Anup Das

The tropical and sub-tropical forests in the foothills of the Himalayas are under threat of deforestation and degradation. With launch of India's EOS-04 C-band SAR, it is important to assess its capabilities to monitor these forests. This study uses multi-sensor C-band SAR data from EOS-04 and S-1 for deforestation mapping across multiple forests in India. To understand the performance of these two datasets in a controlled environment with a sufficient number of logging events within a two-year duration, the analysis is carried out in the managed forests of Haldwani, India. Change maps were generated with a statistical change detection algorithm - Cumulative Sums of Change. The VH (S-1) and HV (EOS-04) cross-pol channels were found to give comparable results with values of overall accuracy at 0.71. The kappa coefficient and F1 Score were 0.58 & 0.65 (VH S-1), and 0.64 & 0.54 (HV EOS-04), respectively. However, it was observed that the detected change areas were better demarcated using S-1 data with preserved compartment boundaries and homogeneous enclosed area. The impact of inaccurate Forest/Non-forest mask resulted in grazed grasslands classified as deforested area with S-1 data. The findings of this study highlight the impact of satellite datasets, polarization channel, and spatial resolution for deforestation mapping. The results show that EOS-04 and Sentinel-1 based change maps have comparable accuracy depending on the temporal data availability.

India launched the C-band synthetic aperture radar (SAR) satellite Earth Observation Satellite (EOS)-4 on 14 February 2022. The payload, operating at 5.4 GHz, is a state-of-the-art sensor, and the improved data products give the user community a set of data that can address research as well as operational user requirements. A lot of applications have been made operational over the Indian region as well as Antarctica by the Indian science community. Here, we showcase the utilization of EOS-4 data related to various global applications, such as agriculture, forest, soil moisture (SM), hydrology, cryosphere, geoscience, oceanic, and coastal applications. Since the data are made globally available, the global user community can address its data needs from a SAR sensor, where full hybrid and dual polarizations are available from electronically steerable beams. At a 17-day revisit time, the satellite takes care of many applications that need closer observations. The satellite has capabilities to address almost all the requirements of the user community, with repeat-pass interferometry being introduced on an experimental basis.

Globally, the rapid loss of mangrove forests often creates long-lasting environmental damage alongside coastlines, mudflats, and river banks. All-weather, physical monitoring of such areas is almost impossible because of inaccessibility to swampy areas and a hostile substrate. Subsequently, conventional field surveys are relatively unavailable to monitor human encroachment in coastal areas like the Mumbai mangrove region of India. In this context, the polarimetric synthetic aperture radar (PolSAR) remote sensing tool becomes a potential candidate for mangrove conservation and management. Since the Mumbai mangrove region of India exists over the extensive land cover, the large-scale classification can articulate the continuous encroachment because of the location of human settlements or activities across this metropolitan coastal city. For this, the traditional algorithms need the essential improvisation to apply for large-scale data analysis, aiming simultaneously at getting the highest decisive and time efficiency. Here, we introduce a shallow learning model of MapReduce-based Multi-Layer Perception (MLP) algorithm to classify the hybrid compact polarimetric (CP) and fully polarimetric (FP) feature space. Even though a shallow learning model of the automated method is easily scalable, it requires a distinctive shallow learnable feature set for better land cover classification. In this effort, this paper investigates the efficacy of derived feature space compared to direct polarimetric measurements of sensors and shows that the shallow learnable feature set is more effective with both CP & FP observations. Simultaneously, the relevancy of the proposed distributed model of MLP is also justified compared to distributed extreme learning machine (DELM) algorithm and provides a practically implementable scaled-MLP algorithm of shallow learning model. Ultimately, this paper comes up with a better polarimetric signature of land types for both the CP and FP datasets, which can be used in an alternative manner as per data availability for a multi-sensor data analysis.

Anthropogenic activity is a major driving factor of greenhouse gas emission, leading to climate change worldwide. So, the best natural approach to lowering the carbon from the atmosphere is mangroves which have more potential to sequestrate carbon. But mangroves are under threat due to land use land cover change. This research has been carried out on the mangroves of Gulf of Khambhat, Gujarat, India, where anthropic activity is affecting the mangrove forest cover with spatiotemporal heterogeneity. In the present study, multi-temporal high-resolution satellite data AVNIR-2 (Advanced Visible and Near Infrared Radiometer type-2) and LISS-4 (Linear Imaging Self-Scanning Sensors-4) were used for the demarcation of various land use/land cover class (LULC), and change analysis and assessment of mangroves health for the years 2009, 2014, and 2019. The impact of saltpan/aquaculture on mangroves growth and its health status has been calculated by various MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data products such as gross primary productivity (GPP), enhanced vegetation index (EVI), and leaf area index (LAI) in Google Earth Engine (GEE), and field-based method was also considered. This study suggests that there is a marginal increase (17.11 km²) in mangrove cover during the assessment period 2009–2019; on other side, 65.42 km² was degraded also. However, increase in saltpan/aquaculture is imposing an adverse effect on mangroves’ basal area, plant density, and productivity. Change analysis also suggests a reduction in healthy mangrove area (from 25.20 to 2.84 km²), which will have an impact on ecosystem services.

For more than four decades, it has been known that the dielectric constant of the lunar surface can be retrieved from the Fresnel reflection coefficients. However, theoretical models have met with limited success in validating laboratory test results from the Apollo missions to date. This article is the first study to focus on the use of high-resolution full-polarimetric synthetic aperture radar datasets for the retrieval of the dielectric constant of the lunar surface from the Fresnel reflection coefficients. We initially show that it is possible to retrieve the lunar dielectric constant via the classical Freeman-Furden decomposition (FDD). The performance of the FDD algorithm is found to be unacceptable over regions with surface slopes and craters, and for subsurface soil samples. Accurate estimation is not possible by simply replacing the volume scattering model in the FDD with popular and widely used volume scattering models. Therefore, a model-based three-component decomposition (TCD) algorithm for a robust retrieval of the lunar dielectric constant is proposed. The proposed TCD algorithm implements an efficient branching condition combined with double unitary matrix rotations and provides exceptionally accurate dielectric constant estimation. The proposed TCD algorithm is validated by using L band full-polarimetric datasets acquired by the Chandrayaan-2 mission over Apollo 12, Apollo 15, and Apollo 17 landing sites. Comparisons are also made with other TCD algorithms. Excellent agreement between the estimated values by the proposed TCD algorithm and the reference values for the dielectric constant, available from the literature, has been observed.

The highly polluted Indian cities are retained their normal state during COVID-19 pandemic lockdown. Thus, the study aims to analysis the lockdown impact on NO2 and SO2 in the selected cities. So, the lockdown relative changes (∆C) are calculated based on baseline and pre-lockdown, for which the ground (Central Pollution Control Board data) and satellite (Sentinel 5P data) measured NO2 and SO2 data were utilised. The study reveals that the average pollutants concentration of NO2 and SO2 is declined to below-average level and indicates that the average dropdown level of NO2 and SO2 are -45.4% and -30.2% for ground pollutants; -45.1% and -43.4% for satellite pollutants respectively. The pandemic lockdown has restricted industrial, vehicular emission, and other anthropogenic activities which resulted in a positive effect on air quality.

The crater floors and walls of youngest impact craters on the Moon are some of the roughest surfaces at radar wavelengths due to the presence of rocky debris and impact melt sheets. In this work, we examine the radar polarization properties of the interior region of three of the youngest craters on the Moon that are Tycho, Jackson, and Giordano Bruno at 25-cm wavelength radar data obtained from the DFSAR instrument onboard Chandrayaan-2, and compare them to the predicted behaviors of theoretical radar scattering models. We suggest that the high radar circular polarization ratio (CPR) values observed from the young crater floors can be explained by a double-bounce dipole (diffuse scattering) model through scattering from and between tens of centimeter scale rock edges and cracks. We also infer that the highest CPR enhancements from the floor of the young impact craters analyzed in this work are caused by dihedral scattering between natural corner reflectors such as rocks many times the size of illuminating wavelength or between rock faces and the regolith.