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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]

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]