Journal of the Indian Society of Remote Sensing最新文献

{"title":"Mapping of Kharif Sown Area Using Temporal RISAT-1A SAR and Optical Data","authors":"P. Srikanth, Anima Biswal, Bhavana Sahay, V. M. Chowdary, K. Sreenivas, Prakash Chauhan","doi":"10.1007/s12524-024-01977-0","DOIUrl":"https://doi.org/10.1007/s12524-024-01977-0","url":null,"abstract":"<p>Timely and accurate information on crop-sown areas during the <i>kharif</i> season (monsoon season in India) is crucial for early identification of drought-prone areas, enabling prompt intervention and mitigation measures to minimize adverse effects on crops and farmers. In this study, two approaches were attempted to estimate the in-season <i>kharif</i> sown area by the end of August using EOS-04 (RISAT-1A) data. Approach 1 utilizes the Coefficient of Variation (CV) of temporal Synthetic Aperture Radar (SAR) backscatter, while Approach 2 integrates optical data with the CV of SAR backscatter. The algorithm based on the temporal CV suggested that the variability of backscatter values over time, captured through temporal analysis, can be a key factor in identifying and delineating cropland areas. The CV of temporal HV backscatter data serves as an indicator of changes in vegetation cover or crop growth stages. In this study, CV values for settlement, forest, and fallow areas were observed to be 0.18, 0.17, and 0.19, respectively, while crops exhibited higher CV values of more than 0.4, which can be attributed to active crop growth. CV threshold optimization was carried out using Youden’s J Score statistical method. The optimal CV threshold value was observed to be 0.3, computed based on the temporal HV backscatter data from four study districts, which was further validated over two other districts. Accuracies of around 80% were achieved in both test and validation districts using the SAR only approach. Integration of optical data with SAR data led to improved overall accuracies, ranging from 85 to 89% in all test and validation districts. The findings suggest that CV analysis of backscatter values, complemented with optical data, can be a valuable tool for early discrimination between different land cover features, with croplands standing out due to their higher CV values attributed to the dynamic nature of crop growth. Using Youden’s J Score for threshold optimization adds statistical rigor to the methodology and demonstrates its potential for accurate in-season <i>kharif</i> sown area estimation for its operational use over large areas.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"33 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CsdlFusion: An Infrared and Visible Image Fusion Method Based on LatLRR-NSST and Compensated Saliency Detection","authors":"Hui Chen, Ziming Wu, Zihui Sun, Ning Yang, Muhammad llyas Menhas, Bilal Ahmad","doi":"10.1007/s12524-024-01987-y","DOIUrl":"https://doi.org/10.1007/s12524-024-01987-y","url":null,"abstract":"<p>Image fusion methods may lose their ability to retain crucial image information when faced with suboptimal conditions, such as poor contrast, excessive noise, or intense illumination, leading to the loss of valuable image features. In this work, an improved CsdFusion algorithm is proposed to increase the visibility of infrared targets in fused images. Firstly, to accomplish clear background textures and structural information, a hybrid image decomposition model combining LatLRR and NSST is established. This process entails the division of the original infrared and visible images into low-rank components (base layers) and salient components (saliency layers) through the Latent Low-Rank Representation (LatLRR) approach. Subsequently, the base layers of both the infrared and visible images undergo the Non-Subsampled Shearlet Transform (NSST), decomposing them into high-frequency and low-frequency layers. The processed high-frequency and low-frequency layers are then subjected to inverse NSST to obtain the fused base layer, ensuring that the fused image retains maximum background information while effectively filtering noise. Secondly, to identify and extract the most significant regions or features in infrared images, the Central-contrast priori Saliency Map (CSM) algorithm is applied. This algorithm calculates the central prior saliency value using Harris corners and the contrast prior saliency value using guided filtering and background suppression. It then combines these two prior saliency values using a feature compensation strategy to compute the infrared saliency map. To validate the effectiveness of the proposed algorithm, comparative evaluation studies on benchmark open datasets are carried out. The results thus obtained through the proposed algorithm demonstrate superior performance in both subjective and objective experiments, generating fused images that not only preserve the crucial details and characteristics of both infrared and visible images but also reflect significant enhancement in visibility and discriminability of target objects, outperforming 10 state-of-the-art image fusion algorithms.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"79 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Based PM 2.5 and 10 Concentration Modeling for Delhi City","authors":"Vikhyat Gupta, Dhwanilnath Gharekhan, Dipak R. Samal","doi":"10.1007/s12524-024-01962-7","DOIUrl":"https://doi.org/10.1007/s12524-024-01962-7","url":null,"abstract":"<p>The global decline in air quality, attributed to pollutants from various sources such as fossil fuel usage, industrial expansion, and heightened commercial activities, underscores the importance of monitoring and forecasting air quality levels. This study delves into 3 years of daily particulate matter data spanning the pre-COVID (2019), COVID-era (2020), and post-COVID (2021) periods across thirty-seven monitoring stations in Delhi. Prior to analysis, the dataset underwent preprocessing to address missing and outlier values. Analysis of the dataset aimed to discern pollutant trends across stations and timeframes, identifying influential factors such as air temperature, surface pressure, and precipitation for modeling particulate matter concentrations. An Artificial Neural Network employing backpropagation was utilized for modeling. Training the model with 80% of the dataset, the remaining 20% served as the test dataset. Validation of the model's performance utilized standard statistical metrics including R², r, root mean square error, and mean absolute error. Notably, the R² for the training dataset were 0.82 and 0.84 and r for training dataset were 0.90 &amp; 0.91 for PM 10 and PM 2.5, respectively. While the R² for the test dataset were 0.78 and 0.79, r values for the test dataset stood at 0.88 for both PM 10 and PM 2.5. Furthermore, the model facilitated upscaling of observations to a spatial scale, broadening the scope of observations via simulations to enhance regional understanding.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"25 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delineating Blue-Dust Enriched Zones Within Banded Hematite Quartzite Using PRISMA Data: A Study in the Bolani Region, Odisha, India","authors":"Debasis Singh, Jagadish Kumar Tripathy, Sushree Sagarika Behera","doi":"10.1007/s12524-024-01980-5","DOIUrl":"https://doi.org/10.1007/s12524-024-01980-5","url":null,"abstract":"<p>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.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"41 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AM-UNet: Road Network Extraction from high-resolution Aerial Imagery Using Attention-Based Convolutional Neural Network","authors":"Yashwant Soni, Uma Meena, Vikash Kumar Mishra, Pramod Kumar Soni","doi":"10.1007/s12524-024-01974-3","DOIUrl":"https://doi.org/10.1007/s12524-024-01974-3","url":null,"abstract":"<p>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 <i>Precision</i>, <i>recall</i>, <i>Acc</i> and <i>F1-score</i> parameters.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"29 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Phenology Model of Oil Palm Plantation Based on Biophysical Parameter on Sentinel-1A Using Multiple Linear Regression (MLR)","authors":"Rika Hernawati, Ketut Wikantika, Soni Darmawan, Agung Budi Harto, Josaphat Tetuko Sri Sumantyo, Sitarani Safitri","doi":"10.1007/s12524-024-01973-4","DOIUrl":"https://doi.org/10.1007/s12524-024-01973-4","url":null,"abstract":"<p>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 <span>(MLR=38.839+0.984*{CHM}_{i}+(-97.214)*{RVI}_{i}+2.476*{VV}_{i})</span>+ (-0.893)<span>(*{VH}_{i})</span> with R² is 0.977 and RMSE is 1.290.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"36 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geostatistical Kriging Interpolation for Spatial Enhancement of MODIS Land Surface Temperature Imagery","authors":"Kul Vaibhav Sharma, Vijendra Kumar, Deepak Kumar Prajapat, Aneesh Mathew, Lilesh Gautam","doi":"10.1007/s12524-024-01959-2","DOIUrl":"https://doi.org/10.1007/s12524-024-01959-2","url":null,"abstract":"<p>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.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"29 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Simulation Study of Volumetric Soil Moisture Evaluation Using NavIC–IR","authors":"C. D. Raisy, Sharda Vashisth, Amitava Sen Gupta","doi":"10.1007/s12524-024-01965-4","DOIUrl":"https://doi.org/10.1007/s12524-024-01965-4","url":null,"abstract":"<p>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.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"33 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of Remotely Sensed Data and the Petrographic Analysis for Lithological Mapping of Neoproterozoic Basement Rocks at Um Had Area, Central Eastern Desert, Egypt","authors":"Ibrahim H. Fangary, Mostafa A. Kamel, Abdellah S. Tolba, Ahmed M. Orabi, Lotfy M. Abdel-Salam","doi":"10.1007/s12524-024-01960-9","DOIUrl":"https://doi.org/10.1007/s12524-024-01960-9","url":null,"abstract":"<p>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.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"23 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Soil Moisture Variations with Fusion-Based Change Detection Algorithm for MODIS and SCATSAT-1 Datasets","authors":"Ravneet Kaur, Reet Kamal Tiwari, Raman Maini","doi":"10.1007/s12524-024-01967-2","DOIUrl":"https://doi.org/10.1007/s12524-024-01967-2","url":null,"abstract":"<p>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.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"36 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}