Journal of the Indian Society of Remote Sensing最新文献

{"title":"Exploring the Capability of Earth Observation Data and a New ‘4S’ Framework for Disaster Risk Reduction: An Experience from July 2022 Flash Floods in Amarnath Valley, India","authors":"Jayesh Mukherjee, Anuva Chowdhury, Surajit Ghosh","doi":"10.1007/s12524-025-02360-3","DOIUrl":"https://doi.org/10.1007/s12524-025-02360-3","url":null,"abstract":"Abstract The holy cave shrine of Amarnath is thronged annually by over three lakh devotees. However, the cryogenic-sensitive region is increasingly becoming vulnerable to anomalies in precipitation events and rising anthropogenic footprints. On the evening of 8 July 2022, a highly localized extreme rainfall event took place leading to a short-lived flash flood along with unsorted debris flow in the Amarnath valley, surmounting heavy loss of lives and local livelihoods. This article uses Earth Observation (EO) datasets to capture and understand the implications of using such data applications in mitigating disasters for remote and inaccessible areas. Despite the valuable insights provided by EO data, their applicability is often restricted by the temporal limitations, particularly those derived from open-source radar and optical satellite imagery, which are frequently incapable of capturing ephemeral or rapidly evolving phenomena. Some meaningful information about the present study was captured with the use of GPM (IMERG) satellite-based rainfall data, while others failed to gauge the situation. Eight topographical parameters have been examined to understand the local factors contributing to flash flood conditions in the Amarnath watershed. An AHP-based flash flood susceptibility zonation (FFSZ) was derived using Google Earth Engine (GEE) along with an interactive user interface was developed for visualization of the computed parameters. The FFSZ contains five classes with their areal percentages: Very Low (20.03%), Low (19.69%), Moderate (20.43%), High (20.14%) and Very High (19.71%) respectively. Our findings suggest the need for more ground-based automated weather stations (AWS) complementing satellite-based EO systems' limitations for providing high-precision regular interval observation. Finally, we propose a new ‘4S’ framework, namely, ‘source’, ‘setting’, ‘susceptibility’ and ‘solution’ for flash flood risk assessment. This framework has also been discussed in complementary to a cross-sectoral interface containing ‘science-governance-disaster risk reduction (DRR)-society’ aspects alongside major targets based on Global Goals (UN SDGs) and India’s national DRR agenda points.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12524-025-02360-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147902647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"National-Scale Crop Phenology Monitoring in India Using High-Temporal EOS-06 Data","authors":"Anima Biswal, P. Srikanth, K Priyanka, Bhavana Sahay, V. M. Chowdary, K. Sreenivas, Prakash Chauhan","doi":"10.1007/s12524-025-02195-y","DOIUrl":"https://doi.org/10.1007/s12524-025-02195-y","url":null,"abstract":"","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147920155","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":"Improvements in Precipitation Product with Newer NASA/GPM IMERG Algorithm on TRMM/TMPA Data During Summer Monsoon Period over India","authors":"M. Venkatarami Reddy, Kondapalli Niranjan Kumar, Ashis K. Mitra, K. Amarjyothi, Imranali M. Momin, Saji Mohandas, V. S. Prasad","doi":"10.1007/s12524-025-02194-z","DOIUrl":"https://doi.org/10.1007/s12524-025-02194-z","url":null,"abstract":"","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"53 11","pages":"3917-3934"},"PeriodicalIF":0.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147891527","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":"Farmland Extraction from UAV Remote Sensing Images Based on Improved SegFormer Model","authors":"Yuqing Chen, Xiuxin Wang","doi":"10.1007/s12524-024-02004-y","DOIUrl":"https://doi.org/10.1007/s12524-024-02004-y","url":null,"abstract":"<p>To further improve the accuracy of extracting farmland spatial distribution information, this thesis proposes an improved SegFormer model for extracting farmland spatial distribution information from unmanned aerial vehicle images. This method first introduces Efficient Channel Attention to optimize each transformer block in the encoder. Then, input the output results of each optimized block into the introduced BiFPN layer for enhanced feature extraction, and input the weighted fused multi-level features from the encoder into the decoder. By aggregating multi-level features through the Multi Layer Perceptron, local and global attention are combined, and then further weighted feature fusion is achieved through BiFPN. Finally, tthe Squeeze Excitation and Efficient Channel Attention was proposed to enhance channel features and improve model performance. The experimental results indicate that the improved SegFormer model’s mean intersection over union and mean pixel accuracy were 96.91 SegFormer model, it has increased by 1.55 union and pixel accuracy for farmland is 98.42 than other semantic segmentation models, effectively extract the extraction accuracy of farmland edges and small farmland from drone images.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"9 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269746","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 Heuristic Approach of Radiometric Calibration for Ocean Colour Sensors: A Case Study Using ISRO’s Ocean Colour Monitor-2","authors":"Anurag Gupta, Mini Raman, K. N. Babu, Syed Moosa Ali, Bimal K. Bhattacharya","doi":"10.1007/s12524-024-02009-7","DOIUrl":"https://doi.org/10.1007/s12524-024-02009-7","url":null,"abstract":"<p>Ocean colour spectral observations play a significant contribution in mapping the earth marine resources through measurements with its inverted geo-physical/biophysical parameters. The retrieval of parameters from the basic sensor measurements highly depends on atmospheric scattering and absorption of light energy by its constituents. Hence the quantitative applications using these datasets are directly affected by the uncertainty in radiative transfer modeling towards atmospheric scattering and absorption and associated sensor degradation with time. Here authors presented an automation of radiometric calibration approach for ocean colour monitor of Oceansat-II (Jan 2017–Dec 2017) dataset through top of the atmosphere radiance simulation using a non-linear optimization technique. This algorithm also provides an alternative approach of calibrating the sensor vicariously through reduced dependency of systematic congruent in-situ measurements. Since Kavaratti in Lakshadweep, India is already a well-known site for calibrating the ocean colour sensors. The OCM cloud free images over this calibration site are utilized to perform its radiometric assessment for the year 2017 using radiative transfer model coupled with bio-optical model where the synchronous, relevant model inputs are simulated. The significant variations in the radiometric calibration coefficients were realized across the spectral bands 412 to 865 nm i.e. 5.5% to 11.8% change were recorded in the year 2017 followed by 2.65 to 5.23% change within a month of March respectively.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"2 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262046","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":"Self Organizing Map based Land Cover Clustering for Decision-Level Jaccard Index and Block Activity based Pan-Sharpened Images","authors":"S. Jayashree, Karki V. Maya, K. Indira, P. A. Dinesh","doi":"10.1007/s12524-024-01970-7","DOIUrl":"https://doi.org/10.1007/s12524-024-01970-7","url":null,"abstract":"<p>Pan-sharpening is very often employed in remote sensing to transform low-resolution multispectral (LMS) images into equivalent high-resolution multispectral images (HMS). Images resulting from pan-sharpening are sharper and more detailed that is resulted by improving spatial features of the multispectral image. One such approach of jointly processing LMS and Panchromatic images is discussed in the present study. The decision-level fusion suggested here involves choosing or combining details from numerous sources by taking decisions while analyzing features recovered from the input images. The proposed methodology is an amalgamation of principal component analysis used for separating spatial and spectral details of LMS, non-subsampled contourlet transform for feature analysis, and Jaccard similarity index and block activity measurement for localized decision-based fusion. The algorithm tries to provide an adaptive approach to address the trade-off between spectral and spatial resolution. Self-Organizing Maps based clustering technique is employed with the intension of grouping the image pixels into three categories soil, water and vegetation. The paper highlights the performance comparison of proposed method with various pixel-level fusion techniques ranging from techniques from Intensity, Hue and Saturation (IHS) transform to Neural Networks based pan-sharpening methods. This comparison is implemented using various reference and non-reference indicators along with Kolmogorov–Smirnov test. Additional analysis using Kolmogorov–Smirnov test is done to statistically analyze spectral degradation. The comparative analysis provides enough evidence that the suggested method yields fused images with enhanced edge details without forgoing the spectral features which was also evident from the mutual information obtained from clustered images. The resulting sharpened images tend to possess good spatial and spectral details that would simplify the automatic image analysis.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"18 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142262047","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":"Improved Building Extraction from Remotely Sensed Images by Integration of Encode–Decoder and Edge Enhancement Models","authors":"Somenath Bera, Vandita Srivastava, Vimal K. Shrivastava","doi":"10.1007/s12524-024-01992-1","DOIUrl":"https://doi.org/10.1007/s12524-024-01992-1","url":null,"abstract":"<p>Building extraction from high-resolution images has been a fundamental task in the remote sensing field. It helps in monitoring natural disasters and developing urban areas. Encoder–Decoder based convolutional neural network (CNN) has provided a paradigm for automatic building extraction. However, extracting building information is difficult due to many reasons like diverse scales, complex background and variety of building structures. Moreover, achieving accurate boundary information remains challenging due to various impediments surrounding buildings. To deal with these challenges, in this article, we proposed a dual-branch model. One branch is the segmentation branch that includes an encoder–decoder framework (based on Attention-ResUNet architecture) combining residual unit and attention network, to generate the segmentation mask. The residual unit improves the ability to learn the deep and complex building features whereas the attention network focuses on the informative spatial information. In addition, a dilated module is positioned at the end of the decoder of Attention-ResUNet to capture the multiscale information. Another branch is the edge branch consisting of canny edge extraction, morphological operation and squeeze-excitation network, to improve the boundary information. The canny edge detection method extracts the edges of the buildings which is further enhanced through the morphological operation. In addition, a squeeze-excitation network is added for fine adjustment of generated feature maps. At the end, our proposed model integrates the segmentation mask obtained using the segmentation branch and boundary information generated by the edge branch to produce the refined segmentation mask. Experiments have been performed on the Massachusetts building dataset and the WHU-I building dataset. The performance of proposed model is compared with state-of-the-art models such as SegNet, DeepLabV3Plus, UNet, Attention-UNet, ResUNet and Attention-ResUNet. The results demonstrate that the proposed approach improves the performance for both the datasets. Hence, we can conclude that the proposed approach has a great potential in extracting multiscale information and enhancing the boundary information of buildings.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"29 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216359","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":"Enhancing Change Detection Accuracy in Remote Sensing Images Through Feature Optimization and Game Theory Classifier","authors":"Gandhimathi Alias Usha Subramanian, Kavitha Kaliappan","doi":"10.1007/s12524-024-01985-0","DOIUrl":"https://doi.org/10.1007/s12524-024-01985-0","url":null,"abstract":"<p>Satellite-based change detection involves comparing multi-temporal images to identify modifications in land cover features. This work investigates the application of a game theory classifier to enhance accuracy in medium-resolution multispectral remote sensing images. The proposed post-classification approach includes segmentation, feature extraction, classification, and image differencing to detect changes in multi-temporal images. To optimize multispectral images, land cover types are segmented using a proximal splitting algorithm. Boundary and texture features are then extracted using the Difference of Offset Gaussian Filter and Gray Level Co-occurrence Matrix. Principal Component Analysis is subsequently applied to reduce the dimensionality of the extracted features. Finally, the reduced features are classified using a game theory classifier, which effectively handles the uncertainty and variability inherent in non-smooth multispectral data. Experiments were conducted using Landsat datasets from the Hanoi and Balcoc regions, evaluating parameters such as misclassification rate, mean square error, color peak signal-to-noise ratio, and validity index. Quantitative analysis showed that the proposed approach achieved misclassification rates of 0.10 and 0.11 for dataset 1 and 2, respectively. Qualitatively, the results underscore the effectiveness of the extracted features in aiding the game theory classifier to discern subtle differences among feature classes.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"283 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216439","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":"Delineation of Climate-Change Induced Flood Susceptible Zones: An Integrated Approach of Impact Assessment","authors":"B. Deepika, S. Rajakumari, R. Madhumitha, M. Malathi","doi":"10.1007/s12524-024-01999-8","DOIUrl":"https://doi.org/10.1007/s12524-024-01999-8","url":null,"abstract":"<p>The occurrence of heavy rains caused by cyclones has emerged as a significant factor leading to the occurrence of floods in the state of Andhra Pradesh, located in South India. The current investigation utilized a combination of GIS and AHP techniques to determine the flood-prone zonation of nine administrative units situated along the Vamsadhara River in the Srikakulam district. The analysis incorporated 16 parameters to identify the Flood Susceptible Zone (FSZ) and involved sensitivity analysis of the variables employed to enhance the reliability of the findings. The FSZ maps obtained were divided into five categories: very high, high, moderate, low, and very low. From the results, it was determined that 19% of the entire study area fell into the very high FSZ classification, while 34% were classified as high FSZ. Additionally, 29% of the area fell into the moderate FSZ category, followed by 14% in the low category, and 4% in the very low category. Among the 9 mandals selected for study, a majority of over 50% of the land area in Gara, Lakshminarsupeta, Narasannapeta, Polaki, and Sarubujjili faced susceptibility that varies from very high to highly susceptible to inundations. Overlay analysis of the water area on the FSZ map before and after a Cyclone demonstrates that the waterlogged regions predominantly coincide with the high and very high susceptibility categories. The results presented in the paper will provide valuable assistance to state and local officials by offering profound insights to support the implementation of effective strategies to reduce future risks.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"10 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216422","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":"Assessment of Flood Risk in the High Rainfall Coastal Area of Cuddalore Taluk, Southeast India, Using GIS-Based Analytic Hierarchy Process Techniques","authors":"A. Saranya, Vivek Sivakumar, S. Satheeshkumar, A. Logeshkumaran","doi":"10.1007/s12524-024-01998-9","DOIUrl":"https://doi.org/10.1007/s12524-024-01998-9","url":null,"abstract":"<p>Flooding stands as the most prevalent and financially burdensome natural disaster impacting nations worldwide. This study focuses on flood risk assessment within the Cuddalore taluk, aiming to leverage Geographic Information System (GIS)-based Analytic Hierarchy Process (AHP) techniques for analyzing flood hazards, vulnerabilities, and risks in the region. Seven key causal factors—elevation, slope, drainage density, river distance, rainfall, soil, and geology—were utilized to construct the flood hazard map. Results indicate that the taluk encompasses very low, low, moderate, high, and very high flood hazard zones, covering 7%, 22%, 34%, 25%, and 12% of its total area, respectively. Additionally, a flood vulnerability map was generated using five spatial layers: land use/cover, population density, distance to road, literacy rate, and population under the age of 6. Integration of the flood hazard and vulnerability maps facilitated the creation of a comprehensive flood risk map. The findings reveal that within the Cuddalore Taluk, zones classified as very low, low, moderate, high, and very high flood risk constitute 51%, 6%, 12%, 18%, and 12%, respectively. While the majority of the coastal region faces susceptibility to flooding within the very low, low, and moderate ranges, select areas are at risk of high and very high flooding. Disseminating flood hazard, vulnerability, and risk maps to relevant authorities is imperative for raising awareness regarding flood-prone locations. The coastal regions, along with adjacent areas, predominantly fall under the category of very high-risk zones, necessitating effective mitigation strategies. Specific locales such as Pillayarkuppam, Cuddalore, Tiruvandipuram, Kayalpattu, Nellikuppam, and Punjimangattuvalkkai demand focused efforts to mitigate high flood risks. Conversely, areas with very low and low flood risks, including Vadakuthu, Neyveli T.S., Sorathur, Panruti, Aierpali, and Pewndur, require preservation measures. Additionally, zones such as Arunam and Mettukuppam, exhibiting moderate flooding risks, warrant attention for preservation efforts in their immediate surroundings.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142216423","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}