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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.

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.

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.