forests; another is the Kolar area with gently undulating terrain with agricultural and forested lands; and th last was Bhuj, an arid plains region which is close to the Rajasthan Thar desert, India. Six interferograms of the Bhuj area derived from two tandem pairs were analysed and it was found that there were varying atmospheric effects of the order of 0.2λ to 1λ and at specific locations more than λ. In the case of the Mumbai and Kolar study areas with two interferograms each, the atmospheric effects were almost negligible. In these cases, it is shown that it was also possible to estimate the height of buildings to an accuracy of 1-2 m. Therefore, it is concluded that one should be cautious when using InSAR techniques for generating DEMs of desert/arid regions where the climate may be very dynamic and only small amounts of water vapour can cause strong atmospheric artefacts. [ABSTRACT FROM AUTHOR]

Majuli, the world's largest river island, is situated to the north of Jorhat in India and at the confluence of the Subansiri and Brahmaputra rivers. Flooding ravages the population of 0.13 million every year, robbing them of the basic requirements of life. This island, which was over 1245 km² in the 1950s, was reduced to 376.93 km² in 2002 due to continuous erosion of banks by the Brahmaputra river, wiping out scores of villages and displacing thousands of people. The heritage of the people of Majuli, the civilization they have nurtured for the last five centuries, is about to be buried, Burial of this civilization will be a loss to the whole of humanity. Therefore, a global approach to this problem of flooding and subsequent erosion has become inevitable, and is urgently needed to save the world's largest river island as well as its glowing cultural heritage. This paper focuses on the study of erosion problems of the decaying island, deploying remote sensing and an artificial neural network (ANN) technique. [ABSTRACT FROM AUTHOR]

Many vegetation classification strategies in tropical ecosystems involving conventional image processing of original satellite imagery bands require considerable prior site knowledge, statistical assumptions, and are difficult, expensive and inconsistent. In this paper we show that the intra-annual variation and rates of change in NDVI for different parts of a large forest area in combination with rules derived from a tree model can be used for detailed vegetation mapping. We used three-date NDVI data for the Biligiri Rangaswamy Temple Wildlife Sanctuary in Karnataka, southern India comprising mean NDVI, coefficient of variation (CV) and two NDVI change vectors corresponding to intraseasonal NDVI differences. A rule-based classification using a tree model was developed at two levels. The overall kappa statistic is 0.61 at level 1 classification, indicating a strong correspondence with the raster version of the available vector reference map based on ground data, even though the two maps are not strictly comparable. Several limitations of the available reference map have been highlighted by the new technique, especially the absence of ecotones. At level two the tree model map has provided detailed classification of dry deciduous forests and new classes not available in the reference map. The method in combination with reference data also provides a framework for fuzzy classification. This technique offers a relatively simple cost-effective alternative to existing classification strategies, especially for areas with diverse evergreen and deciduous vegetation elements. [ABSTRACT FROM AUTHOR]

This paper presents the experimental results of using a scattering coefficient to retrieve chlorophyll at X-band. The observations were carried out in India, during November 1997 to March 1998, by a ground-based X-band scatterometer. The wheat chlorophyll was obtained at a regular interval of 12 days. The scattering coefficient on wheat chlorophyll was measured as a function of the incidence angle and polarization. The scattering coefficient increased as the chlorophyll (chl) increased in the wheat. Chlorophyll is more sensitive for VV-polarization than HH-polarization. There is a significant relationship between chlorophyll and the polarimetric discrimination ratio (D) at an incidence angle of 40°. Using the obtained results, an empirical model for the scattering coefficient is proposed for accessing the chlorophyll of wheat at X-band. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to identify soil erosion zones and to suggest appropriate measures for control of soil erosion using remote sensing, GIS and conventional technique in the Phulang Vagu watershed in the Sriramsagar catchment area of Andhra Pradesh. The digital imagery data of the study area is obtained from the IRS-IC (LISS-III) satellite whereas the toposheets and rainfall data of the study area were obtained from the Survey of India. Satellite images were interpreted to prepare land use/land cover maps by using ERDAS image processing system. Out of 725.983 km[sup 2] of the study area, about 301.435 km[sup 2] is wasteland which is identified as susceptible for soil erosion. Toposheets of the study area were used to prepare drainage and slope maps. Drainage pattern is mainly dendritic with a density of 1.26 km[sup -1] and the stream slope is 0.00614. The arithmetric average method is used to find average annual rainfall. The above parameters were used to calculate the amount of soil erosion from the catchment area. It was found that 882.389 m[sup 3]km[sup -2]year[sup -1] of soil is being eroded from the catchment area which is more than the value adopted in the design of Sriramsagar reservoir. Therefore soil conservation measures such as vegetative cover in the waste land are needed and 12 check dam sites have been proposed by superimposing drainage map and slope map in conjunction with land use/land cover map. With these soil conservation measures, the soil erosion could be kept within the design value of Sriramsagar reservoir. [ABSTRACT FROM AUTHOR]

In the arid and semi-arid tropics, low annual rainfall together with high intensity rains has resulted in excess runoff, soil erosion and low moisture intake leading to poor crop yields. Therefore, adoption of soil and water conservation measures is necessary for the optimal utilization of natural resources and to increase the productivity of land on a sustainable basis. Remote sensing and GIS techniques can be used for generating development plans for the watershed area in consonance with the production potential and limitation of terrain resources, and can also be used for assessing the impact of these measures before actual implementation in the field. This paper describes a case study for the Jasdan taluka (district) of Rajkot in Gujarat, India. The aims are to prioritize watersheds on the basis of runoff generated, expressed as yield, due to existing land use conditions; to suggest soil and water conservation measures; and to evaluate the hydrologic response of these measures on runoff. The Soil Conservation Service Curve Number (CN) method was used for computing the runoff; subsequently runoff yield in percentage was calculated for prioritizing the watersheds. Satellite and other collateral data were used to identify the problems and potential in the watersheds and recommend measures for soil and water conservation. The impact of these measures was assessed by computing runoff under alternative land use and management practices. It was found that the runoff yield decreased by 42.88% of the pre-conservation value for the watershed. [ABSTRACT FROM AUTHOR]

This paper reports the results of a pilot study carried out in RajpuraDariba area, Rajasthan, for locating favourable zones of lead-zinc-copper (Pb-Zn-Cu) mineralization using remote sensing, Geographical Information System (GIS) and geostatistical modelling techniques. Remotely sensed data, both aerial and satellite, were used to update the existing geological map. ATLAS GIS software and multivariate geostatistical techniques were used to analyse and integrate different types of geological and geophysical datasets. The Favourability Index (FI) maps prepared during this study show the occurrence of three favourable zones for Pb-Zn-Cu mineralization. They are: (i) around and north of Rawan ka Khera; (ii) isolated spots between Ruppura and Bhupalsagar; and (iii) north of Dhani. Selective geochemical sampling and resistivity profiling carried out in these favourable zones indicated the presence of geochemical anomalies (anomalous concentrations of Zn and Cu) and low/moderate resistivity zones, respectively. Recent drilling carried out by the Department of Mines and Geology (DMG), Rajasthan, at about 2.5 km north of Rawan ka Khera (one of the predicted favourable zones) indicated evidence of Cu mineralization at a depth of about 70 m. [ABSTRACT FROM AUTHOR]

In this paper, the rotational transformation process is explained as a problem of rotation of remotely sensed data in the variance-covariance space. In particular, the rotation which maximizes the covariance-variance ratio is examined in detail for various land use and land cover classes in the Bombay suburban and Thana district area in India. A statistical approach to determine transformed components and other statistical variables on different band combinations is discussed. The results are analysed, and the best possible combinations selected for accurate classification are presented. [ABSTRACT FROM AUTHOR]

In this paper, the hyperspherical direction cosine transformation process of remotely-sensed data is explained. In particular, this transformation process is utilized to examine various land use and land cover classes in the Bombay suburban and Thana district area in India. A statistical approach to determine the transformed components and other statistical variables on different band combinations is discussed. The results are analysed, and the best possible combinations selected for accurate classification are presented. Separation of topographically induced illumination effects and spectral information (cover type) in digital scanner data can be accomplished by projecting measurement vectors on to a hypersphere. [ABSTRACT FROM AUTHOR]

Continuous and adequate supplies of potable water from ground reservoirs are important for sustained agriculture, industry and domestic use throughout huge semi-arid regions of India. The present paper describes an approach to investigating groundwater potential over extensive geographical areas and illustrates its potential with reference to watershed planning in the large Varaha River Basin (VRB), Andhra Pradesh, India. The method involves the creation of a systematic database of information from satellite data for reconnaissance survey before going for field exploration. Colour composite images from Landsat Thematic Mapper and Indian Remote Sensing (IRS) satellite were used to interpret various thematic maps of the Varaha river basin. SPOT 1 MLA data of band 3 on a 1:250 000 scale was used for improving the accuracy of interpretation of topographic units due to its higher resolution and stereo coverage. Slope and other coverages were derived from topographic maps. The thematic and topographic information was digitized and ERDAS Imagine GIS software was used to analyse this information. Groundwater potential zones were delineated through subjective weights assigned to interpreted thematic and derived topographic units according to their likely infiltration capacities. Seven categories of groundwater potential ranging from very good to poor were derived automatically. Field measurements were then made within a selection of these categories to check the groundwater potential at selected sites. The validity and effectiveness of using remote sensing and GIS techniques for improving the targeting of field observations for groundwater for a huge river basin is shown by comparing the inferred groundwater potential with the field measurements. [ABSTRACT FROM AUTHOR]

0 An attempt has been made to study the subsurface geological and geomorphological details of the Banda area and to demonstrate their relationship to aquifer geometry in the region. In this study a systematic analysis of a basin was made by integrating satellite data with the available spatial ground dataset using ILWIS (Integrated Land and Water Information System). The approach involved regional interpretation of geomorphological and structural features exposed at the surface and relating these to the subsurface relief. The area chosen for the study is situated within the marginal Gangetic alluvial plain in Banda district of Uttar Pradesh, India. The region has a varying thickness of alluvium composed of alternating sand/kankar (carbonate concretions) and clay strata deposited on an uneven basement. Different image processing techniques such as image smoothing, contrast stretching, false colour composite, and principal component analysis were used to enhance the lithological and geomorphic features and their effect on associated features like vegetation and soil. Subsurface features of importance in ground water exploration such as buried channels were identified. Several geomorphological features such as sand bars, point bars, meander scrolls, abandoned channels and ravines were identified and the region was classified in terms of five geomorphic units. A methodology to generate a digital elevation model of subsurface topography with the help of depth to bedrock contours is proposed. On the basis of this model, the configuration of the basement topography in the region was previously reported by the authors In this paper sites for favourable aquifer disposition and various subsurface geomorphic features which are potential sites for ground water development are identified using Geographic Information System (GIS) capabilities. An overlay of the enhanced image on a map showing the thickness of granular zones on the digital terrain model of the basement has led to an understanding of the exact subsurface geometry of the aquifers and their relationship to surficial geomorphic features. Ground water hydrogeological status has been inferred from integration of the information from structural, lithological, geological, topographic and hydrological data. [ABSTRACT FROM AUTHOR]

Spatial information on crop calendars in the command areas of irrigation systems is useful to irrigation engineers in order to achieve chronological synchronization between water delivery pattern and crop life cycle. Such synchronization is vital for efficient use of water. The capability of satellite remote sensing technology to generate spatial crop calendar information in an irrigated command area and its usefulness in the evaluation of water delivery patterns are demonstrated in this paper. The study pertains to the major crop paddy during the rabi season (December to June) of 1992-93, in the Bhadra project command area of Karnataka state, India. Analyses of multidate Normalized Difference Vegetation Index (NDVI) profiles of paddy crop generated from Indian Remote Sensing (IRS) satellite data for each distributary command reveal three distinct growth patterns in the study area with each pattern characterized by a particular crop calendar. The spatial variability in crop calendar over the total command area has thus been derived. The ground truth data obtained in crop cutting experiments (CCEs) validate the satellite derived crop calendar. Distributary wise, water delivery data have been studied in conjunction with the satellite derived crop calendar to determine whether the existing pattern of water delivery covers the required length of crop life cycle in the command area. It was found that the water supply was stopped about 30 days before harvesting in some distributaries and in some about 20-30 days before harvesting. A list of distributaries with greater lags between cessation of water supply and crop harvest was provided to irrigation system engineers to aid their plans for providing a reliable and predictable irrigation service. This is possible either through reorganization of canal operation schedule or through educating farmers about the need for adjusting their agricultural activities to match water supply patterns. [ABSTRACT FROM AUTHOR]

In this paper, methods for the generation of residual and prospecting geoids and free-air gravity anomaly using satellite altimeter data are developed. Maps of residual geoid, gravity anomaly, and their spectral components generated over Indian offshore using ERS-1 altimeter data are presented and interpreted. A number of known megastructures over the study area, for example, Bombay High, Saurastra platform, 90 east ridge, etc., are interpreted successfully from these maps. Some of the anomalous zones (the basins near Mangalore and off Bombay High in the western offshore and Palar basin off Madras coast in the eastern offshore) have been identified as potential sites for occurrences of hydrocarbon-bearing structures, for which detailed ship-borne geophysical surveys have been commissioned by the Oil & Natural Gas Corporation (ONGC), Dehradun, India. [ABSTRACT FROM AUTHOR]

Editorial. Highlights the International Conference on Remote Sensing and GIS/GPS 1997 held from June 18-21, 1997 in Hyderabad, India. Number of research papers presented.

The article presents information on images on the cover of the "International Journal of Remote Sensing." It shows the damaged areas in the earthquake of January 26, 2001 in Gujarat State, India, estimated using the bi-temporal images method with night-time data from the DMSP-OLS. It is taken from figure 8 of the paper by M. Kohiyama on early damaged area estimation system using DMSP-OLS night-time imagery, which is published in this issue.