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The coastal flood during the Tauktae cyclone, 2021, at Chellanam coast, Kerala, India, has invited wide attention as the wave overtopping severely affected coastal properties and livelihood. We used a combination of WAVEWATCHIII and XBeach to study the coastal inundation during high waves. The effect of low-frequency waves and rise in the coastal water level due to wave setup caused the inundation at Chellanam, even during low tide with negligible surge height. Wave setup raised the water level at the coast with steep slopes to more than 0.6 m and peaked during low tide, facilitating wave breaking at the nearshore region. The coastal regions adjacent to these steep slopes were subjected to severe inundation. The combined effect of long and short waves over wave setup formed extreme wave runup that flooded inland areas. At gently sloping beaches, the longwave component dominated and overtopped the seawalls and damaged households along the shoreline. The study emphasizes the importance of longwave and wave setup and its interaction with nearshore bathymetry during the high wave. The present study shall lead to the development of a coastal inundation prediction system for the low-lying hot spots using the combination of WAVEWATCHIII and XBeach models.

Cyclone-induced coastal inundation along the east coast of India is simulated using the state-of-the-art advanced circulation model coupled with Simulating Waves Nearshore model. Bathymetry of the computational domain is prepared by integrating digital coastal bathymetric chart with global digital elevation model (DEM) ETOPO-2. Topography of the coastal land region is created by merging data from the fine resolution airborne DEM with that from the Indian satellite CARTOSAT-2 derived CARTO2 DEM and Shuttle Radar Topography Mission DEM. The major shortcomings of coastal inundation simulation due to inaccurate representation of intricate networks of river, creeks and lagoons have been overcome by careful description of the critical coastal wetland features influencing the landward inundation of storm surge in the present work. Experimental simulation of coastal inundation is carried out for the Phailin (2013) and Hudhud (2014) cyclones. The computed surge residual for Phailin and Hudhud are compared with available tide-gauge observations, which have resulted in root mean square error of 0.06 m and 0.11 m, respectively. The simulations show realistic coastal inundations, which are compared with the Indian meteorological department records. The model set up is then used to generate forecast of coastal inundation due to Titli (2018) cyclone with a lead period of 48 h. The Weather Research and Forecasting model forecast winds at 5 km spatial resolution are used as the primary forcing parameter. The coastal inundation forecast due to Titli cyclone is validated by assessing the spatial coherency of simulated inundation with the water logged area demarcated from synthetic-aperture radar image taken from Canadian satellite RADARSAT-2. The results clearly indicate acceptability of the model performance with a potential to predict the coastal inundation during a cyclone for operational early warning.

Coastal areas are dynamic transition zones between land and sea. Their vulnerability is increasing due to human interventions with the natural processes. In the present study, we focused on coastal vulnerability assessment along Karnataka, south-west coast of India to identify the erosion-prone areas by integrating thematic datasets such as shoreline dynamics, land-use/land-cover, geomorphology, geology, elevation and bathymetry using remote sensing and GIS techniques. The 304 km coastline of Karnataka is divided into fourteen littoral cells, and each into a number of transects at uniform intervals. In this coastal stretch, ~ 265 km (87%) is vulnerable to erosion of which ~ 179.5 km (59%) is undergoing erosion at various magnitudes. Littoral cells in the south are subjected to high erosion and vulnerability, and the areas having human interventions are the most erosion-prone. This approach provides valuable information on the degree of potential vulnerability risk which serves as a guide to develop adaptation measures by the coastal zone management authority.

Oil spill detection techniques using various decomposition algorithms have been studied by researchers and are still evolving. In this paper, two oil spill detection approaches based on Polarimetric decomposition are studied and a combination of useful features is used for efficient oil spill detection and differentiation. The first approach is based on coherent decomposition algorithm i.e. Pauli Decomposition and the second is based on the incoherent decomposition algorithm i.e. H/A/\(\alpha \) decomposition. The H/A/\(\alpha \) decomposition detects the different types of weathered oil spill but fails to discriminate the oil spill from look alike. Pauli decomposition detects and discriminates oil spill from the look-alike but fails to discriminate the type of oil spill. An improved technique is proposed to detect and characterize the type of weathered oil using the combination of the features of H/A/\(\alpha \) and Pauli decomposition. The proposed approach is implemented and validated using the L band UAVSAR data acquired from the Deepwater Horizon Oil spill at Gulf of Mexico in June 2010. The accuracy analysis of the proposed approach using the SVM classification shows that the proposed combination not only detects the oil spill patches but also distinguishes the type of weathered oil with higher accuracy as compared to individual approaches.

The coastal zone of the Gulf of Kachchh (GoK) is well known for their large tidal range and a vast expanse of intertidal mudflats. Since monitoring of the intertidal mudflats with field observations is not feasible, the paper presents the utilization of microwave remote sensing technique with RISAT-1 dual polarized data for the geomorphological studies of intertidal mudflats. Radar measures backscattering coefficient, which depends on surface roughness parameters, where the surface roughness of intertidal mudflats are functions of sediment ripple height and space between consecutive ripples. Ripple’s height and the distance between ripples (its wavelength) are represented here as root-mean-square height (RMSH) and correlation length (CL) respectively. A semi-empirical model is used to derive RMSH using cross-polarized backscattering coefficient, and CL is calculated using inversion algorithm on a ratio of backscattering coefficient. A surface sediment type map is generated, based on the retrieved ...

Turbidity is one of the important water quality parameters, essentially a proxy to assess eutrophication state in inland coastal systems. In this article, a method of combined near-infrared–shortwa...

Prediction of coastal erosion is a challenging field of research with immense application potential in coastal management plans. In the present study, we simulated coastal morphological changes that occurred under normal and extreme wave conditions during a four-month winter monsoon period along a pocket beach at Yarada near Visakhapatnam city. We used Xbeach, a process-based numerical model to simulate the morphological changes by forcing the model with buoy observed wave parameters. Initial beach topography was generated from DGPS profiles surveyed in November 2014 at regular spatial intervals. Model simulated changes in the beach elevation due to accretion and erosion are validated by DGPS profiles re-surveyed in March 2015. During the four-month period, the net sediment transport at Yarada is generally southerly and becomes strong during high wave activity inducing erosion at the northern sector and accretion at the southern sector. The model has simulated the changes in elevation with a high degree of accuracy for the eroded northern sector, and with some disparity along the accreted southern sector. The swash and nearshore processes responsible for the coastal erosion were found to exist throughout the simulation period and intensified during the high wave condition. The paper highlights the importance of modelling studies for conceptual understanding of the beach response to normal and extreme conditions, and for identifying vulnerable sectors of a beach so that appropriate measures can be taken to prevent coastal erosion and loss of land.

The coastal bathymetry often forms complex and transient morphology, especially along the tide-dominated regions like the Gulf of Khambhat (GoK), west coast of India. The high tidal currents within the gulf relentlessly modify the morphology of the bathymetric features that prompts for a frequently updated bathymetric information. The SAR estimated bathymetry represent updated depth values based on the transient bathymetric features observed from the SAR images. The present study has used ALOS PALSAR-1/2 data to update the depth of shallow water bottom features of GoK. The method involves modeling the gradient in the backscatter coefficient (σ°) modulated by the slope of the bathymetric features with the satellite images. The slope corresponding to the bathymetric feature from hydrographic charts (initial guess bathymetry) and the simulated current velocity forms the input to the model. The bathymetry is updated by varying the slopes of the bathymetric features through iteration until the σ° converges. The method is applied to update the depth of various bathymetric features observed from the SAR images of 2008, 2010, 2014 and 2018. We validated the updated depth from 2018 satellite data using available in situ measurements. The comparison of in situ observation with the hydrographic chart has resulted in RMSE of 3.2 m, while the RMSE have improved to 1.5 m when compared with the SAR updated bathymetry. The temporal dynamics of the bathymetric features are studied using the pair of 2008–10 and 2010-14 SAR images. Analysis has comprehended the morphodynamics of bathymetric features like lateral migration of tidal bars, erosion along the tidal channel periphery, and deposition at the tidal spits. We updated the depth of rapidly changing bathymetric features using the 2018 SAR images. The study shows its potential in operational usage to update the complex nearshore bathymetry from the SAR image.

Over the last few decades, anthropogenic activities have triggered the rate of change in the function of mangrove ecosystems in coastal urban areas. Satellite imagery provides valuable information for mangrove mapping and monitoring. Metrics derived from the linear regression analysis of spectral indices (SIs) derived from satellites are commonly used for change analysis. This study examines the robustness of the widely used SIs derived from Landsat satellite image to distinguish mangroves and non-mangrove features and identify the non-linear changes over mangrove forest using the polynomial trend analysis. Airborne Visible and Infra-Red Imaging Spectrometer Next Generation (AVIRIS-NG) data was resampled to simulate the spectral-response of the Landsat sensor. One-way analysis of variance (ANOVA) and mutual information (MI) were applied to the simulated data to identify the optimal SIs. Based on the statistical analysis, modular mangrove recognition index (MMRI), modified normalized difference water index (MNDWI) and normalized difference built-up index (NDBI) were identified to delineate the mangrove, inundated and built-up region. Finally, time-series profiles of the identified spectral indices were generated using Landsat-8 (L8) and Landsat-7 (L7) data to analyse the change dynamics of the mangrove vegetation from 2002 to 2019. The proposed methodology was applied to study the changes in mangroves in the coastal areas of Kochi (Ernakulam District, Kerala State, India). The temporal-non-linear changes in the mangrove area were identified based on polynomial regression that revealed abrupt decline (42% decrease) in the mangrove area due to large-scale infrastructure development projects. The proposed approach is easy to implement, which enables the frequent monitoring of extensive mangrove forests.

The Indian and French Space Agencies, ISRO and CNES, have conceptualized a space-borne Thermal Infrared Reflectance (TIR) mission, TRISHNA (Thermal infRared Imaging Satellite for High-resolution Natural Resource Assessment). The primary design drivers of TRISHNA are the monitoring of (i) terrestrial water stress and use, and of (ii) coastal and continental water. A suit of four TIR bands and six optical bands is planned. The TIR bands will be centred at 8.6 μm, 9.1 μm, 10.3 μm and 11.5 μm to provide noon-night global observations at 57m nadir resolution over land and coastal regions. The field of view (FOV) is ±34° and the orbit of 761 km altitude was designed to allow 3 sub-cycle acquisitions during the 8-day cycle. The optical bands correspond to blue, green, red, and NIR plus two SWIR bands at 1.38 μm and 1.61 μm. The green, red, NIR and the 1.61 μm SWIR bands will have better radiometry quality than those of AWiFS. ISRO and CNES will develop optical and TIR payloads, respectively. Assessing evapotranspiration and furthermore Gross and Net Primary Productivity (GPP and NPP) will in turn assist in quantifying water use in rainfed and irrigated agriculture, water stress and water use efficiency, with expected applications to agricultural drought and early warning, crop yield prediction, water allocation, implementation of water rights, crop insurance business and agro-advisories to farmers. The other scientific objectives of TRISHNA are also briefly described. TRISHNA instrument will fly aboard a ISRO spacecraft scheduled to be launched from 2024 for a minimum period of 5 years’ mission lifetime.

International audience; The monitoring of the water cycle at the Earth surface which tightly interacts with the climate change processes as well as a number of practical applications (agriculture, soil and water quality assessment, irrigation and water resource management, etc...) requires surface temperature measurements at local scale. Such is the goal of the Indian-French high spatio-temporal TRISHNA mission (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment). The scientific objectives of the mission and research work conducted to consolidate the mission specifications are presented. Progress in modelling of surface fluxes is then discussed. The main specifications of the mission such as the revisit, the spatial resolution, the overpass time, the spectral bands and the orbit are analyzed and justified. The resulting baseline of the mission is given.

Murali, R.M.; Reshma, K.N.; Kumar, S.S.; Balaji, S.A.; Raju, D.M.K., and Ramakrishnan, R., 2020. Spatio-temporal coastal morphological changes of Godavari delta region in the east coast of India. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 626–631. Coconut Creek (Florida), ISSN 0749-0208.Understanding the characteristics of coastal morphology is necessary to analyse the causes of its changes and its consequences. The present study focuses on the satellite-derived and in-situ measurement based changes along the Godavari delta region to estimate the changes of the geomorphologic units. Three years (2017 – 2019) of seasonal observations along the coast of Godavari delta with fifteen beach profiling stations were carried out to estimate the volumetric changes along this coast. Out of these 15 transects, three stations, S2(i), S6(i), and S6(iv), experienced dynamic changes. The station at Odalarevu [S6(ii)], near Vainateyam river mouth, has undergone severe erosion, indicating an overall shoreline change of 670 m. The changes near this river mouth eventually eroded the nearby two stations by 25m3/m and 11m3/m in volume, respectively. Similarly, Chirra Yanam [S2(i)] showed a gradual accretion of 46m3/m in the same period. Shoreline change study from 1973-2019 using satellite images show erosion of 300 m near Vainateyam river mouth, 140m in the intermediate station, and 50 m in Odalarevu beach. An erosion of 70m is also noted in Chirra Yanam. These results are attributed to the combination of anthropogenic and the response of this delta region for the regional and global processes. The combination of in-situ and satellite observation gives a better insight into the quantitative estimate of morphological changes and their response behavior.

Maximum Cross Correlation (MCC) technique have been used to derive coastal currents from sequential OCM derived suspended sediment concentration (SSC) maps and the MCC currents are integrated with the Ocean surface current analysis-Real time (OSCAR) ocean currents along the coastal regions of the Southern India. OSCAR currents are mainly depended on the oceanographic measurements from the satellite altimeter which have limitations in the coastal region and in representing the mesoscale properties dominating the coastal ocean. Sequential pair of atmospherically corrected and georeferenced OCM image is used to derive SSC, along the southern coast of India. Maximum Cross-Correlation (MCC) method is applied on the sequential time lapsed images of OCM for matching the suspended sediment dispersion patterns. The MCC technique involves computing of cross-correlation coefficients and identifying correlation peaks. The spatial change of the sediment pattern occurs due to the advection by the currents and the extent of the change corresponds to the speed and direction of the current. From the first image, template window is selected, which is searched in the second image within large region defined as the search window. The movement of the SSC pattern is calculated based on the maximum cross correlation in successive images. Error statistics are calculated between the spatially coherent MCC vectors and OSCAR currents. Based on statistical analysis, an error threshold of 30 degree for current direction and 0.06m/sec for current speed was given to MCC currents and MCC currents beyond the error limit are rejected, while the MCC current within the error limit is merged with the OSCAR ocean currents. The merging of MCC currents has spatially enhanced the OSCAR currents. MCC method is significant in calculating the coastal currents and its mesoscale properties, which dominate the coastal surface flow field and can be used in supplementing the OSCAR ocean currents. Merging of both the currents, have effectively resulted in preparing contiguous maps of coastal ocean currents.

Complexity of the temporal sediment variability within the Gulf of Kachchh is resolved into major modes of variations using empirical orthogonal function (EOF) analysis on sequential OCM derived suspended sediment concentration (SSC) images during spring intermonsoon period of 2011. Variance accounted collectively by the first four modes is around 80% and the principal component (PC) of each mode is correlated with different hydro-meteorological forces influencing the hydrodynamics of the gulf. Hydro-meteorological forces are classified in the order of precedence of influencing the sediment dynamics of the region. PC1 contributing 50% of the total variance is correlated with average of the simulated current velocities between consecutive images, indicating the variation of tides from neap-spring ranges ( ${\rm{r}^{2}} = 0.86$ ). The ocean state parameters like tidal height, wind speed, and current speed at the time of OCM pass contribute to around 20%, 6%, and 4%, respectively, to the total variability of the temporal dynamics of SSC. The interchange of tides from neap and spring is the major contributor toward the temporal variability of sediment concentration within the gulf, where the sediments are subjected to deposition and resuspension. The gulf being highly tide dominated, the suspended sediment variability also reverberates with the variability of tidal characteristics and subtly with the wind conditions.

Assessing the preparedness of the coastal region to the anticipated sea level rise is a major task among the coastal management authority, where identification of relative vulnerable of coast is a prime concern. The present research work has utilized Remote Sensing and GIS techniques for assessing vulnerability of the Gujarat coast due to predicted sea level rise. Synergetic use of satellite remote sensing, numerical simulations and filed observations has been done to generate coastal thematic information followed by validation. Coastal vulnerability assessment of the entire Gujarat coast on 1:50,000 scale were carried out considering five physical parameters. They are; coastal geomorphology, coastal slope, shoreline change rate, mean spring tidal range, and significant wave height. The rank values of all the five physical variables are integrated through additive method to compute a Coastal Vulnerability Index (CVI) where the relative ranking is assigned to various coastal segments based on the vulnerability level. The CVI values categorize Gujarat coast into four risk level classes. The results show that 785 km (45.67 %) of the Gujarat coast is under high to very high risk category and 934 km (54.33 %) of the Gujarat coast is under Moderate to Low risk category due to an anticipated rise in sea level. The coastal regions under very high risk category are along northwestern parts of the Gulf of Khambhat, the northernmost parts of the Gulf of Kachchh and western parts of the Kachchh coast. The southern parts of the Saurashtra coast and southwestern parts of the Kachchh coast is under the low risk category along with isolated segments along the eastern parts of the Gulf of Khambhat. The approach developed for assessing vulnerability of the coast to predicted sea level rise using remote sensing and GIS techniques can be used effectively by coastal management authorities and decision makers to devise better coastal zone management plans as well as to ensure efficient mitigation measures to minimize the losses due to possible impact of predicted sea level rise through which appropriate measures to protect life, property and environment can be timely planned.

Sharpness and contrast have great impact on perceived quality of an image. This paper focuses on sharpness and contrast measures to evaluate quality of Thermal Infrared (TIR1) channel of Indian National Satellite-3D (INSAT-3D) without using any reference image. Most of the sharpness metrics can scarcely manage to discern image quality degradation against high frequency behavior due to noise. Six Image Quality Measures (IQMs) are employed to study their behavior in terms of blur, noise and intensity changes simultaneously. Results show that (1) change in value of Measure Of Enhancement By Entropy (EMEE) is more discernible with change in contrast of an INSAT-3D image as compared to other measures and (2) Second Derivative Like Measure Of Enhancement (SDME) has a potential to distinguish high frequency content due to sharpness arisen due to un estimated noise up to some remarkable level in case of TIR1 INSAT-3D satellite images. Performance comparison of six measures against blur, noise, contrast and sharpness changes is presented.

Though georeferencing satellite images is a solved industry problem long back, every satellite sensor data is unique to process due to its proprietary information and expected geometric accuracies usually demands use of reference data. Industry is moving towards unifying frameworks, sensor models where the users have to just plug-in details. The growing archive is inevitable and therefore (re) processing requirements have to handle huge image archives with state of art georeferencing standards automatically. Use of reference data is necessary to georeference the data sets to better accuracies beyond systematic corrections. This paper presents an approach for such tedious task of automatically reliably georeference huge image archives involving reference image/control points use in georeferencing Indian Remote Sensing satellite images.

The eustatic sea level rise due to global warming is predicted to be about 26–82 cm by the 2100 (5th IPCC 2013), which necessitates identification and protection of vulnerable sections of coasts. The majority of formerly developed coastal vulnerability/sensitivity indices acknowledge that the addition of socioeconomic variables would assist to identify vulnerable areas. The present study therefore is an attempt to develop an integrated coastal vulnerability index (ICVI) for the South Gujarat coast using both physical and socioeconomic variables. Five physical variables, namely coastal slope, Coastal landforms/features, Shoreline change rate, Mean spring tidal range, and Significant wave height, are used for the calculation of the physical vulnerability index (PVI), whereas four variables such as population density of adjacent coastal villages, land use/land cover, proximity to road network and settlement are used to assess the social vulnerability index (SVI). The weights for PVI and SVI are calculated using the analytical hierarchical process (AHP) method, as an improvement to the existing methodologies for vulnerability assessment. Based on the weights and scores derived using AHP, vulnerability maps are prepared to demarcate areas with very low, low, medium, high, and very high risk. The PVI and SVI values are integrated to compute the ICVI. According to the criteria of coastal vulnerability, as defined in this study, the coastal segment with low to very low risk of ICVI rating is 52.51 % of the study area, whereas 13.47 % of the coastal stretch falls under the high- to very high-risk category.

Indian geostationary satellite Kalpana-1 (K1) offers a potential to capture the diurnal cycle of land surface temperature (LST) through thermal infrared channel (10.5–12.5 μm) observations of the Very High Resolution Radiometer (VHRR) sensor. A study was carried out to retrieve LST by adapting a generalized single-channel (SC) algorithm (Jimenez-Munoz and Sobrino, 2003) for the VHRR sensor over India. The basis of SC algorithm depends on the concept of Atmospheric Functions (AFs) that are dependent on transmissivity, upwelling and downwelling radiances of the atmosphere. In the present study AFs were computed for the VHRR sensor through the MODTRAN simulations based upon varying atmospheric and surface inputs. The AFs were fitted with the atmospheric columnar water vapour content and a set of coefficients was derived for LST retrieval. The K1-LST derived with the SC algorithm was validated with (a) in situ measurements at two sites located in western parts of India and (b) the MODIS LST products. Comparison of K1-LST with the in situ measurements demonstrated that SC algorithm was successful in capturing the prominent diurnal variations of 283–332 K in the LST at desert and agriculture experimental sites with a rmse of 1.6 K and 2.7 K, respectively. Inter comparison of K1-LST and MODIS LST showed a reasonable agreement between these two retrievals up to LST of 300 K, however a cold bias up to 7.9 K was observed in MODIS LST for higher LST values (310–330 K) over the hot desert region.

An attempt has been made to use synthetic aperture radar (SAR) data for detection and monitoring of offshore oil seeps in the eastern offshore areas of the Krishna–Godavari Basin, which has been supplemented and correlated with collateral free-air gravity and seismic data. Images of the study area obtained from ENVISAT ASAR image mode were processed and analysed in detail. A number of natural oil seepages were identified and distinguished from pollution and biogenic slicks. These were subsequently studied using different parameters to assign various degrees of confidence. The repetitiveness of the identified seepages was studied and a total of five areas of seep repetitions had been recognized in the study area. The seeps that are repeated in images of different dates are more likely to be of natural origin than others. Simulation and modelling of a particular oil slick arising has been attempted over the Krishna–Godavari offshore using MIKE 21 software.

A spectrally integrated clear-sky and three-layer cloudy-sky models were developed to determine atmospheric transmittances and instantaneous surface insolation. Half-hourly observations at 8 km spatial resolution in optical and thermal infrared bands from an Indian geostationary satellite Kalpana-1 Very High Resolution Radiometer VHRR sensor were used to provide inputs to these models in addition to global 8 day aerosol optical depth and columnar ozone. Sensitivity analysis of the clear-sky model showed a higher influence of aerosol on global insolation, diffuse insolation, and its fraction as compared with water vapour and ozone. The root mean square error RMSE of insolation estimates of the daily integral was found to be 2.05 MJ m−2 ∼11.2% of measured mean with a high correlation coefficient r = 0.93 when compared with in situ measurements during 1 August 2008 to 31 March 2010 over six locations in India. The errors were found to reduce to 7.5% over 3 to 5 day averages. The comparison of annual estimates and equivalent reanalysis fields showed a mean difference of the order of ±1.7 MJ m−2 over the majority of the Indian landmass.

Recent developments in space technology and exponential increase in demand of earth observation data from space have generated a requirement of a data processing environment, where users can discover the data and process, based on their requirements. Grid Services for Earth Observation Image Data Processing (GEOID) is proposed with a motivation to cater to future earth observation applications requirements of digital earth. This paper discusses the overview of the GEOID architecture, its deployment scenario, use-cases and simulation results. Core technologies used for implementation include Grid computing and Service Oriented Architecture. GEOID provides capability to address requirements of applications such as real-time monitoring, time series data processing and processing with user required quality to meet the requirements of end user applications. GEOID allows users to access the archive products or the raw satellite data stream and process their area of interest. Simulations show that appli...

Suspended sediment transport in the Gulf of Kachchh is simulated utilizing the suspended sediment concentration (SSC) derived from Oceansat OCM imagery, as the initial condition in MIKE-21 Mud Transport model. Optimization of the model mud parameters, like settling velocity and critical shear stress for erosion are realized with respect to the sediment size distribution and the bottom bed materials observed in the Gulf. Simulated SSCs are compared with alternate OCM derived SSC. The results are observed to be impetus where the model is able to generate the spatial dynamics of the sediment concentrations. Sediment dynamics like deposition, erosion and dispersion are explained with the simulated tidal currents and OCM derived sediment concentrations. Tidal range is observed as the important physical factor controlling the deposition and resuspension of sediments within the Gulf. From the simulation studies; maximum residual current velocities, tidal fronts and high turbulent zones are found to characterise the islands and shoals within the Gulf, which results in high sediment concentrations in those regions. Remarkable variability in the bathymetry of the Gulf, different bed materials and varying tidal conditions induces several circulation patterns and turbulence creating the unique suspended sediment concentration pattern in the Gulf.

The magnitude of precipitation plays an important role in the yield and supply of terrigenous matter into the sea through fluvial supply. The influence of climate on the influx of total suspended matter (TSM) into the continental margin of the SE Arabian Sea has been evaluated from subweekly synoptic variations in TSM and its advection rates, currents and winds during the southwest monsoon (SWM) and postmonsoon season. Our study endorses the high influx of TSM during the SWM (>82 mg l−1; advection 26–110 mg m−2 s−1), albeit that most of it is sequestered into the shallow coastal region. Over the mid-outer shelf, there is uniformly low TSM (12–24 mg l−1) and a weak TSM advection (9–4 mg m−2 s−1) throughout the year. This trend is persistent also in areas having upwelling-induced high marine productivity. We therefore surmise that higher fluvial influx and primary production during the SWM do not necessarily enhance the supply of particulate matter into the deeper offshore regions of the SE Arabian Sea. We ...

Seamless mosaic generation is a challenging issue in the field of image processing. Multi date and multi time images vary in terms of radiometry as well as geometry of viewing, this makes the task at hand more intricate. These variations are taken care of with the help of radiometrie normalization and geo-correction. In this paper an approach is proposed for mosaicing geo corrected images acquired on different days. This approach uses inter scene normalization of the images and utilizes a mathematical morphological operator to find out the best possible seam line from the overlap area. Overall visual quality of the mosaiced product is analyzed to verify the algorithm.

Remote sensing data products need to meet stringent geodetic and geometric accuracy specifications irrespective of intended user applications. Georeferencing is the basic processing step towards achieving this goal. Having known the imaging geometry and mechanism, the mathematical models built with the use of orbit and attitude information of the spacecraft can correct the remote sensing data for its geometric degradations only up to system level accuracy (IRS-P6 DP Team, 2000). The uncertainties in the orbit and attitude information will not allow the geometric correction model to generate products of accuracy that can meet user requirements unless Ground Control Points (GCP) are used as reference geo-location landmarks. IRS-P6 data processing team has been entrusted with developing a software system to generate data products that will have desired geodetic and geometric accuracies with known limitations. The intended software system is called the Value Added Data Products System (VADS). Precision corrected, Template Registered, Merged and Ortho Rectified products are the value added products planned with VADS.

This paper discusses algorithms for compressing floating point data which is encountered while performing resampling algorithm for rectifying geometric distortions on the images transmitted by the remote sensing satellite on a distributed computing environment. Two efficient algorithms for encoding and decoding floating point data compression, (i) 3-byte packing and (ii) 3-byte + 2-bit packing have been proposed. Both algorithms are tested to execute resampling algorithm on radiometrically corrected for IRS- LISS-III 4 bands data on a distributed system. First algorithm compresses 4-byte floating point data to 3-byte obtaining 25% compression while later one compresses 4-byte floating point data to 3-byte + 2-bit achieving about 18.75% compression. The computational time is reduced by 22% as compared to the distributed resampling algorithms without compression. Further it is found that in lossy compression algorithm only 220 pixels out of 37.4 MB pixels have utmost one Gray count difference, which will not pose any issues for digital classification or any other methods that will be employed in the corrected image by Image Analysts.

Panchromatic data of pixel resolution 5.8 m obtained from IRS-1C and IRS-1D satellites proved to be very useful for mapping purposes. One of the popular data product is the 70 km swath mosaic which is covered by a combination of 3 CCD line sensors, each with 4096 pixels. Each CCD-line sensor with different imaging times causes geometric problems of mosaicing three strips data together. In this paper, we propose the details of the design elements of system that caters to the need for accurate and automatic multi strip image registration without any second resampling of the data. The systematic geometric correction grid mapping is improved to facilitate accurate mosaicing by automatic image registration task that makes use of the overlap data within image strips and image registration is achieved up to sub-pixel level.

In this paper, we have described a model to parallelize the resampling routine, which is used in the geometric correction of data provided by remote sensing satellites. Our model is a typical master-slave model consisting of N machines termed as hosts out of which one is designated as the master. The input image data resides on the master. Processing of the input image data is done in parallel on the N machines. Issues related to load-balancing and various error conditions that may occur during execution like one of the machines going down have been studied and are incorporated in the model. It also provides the flexibility to add or delete the hosts during the execution of the resampling routine. The serial version of this routine involves huge amount of computations and takes substantial amount of time even for an image of 473 MB. We have implemented our model with the help of PVM which is most often used in distributed computing environment. Our approach has been tested for geometric correction on LISS-III 4 band data of size 473 MB. It is found that if one uses 2, 3 or 4 hosts the overall execution time is reduced by 33%, 42% and 49%, respectively.

Remote sensing satellites use onboard compression techniques to overcome the limited bandwidth and increasing data volume requirements of images. On-board compression using Differential Pulse Code Modulation (DPCM) is implemented for Resourcesat-2, LISS-3 and LISS-4 sensors. Implemented DPCM is a lossy compression and hence renders artifacts, when images are decompressed on ground. In this paper a technique for restoration of DPCM artifacts is proposed and its performance is evaluated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). Proposed technique is used as part of operational data products generation software and sample results are shown.

Choosing a suitable cost function for satellite image registration is always a challenging task. Satellite images poses a unique challenge in image registration field as images may consist of noise, clouds, highly undulating terrains etc. Registration of Satellite images lie in the category of multi temporal as well as sometimes multi modality cases. Till today there is no generalized solution suitable for all kind of cases. Choice of cost function plays an important role in image registration task. Different types of cost functions are available like intensity difference, correlation, entropy based methods etc. Here detailed exercise is done on existing similarity metrics, with a final conclusion that mutual information based similarity metric covers most of the cases with satisfactory results.

Satellite Image fusion generates single hybrid image from a collection of input satellite images and helps us to extract maximum information from the remotely sensed datasets to achieve optimal spatial and spectral resolution. The critical steps of image fusion framework are co-registration of Synthetic Aperture Radar(SAR) data with corresponding optical scene, enhance the images for visual clarity and then merge the multi sensor data with a standard fusion technique. The image fusion system should perform all these steps in an automatic manner for providing ease to the user. The primary attention of this work is to examine the improvement that can be obtained by fusion of low resolution multi spectral data obtained from optical Resourcesat-2 platform (LISS-4MX/LISS-III/AWIFS Sensor having 5m/24m/56m spatial resolution) with high resolution RISAT-1 (Fine Resolution STRIPMAP (FRS-1)/Medium Resolution SCANSAR(MRS) mode data having 3m/18m spatial resolution) using SAR-Optical image fusion system discussed above. This integration of optical and SAR images from Indian Remote Sensing satellites facilitates better visual and automatic image interpretation. The Maximum Likelihood algorithm is used for classification of fused image and Resourcesat-2 multispectral data. The quality improvement of the fused product can be observed by comparing the classification accuracies of merged data with original multispectral data of the same region.

Automatic satellite image registration is a challenging task of overlaying two images for geometric conformity aligning common features by establishing a transformation model using distinguishable feature points collected simultaneously in both the images in a completely un assisted manner. Remote sensed images capture terrain features in a natural condition subjected to seasonal changes, sun illumination conditions, and cloud presence. The critical steps in image registration are collection of feature points and estimating a spatial transformation especially when outliers are present besides feature matching and resampling the slave image to the master image geometry. In this paper, the details and merit of employing automatic Harris corner detection and building a transformation model using Random Sample Consensus (RANSAC) algorithm is brought out while registering a pair of LISS-3 or AWIFS images from Indian Remote Sensing Satellite (IRS) platform. Potential available with this approach for performing large scale image registration tasks such as time series processing are highlighted.

Image registration of satellite imagery is challenging task as the image pair may have been captured by different sensors, view angles or at different times. Conventional satellite image registration tasks are achieved by feature based techniques which are to be identified and matched before estimating transforms and resample the moving or slave image to a fixed or master image. However, automatic image registration is very important requirement for voluminous data sets. Even if feature selection made automatic, even distribution and the density of feature points is always not ensured. A good alternative is to switch over to intensity based image registration techniques which are somewhat image content independent. We bring out details on an intensity based multi temporal satellite image registration task with metric, transform and optimization components based method. An optimization technique employed to find optimal transform parameters by maximizing the chosen similarity measure criteria, provides a robust image registration framework. Adaptive Stochastic Gradient Descent optimization (ASGD) for satellite image registration is a promising new technique, details of which are reported here.

Machine learning is a scientific computing discipline to automatically learn to recognize complex patterns and make intelligent decisions based on the set of observed examples (training data). Support Vector Machine (SVM) is a supervised machine learning method used for classification. An SVM kernel based algorithm builds a model for transforming a low dimension feature space into high dimension feature space to find the maximum margin between the classes. In the field of geospatial data processing, there is a high degree of interest to find an optimal image classifier technique. Many image classification methods such as maximum likelihood, K-Nearest are being used for determining crop patterns, land use and mining other useful geospatial information. But SVM is now considered to be one of the powerful kernel based classifier that can be adopted for resolving classification problems. The objective of the study is to use SVM technique for classifying multi spectral satellite image dataset and compare the overall accuracy with the conventional image classification method. LISS-3 and AWIFS sensors data from Resourcesat-1, Indian Remote Sensing (IRS) platform were used for this analysis. In this study, some of the open source tools were used to find out whether SVM can be a potential classification technique for high performance satellite image classification.

IRS-P4(Oceansat-1) is the first in the series of Indian operational ocean remote sensing satellites. The payloads of this mission are tailored for making measurements of the physical and biological oceanographic parameters. IRS-P4 contains two sensors, namely Ocean Colour Monitor (OCM) and MSMR. OCM has 8-bands, in which first band, intermittently shows vertical stripings in the raw data. An algorithm based on Discrete Cosine Transform(DCT), is proposed here to model these vertical stripings. Some of the basis function of the DCT has very high degree of similarity with the vertical strips. This work elliminates the vertical strips present in the image by modelling the appropriate coefficients of the basis functions. Results of our work shows that the approach followed by us is effective in removing the vertical strips. To avoid the computational overheads of application of this algorithm on large image, we have divided the image into smaller blocks, the issues of block based processing in frequency domain has been taken care by handling the boundary conditions by means of smoothening.

Iron-ore deposits of the Kudremukh region in Karnataka State, India, were discovered at the beginning of the present century. These deposits have only recently begun to be exploited by the Kudremukh Iron Ore Company. As a result the land cover, particularly the grassland areas, are becoming disturbed. This paper is the outcome of a joint study undertaken by three central and state government agencies in India for monitoring the ecological changes in the above region. Multitemporal LANDSAT MSS data together with aerial CIR photographs and ground data were used for the study. The study was mainly addressed to the mapping of land-cover changes, which is one of the most important indicators of ecological monitoring.