Indian Remote Sensing Satellite Data Research Articles

Soil Erosion Hazard Zonation in Bandu Sub-Watershed, India

Abstract Soil hazard zonation in watershed is quite significant to take the necessary actions for soil conservation. The present study attempts to identify soil hazard zones for proper soil conservation and prepare a treatment plan for the Bandu sub-watershed of India through morphometric analysis techniques by giving rank based on priority. The result of the prioritization of eighteen micro-watersheds is entirely satisfactory due to the use of fourteen morphometric parameters. The study quantifies the potential soil loss and identifies the soil eroded zone of the sub-watershed using the universal soil loss equation method and the entire region is categorized into three soil hazard zones with varying degrees. Indian Remote Sensing Satellite data have been used to conduct the whole study. The micro-watershed prioritization has been estimated by applying the composite morphometric value. The micro-watershed having 5.625 composite value ranks first for prioritization (most vulnerable with maximum soil erosion) and having 15.875 composite value ranks last for prioritization (least vulnerable with minimum soil erosion). The result also shows that the soil loss ranges from 0-30 tonne/hectare/year with an average soil loss of 0-10 tonne/hectare/year in maximum areas of the sub-watershed. The soil loss map shows that along the Bandu and in some agricultural fields, the central part of the region is susceptible to soil erosion. The scientific approach of this research could be more effective in maintaining sustainable rural planning. The study can be used as a reference work for determining soil hazard zones in any tropical watershed with high soil loss risk.

Updated Glacial Lake Inventory of Indus River Basin based on High-Resolution Indian Remote Sensing Satellite Data

Updated Glacial Lake Inventory of Indus River Basin based on High-Resolution Indian Remote Sensing Satellite Data

A geo‐spatial approach to assess Trees outside Forest (ToF) in Haryana State, India

AbstractMapping and monitoring the Trees outside Forest (ToF) is gaining significance in the scientific community as they provide critical ecosystem services such as protecting soil and water resources, wildlife habitat, and aesthetics including food, fuel, and fibre. Quantifying ToF can also provide useful information for emission estimation relating to the agriculture, forests, and other land use (AFOLU) category of the Intergovernmental Panel for Climate Change (IPCC). Despite the importance of quantifying ToF, very few studies have attempted to quantify them in India's natural resource inventory programs. In this study, we focused on Haryana State, India, to inventory ToF using very high‐resolution (VHR) Indian Remote Sensing (IRS) satellite data. Haryana's landscape is interspersed with croplands and ToF, thus providing a challenging environment to test VHR satellite data's ability to quantify the diversified landscape structure. We specifically used CARTOSAT‐1 panchromatic (2.5 m) and multispectral LISS‐IV (5.8 m) datasets to quantify the vegetation and build a much‐needed database for ToF. We used a novel classification scheme based on the geometry, that is, point, line, or polygon formations, to quantify ToF at a scale of 1:10,000. The obtained results suggest ToF with linear and block formations extended to 128.83 and 20.51 km2, respectively, accounting for ~3.38% of the Total Georgraphical Area of Haryana State while point formations established 2,774,531 in numbers. This study highlights the usefulness of VHR satellite data and fused imagery to quantify ToF in highly diverse landscape of Haryana. The results will help address vital ecosystem services from ToF, including greenhouse gas emissions quantification from the Agriculture, Forests and Other LandUse (AFOLU) category.

Three Decades of Indian Remote Sensing in Coastal Research

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.

Three Decades of Nationwide Forest Cover Mapping Using Indian Remote Sensing Satellite Data: A Success Story of Monitoring Forests for Conservation in India

India is a biodiversity-rich country. The panorama of Indian forests ranges from tropical evergreen rain forests in the Andaman and Nicobar Islands, the Western Ghats, and the north-eastern States, to Dry Alpine Scrub high in the Himalayas in the north. On the other extreme, tropical dry deciduous and thorn forest is found in the deserts and dry areas of Rajasthan and Gujarat. The variety and distribution of forest vegetation are large, and India is one of the 17 mega biodiversity regions of the world. Forestry in India supports a sizeable non-wood forest product-based economy apart from timber and fuelwood. In forested landscapes people are integrated with forest and make living out of it. On the other hand urbanization and infrastructure development are putting considerable pressure on forests. These issues need effective and efficient forest monitoring system. Hence in late 1980s, India entrusted Forest Survey of India to evolve robust and scientific method of forest monitoring. Thus, India has taken an early lead in establishing a forest monitoring system. The history of forest cover mapping using satellite images in the country goes back to 1983 when National Remote Sensing Agency (NRSA—now known as NRSC-ISRO) prepared the first-ever forest cover map of India. Later, Forest Survey of India has been mandated with the forest cover mapping of the country in the biennial cycle since 1987. Three decades of forest cover mapping on a regular basis have been a remarkable achievement in the field of natural resource monitoring. India’s remote sensing-based periodic monitoring of forest cover, which is unique in the world, has played a major role in conservation and enhancement of forest cover of the country. Evolving with satellite data improvement and technological advancements, the methodology of forest cover mapping by FSI has seen significant changes over the years, wherein Indian remote sensing data, particularly the IRS 1C/1D LISS III data, have been the primary input in the time series mapping. This paper reviews the chronology and significance of this important forest monitoring programme of India.

Performance evaluation of image fusion techniques for Indian remote sensing satellite data using Z-test

Image fusion is being used since last two to three decades in remote sensing for improving visual appearance of coarse resolution imagery using fine spatial resolution data. The resultant outputs are being used successfully in various applications such as image classification, feature extraction, digital change detection, and many more including multi-temporal and multi-scale change detection. Acceptability of a fusion method for a particular application depends upon various factors; one of them is quality of fused image. In this research paper the four different image fusion techniques namely Ehlers, IHS fusion, Brovey and FuzeGo have been evaluated using IRS-P6 (Cartosat-1) and RESOURCESAT-2 (LISS-IV) images of Bhopal city, India. Quality of fusion results is assessed by performing visual analysis between fused image and multispectral (MS) image along with statistical analysis. Visual comparison is done based on better visibility of different land cover features such as roads, buildings, water body and sharpness of edges present in image. For statistical evaluation of fusion process, six statistical parameters i.e. standard deviation (SD), correlation coefficient (CC), entropy/noise, RMSE and ERGAS have been used. In addition to these traditional statistical measures, Z-test is used for combined assessment of fusion techniques. Visual comparisons of fusion results obtained for test site have shown that FuzeGo algorithm has given comparatively better results than other algorithms. Statistical parameter CC, SD are found highest for IHS method, RMSE, and ERGAS are found highest for Brovey method and least noise is added by FuzeGo algorithm. Overall visual analysis and Z-test indicates that FuzeGo has given better results which are followed by Ehlers as compare to other methods.

DEVELOPMENT OF WEB BASED HIMALAYAN GLACIER INFORMATION SYSTEM USING OPEN SOURCE

Abstract. Systematic inventory of glaciers is required for a variety of applications needed for the comprehensive development of the Himalayan region such as: a) disaster warning, b) estimation of irrigation potential, c) planning and operation of mini and micro hydroelectric power stations, etc. A systematic inventory of the Himalayan glaciers at 1 : 50,000 scale was created for Indus, Ganga and Brahmaputra basins using Indian Remote Sensing Satellite data and attempted to modified global standards in GIS environment (Sharma et al, 2013). A robust, user- friendly web-based Himalayan Glacier Information System (HGIS), a first of its kind in the country is developed which facilitates any user to selectively display, query, analyse, compose maps and graphs and print, spatial and aspatial information on glaciers relevant to respective interests. The HGIS architecture is based on Open Geospatial Consortium (OGC) standards and utilises OpenGeo Suite bundled software comprising of Postgresql (PostGIS), Geoserver, GeoWebCache and GeoExplorer each of those having a different function (Anonymous 2010). The spatial and aspatial glacier data sets were stored in a pre-defined format (Sharma et al, 2008) and imbibed into spatially enabled database (PostGIS), having sophisticated functions for spatial data analysis and query. For publishing the data on web page OGC-compliant services are used. The HGIS information content comprise of a) glacier inventory maps and b) inventory data sheet. The map displays the glacier morphology features like accumulation zone and ablation (ice exposed and debris covered) zones, snout location, de-glaciated valleys, moraines and glacier lakes. The basin, sub-basin and administrative boundaries form the background. The inventory data sheet attributes for each glacier provides information on glacier Location, Identification, Dimension, Orientation, Elevation, Classification, etc. The spatial map and datasheet are linked by unique glacier identification number (galc_id) which is a key field present in all corresponding glacier related point, polygon or line layers. All the glacier attribute were made amenable to query and analysis by users. HGIS represents a significant step towards mapping and compiling individual glacier level inventory data in spatial form to fill the void in data and information on the status of Glaciers in the Himalaya and Trans-Himalayan Karakoram region. HGIS provides a basis for assessing the glacier inventory data which has applications in studies related to climate change, water resource planning, hydropower site selection and mitigation of glacial lake outburst flood (GLOF) hazards.

Significant decline of forest fires in Nilgiri Biosphere Reserve, India

Yet there is considerable uncertainty over management effectiveness of many protected areas around the world. In this regard, long-term monitoring and management of disturbances in the protected areas need to be properly addressed and documented. The present study is aimed to analyze spatial extent, frequency and distribution of forest fires in Nilgiri Biosphere Reserve, Western Ghats, India over four decades (1973–2014). Multi-temporal Landsat and Indian Remote Sensing satellite data were used to quantify forest burnt area. The results showed that the spatial extent of burnt area greatly varied between 1318 km2 (in 1989) to 15.7 km2 (in 2013). The monitoring of fires during the recent decade (2005–2014) was observed to have a gradual decline over the reserve. High frequency of fires has been found in dry deciduous forest followed by moist deciduous forest, scrub, grassland and semi-evergreen forest. Among six protected areas, Bandipur National Park had represented more fire incidents followed by Mudumalai Wildlife Sanctuary, Nagarhole and Mukurthi National Parks. Grid analysis shows that 67% of the grids were affected by forest fires. Large burnt patches have steadily decreased in their size while the other patch classes showed a significant reduction in their size over the study period. This study had revealed the role of climate and accessibility in relation to fire occurrences. Based on historical burnt area mapping outputs, fire hotspots have been modeled to address fire risk zones. Indeed, the Mann-Kendall trend test has indicated that the overall trend of fire incidents have been declining and this indicates the management effectiveness in Nilgiri Biosphere Reserve.

Horticultural Fruit Crop Plantations Mapping using Geo-informatics Technology in Gujarat State, India

The cultivation and production of horticultural crops is known as Golden Revolution” and India has emerged as the second largest producer of fruits and vegetables in the world. The Central Government of India has started National Horticulture Mission (NHM) for overall development of Horticulture and Gujarat Government has also started Gujarat State Horticulture Mission (GSHM). This mission being implemented in 16-potential districts and covers major fruit crops in the state like mango, sapota, Aonla, banana, coconut etc. Under the GSHM, mapping of fruit crop plantations using Indian Remote Sensing Satellite (IRS) data in Gujarat State was carried out jointly by the Directorate of Agriculture, Govt of Gujarat and Bhaskaracharya Institute for Space Applications and Geo-informatics (BISAG), Govt of Gujarat. The major objective of this project was mapping of fruit crop plantations at village-level and preparation of village-level Horticultural Atlas of Gujarat State. High spatial resolution digital data from IRS LISS-IV and CARTOSAT-1 with spatial resolutions of 5.8m and 2.5m respectively, covering major fruit growing districts in Gujarat state was analysed for identification and delineations of fruit crop plantations in each survey number on cadastral map. The cadastral maps of each village were geo-referenced with satellite data and survey number boundaries were superimposed on the satellite data. Fruit crop plantations were located during field visits and their GPS locations were transferred to the satellite images with cadastral maps for unique identification and accurate mapping. The fruit crops in different districts of Gujarat State were identified on the high resolution satellite data based on the planting pattern and tree-crown density. The survey numbers having fruit crop plantations were delineated on the CARTOSAT-1 data. The fruit crop plantation maps of each village were prepared and complied at village and taluka level using open-source Quantum GIS software for preparation of village-level horticultural atlas of Gujarat State. The area under fruit crop plantations was also estimated. The results of this alternate method of mapping fruit crop plantations using high resolution, single-band CARTOSAT-1 panchromatic data on cadastral maps with survey numbers gave very good results for preparation village-level horticultural atlas of Gujarat State. Field survey in different villages was also carried out for accuracy assessment of survey numbers mapped as horticultural plantations.

Spatial prediction of landslide susceptibility in parts of Garhwal Himalaya, India, using the weight of evidence modelling.

Garhwal Himalaya in northern India has emerged as one of the most prominent hot spots of landslide occurrences in the Himalaya mainly due to geological causes related to mountain building processes, steep topography and frequent occurrences of extreme precipitation events. As this region has many pilgrimage and tourist centres, it is visited by hundreds of thousands of people every year, and in the recent past, there has been rapid development to provide adequate roads and building infrastructure. Additionally, attempts are also made to harness hydropower by constructing tunnels, dams and reservoirs and thus altering vulnerable slopes at many places. As a result, the overall risk due to landslide hazards has increased many folds and, therefore, an attempt was made to assess landslide susceptibility using 'Weights of Evidence (WofE)', a well-known bivariate statistical modelling technique implemented in a much improved way using remote sensing and Geographic Information System. This methodology has dual advantage as it demonstrates how to derive critical parameters related to geology, geomorphology, slope, land use and most importantly temporal landslide distribution in one of the data scarce region of the world. Secondly, it allows to experiment with various combination of parameters to assess their cumulative effect on landslides. In total, 15 parameters related to geology, geomorphology, terrain, hydrology and anthropogenic factors and 2 different landslide inventories (prior to 2007 and 2008-2011) were prepared from high-resolution Indian remote sensing satellite data (Cartosat-1 and Resourcesat-1) and were validated by field investigation. Several combinations of parameters were carried out using WofE modelling, and finally using best combination of eight parameters, 76.5 % of overall landslides were predicted in 24 % of the total area susceptible to landslide occurrences. The study has highlighted that using such methodology landslide susceptibility assessment can be carried out in vast stretches of Himalaya in short time in order to assess the impact of development as well as climate change/variability. The resultant map can play a critical role in selecting areas for remedial measures for slope stabilisation as well planning for future development of the region.

Development of a scheme for atmospheric correction of Resourcesat-2 AWiFS data

This paper presents a method called SACRS2, a scheme for atmospheric correction of RS2-AWiFS (Resourcesat2-Advanced Wide Field Sensor) data. The SACRS2 is a computationally fast scheme developed from a physics-based detailed radiative transfer model 6SV for correcting large amount of data from the high-repetivity AWiFS sensor. The method is based on deriving a set of equations with coefficients which depend on the spectral bands of the RS2-AWiFS sensor through forward signal simulations by 6SV. Semi-empirical formulations provided in the SMAC method with a few improvements have been used to describe various atmospheric interactions. A total of 112 coefficients for different equations are determined using the best fit equations against the computations of the 6SV. After the specific coefficients for the RS2-AWiFS spectral bands are determined, the major inputs of the scheme are raw digital numbers recorded by RS2-AWiFS sensor, atmospheric columnar water vapour content, ozone content, aerosol optical thickness at 550nm and viewing-illumination conditions. Results showed a good performance of the SACRS2 with a maximum relative error in the SACRS2 simulations ranged between 1% for a reflectance of 0.5 and 8.6% for reflectance of 0.05 with respect to 6SV computations. Validation of retrieved surface reflectance using the SACRS2 scheme with respect to in-situ measurements at two sites indicated a capability of this scheme to determine the surface reflectance within 10%. This is a first of its kind scheme developed for the atmospheric correction of any Indian Remote Sensing satellite data. A package containing the SACRS2 software is available on the MOSDAC website for the researchers.

Mangrove Restoration and Regeneration Monitoring in Gulf of Kachchh, Gujarat State, India, Using Remote Sensing and Geo-Informatics

Indian coast harbors richly diverse and critical coastal habitats like coral reefs and mangroves. Mangroves form one of the most important ecosystems of coastal and marine areas. It safeguards the ecology of the coastal areas and provides livelihood opportunities to the fishermen and pastoral families living in these areas. In real sense, mangrove is the Kalpvriksh (divine tree which fulfills all the desires) for the coastal communities. The restoration and plantation of mangroves have received a lot of attentions worldwide. To assess the impact of mangrove plantation activities and to monitor the mangrove regeneration and restoration in various villages, a joint study under the Integrated Coastal Zone Management Project (ICZMP) was taken up by Gujarat Ecology Commission (GEC) and Bhaskaracharya Institute for Space Applications and Geo-Informatics (BISAG) in the Gulf of Kachchh, Gujarat State. The major objective of this study was to monitor the increase in mangrove cover in coastal areas of Gulf of Kachchh using the Indian Remote Sensing Satellite data of 2005, 2011 and 2014. The mangrove regeneration was monitored using multi-temporal Indian Remote Sensing Satellite (IRS) LISS-III and LISS-IV digital data covering Gulf of Kachchh region. The multi-temporal IRS LISS-III data covering Gulf of Kachchh of October-2005, November-2011 and LISS-IV data of April-2014 was analyzed. The mangrove density and mangrove area in different talukas was estimated based on the analysis of IRS LISS-III digital data. The mangroves have been delineated based on the pink colour observed on satellite images and the area was estimated in the Geographic Information System (GIS) environment. The taluka or block-level mangrove areas were estimated and changes in the areas were monitored during the period of six years from 2005 to 2011. It was observed that the areas where mangrove regeneration activities were carried out with active participation of Community Based Organizations (CBOs), mangrove density as well as mangrove area have substantially increased in the Gulf of Kachchh region.

Real-time validation of Indian Remote Sensing Satellite data down link chain performance and evaluation of online data analysis with BER measurement

Indian Remote Sensing Satellites transmit video data in X-band and telemetry data in S-band. Currently, there is no available mechanism to validate the data link chain performance from onboard satellite to ground station in real-time. In order to achieve the objective, a novel method of Real-time online Bit Error Rate (BER) measurement is formulated using Real-time BER validating system (RTBVS) for all IRS missions. The mechanism is designed, realized and implemented at Satellite ground station to evaluate total satellite data receive chain performance in real-time itself. The Real Time BER Validating System (RTBVS) is tested successfully and is made operational at Satellite Data Reception Station for IRS Satellite Series. The RTBVS design module consists of advanced high speed parallel frame synchronizer for high data rate satellites, flywheel logic for reliability and stability of good frame sync detection and synchronization, and display of BER on the fly in a suitable window logic of one million bits or selectable window greater than 106 bits. RTBVS aids in the routine offline checks viz., local loop checks conducted daily, far field bore sight radiation tests carried out at regular intervals at ground station, before acquisition of satellite for Ground station performance validation. Further, the required test PN data passes from satellite can be avoided by using RTBVS mechanism and that particular orbit can be released for real-time data reception. The routine off line checks validate the satellite data receive chain at ground station only, but the total data receive chain from onboard satellite to ground station can be established using the RTBVS system which displays the online BER reading and helps in evaluating the station performance.

Extracting seasonal cropping patterns using multi-temporal vegetation indices from IRS LISS-III data in Muzaffarpur District of Bihar, India

The advancement in satellite technology in terms of spatial, temporal, spectral and radiometric resolutions leads, successfully, to more specific and intensified research on agriculture. Automatic assessment of spatio-temporal cropping pattern and extent at multi-scale (community level, regional level and global level) has been a challenge to researchers. This study aims to develop a semi-automated approach using Indian Remote Sensing (IRS) satellite data and associated vegetation indices to extract annual cropping pattern in Muzaffarpur district of Bihar, India at a fine scale (1:50,000). Three vegetation indices (VIs) – NDVI, EVI2 and NDSBVI, were calculated using three seasonal (Kharif, Rabi and Zaid) IRS Resourcesat 2 LISS-III images. Threshold reference values for vegetation and non-vegetation thematic classes were extracted based on 40 training samples over each of the seasonal VI. Using these estimated value range a decision tree was established to classify three seasonal VI stack images which reveals seven different cropping patterns and plantation. In addition, a digitised reference map was also generated from multi-seasonal LISS-III images to check the accuracy of the semi-automatically extracted VI based classified image. The overall accuracies of 86.08%, 83.1% and 83.3% were achieved between reference map and NDVI, EVI2 and NDSBVI, respectively. Plantation was successfully identified in all cases with 96% (NDVI), 95% (EVI2) and 91% (NDSBVI) accuracy.

Wetland assessment, monitoring and management in India using geospatial techniques

Satellite remote sensing and GIS have emerged as the most powerful tools for inventorying, monitoring and management of natural resources and environment. In the special context of wetland ecosystems, remotely sensed data from orbital platforms have been extensively used in India for the inventory, monitoring and preparation of action plans for conservation and management. First scientific inventory of wetlands in India was carried out in 1998 by Space Applications Centre (ISRO), Ahmedabad using indigenous IRS (Indian Remote Sensing Satellite) data of 1992–93 timeframe, which stimulated extensive use of geospatial techniques for wetland conservation and management. Subsequently, with advances in GIS, studies were carried out for development of Wetland Information System for a state (West Bengal) and for Loktak lake wetland (a Ramsar site) as a prelude to National Wetland Information System. Research has also been carried out for preparation of action plans especially for Ramsar sites in the country. In a novel research, use of the geospatial technology has also been demonstrated for biodiversity conservation using landscape ecological metrics. A country-wide estimate of emission of methane, a Green House Gas, from wetlands has also been made using MODIS data. Present article critically reviews the work carried out in India for wetland conservation and management using geospatial techniques.

Integrating Indian remote sensing multi-spectral satellite and field data to estimate seagrass cover change in the Andaman and Nicobar Islands, India

Environmental resource managers and policy makers require a reliable tool to quickly assess the spatial extent of any natural resources, including seagrasses, in order to develop management plans. Even small natural or anthropogenic disturbances can cause severe changes in the distributional pattern of seagrass meadows. Satellite imageries provide a suitable means to detect and assess such changes in space and time in remote and inaccessible areas. Present study aims to understand the distribution pattern of seagrasses after the Indian Ocean Tsunami in 2004 with the help of Indian Remote Sensing satellite data and in situ ground surveys with hand held GPS. As no geospatial data bases were available for the pre-tsunami period, the changes in seagrass cover were compared with the ground estimates available in the literature and also using pre-tsunami satellite data sets. The study found severe loss of seagrasses in the northern Andaman particularly in the Interview and North reef islands and in the Nicobar group of islands including Great Nicobar and Trinket islands. The investigation revealed the presence of 2,943.38 ha of seagrass covering the entire Andaman and Nicobar islands, and that 1,619.41 ha of seagrasses had been denuded during this period. The earthquake and subsequent tsunami in 2004 was the major reason for the loss of seagrasses in these islands. The seagrass spatial map generated in the present study can be used for the development of conservation and management plans and also to restore the denuded seagrasses of this region. Keywordsseagrass, remote sensing, IRS, conservation, Andaman and Nicobar islands

Lithological Mapping in the Eastern Part of the Central Iranian Volcanic Belt Using Combined ASTER and IRS data

The study area is located in the eastern part of the central Iranian volcanic belt. Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS ) pan images were used for applying several image classification methods for lithological mapping. ASTER visible-near infrared and shortwave infrared bands were sharpened using IRS pan image. We used classification methods such as Maximum likelihood, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) in order to evaluate the usefulness of these methods for geological mapping. The classification results showed that MLC has the best accuracy and the classified image closely resembles the previously prepared geology map of the area.

A multiple-frame approach to crop yield estimation from satellite- remotely sensed data

Many studies have recently explored information from satellite-remotely sensed data (SRSD) for estimating crop production statistics. The value of this information depends on the aerial and spatial resolutions of SRSD. SRSD with fine spatial resolution is costly and aerial coverage is less. Use of multiple frames of SRSD in the estimation process of crop production can increase the precision. We propose an estimator for the average yield of wheat for the state of Haryana, India. This estimator uses information from Wide Field Sensor (WiFS) and Linear Imaging Self Scanner (LISS-III) data from the Indian Remote Sensing (IRS-1D) satellite and crop-cutting experiment data collected by probability sampling design from a list frame of villages. We find that relative efficiencies of multiple-frame estimators are high in comparison to single-frame estimators.

Providing poplar plantation map by Indian remote sensing (IRS) satellite imagery in Northern Iran

Using satellite imagery processing technique, we can separate planting poplar lands with a low cost, high speed and precision. The present study was conducted in northern Iran using Indian Remote Sensing (IRS) satellite data in July 2006 and ILWIS software to identify the cultivated surfaces of poplar. Field visitation recorded 548 ground control points by GPS in the way of features in various areas of Guilan Province. Ground control point map was overlaid on satellite color composite (sample sat) as training points map (pixels). In supervised classification, box classifier, maximum likelihood, minimum distance and minimum mahalanobis distance were used to determine user’s accuracy, overall accuracy and kappa coefficient of each method, separately. Results indicate that spectrum reflex of poplar species is different from other vegetations and separable; but it has very close interference with conifer forest, natural forest, rice field and canebrake. By determining suitable training points, poplar cultivated surfaces could be identified. Among the classification methods, maximum likelihood with 91.48% of overall accuracy and 90.75% of kappa coefficient is the best method for separating planting poplar lands compared to the other methods. Key words: Poplar planting, supervised classification, maximum likelihood method, Indian remote sensing (IRS), Guilan.

Geospatial modeling of biological richness in Kuldiha wildlife sanctuary of Orissa, India

The paper presents a geospatial modeling approach for the assessment of biological richness in Kuldiha wildlife sanctuary in Orissa located in the northern tip of the Eastern Ghats in India. Indian Remote Sensing satellite data of Resourcesat-1 LISS III and field sampling were used to model biological richness at 1:50,000 scale. It was found that the sanctuary is dominated by Sal mixed dry deciduous forest. The vegetation map prepared through visual interpretation of satellite imagery was subjected to landscape analysis and assessment of biotic disturbance using SPLAM software. The disturbance index together with species richness, ecosystem uniqueness, terrain complexity and total importance value was modeled to access the biological richness in the sanctuary. A total of 3.9 per cent area was found to posses very high plant richness followed by high (21.2%), medium (42.1%) and low (32.8%) in the sanctuary. The study demonstrated the geospatial technology in conjunction with landscape analysis, ground inventory and geospatial modeling seizes good potential for rapid assessment of biological richness. The fringe areas of the sanctuary having disturbance more because most of the small villages which are relocated from sanctuary, settled in those areas.