Journal of the Indian Society of Remote Sensing最新文献

The aim of this paper lies in improving the accuracy of multiple signal classification (MUSIC) inversion in Synthetic Aperture Radar Tomography (TomoSAR), while using scattering statistical models. We propose new combination algorithms between MUSIC inversion and scattering statistical models. We exploited three volume scattering models, the uniform model, the exponential model, and the Gaussian model. For each probability model, the analytical expression of the corresponding inversion was computed. In order to verify the proposed method, we exploited the dataset of the BioSAR-2 project. The data was acquired in a boreal forest located in north Sweden. The attained results for the suggested new approaches were analyzed quantitatively by computing the detection rate corresponding to the area under study according to the relative error measured for the vegetation height. Qualitatively, by evaluating for each algorithm, the generated digital surface model (DSM), the relative error, and the histograms of selected zone with strong forest densities. It was shown that combining MUSIC inversion and the uniform probability model, we achieved the highest detection rate of 60.7% for a 0.3 relative error. For the exponential distribution, we obtained a detection rate of 60.2%, and the detection rate for the Gaussian distribution was 54%. For the standard MUSIC, it achieved a weak detection rate of 25.5% for a 0.3 relative error, and for the standard CAPON, it achieved a detection rate of 38.6% for a 0.3 relative error. These results indicate that the proposed approach increases the achievement of the MUSIC inversion by 35.2%, and outperforms the standard CAPON by 22.1%. This shows the importance of using probability models in MUSIC inversion for a better estimation of vegetation height in SAR tomography.

Recent environmental issues, rising water demand, and the decreasing supply of natural water resources require the provision of additional quantities of water to ensure the sustainability of ecosystems and water resources. In this study, a systematic approach was used to choose suitable sites for rainwater harvesting (RWH) using an analytic hierarchy process-based multi-criteria evaluation approach in Wadi Sarkhar, Iraq. In order to produce the suitability map, seven criteria layers were used: precipitation, slope, elevation, drainage density, Normalized Difference Vegetation Index (NDVI) obtained from Sentinel 2 data, type of soil, and soil moisture. The area of study has a considerable topographical disparity in altitudes that was ranging from 10 to 2000 m. Special attention was paid to this fact, so the performance of a slope analysis was necessary to identify the sites for RWH appropriately. After analyses of the slope and drainage density layer, new insight about the hydrologic capacity and characteristics was obtained. Long-term precipitation records were essential for determining the sustainability of RWH especially in semi-arid regions. Moreover, the NDVI layer data were used to detect land cover and vegetation distribution. Soil type and soil moisture were utilized to evaluate the ground capacity to retain water. The study area was classified by the final suitability map into three different zones: low suitability, unsuitable zone, and high suitability. This study outcome will provide a systematic approach to the selection of suitable places for RWH, ensure competent management of water resources, and provide an idea about ecosystems and water resources sustainability.

The influence of landslides (LS) on forest structure, composition, and functionality has gained limited scientific attention compared to socioeconomic aspects. This study aims to fill this gap by investigating the dynamics of pre- and post-LS occurrences in and around the Kali Tiger Reserve (KTR), Western Ghats. Our approach integrates multi-source, multi-temporal earth observation data, vegetation indices, field observations, and machine learning techniques. This study identified 245-LS caused due to a catastrophic rainfall event in July 2021 the most severe over a century that impacted the tropical dense forests. The present study highlights the emergence of invasive alien species (IAS), particularly Chromolaena odorata, following these landslide incidents. Field observations revealed a significant loss of large trees, which corroborated with the Global Ecosystem Dynamics Investigation (GEDI) based Canopy Height Model (CHM) and very high-resolution (VHR) data. The affected areas witnessed a significant rise in land surface temperature (LST) and a decrease in vegetation moisture. A comparative analysis with operational tree loss monitoring using optical (30-m Landsat based Global Forest Watch (GFW), and microwave (L-band Synthetic Aperture Radar (SAR) JICA-JAXA (ALOS-2) Forest Early Warning System) revealed improved performance in mapping small landslides with current approach. These results emphasize the necessity of conducting local and large scale investigations of forest dynamics before and after landslides to meet environmental commitments at various levels. The landslide events will likely induce significant alterations in the forest's microclimate. Our research recommends an immediate action plan to restore affected sites, remove IAS, and encourage the planting of native vegetation for biodiversity conservation.

Optical remote sensing images exhibit complex characteristics such as high density, multiscale, and multi-angle features, posing significant challenges in the field of salient object detection. This academic exposition introduces an integrated model customized for the precise detection of salient objects in optical remote sensing images, presenting a comprehensive solution. At the core of this model lies a feature aggregation module based on the concept of hybrid attention. This module orchestrates the gradual fusion of multi-layer feature maps, thereby reducing information loss encountered during traversal of the inherent skip connections in the U-shaped architecture. Notably, this framework integrates a dual-channel attention mechanism, cleverly leveraging the spatial contours of salient regions within optical remote sensing images to enhance the efficiency of the proposed module. By implementing a hybrid loss function, the overall approach is further strengthened, facilitating multifaceted supervision during the network training phase, covering considerations at the pixel-level, region-level, and statistical levels. Through a series of comprehensive experiments, the effectiveness and robustness of the proposed method are validated, undergoing rigorous evaluation on two widely accessed benchmark datasets, meticulously catering to optical remote sensing scenarios. It is evident that our method exhibits certain advantages relative to other methods.

The Zat region has a Precambrian basement that is partially covered by deformed Phanerozoic terrains. The last three orogeneses that Morocco experienced, from the Precambrian to the Quaternary, shaped this region. We used optical imagery from Landsat 8 OLI and ASTER DEM to map the tectonic fractures in this region. First, radiometric and geometric corrections were taken into account. Then, during the automatic extraction of lineaments, directional filters were used. Many approaches were used in the validation procedure, including the creation of false colour images, principal component analysis, and the removal of artificial lineaments by superimposing them on geological and topographic maps, Google Earth data, and field measurements. The listed lineaments have four major directional ranges: N–S, NW–SE, E–W, and NE–SW. The Hercynian and Alpine fractures are designated by the N–S and NE–SW directions, respectively. However, Precambrian filled fractures are distinguished by lineaments that fluctuate in the WNW–ESE direction. The geographical distribution of lineaments demonstrates the presence of two hard nuclei (Ourika gneissic massif and Afra ignimbritic massif) having controlled the region’s deformation. The region’s tectonic intensity decreases at the level of these nuclei and increases at the level of the surrounding terrains, which may include mineralising indices. This study highlights a region that is likely to be mined because of its geological and structural heritage.

The Chandrayaan 3 (Ch-3) mission, as of the day (23rd of August 2023), is all set to explore the moon’s surface in great detail. Scientists have carefully chosen the landing site based on data gathered from previous missions, namely Chandrayaan 2’s (Ch-2) Imaging Infrared Spectrometer (IIRS) and Chandrayaan 1’s (Ch-1) Moon Mineralogy Mapper (M³). Our research analyzes the data from the selected Ch-3 landing site by using sophisticated techniques to remove unwanted noise from the hyperspectral images provided by IIRS and M³. The IIRS on Ch-2 and M³ on Ch-1 captured valuable information differently, giving us a better understanding of the moon’s composition and features. We aim to improve the quality of the Ch-3 landing site data by eliminating any interference caused by noise, making the images clearer and more useful. To achieve this, we’re employing two denoising methods- HyRes (Automatic Hyperspectral Image Restoration Using Sparse and Low-Rank Modeling) and HyMiNoR (Hyperspectral Mixed Gaussian and Sparse Noise Reduction). These smart algorithms will help us reveal the true nature of the lunar landscape hidden beneath the noise, giving us better insights into the landing site’s characteristics.

The landfill method is recognized as the cheapest and most widely used solid waste management system. However, improper disposal of solid waste is a serious problem in urban areas of Ethiopia like Areka town due to the rapid growth of population and urbanization. The objective of this study is to identify suitable landfill sites for solid waste using geospatial-based multi-criteria decision analysis techniques. To achieve this objective data were collected from field observations, focus group discussions, key informant interviews, residential areas, protected areas, elevation, slope, and roads were used to identify suitable landfill sites. ArcGIS, QGIS, and Erdas Imagine software were used to prepare the criteria maps. The analytical hierarchy process was also applied to derive the relative weights of criteria maps and then the weighted overlay tool was applied for the preparation of the final suitability map. Accordingly, 5%, 60.1%, 32.8%, and 2.1% of the study area was not, less, moderate, and highly suitable for solid waste disposal. The final result shows that Landfill 1 (4.9 ha) and Landfill 2(6.6 ha) were the 1st and 2nd most suitable sites for proposing a new landfill with the least negative impact on the environment and human health. Thus, this study strongly recommends town municipality should use proposed landfill sites 1 and 2 for solid waste disposal and future studies should also consider various factors overlooked in this study.

Soil moisture is one of the least monitored of all the hydrologic variables. It is greatly influenced by unpredictable and intermittent precipitation, varying evapotranspiration rates, heterogeneous soils, land cover, topography, and is extremely changeable in both space and time. The aim of this study is to retrieve surface and rootzone soil moisture in fallow land at a field scale using Sentinel-1A SAR data. The study explores the potential of obtaining surface soil moisture over fallow land at two different soil types from C-band SAR data. The study area consists of two plots having different soil types. The study area was divided into 80 grids, each measuring 10 × 10 m, to collect soil samples which are synchronized with Sentinel-1A passes. The soil moisture which are retrieved from plot 1 were used to develop the model. The developed model was validated in plot 2. In order to study the impact of soil moisture and dielectric constant on backscattering coefficients, a multiple regression analysis was used to create a semi-empirical model. Rootzone soil moisture retrieval model was developed by considering the backscattered coefficient, volumetric surface soil moisture as an independent variable and volumetric rootzone soil moisture as dependent variable. The predicted surface soil moisture using the regression model were identical to in-situ observed surface soil moisture, with R² of 0.77, RMSE of 1.31 m³/m³, and NSE of 0.75. The estimated rootzone soil moisture matches the in-situ observed rootzone soil moisture identically with R² = 0.74, RMSE = 1.23 m³/m³, NSE = 0.73. This study aids local farmers in their irrigation water management.

The present study reports a cloud feature observed by the Mars Colour Camera (MCC) onboard India’s first Mars Orbiter Mission in both the Martian terminator during the MY 32 to 34: “The Twilight Cloud”. Twilight clouds used to show latitudinal expansion, covering over 5,000 km² and used to appear between 19:00 LT and 20:00 LT for evening and 04:00 LT and 05:00 LT for morning terminator. These clouds often reached altitudes of at least 15 to 40 km. We further compare these observations to Mars Climate Sounder (MCS) data. The TOA (Top of the Atmosphere) reflectance varies from 0.030 to 0.035 in the blue channel and 0.025 to 0.030 in the red channel indicates the presence of both dust and water ice at the observed altitude level. The MCD-GCM (Mars Climate Database Web Interface- General Circulation Model) simulations used to estimate the mixing ratio. MCS extinction data along with simulated MCD results, estimated the effective radius of the particle to be varying from 0.3 to 3.0 μm. The work also infers the seasonal behaviour of these clouds, especially during NHS (Northern Hemisphere Summer) and LNHA (Late Northern Hemisphere Autumn). The present work indicates that the daily thermal variation is one of the plausible reasons for the formation of the clouds.

Wildfires are a natural disaster that results in significant harm and catastrophic destruction. Forest areas tend to be more prone to the devastating effects of wildfires. Global warming causes wildfires to occur more frequently and with severe effects, forcing them to spread across wide amount of land areas, causing unimaginable harm and even claiming lives. In this paper, we propose a novel methodology to analyze the effects of wildfire and estimating its probability to spread using satellite data. The severity of wildfire is determined through fire and smoke detection via deep learning approach Modified-Residual Unet. To categorize areas based on their susceptibility to wildfires, NDVI imagery is given to the ZFNet classifier which determines the region's risk of being prone to wildfire. It achieves an impressive accuracy of 98.3% proving its ability in classifying wildfire risk. A novel Deep Probabilistic (P) Learning along with Cellular Automaton and Diffusion Limited Aggregation Algorithm is used to simulate the spread of wildfires and estimates are made by Anisotropic Generalized Regression Neural Network for the impacted areas. Thus, the efficiency of this novel approach has been tested with various datasets and our approach proves to have notable merits with greater accuracy and substantially lesser time when compared to other methods.