Journal of the Indian Society of Remote Sensing最新文献

This study employs advanced technologies, specifically remote sensing (RS) and geographic information systems (GIS), to investigate the impact of earthquakes on critical infrastructure in Kahramanmaraş. Critical infrastructure encompasses physical and digital systems crucial for national security, economic stability, and public well-being. Disruption or failure of these interdependent systems, including energy, transportation, communication, water supply, healthcare, and emergency services, can have profound impacts on regional security and societal necessities. Protecting and prioritizing critical infrastructure during disaster response is vital for minimizing damage and expediting recovery. The study employs an innovative approach by integrating building damage assessment results with Point of Interest (POI) data to swiftly assess earthquake effects on critical infrastructure in Kahramanmaraş. Real-time earthquake vulnerability of 57 critical infrastructure elements in 15 POI categories is analyzed. Results indicate financial institutions and commercial areas as the most damaged POIs, while muster points exhibit the least damage. Historical facilities, health facilities, governmental institutions, road facilities, and sports facilities also show varying degrees of damage. Overall, 34% of critical infrastructure structures experienced damage. The proposed method offers a pragmatic approach for rapidly identifying damaged critical infrastructure POIs during disaster-based assessments, addressing a research gap.

The present work proposes an efficient optical transceiver system for secured transmission of compressed satellite images. However, most of the reported techniques fail to achieve a good balance between the compression ratio and Peak Signal to Noise (PSNR) value of the restored image maintaining system robustness against illegal eavesdropping at the same time. To improve these weaknesses, we have introduced an encryption technique combining 6D hyperchaotic sequence with random number controlled by the designed logic circuit for enciphering images before transmission. To optimize the transmission bandwidth through the optical channel, the Modified Run Length Encoding and decoding compression-decompression algorithm is used which is proven to provide a better compression ratio in comparison to existing methods. Furthermore, efficient restoration of the transmitted image with a sufficiently high PSNR and an appropriate correlation coefficient are the two crucial aspects of any authentication-based image compression technique addressed in this communication. The results are examined in terms of correlation parameter, PSNR, information entropy, histogram analysis, key sensitivity analysis, key space analysis, differential analysis and compression ratio. Robustness of the cryptosystem is verified by Known Plaintext Attack (KPA), Chosen Plaintext Attack (CPA), Chosen Ciphertext Attack (CCA), cropping attack and noise attack. Randomness of the bit stream generated by the cryptosystem is tested by the NIST SP 800-22 Randomness Test Suite. Simulation results show that the proposed technique is efficient enough for proper recovery of images with very high PSNR and correlation coefficient of value 1. The compression ratio achieved by our proposed technique is 2.008, which is proven to be much better as compared to contemporary lossless compression techniques.

The North Eastern Arabian Sea (NEAS) is one of the regions highly vulnerable to oil spill pollution due to massive shipping activity, international oil tanker routes, and oil rig operations. Recent studies have indicated that Bombay High Field, located in the NEAS, significantly contributes to oil spill pollution into the sea. This study pioneers the utilization of ISRO’s EOS-04 SAR data in assessing the oil pollution in the NEAS. We also tested the performance of various speckle filters for reducing speckle noise in the EOS-04 data with an oil spill event. We found that the Medain speckle filter performed better and additionally, demonstrated an example of EOS-04 oil-spill look-alike case over the Bombay High region. An inter-sensor comparison of EOS-04 against Sentinel-1 A is also carried out to evaluate the performance of EOS-04 in mapping the ocean features of oil spills and ships. Subsequently, a time series data of EOS-04 from March 2022 to November 2023 is used to quantitatively estimate the oil pollution due to oil leaks in each month in terms of spill area and volume. The distribution of oil pollution in terms of spreading area is high in October 2023 with 556 sq km (25.25%) and very low in August 2023 with 6.79 sq km (0.31%). During the study period, we estimated the total surface contamination of oil pollution to be 2193 sq km and oil released is around 435.35 tons. This study emphasizes the need to regularly monitoring and safeguard the marine environment from the unaccounted and unreported oil entering the Arabian Sea from the Bombay High Region.

This research aimed to evaluate the capability of the combination of aerial UAV multispectral imagery and an equation-oriented approach for monitoring nitrogen status and variable-rate nitrogen fertilizer management in forage maize farms. To achieve this goal, four levels of nitrogen fertilizer were applied in a randomized complete block design (0, 50, 100, and 150%) in eight-leaf and tasseling growth stages. A method based on the biomass of aerial organs and leaf nitrogen content was used to estimate variable rate nitrogen application. Among vegetative indices extracted from aerial images, the correlation between the Normalized Difference Vegetation Index (r = 0.77, P ≤ 0.01), Nitrogen Reflectance Index (r = 0.70, P ≤ 0.01) and Modified Triangular Vegetation Index2 (r = 0.67, P ≤ 0.01) with leaf nitrogen content were positive and significant at the eight-leaf growth stage. Similarly, the Normalized Difference Vegetation Index (r = 0.77, P ≤ 0.01), Nitrogen Reflectance Index (r = 0.87, P ≤ 0.01) and Modified Triangular Vegetation Index2 (r = 0.66, P ≤ 0.01) had a high correlation with the leaf nitrogen content at the tasseling growth stage. Based on the obtained results, a total of 223, 192, 173, and 100 kg/ha urea fertilizer were estimated to be applied in 0, 50, 100, and 150% nitrogen fertilizer plots, respectively. Findings suggested that the nitrogen changes and nitrogen rate needed to apply were detected by aerial multispectral imagery with good accuracy.

The inherent composition of roads and buildings project spectral and hierarchically similar characteristics in remote-sensing images. Gray values of both background pixels and roads overlap when a large area of a remote-sensing image is considered. As a consequence, segmenting road networks and buildings in an urban environment presents critical challenges. So far, the literature suggests that supervised algorithms outperform their unsupervised counterparts when it comes to segmenting roads and buildings. However, supervised algorithms require a massive database in the training stage. This can cause a bottleneck as the percentage of pixels in urban remote sensing images depicting roads is very low when compared to the background. Index integrated spatially constrained Gaussian Mixture model (IISC-GMM), a novel unsupervised algorithm that overcomes the aforementioned constraints by integrating a Morphological Building Index (MBI) mask with a novel Gaussian mixture model (GMM) is proposed. To better distinguish foreground from background pixels, this novel algorithm blends localized spatial smoothness of neighboring pixels with spectral information. The gaps in the road network are eliminated by applying path morphology. The algorithm generates a Dice coefficient of 80.00%, a Completeness of 77.41%, a Correctness of 82.75%, a Quality of 73.80%, and a Misclassification rate (MCR) of 11.36% when validated on the Massachusetts Road dataset. In addition to being faster and less computationally intensive, the results obtained by IISC-GMM are comparable to those obtained by the computationally intensive Deep Learning methods.

In developing countries, exploration and exploitation of mineral resources help create the infrastructure that can sustain the population and economic growth. Remote sensing makes a brilliant tool to boost this growth. Hyperspectral Remote Sensing has been a key technology in mineral exploration and mapping for some time now. In this study, Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) data with the high spatial and spectral resolution was deployed to map the metasedimentary rocks in parts of the Bhukia region of Banswara district, Rajasthan. Similarly, PRecursore IperSpettrale della Missione Applicativa (PRISMA) data is also utilized to achieve a similar goal. The rock spectra obtained from both the surface reflectance hyperspectral datasets after applying the processing techniques were compared against the USGS spectral library and the field/ laboratory spectra to identify the diagnostic spectral features. The spectral Angle Mapper algorithm was applied to both datasets and detected limestone, dolomite, phyllite, and quartzite rocks. Furthermore, absorption band parameters were estimated and interpreted to corroborate the chemistry of the rock which helped in the identification of limestones based on Al³⁺/ Mg²⁺ content.

Variations in glacier facies may signify the glacier’s response to the surrounding climate, and continuous monitoring of glacier facies can reveal a lot about the glacier’s behavior and stability. The swift development of remote sensing and the handiness of polarimetric SAR data has gained popularity for monitoring glaciers and their dynamics. We used ALOS-1/PALSAR-1 L-band data over the Siachen glacier in the Karakoram Himalayan region for this study. For glacier facies/zones classification, we employed a two-stage scattering model-based SVM classification scheme for improved glacier facies mapping. Results showed that two-stage classification using 6SD-SVM is effective, with a kappa coefficient of 0.82 and an overall accuracy of 87.58%. Integration of scattering-based polarimetric information extends a new dimension in glaciated terrain classification, and generates enhanced accuracy in classified images. Even though the employed technique produces satisfactory results, but classes for mid- & low-percolation and debris cover are misclassified. To further clear up any ambiguity between the aforementioned classes, the probability difference between surface and volume backscattering has been added as a second step in the second stage of the classification process. In comparison, 6SD-SVM outperforms the backscatter [T]-SVM classification and the overall accuracy is enhanced by 7%.

The load of aerosols in the atmosphere has been increasing gradually due to industrialization and urbanization. This increase has contributed to change in the Earth’s radiation budget through the absorption or scattering of radiation. The aerosol direct radiative forcing (ADRF) is a measurement utilized to comprehend the impact of cooling or warming up of the atmosphere directly by aerosols. Our study examined the impact of aerosols during the COVID-19 pandemic by comparing them to the average from the preceding 5-year period (2015–2019) in peninsular India. The measure of aerosols deployed in this study is the Aerosol Optical Depth (AOD), and the study was carried out on three distinct time frames: prior to lockdown, during lockdown, and post lockdown. The study revealed that the ADRF increased during all the three time frames of 2020 compared to the average of 2015–2019, and the other time scales experienced an increase in ADRF as well. The most notable rise in ADRF and decrease in temperature occurred in the tropical savanna and warm semi-arid climate regions during the pre-lockdown period. During lockdown, the increase in ADRF was seen throughout the study area, and a decrease in temperature was observed only in the tropical monsoon region. In the post-lockdown period, the decline in ADRF was accompanied by a fall in temperature in the tropical savanna region. This study provides insights into the effect of aerosols on ADRF in peninsular India and highlights the importance of monitoring and regulating aerosol emissions to mitigate the changes in temperature.

This study aims to examine the seasonal spatial variability of column-averaged of dry air mole fraction of CO₂ (X_CO2) and Solar induced chlorophyll fluorescence (SIF) in the state of Mato Grosso in the Midwest region of Brazil, employing ordinary kriging (OK) as the spatial interpolation method. The X_CO2 and SIF remote sensing data were collected on the Orbiting Carbon Observatory-2 (OCO-2) platform. Descriptive statistical analysis also incorporated spatial variability by using OK on the data that had been segregated into dry and wet seasons. The wet season mean for X_CO2 ranged from 393.96 ± 0.10 to 394.14 ± 0.10 ppm between 2015 and 2018, and no variation in the mean of X_CO2 was observed between these years in the dry season. SIF was higher in the rainy season compared to the dry season. A significant and more substantial negative relationship (r = − 0.84, p < 0.01) was observed between X_CO2 and SIF. Higher values of X_CO2 and SIF were observed in the northern region of the state, under the Amazon biome. Therefore, the seasonal variability of X_CO2 in the Mato Grosso State, Brazil, is positively related to SIF. It indicates that photosynthesis plays an essential role in this carbon dynamics under regional conditions.

Erosion risk assessment is essential for implementing effective soil and water conservation (SWC) measures, presenting complex challenges, especially in data-scarce semi-arid regions of India. This study addresses these challenges by applying a comprehensive approach to prioritize intervention areas, thus enhancing erosion management efficiency in the Devgadh Baria Watershed (DBW) in Gujarat, India. The primary objective is to systematically prioritize sub-watersheds (SWs) through geomorphometric and LULC analyses and propose appropriate SWC measures for high-priority areas. Utilizing remote sensing (RS) and geographical information systems (GIS) techniques, the study delineates SWs and assesses their vulnerability using seven distinct morphometric parameters and LULC classes, including agricultural land, forest, wasteland, and built-up areas. The combined analysis integrates these parameters to produce compound values for all 30 SWs, resulting in a refined priority ranking. SW26, initially very high priority in morphometric analysis due to steep slopes and minimal drainage density, shifted to medium priority in the combined analysis, reflecting effective agricultural management practices that reduce erosion. Conversely, SW7 remained a very high priority across both analyses, indicating consistent high erosion risk due to a significant built-up area and limited forest cover. SW30 shifted from high to medium priority, influenced by balanced agricultural activities and lower slopes. SWs 6 and 24 transitioned from very high to medium priority, while SW22 remained high, supported by moderate forest cover and beneficial soil types mitigating erosion. This research underscores the scientific importance of integrating morphometric and LULC analyses for precise SW prioritization. The combined approach enhances erosion risk assessments and supports targeted SWC strategies, crucial for effective watershed management in semi-arid regions. The findings provide actionable insights that align with global sustainability goals, contributing to improved soil conservation and water resource management.