Discharge water temperature assessment of thermal power plantusing remote sensing techniques

Thermal power plants are generally constructed near to sea coast to meet their requirement of coolingwater. The warm water discharge from the thermal power plant is one of the major environmentalconcerns in view of the thermal pollution in the sea water. The temperature limit for the warm waterdischarge from the thermal power plant has to be monitored and controlled. Coastal Gujarat PowerLimited (CGPL) operates (24×7) at an “once-through system” based sea water circulation for powergeneration. The used sea water is then discharged into the sea through an outlet channel. As per environmental norms, the discharge water temperature needs to be maintained below the stipulated “delta”rise (+7 ℃) with respect to ambient sea surface temperature at the inlet. We demonstrate the applicability of thermal remote sensing data in understanding the seasonal and temporal variations of thetemperature difference between the discharge water and the ambient sea water. We used thermal banddata from Landsat-8 satellite imagery to map water surface temperature and create temperature profilesalong the intake and outflow channels (till the sea), to understand the variation of temperature andestimate the “DT” between intake point and various observation points along the outflow. This analysiswas carried out for all 11 months (except June) of the year 2018 to correlate temperature variations withseasonal changes. Tidal conditions during the time of data acquisition were also considered to accountfor the effect of tides on DT. The result shows that the average temperature rise between intake andoutflow are maintained at ~3 ℃ across all the months of 2018, with minor variations in the months ofJuly and August. Further, average temperature drop from outflow to cooling channel (before diaphragm)is seen to be ~2 ℃ across all the months with similar seasonal fluctuations.

Importance of Aerosol Optical Depth in the Atmospheric Correction of Ocean Colour Remote Sensing Data

Atmospheric aerosols being an important component of the atmosphere play an important role in global and regional climate change. Aerosols can affect air quality, climate change and human health and have a significant effect on the solar energy budget. To study their quantitative effect is a challenging task due to their high spatial and temporal variability. This parameter represents one of the extinction coefficients of solar radiation and rate of suspended particles in the atmosphere. Ocean Colour Remote Sensing provides information about the four major geophysical parameters like chlorophyll and total suspended sediment concentration, vertical diffuse attenuation co-efficient and the aerosol optical thickness measured at 865 nm. Aerosol optical depth can be expressed as function of wavelength through Angstrom’s equation ?, where “α” and “β” are known as Angstrom parameters. The Angstrom exponent, “α” is related to the size distribution of the aerosol particles and “β” represents the amount of aerosols present in the atmosphere. In this present study, an attempt is made to study the impact of these two parameters by changing values from 0 to 0.05 for “α” and from 0.0 to 0.6 for “β” in SeaDAS processing for estimating the aerosol optical depth. From this study, it is clear that for most of the applications either in the coastal or open ocean waters, alpha value varies from 0.0 to 0.3 over the north Indian Ocean. However, this has been further evaluated by various combinations for retrieving the AOD using OCM-2 data.

Issues and Limitations of Broad Band Remote Sensing of Kimberlite—A Case Example from Kimberlites of Dharwar Craton, India

Present study attempts to understand the potential of multispectral ASTER (Advanced space borne thermal emission and reflection radiometer) data for spatial mapping of kimberlite. Kimberlite is an economic rock known for hosting diamond. Kimberlite also has petrogenetic importance for giving us clue on the composition of lower part of the mantle. Kimberlites often contain serpentine, carbonate minerals;which have their diagnostic spectral signatures in short wave infrared (SWIR) domain. In the present study, attempt is made to delineate kimberlite from adjacent granite-granodiorite gneiss based on processing of the ASTER data as ASTER’s spectral channels can detect some of the diagnostic absorption features of kimberlites. But it has been observed that the kimberlites are difficult to be delineated by processing the ASTER data using correlative information of both sub-pixel and per-pixel mapping. Moreover, smaller spatial size of kimberlites with respect to pixel size of ASTER SWIR channels further obscures the spectral feature of kimberlite. Therefore, an attempt is also made to understand how intra pixel spectral mixing of kimberlite and granite granodiorite-gneiss modifies the diagnostic spectral feature of kimberlite. It is observed that spectral feature of kimberlites would be obscured when it is has very small spatial size (one-tenth of pixel) with respect to pixel size. Moreover, calcrete developed in the adjacent soil has identical absorption feature similar to the spectral features of kimberlites imprinted in the respective ASTER convolved spectral profiles. This also has resulted false-positives in ASTER image when we use spectral feature as a tool for spatial mapping of kimberlite. Therefore hyperspectral data with high spatial and spectral resolution is required for targeting kimberlites instead of using broad band spectral feature of kimberlites.

Mapping of Aluminous Rich Laterite Depositions through Hyper Spectral Remote Sensing

Increased dimensionality of the satellite data proves to be very useful for discriminating features with very close spectral matching. Present study concentrates on the retrieval of reflectance spectra from the level one radiometrically corrected data in Koraput district (Orissa) for the Bauxite ore. In the present study, atmospheric correction model FLAASH has been used to retrieve reflectance from the radiance data. Preprocessing of the dataset has been done before applying atmospheric correction on the dataset. Spectral subsetting of noise prone bands has been successfully done. Local destriping of the affected bands has been done using a 3*3 local mean filter. Spectral signatures of samples were derived from the processed data. Spectral signature of each sample and derived features vectors were correlated with the satellite image of the area and distribution of each feature was demarcated. Spatial abundance of each feature was used in preparation of mineral abundance map. Accuracy of the map was assessed using training sets of representative geological units. The mineral abundance mapping using the spectral analysis of the reflectance image involves the endmember collection using the N-Dimensional visualizer tool in ENVI software. Laterite, Bauxite, Iron and silica rich Aluminous laterite soil, Alluvium and Forest were selected as the end members after understanding the geology and analysis of the reflectance image. Various mapping techniques were applied to generate the final classified mineral abundance Map, Linear Spectral Unmixing, Mixture Tune Matched Filtering, Spectral Feature Fitting, Spectral Angle Mapper were the techniques used. Results have revealed the ability of Hyper spectral Remote sensing data for the identification and mapping of Hydrothermal altered products like Bauxite, Aluminous Laterite. This technology can be utilized for targeting minerals in the altered zone.

Performance validation of High Mountain Asia 8-meter Digital Elevation Model using ICESat-2 geolocated photons

High Mountain Asia(HMA),recognized as a third pole,needs regular and intense studies as it is susceptible to climate change.An accurate and high-resolution Digital Elevation Model(DEM)for this region enables us to analyze it in a 3D environment and understand its intricate role as the Water Tower of Asia.The science teams of NASA realized an 8-m DEM using satellite stereo imagery for HMA,termed HMA 8-m DEM.In this research,we assessed the vertical accuracy of HMA 8-m DEM using reference elevations from ICESat-2 geolocated photons at three test sites of varied topography and land covers.Inferences were made from statistical quantifiers and elevation profiles.For the world’s highest mountain,Mount Everest,and its surroundings,Root Mean Squared Error(RMSE)and Mean Absolute Error(MAE)resulted in 1.94 m and 1.66 m,respectively;however,a uniform positive bias observed in the elevation profiles indicates the seasonal snow cover change will dent the accurate estimation of the elevation in this sort of test sites.The second test site containing gentle slopes with forest patches has exhibited the Digital Surface Model(DSM)features with RMSE and MAE of 0.58 m and 0.52 m,respectively.The third test site,situated in the Zanda County of the Qinghai-Tibet,is a relatively flat terrain bed,mostly bare earth with sudden river cuts,and has minimal errors with RMSE and MAE of 0.32 m and 0.29 m,respectively,and with a negligible bias.Additionally,in one more test site,the feasibility of detecting the glacial lakes was tested,which resulted in exhibiting a flat surface over the surface of the lakes,indicating the potential of HMA 8-m DEM for deriving the hydrological parameters.The results accrued in this investigation confirm that the HMA 8-m DEM has the best vertical accuracy and should be of high use for analyzing natural hazards and monitoring glacier surfaces.

Structure and floristic composition of tree stand in tropical forest in the Eastern Ghats of northern Andhra Pradesh, India

The changes in species composition, abundance and forest stand structure were analyzed across altitudinal regimes in tropical forests of Eastern Ghats of northern Andhra Pradesh, India. Three 1-ha plots were established with one each in low, medium and high altitudes. A total of 153 species, 2129 stems （709 stems .ha^-1） of ≥10 cm girth were enumerated. Species richness and diversity pattern varied along altitudinal gradient and increased with the altitude. Species richness varied from 52 to 110 species-ha^-1 and stand density from 639 to 836 stems.ha^-1 with average basal area of 34.39 m^2.ha^-1. Shannon-Wiener index （H＇） ranged from 4.55 to 5.17. Low altitude （i.e., Site 1） is dominated byXylia xylocarpa （59.22） and Lagerstroemia parviflora （23.90）, medium altitude （i.e., Site 2） by Xylia xylocarpa （45;50） Bursera serrata （17.29）, and high altitude （i.e., Site 3） has Schleichera oleosa （28.25） Pterocarpus marsupium （26.55） as predominant species. Taxonomically, Rubiaceae （12 species）, Fabaceae （12）, Euphorbiaceae （11）, Rutaceae （7） and Lanraceae （7） were dominant families. Density-wise, Fabaceae, Combretaceae, Euphorbiaceae, Anacardiaceae and Myrtaceae were abundant. Thus, conservation assessment based on altitudinal regimes and the information on species structure and function can provide baseline information for monitoring and sustaining the biodiversity.

Assessment of geodetic strain and stress variations in Nepal due to 25 April 2015 Gorkha earthquake:Insights from the GNSS data analysis and b-value

A magnitude M;7.8 earthquake occurred on 25 April 2015(referred as Gorkha earthquake).We have analyzed the spatial variation of b-value and two-dimensional strain within Nepal Himalaya before and after the Gorkha earthquake.We have used continuous Global Navigation Satellite System(GNSS)data from 30 stations in the Nepal region for geodetic strain estimation and earthquake data for b-value estimation.The GNSS data were processed using double differencing technique for the accurate position of each station.The precise velocity vectors show a general azimuth of north east for all the stations and have been used to derive two-dimensional strain.Between epicenters of Gorkha(25 April 2015)and Dolakha earthquakes(12 May 2015),we observed high co-seismic horizontal displacements(0.2 m to 2 m).In the Pre-seismic deformation study,maximum strain accumulation(56.40×10;)and low bvalue(0.79-0.89)was observed in and around the Western Nepal region,which may be responsible for the 2015 Gorkha earthquake.The potential seismic zones were identified by GIS based integration of geodetic strain and b-value map and superimposition using weighted overlay method.The Maximum strain and low b-value are now observed in the eastern part of Nepal.Hence,the spatial disposition of elastic energy has changed after the two major earthquakes and continuous seismic hazard assessment is required in the eastern Nepal.

Damage and geological assessment of the 18 September 2011 M_w 6.9 earthquake in Sikkim,India using very high resolution satellite data

Post-disaster very high resolution（VHR） satellite data are potential sources to provide detailed information on damage and geological changes for a large area in a short time.In this paper,we studied landslides triggered by the M_w 6.9 earthquake in Sikkim,India which occurred on 18 September 2011 using VHR data from Cartosat-1,GeoEye-1,QuickBird-2 and WorldView-2 satellites.Since the earthquake-affected area is located in mostly inaccessible Himalayan terrain,VHR data from these satellites provided a unique opportunity for quick and synoptic assessment of the damage.Using visual change analysis technique through comparison of pre- and post-earthquake images,we assessed the damage caused by the event.A total of 123 images acquired from eight satellites,covering an area of4105 km2 were analysed and 1196 new landslides triggered by the earthquake were mapped.Road blockages and severely affected villages were also identified.Geological assessment of the terrain highlighted linear disposition of landslides along existing fault scarps,suggesting a reactivation of fault.The landslide inventory map prepared from VHR images also showed a good correlation with the earthquake shake map.Results showed that several parts of north Sikkim,particularly Mangan and Chungthang,which are close to the epicentre,were severely affected by the earthquake,and that the event-based landslide inventory map can be used in future earthquake-triggered landslide susceptibility assessment studies.

Satellite Derived Geospatial Irrigation Performance Indicators for Benchmarking Studies of Irrigation Systems

Development of irrigation infrastructure and its efficient management is the primary concern for sustainable food production. The assessment of irrigation infrastructure creation, its utilization, diagnostic evaluation of the various performance indices (monitoring) are important to measure the efficiency. Benchmarking of Irrigation Systems (BIS) is for the diagnostic analysis of irrigation performance indicators comprising of Irrigation Infrastructure System (IIS), Agricultural System (AS), Water Delivery Dynamics (WDD). Since, the performance of an irrigation command varies with space and time, utilization of spatial information technologies viz. Remote Sensing (RS), Geographical Information Systems (GIS), Global Positioning Systems (GPS) useful to provide spatial information on several indices in the process of benchmarking (BM). Information requirements for BIS at different stages, utilization of spatial information technologies to derive irrigation performance indicators was discussed with suitable examples and demonstrated in this study. The studies carried out indicates that the geospatial approach for BIS enabled the improvements in data collection methods, diagnostic analysis, spatio-temporal visualisation of BM indicators at disaggregated canal level which would be useful for decision support during the corrective management measures. The conjunctive use of multi-date (medium resolution) satellite data, high spatial resolution data, field data on water deliveries was found to be an alternative to the conventional non-spatial approaches for BIS and thereby better water resources planning and management.

Impact of Cyclone PHAILIN on Chlorophyll-a Concentration and Productivity in the Bay of Bengal

Ocean colour remote sensing is one of the conventional methods in satellite oceanography used to study the biological response of the upper ocean to the tropical cyclones. This paper aims to study the impact of the Very Severe Cyclonic storm PHAILIN, and its consequence on the surface chlorophyll-aconcentration distribution in the Bay of Bengal using Oceansat-2 Ocean Colour Monitor (OCM). The impact of this cyclone on ocean primary productivity has been studied using MODIS-A data. Sea surface temperature (SST) plays an important role in the generation of primary productivity along with the other oceanographic parameters;SST patterns in the Bay of Bengal during the cyclone period were studied. From the analysis, it is observed that the chlorophyll-aconcentration has increased from 1.08 (before) to 7.06 mg/m3 after the cyclone with an SST drop of ~3°C (29.19°C to 26°C). The primary productivity has increased from 410.0506 to 779.9814 mg/C/m2/day after the cyclone. In addition to the above analysis, an attempt has also been made to study the impact of cyclone intensity on the chlorophyll concentration. The study shows that the comparison between cyclone intensity (CI) and chlorophyll concentration shows a positive relationship.

Delineation of sensitive coastal zone of northern Ramanathapuram coast,Tamilnadu,India,using a GIS approach

This research aims to assess the sensitive coastal zones by analyzing the shoreline changes such as accretion and erosion in the eastern coastal part of Ramanathapuram District,Tamil Nadu,India.The study focused on Mandapam,Pirappanvalsai,Enmanamcondam,Nagachi,Terbogi,Athyuthu,Chitrakkottai,Pathengal,Attangarai,Alagankulam,and Devipattinam villages.Some of these areas are distinguished as highly sensitive ecological zone.The erosion of an area leads to irreplaceable disturbances to nature.Hence,erosion and accretion studies are top priorities when studying the coastal area.In addition to GIS technology,which has been widely used in the past 20 years,the End Point Rate(EPR)and Linear Regression Rate(LRR)analyses were conducted to better understand the shoreline changes.The stability of the coastal region was analyzed by observing the slope variations of the beach during the profile survey.In the study,we analyzed that the Quaternary deposits of sand are the dominant geological feature of the study area.Coral reefs and salt pans are highly distributed along the coastal stretch.The coast of Mandapam village has been detected as a highly sensitive region,with an average erosion rate of-3.1 m/yr and an average accretion rate of 1.22 m/yr,resulting in high erosion and low accretion.The coast is a steep coast with an average slope angle of 0.24°.This study provides insight into sustainable coastal zone management.

GIS-based assessment of non-equilibrium pattern between groundwater recharge and irrigation draft in a semi-arid region of Rajasthan, India

Over-exploitation of groundwater for irrigation can result in drastic reduction in groundwater level in Jodhpur district of western Rajasthan, India. In this study, we used the long-term trend analysis of seasonal groundwater level data to predict the future groundwater scenario in 33 villages of Jodhpur district, assessed the impact of water harvesting structures on groundwater recharge and explored the non-equilibrium between groundwater recharge and irrigation draft in the study area. Analysis of groundwater level data from 26 observation wells in 33 villages in the pre-monsoon period showed that groundwater level decreased continuously at the rate of 2.07 m/a. With this declining rate, most of the tube wells （including the well with the maximum depth of 193 m） are predicted to become completely dry by 2050. Behavior of temporal groundwater level data in the study period （from 2004 to 2012） can be explained by different geospatial maps, prepared using ArcGIS software. Statistical analysis of the interpolated maps showed that the area with the maximum positive groundwater recharge occupied 63.14% of the total area during 2010-2011 and the area with the maximum irrigation draft accounted for 56.21% of the total area during 2011-2012. Higher groundwater recharge is attributed to the increase in rainfall and the better aquifer condition. Spatial distribution for the changes of average groundwater recharge and draft （2008-2009 and 2011-2012） showed that 68.50% recharge area was in positive change and 45.75% draft area was in negative change. It was observed that the area of the irrigation draft exceeded that of the groundwater recharge in most of the years. In spite of the construction of several shallow water harvesting structures in 2009-2010, sandstone aquifer zones showed meager impact on groundwater recharge. The best-fit line for the deviation between average groundwater fluctuation due to recharge and irrigation draft with time can be represented by the polynomial curve. Thus, over-exploitation of groundwater for agricultural crops has result in non-equilibrium between groundwater recharge and irrigation draft.

Exploring the nexus between Indian forestry and the Sustainable Development Goals

Forests are considered as a sacred asset in India and have guided the way of living throughout its history.Indian forests not only accommodate the myriad species but also act as a survival support system to the communities that depended on them.The 2030 Agenda for Sustainable Development proposed by the United Nations(UN)has gained momentum and becomes an integral part of the recent efforts of Indian governance.In this investigation,we examined the potential nexus between Indian forestry system(biodiversity-enriched assets,ecosystem services,constitutional mechanisms,and governances)and Sustainable Development Goals(SDGs)through theoretical underpinnings from literature that selected from database like Google Scholar,Indian forest survey reports,and information retrieved from Indian government websites.The significance of this review is that it presents comprehensive information about Indian forestry,biodiversity-rich assets,and sustainable forest management practices.The results show that Indian forestry as a whole is an integral part of the food-energy-water cycle and contributes to all dimensions of sustainable development,i.e.,economic sustainability,social sustainability,and environmental sustainability.The investigation confirms that besides partly contributing to the economy and life support systems to many dependent species,forests also act as boosters in the areas of food security and health.Targets related to the climate action,peace,and partnership goals are well in place through various forestry interventions and environmental commitments by the Government of India.

Geospatial Opinion on Unusual Locust Swarm Invasions during Amphan Cyclone

The desert locust, Schistocerca gregaria Forsk. (Orthoptera: Acrididae) is a peculiar insect that exhibits density-dependent phase polyphenism. Gregarious phase enables desert locust to form a large groups and mass migrations during which significant crop loss and food security issues may occur. Currently, the most recent upsurge of desert locust is prevailing across Eastern Africa and Southwestern Asia. Usually desert locusts are restricted to regions like semi-arid and arid regions. A super cyclonic storm, namely Amphan originated during May 2020, in the Bay of Bengal of North Indian Ocean has influenced numerous swarms to migrate from the Thar desert region to other parts of India. In this research, satellite data were used to investigate the reasons for this sudden swarm migration. The results that revealed the wind jets passing above the Thar desert region have integrated with the cyclogenesis of Amphan and led to the formation of conduits. The favorable conditions in the conduits include the open sky conditions, air temperature and fresh vegetation availability. Moreover, the wind settings in the conduits have enabled the swarm migrate to follow the downwind facing the sunrays during early mornings for an easy and successful take-off. It is observed that during the take-off session the wind speed at 850 hPa pressure level is mostly double than that of surface wind in both the conduits.

Morphological Profiles of Sand Dunes from ICESat-2 Geolocated Photons

Aeolian process leads to the transportation and accumulation of sand particles that result in sand dune landforms. The structure and shape of the sand dunes are driven by the parameters of interacting wind force and the material composition of sand within. Cross-section profiles over the sand dunes will essay the geomorphological parameters through which the steady state and rate of sand transport can be computed. National Aeronautics and Space Administration’s novel satellite namely Ice, Cloud and land Elevation Satellite-2 (ICESat-2) hosts a solo sensor namely Advanced Topographic Laser Altimeter System (ATLAS) which is a photon counting instrument that measures the round-trip time of the light pulse being emitted and reflected back from the surface determines the true height of the topographic feature on the Earth. In this article, cross-section profiles generated from the beams of ICESat-2 ground-tracks acquired over sand dunes of the Thar Desert region were analysed for detecting the geomorphological parameters. Observations from the cross-section profiles have resulted in giving unprecedented details about the shapes and morphological settings of various types of sand dunes like barchanoids, parabolic, longitudinal, and transverse dunes. Morphological parameters of sand dunes like the length of the stoss slope, crest height, slip face details, inter-arms spacing, height of the trailing arms, length of the depositional lobes, and sinuosity of the recurring crest lines were retrieved with ease from the Level-2A data product namely ATL03 of ICESat-2/ATLAS.

Synergetic Use of Satellite Image and DEM for Identification of Landforms in a Ridge-Valley Topography

Multispectral satellite image and digital elevation model (DEM) have emerged as the primary data sources for the mapping of landforms. They offer an opportunity to gain a fresh insight into the geomorphology of an area through landform identification using spatial, temporal, spectral and radiometric resolution of satellite images, and through the synergetic approach using DEM derivatives such as profile, slope, hill shading, contour pattern and 3-D flythrough visuals. This paper demonstrates an integrated study of Resourcesat-1 LISS III (23.5 m) image with ASTER DEM (30 m) for the identification of landforms in a rugged topography where conventional field based methods are difficult to adopt due to terrain inaccessibility. Satellite image aided by 3-D visualisation helped in accurate identification of landforms such as hogback, cuesta, plateau top and intermontane valley. The results showed that although denudational processes are active in this area, structural imprints of the strata that is mostly of proterozoic era, are still preserved.

Application of remote sensing for sustainable agriculture and forest management

Global warming resulting from greenhouse effect has threatened to the sustainability of agriculture,forest and land surface globally.A significant rise in extreme weather events has been observed worldwide,particularly in developing countries[1].In the changing climatic era,the economic importance of the remote sensing and geographical information system(GIS)to monitoring forests and agricultural resources is imperative to the development of agro-ecosystem services and the products responding to user needs[2].The applications of the remote sensing for forestry and agriculture,including the estimation of the land surface,biophysical variables,forest vegetation’s,methods for mapping land cover,primary soil properties,the estimation of the forest cover area,and the contribution of remote sensing for crop and water monitoring to manage adverse conditions.

Assessment of spatial and temporal dynamics of tropical forest cover： A case study in Malkangiri district of Orissa, India

Tropical forests have been recognized as having global conservation importance. However,they are being rapidly destroyed in many regions of the world. Regular monitoring of forests is necessary for an adaptive management approach and the successful implementation of ecosystem management. The present study analyses the temporal changes in forest ecosystem structure in tribal dominated Malkangiri district of Orissa,India,during 1973-2004 period based on digitized forest cover maps using geographic information system （GIS） and interpretation of satellite data. Three satellite images Landsat MSS （1973）,Landsat TM （1990） and IRS P6 LISS III （2004） were used to determine changes. Six land cover types were delineated which includes dense forest,open forest,scrub land,agriculture,barren land and water body. Different forest types were also demarcated within forest class for better understanding the degradation pattern in each forest types. The results showed that there was a net decrease of 475.7 km2 forest cover （rate of deforestation = 2.34） from 1973 to 1990 and 402.3 km2 （rate of deforestation = 2.27） from 1990 to 2004. Forest cover has changed over time depending on a few factors such as large-scale deforestation,shifting cultivation,dam and road construction,unregulated management actions,and social pressure. A significant increase of 1222.8 km2 agriculture area （1973-2004） clearly indicated the conversion of forest cover to agricultural land. These alterations had resulted in significant environmental consequences,including decline in forest cover,soil erosion,and loss of biodiversity. There is an urgent need for rational management of the remaining forest for it to be able to survive beyond next decades. Particular attention must be paid to tropical forests,which are rapidly being deforested.

Optimization of Threshold Ranges for Rapid Flood Inundation Mapping by Evaluating Backscatter Profiles of High Incidence Angle SAR Images

Rapid satellite-based flood inundation mapping and delivery of flood inundation maps during a flood event can provide crucial information for planners and decision makers to prioritize relief and rescue operations. The present study is undertaken to optimize the threshold ranges for the classification of flood water in Synthetic Aperture Radar(SAR) images(of 20° to 49° incidence angles) for quick flood inundation mapping and response during flood disasters. This is done through assessing the signature of flood water in Horizontal transmit and Horizontal received(HH), Horizontal transmit and Vertical received(HV), Vertical transmit and Horizontal received(VH), and Vertical transmit and Vertical received(VV) polarization radar data. The mean backscattering signature profiles of various water bodies were analyzed to discriminate flood water from other water bodies. The study shows that there is better demarcation of land-water surface in HH polarization. VV polarization has the potential to identify partially submerged features, which can be useful in flood damage assessments. The backscatter of flood water in HV and VH is the same and both HV and VH polarizations are adequate for the mapping of flood water. At near range to far range,-8 to-12 d B,-15 to-24 d B, and-6 to-15 d B can be used as optimum ranges for the classification of flood water in HH, HV, and VV polarizations. These optimum threshold ranges can be applied to the automation of flood mapping using SAR images in near-real time, where much time was often spent on finding the thresholds in order to produce flood inundation maps in a short time from the onset of flood disasters and deliver such maps to the concerned agencies.

Segmentation and classification of high spatial resolution images based on Hölder exponents and variance

Pixel-based or texture-based classification technique individually does not yield an appropriate result in classifying the high spatial resolution remote sensing imagery since it comprises textured and non-textured regions.In this study,Hölder exponents(HE)and variance(VAR)are used together to transform the image for measuring texture.A threshold is derived to segment the transformed image into textured and non-textured regions.Subsequently,the original image is extracted into textured and non-textured regions using this segmented image mask.Afterward,extracted textured region is classified using ISODATA classification algorithm considering HE,VAR,and intensity values of individual pixel of textured region.And extracted non-textured region of the image is classified using ISODATA classification algorithm.In case of non-textured region,HE and VAR value of individual pixel is not considered for classification for significant textural variation is not found among different classes.Consequently,the classified outputs of non-textured and textured regions that are generated independently are merged together to get the final classified image.IKONOS 1 m PAN images are classified using the proposed algorithm,and the classification accuracy is more than 88%.