Showing papers in "Journal of The Indian Society of Remote Sensing in 2010"

Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches

Abstract: Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).

Assessment of forest fragmentation in the conservation priority Dudhwa landscape, India using FRAGSTATS computed class level metrics

Abstract: The Dudhwa landscape, a priority conservation area representing Terai ecosystem (woodland-grassland-wetland complex) has witnessed a sea change in past 150 years or so on account of long history of forest management, changes in land use, and rapid economic development. We assessed fragmentation in two constituent protected areas (Dudhwa National Park-DNP and Katerniaghat Wildlife Sanctuary-KAT) of the landscape due to forest management activities (clear cutting, development of rail and road network, and plantations) and compared the magnitude among them using select metrics at the forest class level. We applied FRAGSTATS spatial pattern analysis software (ver.3.3) on different forest classes deciphered by land use/ cover maps generated using IRS P6 LISS IV digital data. Study amply revealed that the forests in DNP are less fragmented and of better habitat quality than forests of KAT. The set of seven metrics (patch density, mean patch size, edge density, mean shape index, mean core area, mean nearest neighbour, and interspersion and juxtaposition index) at the class level quantified in the present study are simple and proved useful for quantifying complex spatial processes and can be used as an effective means of monitoring in Dudhwa landscape.

Evaluation of morphometric parameters derived from ASTER and SRTM DEM — A study on Bandihole sub-watershed basin in Karnataka

Abstract: Morphometric parameters derived from three different sources viz., Survey of India topographic map (1:50,000), SRTM (Shuttle Radar Topographic Mission 90 m) and DEM derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer — 30 m) are evaluated to examine any difference within the results for the proper planning and management of the watersheds. Extracting drainage network from DEMs is mainly based on the flow of water from higher to lower elevation and steepest descent in a pixel. Common morphometric parameters are considered for analysis. The results show that the morphometric parameters derived from the SRTM and ASTER data provide good and satisfying results. The results will be more efficient when the DEM cell size is smaller or the resolution of the image is higher.

Cellular Automata Based Model of Urban Spatial Growth

Abstract: In the study reported in this paper an attempt has been made to develop a Cellular Automata (CA) model for simulating future urban growth of an Indian city In the model remote sensing data and GIS were used to provide the empirical data about urban growth while Markov chain process was used to predict the amount of land required for future urban use based on the empirical data Multi-criteria evaluation (MCE) technique was used to reveal the relationships between future urban growth potential and site attributes of a site Finally using the CA model, land for future urban development was spatially allocated based on the urban suitability image provided by MCE, neighbourhood information of a site and the amount of land predicted by Markov chain process The model results were evaluated using Kappa Coefficient and future urban growth was simulated using the calibrated model

Space based disaster management of 2008 Kosi floods, North Bihar, India

Abstract: One of the most important elements in flood disaster management is the availability of timely information for taking decisions and actions by the authorities. During the August 18, 2008 Kosi floods which impacted India and Nepal and affected more than three million people, aero-space technology proved to be a critical input for providing vital information on flood inundation. The satellite based flood inundation maps were extensively used for identifying marooned villages, submerged roads and railway tracks and carrying out the relief and rescue operations by the state agencies. Decision Support Centre (DSC) at National Remote Sensing Centre (NRSC) kept a constant watch on the flood situation. More than 200 flood inundation maps, using about 30 satellite datasets were generated and provided in near real time mode to the state agencies during August to October, 2008. DSC efforts were primarily focused in providing an overall picture of the flood situation in a short span of time to the state agencies. The present paper discusses about the operational use of remote sensing technology for near real time flood mapping, monitoring of Kosi floods and the satellite based observations made for the Kosi river breach.

Application of landscape ecology and remote sensing for assessment, monitoring and conservation of biodiversity

Abstract: Landscape ecology, inter alia, addresses the question as to how altered landscape patterns affect the distribution, persistence, and abundance of a species. Landscape ecology plays an important role in integrating the different scales of biodiversity from habitat patch to biome level. Satellite remote sensing technology with multi-sensor capabilities offers multi-scale information on landscape composition and configuration. Advances in geospatial analytical tools and spatial statistics have improved the capability to quantify spatial heterogeneity. Globally, landscape level characterization of biodiversity has become an important discipline of science. Considering the vast extent, heterogeneity, and ecological and economic importance of forest landscapes, significant efforts have been made in India during the past decade to strengthen landscape level biodiversity characterization. The generic frame work of studies comprises preparation of national databases providing information on composition and configuration of different landscapes using multi-scale remote sensing techniques, understanding the landscape patterns using geospatial models to elicit disturbance and diversity patterns and application of this information for bioprospecting and conservation purposes. Studies on hierarchical linkage of multi-scale information to study the processes of change, landscape function, dynamics of habitat fragmentation, invasion, development of network of conservation areas based on the understanding of multi-species responses to landscape mosaics, macro-ecological studies to understand environment and species richness, habitat and species transitions and losses, landscape level solutions to adaptation and mitigation strategies to climate change are a few of the future challenges. The paper presents the current experiences and, analyses in conjunction with international scenario and identifies future challenges of Indian landscape level biodiversity studies.

Mapping of Cropping System for the Indo-Gangetic Plain Using Multi-Date SPOT NDVI-VGT Data

Abstract: The present study has been carried out to delineate the existing cropping systems in the Indo-Gangetic Plains (IGP) using 10 day composite SPOT VEGETATION (VGT) NDVI data acquired over a crop year (June–May). Results showed that it is feasible to identify the major crops like rice, wheat, sugarcane, potato, and cotton in the dominant growing areas with good accuracy. Double cropping pattern is the most prevalent. Rice-wheat, sugarcane based, cotton-wheat, rice-potato, rice-rice, maize/millet-wheat are some of the major rotations followed. Rice-wheat is the dominant rotation accounting for around 40% of the net sown area. Triple crop rotations was less than 5% of the area and observed in some parts of Uttar Pradesh, Bihar and West Bengal. Single crop rotation of rice-fallow is significant only in West Bengal.

Spatial and Temporal Dynamics of Urban Heat Island and Their Relationship with Land Cover Changes in Urbanization Process: A Case Study in Suzhou, China

Abstract: One of the significant environmental consequences of urbanization is the urban heat island (UHI). In this paper, Landsat TM images of 1986 and 2004 were utilized to study the spatial and temporal variations of heat island and their relationships with land cover changes in Suzhou, a Chinese city which experienced rapid urbanization in past decades. Land cover classifications were derived to quantify urban expansions and brightness temperatures were computed from the TM thermal data to express the urban thermal environment. The spatial distributions of surface temperature indicated that heat islands had been largely broadened and showed good agreements with urban expansion. Temperature statistics of main land cover types showed that built-up and bare land had higher surface temperatures than natural land covers, implying the warming effect caused by the urbanization with natural landscape being replaced by urban areas. In addition, the spatial detail distributions of surface temperature were compared with the distribution of land cover by means of GIS buffer analysis. Results show remarkable show good correspondence between heat island variations with urban area expansions.

Terrain characterization using SRTM data

Abstract: Earth’s surface possesses relief because the geomorphic processes operate at different rates, and geologic structure plays a dominant role in the evolution of landforms (Thornbury, 1954). The spatial pattern of relief yields the topographic mosaic of a terrain and is normally extracted from the topographical maps which are available at various scales. As cartographic abstractions are scale dependent, topographical maps are rarely good inputs for terrain analysis. Currently, the shuttle radar topography mission (SRTM) provides one of the most complete, highest resolution digital elevation model (DEM) of the Earth. It is an ideal data-set for precise terrain analysis and topographic characterization in terms of the nature of altimetric distribution, relief aspects, patterns of lineaments and surface slope, topographic profiles and their visualisation, correlation between geology and topography, hypsometric attributes and finally, the hierarchy of terrain sub-units. The present paper extracts the above geomorphic features and terrain character of part of the Chotonagpur plateau and the Dulung River basin therein using SRTM data.

Integrated remote sensing and GIS study for hydrogeomorphological mapping and delineation of groundwater potential zones in Khallikote block, Ganjam district, Orissa

Abstract: The present study attempts to delineate different groundwater potential units using remote sensing and geographic information system (GIS) in Khallikote block of Ganjam disrict, Orissa. Thematic maps of geology, geomorphology, land use and land cover, drainage density, lineament density, slope and DEM (digital elevation model) were prepared using the Landsat Thematic Mapper data in 3 spectral bands, band 7 (mid-infrared light), band 4 (near-infrared light), Band 2 (visible green light). Relationship of each layer to the groundwater regime has been evaluated through detailed analysis of the individual hydrological parameters. The SMCE (Spatial Multi-Criteria Evaluation) module in ILWIS (Integrated Land and Water Information System) supports the decision-making process for evaluating the ground water potential zones in the area. The study shows that more than 70% of the block is covered by medium to excellent category having good ground water potential.

ALCM: Automatic land cover mapping

Abstract: It may be quite important for resource management people to extract single land cover class, at sub-pixel level from multi-spectral remote sensing images of different areas in single step processing. It has been observed, that neural network can be trained to extract single land cover class from multi-spectral remote sensing images, but they have problems in setting various parameters and slow during training stage. This paper present single land cover class water, extraction from mixed pixels present in multiple multi-spectral remote sensing data sets of same bands of AWiFS sensor of Resoursesat-1 (IRS-P6) satellite from different areas. In this work fuzzy logic-based algorithm, which is independent of statistical distribution assumption of data, has been studied at sub-pixel level to handle mixed pixels. It has been found; possibilistic c-means (PCM) algorithm takes the possibilistic view, that the membership of a feature vector in a class has nothing to do with its membership in other classes. Due to this, it was observed that PCM can extract only one class, from remote sensing multi-spectral data and it has produced 93.7% and 97.1% overall sub-pixel classification accuracy for two different data sets of different places using LISS-III (IRS-P6) reference data of same dates as of AWiFS data.

Geospatial modeling of Brown oak (Quercus semecarpifolia) habitats in the Kumaun Himalaya under climate change scenario

Abstract: The study explores the use of multiple criteria decision techniques in predicting spatial niche of Brown oak (also known as Kharsu oak, Quercus semecarpifolia Sm.) formation in midaltitude (2,400–3,500 meter amsl) Kumaun Himalaya. Predictive models using various climatic and topographical factors influencing Brown oak’s growth and survival were developed to define its current ecological niche. Analytical Hierarchical Process (AHP) method involving Saaty’s pair-wise comparison was performed to rank the explanatory powers of each compared variable. Variables were suitably weighted using fuzzy factor standardization scheme to reflect their relative importance in defining species niche. An optimum indicator was then chosen for deriving a site suitability map of brown oak. This study establishes the role of aspect in the current distribution of the species along with known influence of altitude. Future niches of oak has been tracked in the projected climate change scenario of +1°C and +2°C rise in temperature and 20 mm in precipitation. The results show that on predicted +1°C and +2°C increase in temperature, present habitat of brown oak distribution may be reduced by 40 per cent and 76 per cent respectively.

Variability analysis of groundwater levels — AGIS-based case study

Abstract: The present study has analyzed the variability in depth to water level below ground level (bgl) vis-a-vis groundwater development and rainfall from 1987 to 2007 in agriculture dominated Kaithal district of Haryana state in India. Spatial distribution of groundwater depth was mapped and classified into different zones using ILWIS 3.6 GIS tools. Change detection maps were prepared for 1987–1997 and 1997–2007. Groundwater depletion rates during successive decades were compared and critical areas with substantial fall in groundwater levels were identified. Further, block wise trends of change in groundwater levels were also analyzed. The water table in fresh belt areas of the district (Gulha, Pundri and Kaithal blocks) was observed to decline by a magnitude ranging from 10 m to 23 m. In Kalayat and Rajaund blocks, the levels were found fluctuating in a relatively narrow range of 4–9 m. During 1997–2007, the depletion has been faster compared to the preceding decade. Excessive groundwater depletion in major part of the district may be attributed to indiscriminate abstraction for irrigation and decrease in rainfall experienced since 1998. Changes in cropping pattern and irrigation methods are needed in the study area for sustainable management of the resource.

Spatial analysis of anthropogenic disturbances in mangrove forests of Bhitarkanika Conservation Area, India

Abstract: The dependence of coastal communities on mangrove forests for direct consumptive use due to the scarcity of alternate resources makes them one of the highly disturbed landscapes. This paper examines the spatial characteristics and extent of anthropogenic disturbances affecting the mangrove forests of Bhitarkanika Conservation Area situated along the east coast of India by using remotely sensed data and GIS, supplemented with socioeconomic surveys. The study reveals that resource extractions from these forests were considerable despite the protected status. Around 14% of the total fuel wood consumed annually in each of the household came from the mangrove forests of the Park. The patterns of consumption were spatially heterogeneous, controlled by the availability of alternatives, ease of accessibility, presence of markets, human density, and forest composition. The disturbance surface showed 30% of the major forest classes to be under high to very high levels of disturbance especially at easy access points. Besides, the distribution of economically useful species also determined the degree of disturbance. Resource use surfaces clearly identified the biotic pressure zones with respect to specific mangrove use and could be combined with the disturbance regime map to prioritize areas for mangrove restoration.

Geospatial modelling for identification of potential ecological corridors in Orissa

Abstract: Development of forest connectivity and corridors are critical for biodiversity conservation and also ensures energy and genetic exchange across greater spatial extent. A model has been developed to geospatially identify the potential ecological corridors based on the vegetation type and land cover data in association with spatial disturbance profile. The model formulates the route of least impedance due to (1) disturbance (a function of fragmentation, juxtaposition, Interspersion & proximity to roads/settlements) and (2) vegetation type and land cover. This is because the movement of genetic information and materials follow the path of least resistance across a landscape. The paper explores the utility of the approach to spatially generate ecological corridors connecting 14 protected areas of Orissa. The model has been able to identify the potential route connecting the different protected regions with 85–87% of the corridor in the natural areas. Of the 14 protected areas, only 12 could be connected by the model as they confirm to the criteria for the corridor establishment.

Land suitability studies for major crops in Pavagada taluk, Karnataka using remote sensing and GIS techniques

Abstract: Pavagada taluk of Tumkur district in Karnataka is one of the most backward taluks receiving less than 500 mm annual rainfall. The maximum area of the taluk is under monocropping, reasons for the same were not documented well. The present study was carried out using remote sensing data along with field survey and laboratory analysis for assessing the potentials and limitations of soil. Using the basic information on soil, climate and topography based on the matching exercise between the growth and production requirements of the crop, suitability of soils for groundnut, paddy and finger millet was assessed as per FAO land evaluation. The soil suitability maps were prepared using Arc GIS software. About 48 per cent of the total area was moderate to marginally suitable and 13 per cent of the area was not suitable for both groundnut and finger millet. Lowland areas covering 12 per cent of the area was highly suitable, 15 per cent was moderate to marginally suitable and 20 per cent was not suitable for paddy cultivation.

Monitoring growth of built-up areas in indo-gangetic plain using multi-sensor remote sensing data

Abstract: This paper provides an approach for rapid and accurate estimation of built-up areas on a per pixel-basis using a integration of two coarse spatial resolution remote sensing data namely DMSP-OLS and MODIS NDVI. The DMSP-OLS data due to its free availability, high temporal resolution and wide swath was used for regional level mapping of built-up areas. However, due to its low radiometric resolution, the built-up areas cannot be estimated accurately from the DMSP-OLS data. In present study, the DMSP-OLS data was combined with MODIS NDVI data to develop an Human Settlement Index (HSI) image, which estimated the fraction of built-up area on a per pixel basis. The resultant HSI image conveys more information than both the individual datasets. These temporal HSI images were then used for monitoring urban growth in Indo-Gangetic plains during the 2001–2007 time period. Thus, the present research can be very useful for regional level monitoring of built-up areas from coarse resolution data within limited time and minimal cost.

Groundwater prospects evaluation-based on hydrogeomorphological mapping: A case study in Kancheepuram district, Tamil Nadu

Abstract: Water is the most important natural resource which forms the core of the ecological system. The advent of remote sensing has opened up new vistas in groundwater prospect evaluation, exploration and management. The role of hydrogeomorphological units in tile storage of groundwater from the Kancheepuram distict has been investigated using IRS P6 LISS-III data. The Kancheepuram district exhibits diverse hydrogeomorphological conditions where the groundwater regime is controlled mainly by topography and geology. The extent of various water prospectus zones in terms of percentage includes, maximum area, particularly the north-western, and central part which is characterized by good potential occupying about 43% of total area. The moderate potential is marked by only 35%, and is scattered along the northern and southern side of the study area, the remaining 12% is of poor prospectus, which is falling in the coastal region of the study area.

Estimation of glacier depth and moraine cover study using ground penetrating radar (GPR) in the Himalayan region

Abstract: Himalayan region has one of the largest concentrations of mountain glaciers whose areal extent is changing due to global warming. In order to assess future changes in glacier extent due to global warming, information about glacier depth and debris cover is important. In this paper, application of ground penetrating radar (GPR) is discussed to assess glacier depth and debris cover. This investigation was carried out at Patseo and Samudratapu glaciers in Himachal Pradesh (West Himalaya). Antennas of frequency 50 and 100 MHz have been used for glacier depth and 500 MHz for moraine depth estimation. GPR signatures of glaciers were collected and further analyzed using velocities of electromagnetic waves in different media. The depth of Patseo glacier was estimated as 40 m. However, depth of the larger Samudra Tapu glacier could not be estimated using 50 and 100 MHz antennas. The depth of moraines was estimated using 500 MHz antenna and it varies from 0.35 cm to 0.85 cm for medial and around 1–2 m for lateral moraine at the experimental site.

An integrated study of geospatial information technologies for surface runoff estimation in an agricultural watershed, India

Abstract: Runoff is one of the important hydrologic variables used in most of the water resources applications. The Soil Conservation Service-Curve Number (SCS-CN) method is adopted for the estimation of surface runoff in the Mehadrigedda watershed area, Visakhapatnam district, India using multispectral remote sensing data, curve number approach and normal rainfall data. The main source of water in the Mehadrigedda watershed area is by rain, most of it drains off and only a little percolates into ground. The weighted curve number is determined based on antecedent moisture condition (AMC)-II with an integration of hydrologic soil groups (HSGs) and land use/land cover LULC categories. An integrated approach is applied to delineate the land use/land cover information as adopted from NRSA classification. The recording of daily rainfall data during the years 1997–2006 is collected from Indian Meteorological Department (IMD) rainguage center at Kottavalasa. It is observed that the annual rainfall-runoff relationship during 1997–2006, which is indicating that the overall increase in runoff with the rainfall of the watershed area. Integration of remote sensing (RS) and geographical infomation system (GIS) techniques provide reliable, accurate and up-to-date information on land and water resources.

Landscape characterisation of the forests of Himalayan foothills

Abstract: Deforestation and degradation are important aspects of landscape dynamics and have global significance. Quantification of landscape pattern using landscape metrics help in characterisation of landscapes and thus overall health of the forest cover. Himalayan foothills are one of the most important and fragile landscapes. Developmental activities and depdendence on the forest resources have altered the spatial pattern of these natural landscapes to a great extent. These changes in the landscape were analysed using satellite data from 1990, 2001 and 2006. The vegetation type maps of Dehradun forest division were prepared by supervised classification technique in order to study the landscape dynamics. Patch density, edge density, shape index, cohesion index, interspersion and juxtaposition index, normalised entropy, and relative richness are some important landscape metrics used in the study for quantifying the characteristics of landscape. The landscape metrics analysis and transformation analysis show that the forested areas are getting degraded and physical connectedness between the patches have also decreased making them isolated. The study demonstrates the importance of geospatial tools for monitoring the impact of disturbances on the forest ecosystem health, which can further help in landscape management.

Support Vector Machines for Automatic Multi-class Change Detection in Algerian Capital Using Landsat TM Imagery

Abstract: Recently, Support Vector Machines (SVMs) have shown a practical relevance in various image processing applications. This paper investigates their applicability for land cover and land use change detection using multi-sensor images of remote sensing. Then, the most widely used approaches for multi-class SVMs, which are the One-Against-All and the One-Against-One with both Max-Win and DDAG decision rules are implemented to perform multi-class change detection. SVMs are evaluated in comparison with artificial neural networks using different accuracy indicators. The results obtained showed that SVMs are much more efficient than artificial neural networks and highlighted their suitability for land cover change detection.

Geospatial modelling of biological richness in Sunderbans

Abstract: The present study attempts to assess the biological richness in Sunderban Biosphere Reserve (SBR) using a three-pronged approach i.e. satellite image (IRS 1D LISS-III) for vegetation/land use stratification, landscape analysis for disturbance regimes assessment and the disturbance regimes together with the ecosystem uniqueness, species richness and importance value for biological richness modelling. The study showed that four mangrove categories, viz., Avicennia, Phoenix, mixed mangroves and mangrove scrub, cover 23.21 per cent of the total geographical area of SBR. The largest area is occupied by mixed mangroves (18.31%). The overall accuracy of the vegetation/land use map worked out to be 91.67 per cent. The disturbance analysis revealed that the vegetation types were not much disturbed. Shannon-Weaver’s index of diversity was highest in case of mixed mangrove. The results revealed that 75 per cent forest area has high biological richness.

Mining Land Cover Information Using Multilayer Perceptron and Decision Tree from MODIS Data

Abstract: Land cover (LC) changes play a major role in global as well as at regional scale patterns of the climate and biogeochemistry of the Earth system. LC information presents critical insights in understanding of Earth surface phenomena, particularly useful when obtained synoptically from remote sensing data. However, for developing countries and those with large geographical extent, regular LC mapping is prohibitive with data from commercial sensors (high cost factor) of limited spatial coverage (low temporal resolution and band swath). In this context, free MODIS data with good spectro-temporal resolution meet the purpose. LC mapping from these data has continuously evolved with advances in classification algorithms. This paper presents a comparative study of two robust data mining techniques, the multilayer perceptron (MLP) and decision tree (DT) on different products of MODIS data corresponding to Kolar district, Karnataka, India. The MODIS classified images when compared at three different spatial scales (at district level, taluk level and pixel level) shows that MLP based classification on minimum noise fraction components on MODIS 36 bands provide the most accurate LC mapping with 86% accuracy, while DT on MODIS 36 bands principal components leads to less accurate classification (69%).

Synergistic use of multi-temporal Radarsat SAR and AWiFS data for Rabi rice identification

Abstract: A national level project on kharif rice identification and acreage estimation is being carried out successfully for several states in the country. A similar methodology based on the temporal profile for identification and delineation of various land cover classes has been followed for the Rabi rice acreage estimation. To define rabi rice, rabi season in India starts from November — February to March — June. Though the main growing season is predominantly winter but the uncertainty of getting cloud free data during the season has resulted in the use of microwave data. A feasibility study was taken up for early forecasting of the rabi rice area using microwave data. Hierarchical decision rule classification technique was used for the identification of the different land cover classes. Land preparation, puddling and transplantation were the reasons for the specific backscatter of rice growing areas. The increase or decrease in the SAR backscatter due to progress in the crop phenology or due to delayed sowing respectively forms the basis for identifying the rice areas. In addition the potential of optical data of a later date has been utilized in the form of various indices from bands including MIR to distinctly separate the late sown areas and also the puddled areas from other areas. This study emphasizes the synergistic use of SAR and optical data for delineating the rabi rice areas which is of immense use in giving an early forecast.

Assessment and validation of biological richness at Landscape Level in part of the Himalayas and Indo-Burma Hotspots using geospatial modeling approach

Abstract: Validation is a necessary step for model acceptance and is defined as a comparison of the model’s predictions with real world to determine whether the model is suitable for its intended purpose. We have validated the biological richness index for three states generated in ‘Biodiversity Characterization at Landscape Level’ project under the aegis of Department of Biotechnology and Department of Space of the Government of India. Biological Richness (BR) index, described elsewhere as a cumulative property of ecological habitats and surroundings; was analyzed as an integrated ‘threetier modeling approach’ of (i) utilization of geospatial tools, (ii) limited field survey and (iii) landscape analysis. For validation, we categorized the field plots into 10-groups corresponding to 10-levels of BR using data splitting technique using their GPS-recorded positional information. In general, the number of tree, shrub and liana species and mean BA demonstrated a decreasing trend with lowering of BR for all three states falling under both the hotspots. However, the number of endemic species increased with decrease in BR levels for Meghalaya and Arunachal Pradesh; and decreased for the state of Assam. The study validates the BR index derived using geospatial modeling approach, thereby provides confidence in its acceptance for ecological conservation purposes.

Some issues related with sub-pixel classification using HYSI data from IMS-1 satellite

Abstract: The mixed pixels are treated as noise or uncertainty in class allocation of a pixel and conventional hard classification algorithms may thus produce inaccurate classification outputs. Thus application of sub-pixel or soft classification methods have been adopted for classification of images acquired in complex and uncertain environment. The main objective of this research work has been to study the effect of feature dimensionality using statistical learning classifier — support vector machine (SVM with sigmoid kernel) while using different single and composite operators in fuzzy-based error matrixes generation. In this work mixed pixels have been used at allocation and testing stages and sub-pixel classification outputs have been evaluated using fuzzy-based error matrixes applying single and composite operators for generating matrix. As subpixel accuracy assessment were not available in commercial software, so in-house SMIC (Sub-pixel Multispectral Image Classifier) package has been used. Data used for this research work was from HySI sensor at 506 m spatial resolution from Indian Mini Satellite-1 (IMS-1) satellite launched on April 28, 2008 by Indian Space Research Organisation using Polar Satellite Launch Vehicle (PSLV) C9, acquired on 18th May 2008 for classification output and IRS-P6, AWIFS data for testing at sub-pixel reference data. The finding of this research illustrate that the uncertainty estimation at accuracy assessment stage can be carried while using single and composite operators and overall maximum accuracy was achieved while using 40 (13 to 52 bands) band data of HySI (IMS-1).

Assessment of Fragmentation and Disturbance Patterns in Eastern Ghats: A Case Study in R.V. Nagar Range, Visakhapatnam District, Andhra Pradesh, India

Abstract: At present the biodiversity in Eastern Ghats is threatened by loss of habitats, exploitation and unscientific management of natural resources, forest fire, biological invasion and other anthropogenic pressures. In this context, we have assessed the forest cover changes, fragmentation and disturbance in the R.V. Nagar Range of Eastern Ghats region, Andhra Pradesh using satellite remote sensing and GIS techniques. Satellite data of IRS-1A LISS II of 1988 and IRS-P6 LISS III of 2006 were assessed for forest cover changes in 1 sq.km grid and generated as Sensitivity Index map. Further the road and settlement buffer of 1000 m was generated to represent Threat Index map. From 1988 to 2006, the forest cover had a total cover loss of 35.2 sq.km and increase in scrub cover by 7.2%. Over all change analysis from 1988 to 2006 with reference to forest cover indicates, negative changes (loss of forest area) accounted for 48.1 sq.km area and positive changes (gain of forest) for an area of 12.1 sq.km of area. The results of the change detection using multi-date satellite imagery suggest degradation in forest cover over two decades, which necessitates the conservation measures in this range with high priority.

Assessment and development of algorithms to detection of oil spills using MODIS data

Abstract: Oil spill detection and subsequent monitoring are of major concern for coastal zone management as they form potential marine pollutants. The present study is based on examining the feasibility of Moderate Resolution Imaging Spectroradiometer (MODIS) high resolution bands (250 and 500 m) for detection of oil spill. The MODIS — Aqua for 18, 19 and 20th January 2003 were used to study the oil spill in Lake Maracaibo, Venezuela. The examination of L1B and L2 data revealed that L2 products such as SST, Rrs and BRDF were not very useful due to erroneous atmospheric corrections. Visual examination of raw radiance data i.e. L1B data in the 250 and 500 m spatial resolution was found to be the simplest yet feasible method for spill detection. The study further revealed that atmospherically uncorrected radiances at 469, 555 and 645 nm were showing significant signature of oil spill. Therefore an attempt was made to perform ratio operations to enhance the feature. The study concluded that the ratio of difference and sum at 645 and 555 nm, normalized by 469 nm, provides the best result. The result was validated by comparing with the previous published literature. The result clearly indicates the potential of MODIS-Aqua high resolution data in oil spill monitoring. Therefore, MODIS-Aqua data with daily coverage and high resolution can be reliable and cost-effective for such events.

Assessment of coastal bio-resources of the Palk Bay, India, using IRS-LISS-III data

Abstract: Coastal resources viz., coral reefs, seagrasses, mangroves, and coastal land features viz., sandy beach, mudflats and salt pan/aquaculture ponds were classified and assessed in the Palk Bay region of the south-east coast of India using IRS LISS III satellite image (1996, 2000, 2002 and 2004). The study recorded an areal coverage of 286.95 ha of reef area during 2004, which is 177.54 ha lesser than that of the reef area of 1996. The reef vegetation composed mainly of seaweeds has gained over 29.44 ha during the same period. Likewise, sand over reef area has also increased alarmingly i.e. 120.34 ha between 1996 and 2004. The seagrass beds of Munaikkadu region of the Palk Bay are comparatively protected and have gained over 7.5 ha between 1996 and 2004. It has been found that both the dense (2.99 ha) and sparse (36.45 ha) mangroves have gained their areal coverage considerably between 1996 and 2004. Whereas in Devipattinam region, many anthropogenic pressures are exerted only on the seagrass resources which has led to the reduction of over 785.5 ha of dense seagrass beds between 1996 and 2004. The study clearly indicated that the resources are under the pressures of low to high threats, especially the coral reefs and seagrasses, if the pressures continue, coastal resources of the Palk Bay may face serious threats of destruction in this part of the Bay in the years to come.