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Urban floods are more concerned in recent days due to their substantial effect in loss of human lives and properties. Due to climate change, urban floods are frequently observed in many parts of the world. Flood events in Chennai city are a frequent scenario due to rapid increase in the density of population. Adyar river watershed and surrounding urban cover are focused in the present study. The present study aims at mapping flooded region using Sentinel 1A datasets over Adyar watershed. Series of Sentinel 1A image is collected before, during and after floods for mapping the extent of flood and mapping risk zones in Adyar watershed. Methodologies such as ISODATA Technique, Multi-Temporal Analysis, Thresholding Method, PCA and ICA Analysis and Grey Level Co-Occurrence Matrix are adopted for the extraction of flooded extent from the SAR datasets. Analysis performed over the Adyar watershed provided promising results in the extraction of flooded extent with Thresholding Method and Grey Level Co-Occurrence Matrix being the dominant of all the methods. Though higher accuracy is obtained in the extraction of flooded extent, limitation of layover, foreshortening and shadow is experienced in the built up region for the extraction of flooded pixels. [ABSTRACT FROM AUTHOR]

Sequences of multi-temporal synthetic aperture radar (SAR) images are routinely used for land-use land-change (LULC) applications, allowing the retrieval of accurate and up-to-date information on the state of the Earth's surface and its temporal variations. Change detection (CD) methods that rely on the exploitation of SAR data are, generally, made of three distinctive steps: (1) pre-processing of the SAR images; (2) comparison of the pairs of SAR images; and (3) the automatic extraction of the "changed areas", employing proper thresholding algorithms. Within this general framework, the reduction in speckle noise effects, which can be obtained by applying spatial multi-looking operations and ad hoc noise filters, is fundamental for the better detecting and classifying of changed regions. Usually, speckle noise filters are singularly and independently applied to every SAR image without the consideration of their inherent temporal relationships. In particular, most use local (spatial) approaches based on determining and averaging SAR backscattered signals related to neighboring SAR pixels. In this work, conversely, we explore the potential of a joint 3D space-time non-local mean filter (NLMF), which relies on the discrimination of similar features in a block of non-local SAR pixels extracted from the same or different SAR images. The theory behind non-local-mean filters is, first, shortly revised. Then, the developed space-time NLMF is applied to a real test case for the purposes of identifying flooded zones due to the massive inundations that hit the Kerala region, India, during the summer of 2018. To this aim, a set of 18 descending SAR images collected from the European (EU) Copernicus Sentinel-1 (S-1) sensor was exploited. The performance of the developed NLMF has also been assessed. It is worth remarking that the proposed method can be applied for the purposes of analyzing a heterogenous set of natural and/or artificial disastrous conditions. Further, it can also be helpful during the pre-processing stages of the sequences of SAR images for the purposes of CD applications. [ABSTRACT FROM AUTHOR]

Spatially explicit measurement of Above Ground Biomass (AGB) is crucial for the quantification of forest carbon stock and fluxes. To achieve this, an integration of Optical and Synthetic Aperture Radar (SAR) satellite datasets could provide an accurate estimation of forest biomass. This will also help in removing the uncertainties associated with the single sensor-based estimation approaches. Therefore, the present study attempts to integrate Sentinel-2 optical data with Sentinel-1 SAR dataset to estimate AGB in the Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. In this study, two non-parametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions—linear, sigmoidal, radial and polynomial and Random Forest (RF) were employed for the prediction of AGB using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA). Ground based AGB was estimated through allometric equation at 35 sampling sites with the help of tree height and Diameter at Breast's Height (DBH). Standalone collinearity analysis among different parameters resulted in poor correlation of AGB with VH (r = 0.05) and IA (r = 0.015), whereas a significantly good correlation with NDVI (r = 0.80) and VV (r = 0.74) were observed. Inclusion of NDVI with VV and VH together also resulted in a better correlation (r = 0.85) than other combinations. The SVM with linear kernel utilizing parametric the combinations of VV + VH + NDVI and VV + VH + NDVI + IA were found to be best performing on the basis of evaluation metrics. The outcome of this study highlighted the significance of machine learning techniques and synergistic use of different remote sensing data for an improved AGB quantification in tropical forests. [ABSTRACT FROM AUTHOR]

The demarcation of linear geomorphological features due to fracture systems, underlying or exposed faults, drainage lines etc. from satellite images has been one of the most widely used applications in geological studies. Synthetic Aperture Radar (SAR) data has been recognized as an effective tool for lineament extraction due to its unique sensitivity to subtle topography, soil moisture and texture. However, the typical SAR sensor parameters such as frequency, polarization and look direction plays a vital role in enhancing the performance for a specific application. Therefore, the study aims to analyse the multi-frequency (C- and L-band), multi-polarization (like and cross) and multi-look direction SAR data for geological lineaments extraction. In this paper, Sentinel-1 and Advanced Land Observation Satellite (ALOS)-2 Phased Array type L-band Synthetic Aperture Radar (PALSAR)-2 datasets acquired over a part of the Indo-Burma ranges of Manipur region (northeastern part of India) are considered for analysis. The lineaments extracted from SAR datasets are compared with reference to lineaments provided by the Geological Survey of India (GSI) through their portal. The study observes that the like-polarization C-band SAR data provide optimal results for this application. It is also observed that the integration of lineaments extracted from multi-look SAR data can significantly improve the performance than that derived from single-look data. Overall, the study also focuses on the spatial distribution of lineaments with respect to the local structural regime for a better understanding of the regional tectonic setup. [ABSTRACT FROM AUTHOR]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]

Strong bidirectional internal solitary waves (ISWs) generate from a shallow channel between Car Nicobar and Chowra Islands of Nicobar Islands, India, and propagate toward the Andaman Sea (eastward) and Bay of Bengal (westward). Batti Malv Island separates this shallow channel into two ridges, north of Batti Malv (NBM) and south of Batti Malv (SBM). First, this study identifies the prominent mode-1 and mode-2 ISWs emerging from NBM and SBM using synthetic aperture radar images and then explores their generation mechanism(s) using a nonlinear, unstructured, and nonhydrostatic model, SUNTANS. During spring tide, flow over NBM is supercritical with respect to mode-1 internal wave. Model simulations reveal that mode-1 ISWs are generated at NBM by a "lee wave mechanism" and propagate both in the east and west directions depending on the tidal phases. However, the flow over SBM is subcritical with respect to mode-1 internal wave. The bidirectional propagating mode-1 ISWs evolve from a long-wave disturbance induced by "upstream influence." But, during spring tide, with an increased tidal flow over SBM, it is observed that the westward propagating ISWs are formed by a dispersed hydraulic jump observed over the ridge. Moreover, the bidirectional mode-2 waves from SBM are generated by a lee wave mechanism. An energy budget comparison reveals that the region surrounding NBM is efficient in radiating low-mode baroclinic energy (0.98 GW), while SBM is highly efficient in converting barotropic to baroclinic energy (4.1 GW). [ABSTRACT FROM AUTHOR]

An optimal model was developed for accounting forest carbon stock from synergistic use of optical data from Landsat TM and synthetic aperture radar (SAR) data from COSMO-Skymed (X-band), Radarsat-2 (C-band) and ALOS PALSAR (L-band) sensors over a tropical deciduous heterogeneous forest of India. The best-fit integrated multiple linear regression model had a model accuracy of 83%, r2 = 0.96, root mean square error = 10.02 Mg/ha and Willmott's index of agreement of 0.98. The model further validated using chi-squared and t-test. Results of models for calculating the aboveground biomass (AGB) were converted to C and CO2 using conversion factors. Average AGB, C and CO2 were 70.5, 35.26 and 130.89 Mg/ha, respectively. The synergistic use of optical and multi-frequency SAR data enhanced the AGB saturation threshold to about 150 Mg/ha for tropical deciduous mixed forests. Hence, the synergistic use of this data is suggested for large-scale AGB and C estimations for tropical forests. Optical remote sensing sensors are extensively used due to greater data availability despite their poor sensitivity toward forest parameters. In contrast, SAR signals are highly sensitive toward forest biophysical and structural parameters, providing a better alternative. This unique integrated approach provides valuable information regarding the spatial distribution and quantification of forest biomass and carbon. [ABSTRACT FROM AUTHOR]

Brahmaputra is one of the perennial rivers in India which causes floods every year in the north-east state of Assam causing hindrance to normal life and damage to crops. The availability of temporal Remote Sensing (RS) data helps to study the periodical changes caused by flood event and its eventual effect on natural environment. Integrating RS and GIS methods paved a way for effective flood mapping over a large spatial extent which helps to assess the damage accurately for mitigation. In the present study, multitemporal Sentinel-1A data is exploited to assess the 2017 flood situation of Brahmaputra River in Assam state. Five data sets that are taken during flood season and one reference data taken during the non-monsoon season are used to estimate the area inundated under floods for the quantification of damage assessment. A visual interpretation map is produced using colour segmentation method by estimating the thresholds from histogram analysis. A new method is developed to identify the optimum value for threshold from statistical distribution of Synthetic Aperture Radar (SAR) data that separates flooded water and non-flooded water. From this method, the range of backscatter values for normal water are identified as − 18 to − 30 dB and the range is identified as − 19 to − 24 dB for flooded water. The results showed that the method is able to separate the flooded and non-flooded region on the microwave data set, and the derived flood extent using this method shows the inundated area of 3873.14 Km2 on peak flood date for the chosen study area. [ABSTRACT FROM AUTHOR]

The applications of synthetic aperture radars (SAR) have increased manifold in the past decade, which includes numerous Earth observation applications such as agriculture, forestry, disaster monitoring cryospheric- and atmospheric- studies. Among them, the potential of SAR for ionospheric studies is gaining importance. The susceptibility of SAR to space weather dynamics, and ionosphere in particular, comes at low frequencies of L- and P-bands. This paper discusses one such scintillation event that was observed by L-band Advanced Land Observation Satellite (ALOS)-2 Phased Array L-type SAR (PALSAR) over southern India on March 23, 2015. The sensors also acquired data sets on four other days on which the ionosphere was quiet. Ionospheric parameter measurements of total electron content (TEC) and amplitude scintillation (S4) index from ground-based Global Navigation Satellite System (GNSS) receiver at Tirunelveli was used to establish the ionospheric conditions on the days of SAR acquisition as well as to corroborate the S4 estimated from SAR. Multi-temporal ALOS-2 data sets were utilized to calculate S4 from two separate methods and the results have a good agreement with GNSS receiver measurements. This highlights the potential of SAR as an alternate technique of monitoring ionospheric scintillations that can be utilized as complementary to the highly accurate and dedicated measurements from the GNSS networks. [ABSTRACT FROM AUTHOR]

Coal mines are the major source of energy in industries and are important indicators of the economic growth of a country. Excessive withdrawal of coal from underground coal mines without stowing leads to surface deformation. In this study, Synthetic Aperture Radar Technology has been used to identify the surface deformation in the Korba Coal Mines of India. Korba coal mines are major coal-producing mines in India that come under South Eastern Coalfields Limited (SECL). It covers three underground mines. Depillaring has been done through the caving method, which may lead to the deformation of the area and its surroundings. Deformation may have devastating consequences if overlooked. So, it requires continuous monitoring of the area. Space-based SAR interferometry is the most preferred technique to monitor any mining area over a period of time. In this technique, known as the DInSAR technique, a deformation map can be generated from images of the same area acquired over different points of time. In the present study, images of the month of February of the years 2015, 2016, 2017, and 2019 have been successfully utilized for DInSAR analysis over the Rajgamar area. The Interferogram allows analysis of an entire mine site including pits and their surrounding area. High precision monitoring over wide coverage enables to bring out accurate deformation results due to the presence of coherent targets and monitoring risk across the study area. Maps generated from data were scrutinized and interpreted. Consequentially, 2015–2016 shows greater deformation than the other two corresponding maps generated 2015–2017 and 2015–2019 data. Results were validated through ground observations. Overall the area of the Rajgamar coal mine is getting deformed at a slow rate. • Rajgamar coalmine is an underground mine which area due to excavation of coal. • The DInSAR technique helps to predict the Deformation with low cost and time. • It helps to identify the affected area with the help of less no. of images with suitable parameters. • This helps to predict the most hazardous zones in the area and helps in planning and management of identical area. [ABSTRACT FROM AUTHOR]