Journal of the Indian Society of Remote Sensing最新文献

Blue dust, a high-grade martite-rich ore commonly found in conjunction with Banded Hematite Quartzite (BHQ) and Banded Hematite Jasper. It holds a distinctive stratigraphic position within Precambrian sedimentary iron ore deposits, and its formation is attributed to the supergene enrichment process. Blue dust, with higher Fe content compared to impure BHQ, is blended during mining with BHQ ore to elevate the Fe grade of low Fe₂O₃ BHQ ore. In this study, we utilized hyperspectral PRISMA data provided by the Italian Space Agency to identify blue dust zones within Banded Hematite Quartzite (BHQ) in the Bolani region of Odisha, India. The Bolani iron ore deposit is situated on the western limb of the renowned horseshoe-shaped Bonai-Keonjhar iron ore belt in Odisha, characterized by the presence of blue dust in fairly large pockets and lenses. Laboratory-generated spectral signatures revealed unique characteristics in blue dust, including a steeper slope in the spectral range from 1196 to 870 nm and greater absorption minima at 870 nm compared to BHQ samples. Leveraging these distinctions, a Relative Band Depth (RBD) image was generated, incorporating PRISMA bands aligned with the diagnostic spectral feature of blue dust observed at 733 nm and 1196 nm (for shoulders) and 870 nm (for absorption minima). A proposed composite image, combining RBD, the first Principal Component (PC-01) image derived from PRISMA bands within the 350–1350 nm spectral range, and a reflectance band at 1047 nm, effectively delineates blue dust zones from BHQ. Validation through field assessments, spectral signature comparisons, and mineralogical analysis of collected samples enhances the accuracy of the results. The findings of this study highlight the substantial potential of the PRISMA dataset for accurately delineating blue dust within BHQ, validating its effectiveness, and opening avenues for future research in optimizing mineral resource exploration.

Roads are an essential element of various information systems such as geographic information systems, transportation systems, etc. The main source of road information is remote sensing data as it covers a large amount of area. Despite recent technological advancements precise road information extraction is still a tedious task. In this work, a computational-efficient deep learning architecture AM-Unet is proposed to extract road information from high-resolution aerial imagery. The proposed method alters the design of Unet architecture for the encoder, decoder, and skip connections. These changes enhance the computational efficiency of the decoder to recapture spatial location information. The experiments are performed on complex high-resolution (HR) aerial images and the results are assessed on diverse quantitative parameters. The experimental results are compared to other deep learning methods which reflects the improvement in results on Precision, recall, Acc and F1-score parameters.

Estimating the biophysical parameters during the phenology cycle are very important and the key parameter for indicating the productivity of oil palm plantations. In many countries, the oil palm plantation has a very large area, therefore remote sensing technology is needed to estimate biophysical parameters in large areas. The special characteristics and potential of Synthetic Aperture Radar (SAR) data in acquiring geometric and dielectric properties of biophysical parameters have led to their identification in the context of vegetation monitoring. This study, we have investigated and developed models for estimating the oil palm phenology by applying multiple linear regression (MLR). The methodology includes the biophysical parameters estimated using Sentinel-1A for extracting the canopy height model (CHM), radar vegetation index (RVI), backscattering on VV and VH, aboveground biomass, texture entropy, and texture energy. Then applied multiple linear regression (MLR) analysis for developing model and assess its ability. The result found the best model for estimating oil palm phenology using 4 parameters. The parameters are CHM, RVI, Backscatter on VV, Backscatter on VH and the best model for estimating oil palm phenology is (MLR=38.839+0.984*{CHM}_{i}+(-97.214)*{RVI}_{i}+2.476*{VV}_{i})+ (-0.893)(*{VH}_{i}) with R² is 0.977 and RMSE is 1.290.

Thermal images play a crucial role in various applications, such as environmental monitoring, energy efficiency, and food safety. However, thermal images are often affected by low spatial resolution, limited accuracy, and noise, which reduce their usefulness and effectiveness. This research paper presents a novel approach for enhancing thermal images and optimizing using Kriging Interpolation KI. The proposed KI method combines a metaheuristic optimization algorithm, Particle Swarm Optimization (PSO), with Kriging, a geostatistical method for interpolation and prediction of spatially continuous variables. The proposed KI method has been evaluated on a set of low-resolution Land surface temperature (LST) images of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and validated with higher resolution LandSat-8 LST. The use of PSO in combination with Kriging provides a powerful tool for efficient and accurate spatial enhancement of thermal images, allowing for the preservation of important thermal features and details while improving the overall quality of the images. The proposed KI algorithm demonstrated the effectiveness of the approach in enhancing the spatial resolution and accuracy of the MODIS thermal images. The results show that the proposed method outperforms traditional statistical LST image enhancement methods, such as DisTrad, TsHarp, and Regression Tree in terms of spatial resolution and accuracy. The proposed method has potential applications in agricultural, metrological, and environmental applications, where thermal images are used to continuously monitor and control temperature-sensitive data.

The sensitivity of the reflectivity of microwave signals to the moisture content of the soil makes it possible for soil moisture evaluation by remote sensing. L5 band signals used by the Indian regional navigation satellite system NavIC can be utilized as signals of opportunity to remotely assess soil moisture. Depending on the amount of water in the soil, the amplitude and phase of these signals alter when they reflect off the ground. As the satellite moves in the sky, a sinusoidal interference pattern is created when the reflected signals combine with the direct signals from it. This is known as NavIC–interferometry/reflectometry or NavIC-IR. The present work is a detailed theoretical simulation of the above-mentioned interference process using a stratified multilayer soil model. The simulation results are in good agreement with the previously reported experimental results by other groups in India using NavIC signals. There is a linear dependence between the phase of the interference pattern and the volumetric soil moisture, which is in good agreement with the previous empirical experimental findings.

This study aims to map the rock types in the Um Had region by integrating remote sensing applications of Landsat-8 (OLI) image processing, field studies, and petrographic investigations. The present work involves updating the existing geological map of the Um Had area in the central Eastern Desert, Egypt, due to the lack of a precise and accurate geological map. Several rock types dating to the Neoproterozoic Era, including oceanic crust (ophiolitic and island arc) and continental crust assemblages, originated in the region during two tectonic stages (late to post-orogenic and syn-orogenic). Remote sensing technology is already widely utilized for various geological domains like mineralogy, lithology mapping, geomorphology, and others. In our study, it is specifically used for lithological mapping. We utilized the optimum index factor and correlation coefficient methods to identify the most effective results from False-Color Composite (FCC), Principal Component Analysis (PC), and Band Ratio (BR). These techniques, combined with supervised classification, enabled us to distinguish among different rock units based on their spectral signatures. All results were combined with the previously mentioned techniques that include principal component images (PC1, PC4, and PC3; PC2, PC3, and PC4) and band ratio images (2/4, 5/7, and 5/3 × 2; 4/2, 5/6, and 6/7). Consequently, this supported the geological mapping and confirmed the field and petrographic investigations. This approach enabled the identification of seventeen distinct rock units, namely serpentinite, biotite schist, talc schist, metabasalt, metaandesite, metadacite, metarhyolite, metagabbro, quartz diorite, tonalite, rhyolite, granodiorite, monzogranite, syenogranite, siltstone, graywacke, and conglomerate. A comparative analysis of the newly modified and created lithological maps with previously published maps of the Um Had region significantly enhanced the accuracy and robustness of geological mapping and rock unit identification.

Soil moisture is a vital parameter in the study of hydrology, agriculture and meteorology. The estimation of soil moisture is important for crop yield estimation, crop growth analysis and water resource management. Remote sensing is a significant way of mapping and monitoring crop fields’ soil moisture content globally, using optical and microwave satellite datasets. In previous literature, many attempts have been made to compute soil moisture using optical and microwave-based remote sensing datasets. However, the applicability of optical data is limited due to the presence of atmospheric/cloud effects, while microwave applications are restricted due to limited resolution. In this article, a fusion-based change detection approach has been proposed to detect the soil moisture variation with multispectral and microwave satellite datasets. This study has been conducted in three stages i.e., (a) image-fusion of moderate resolution imaging spectroradiometer (MODIS) and scatterometer satellite (SCATSAT-1) at HH and VV polarization using different fusion algorithms i.e., nearest neighbour-based fusion (NNF), Gram–Schmidt (GS), Brovey transformation (BT) and principal component (PC) spectral; (b) Neural Net based classification of fused datasets to deliver the thematic maps, and (c) perform the post-classification change detection (PCD) to develop the change maps. The classified and change maps have been further utilized to detect the level of soil moisture. From the experimental outputs, it has been evaluated that the NNF-based PCD performed well enough in the development of the change maps as compared to other methods i.e., GD, BT and PC spectral. The present work can aid crop yield estimation, agricultural water and precision irrigation management.

Flash flooding is one of the most noteworthy natural disasters in arid regions, especially in urban areas. The Helwan-Atfih area is a heavily populated region and part of the Eastern Desert drylands of Egypt. It is characterized by ten drainage basins that dissect it and drain toward the Nile River (Wadies of Degla, Hof, Al-Gebbu, Garawy, Hera, Al-Hay, Al-Werg, Al-Nowya, Al-Reshrash, and AL-Atfehe). Landsat-8, STRM-DEM, and CFSR remote sensing satellite data of 15 m, 30 m, and 0.3-degree resolution, respectively, were prepared and utilized to evaluate flooding hazards within the study area using the GIS-weighted overlay technique. Weighted overlay analysis is a GIS-based multi-criteria decision-making technique. This technique was performed to delineate the most vulnerable areas for flooding, depending on 14 thematic layers representing the multi-class factors that influence flood hazard (nine morphometric parameters, slope, relief, lineament density, surface lithology, and surface runoff). According to the morphometric parameters, the basins of the study area are characterized by moderate drainage densities, and moderately permeable subsoil. Limestone occupies 83.41% of the total lithological units within the basins’ area, which indicates a high flooding potential. Steep slopes are primarily observed in the southern basins, especially in the Al-Reshrash basin. Wadi Al-Atfehe and Wadi Al-Reshrash have the lowest lineament density areas, reflecting a higher flooding hazard. The total runoff volume ranges between 2.42 × 10⁶ and 12.08 × 10⁶ m³. Based on the results, Wadi Al-Reshrash receives the highest runoff volume (12.08 × 10⁶ m³) and has the highest slope degree (57^○-71^○). 85.4% of its area is covered with limestone and it has a low to moderate lineament concentration. Accordingly, Wadi Al-Reshrash is the most prone basin to flooding within the study area, followed by Wadi Al-Werg, while the other basins show a moderate flood hazard degree.

Predicting Soil Organic Carbon (SOC) accurately and generating SOC distribution map holds potential for assisting farmers in assessing soil fertility, optimizing and enhancing the resource use efficiency. This study used Mica Sense Red Edge sensor mounted onboard Idea forge Q4c Unmanned Aerial System (UAS) to assess the distribution of SOC in the experimental site. Random Forest (RF) and Support Vector Machine (SVM) techniques were developed with both UAS as well as Sentinel datasets for SOC prediction. Overall, the UAS dataset exhibited greater accuracy in prediction of SOC compared to Sentinel Datasets. Random forest model provided an accurate prediction of SOC when used with the UAS dataset (RPD = 1.09, R²CV = 0.25, RPIQ = 2.57 and RMSECV = 0.06), whereas the Sentinel 2A dataset provided a better prediction of SOC with SVM model (RPD = 0.96, R²CV = 0.10, RPIQ = 0.96 and RMSECV = 0.07). The prediction map of SOC was generated using the UAS dataset with the RF model because it was found to be more accurate compared to the Sentinel and SVM model. The accuracy assessment indicators indicated that UAS based SOC prediction is having the potential in achieving more accurate predictions of SOC, which will offer an optimized agricultural practice and insights for supporting informed decision-making.

The effects of Martian dust storms are not only limited to lower atmospheric regime, but the increased dust storm activity could also affect the vertical structure of the constituents in the thermosphere. To this end, this paper investigates the changes in the vertical mixing of neutral and ionic species densities in the thermosphere before and during a regional (2016) and a global (2018) dust storm event; using Neutral Gas and Ion Mass Spectrometer (NGIMS)/ Mars Atmosphere and Volatile Evolution (MAVEN) observations. Care has been taken to keep a restricted solar zenith angle variation (25º) to avoid the effects of changes in solar illumination on the distribution of thermospheric species densities. Contrasting differences in the vertical distribution of neutral (CO₂, CO, O, N₂, Ar, He) and ionic (CO₂⁺, O⁺, O₂⁺, N₂⁺, CO⁺, Ar⁺) atmospheric species before and during the regional and global dust storm events are noticed.