Journal of the Indian Society of Remote Sensing最新文献_第8页

Improving Gram–Schmidt Adaptive Pansharpening Method Using Support Vector Regression and Markov Random Field 利用支持向量回归和马尔可夫随机场改进格兰-施密特自适应平差方法

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-07-02 DOI: 10.1007/s12524-024-01934-x

Won-Il Choe, Jong-Song Jo, Kum-Su Ri, Kwang-Chol Sok, Yong-Ryong Ri

{"title":"Improving Gram–Schmidt Adaptive Pansharpening Method Using Support Vector Regression and Markov Random Field","authors":"Won-Il Choe, Jong-Song Jo, Kum-Su Ri, Kwang-Chol Sok, Yong-Ryong Ri","doi":"10.1007/s12524-024-01934-x","DOIUrl":"https://doi.org/10.1007/s12524-024-01934-x","url":null,"abstract":"This study aimed to propose an improved Gram–Schmidt adaptive (GSA) pansharpening method using the support vector regression (SVR) and Markov random field (MRF) models in the cases of high ratios between spatial resolutions of LRMS and PAN images. In the present study, the SVR model was used to model the nonlinear relationship between the original LRMS images and the corresponding downsampled PAN image, thereby aiming to obtain the intensity component (({mathbf{I}}_{L})) of the upsampled MS image. Then, the initial pansharpened HRMS image was generated from the GSA pansharpening method with ({mathbf{I}}_{L}) calculated by the SVR model, which is denoted as GSA–SVR in this study. Finally, the quality of the initial pansharpened image was further improved by using the MRF model, which is denoted as GSA–SVR–MRF. A performance comparison of the GSA–SVR–MRF method with competitive pansharpening techniques as well as the GSA–SVR method demonstrated its superiority in maintaining the spatial and spectral details of the PAN and original LRMS images. The GSA–SVR–MRF method was found to be the best in terms of most quality indices.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"16 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516495","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}

引用次数: 0

Satellite-Based Mapping for Seasonal Variations of Air Pollution and its Environmental Effects in Odisha 基于卫星的奥迪沙邦空气污染季节变化及其环境影响绘图

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-07-01 DOI: 10.1007/s12524-024-01931-0

Sudhakar Pal, Arabinda Sharma

{"title":"Satellite-Based Mapping for Seasonal Variations of Air Pollution and its Environmental Effects in Odisha","authors":"Sudhakar Pal, Arabinda Sharma","doi":"10.1007/s12524-024-01931-0","DOIUrl":"https://doi.org/10.1007/s12524-024-01931-0","url":null,"abstract":"Air pollution is an important global environmental issue impacting public health across the world. Innovative satellite-based technology has revolutionized the monitoring of air pollution, enabling assessments on various scales with unprecedented accuracy and coverage. The study attempts to estimate the seasonal and spatial fluctuations of various gaseous pollutants using Sentinel-5P TROPOMI satellite images at the district level in Odisha. In order to comprehend the environmental impact of air pollution, an effort must be made to assess potential greenhouse gas (GHG) emissions and potential acidification levels in Odisha. Results showed that potential emissions of greenhouse gases vary regionally and range from 378.82 g CO2 equivalent/m2 to 386.22 g CO2 equivalent/m2, while potential acidification levels range from 0.008 g SO2 equivalent/m2 to 0.034 g SO2 equivalent/m2. The north-western (Jharsuguda, Sambalpur, Bargarh, Sonepur, and Sundargarh) and north-central (Angul, Dhenkanal, and Deogarh) regions of Odisha exhibit high potential emissions of greenhouse gases and levels of acidification. This is attributed to comparatively higher concentrations of various pollutants stemming from sources like industrial and vehicle emissions. Although the satellite-based study enabled us to characterise the relative air pollution across the state, it necessitated a number of air pollution monitoring stations for validation purposes. A future road map to address climate change and environmental protection may be developed with the aid of local officials and policymakers.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"29 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516494","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}

引用次数: 0

A Computation Framework for LISS-III Analysis Ready Data (ARD) Products for Indian Spatial Data Cube Generation 用于印度空间数据立方体生成的 LISS-III 分析就绪数据 (ARD) 产品的计算框架

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-29 DOI: 10.1007/s12524-024-01928-9

Ashutosh Kumar Jha, Sanjay Kumar Ghosh, Sameer Saran

{"title":"A Computation Framework for LISS-III Analysis Ready Data (ARD) Products for Indian Spatial Data Cube Generation","authors":"Ashutosh Kumar Jha, Sanjay Kumar Ghosh, Sameer Saran","doi":"10.1007/s12524-024-01928-9","DOIUrl":"https://doi.org/10.1007/s12524-024-01928-9","url":null,"abstract":"The velocity and volume of MultiSpectral (MS) remote sensing data have recently increased exponentially. In recent times, however, the absence of a spatial data cube to store analysis-ready data (ARD) products for the Indian sensors’ data delimits its ready use and depreciates its value. Establishing a framework for storing, managing, and providing online processing ARD products for different sensors is necessary. The current work proposes a framework to produce ARD products by radiometrically correcting the data using the 6 S atmospheric correction and Shepherd Diamond-based terrain correction method to provide normalised surface reflectance. The generated ARD product for LISS-III shows a good correlation with the Planet Lab’s surface reflectance ARD product and an excellent correlation with the SACRS2- a Scheme for Atmospheric Correction of ResourceSat-2 corrected product. A frequency-based geometric correction algorithm provides RMSE of less than half a pixel registration error compared to LANDSAT-8 OLI orthorectified imagery. Finally, A Spatial Data Cube (SDC) with CARD4L metadata standard stores the ARD products post ingestion. The paper explains the complete integrated software development with an end-to-end processing chain of LISS III, an Indian optical sensor data.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"343 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505025","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}

引用次数: 0

Role of Simulated Lidar Data for Training 3D Deep Learning Models: An Exhaustive Analysis 模拟激光雷达数据在训练 3D 深度学习模型中的作用：详尽分析

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-27 DOI: 10.1007/s12524-024-01905-2

Bharat Lohani, Parvej Khan, Vaibhav Kumar, Siddhartha Gupta

{"title":"Role of Simulated Lidar Data for Training 3D Deep Learning Models: An Exhaustive Analysis","authors":"Bharat Lohani, Parvej Khan, Vaibhav Kumar, Siddhartha Gupta","doi":"10.1007/s12524-024-01905-2","DOIUrl":"https://doi.org/10.1007/s12524-024-01905-2","url":null,"abstract":"The use of 3D Deep Learning (DL) models for LiDAR data segmentation has attracted much interest in recent years. However, the generation of labeled point cloud data, which is a prerequisite for training DL models, is a highly resource-intensive exercise. Simulated LiDAR data, which are already labeled, provide a cost-effective alternative, but their efficacy and usefulness must be evaluated. This paper examines the role of simulated LiDAR point clouds in training DL models. A high-fidelity 3D terrain model representing the real environment is developed, and the in-house physics-based simulator “Limulator” is used to generate labeled point clouds through various realizations. The paper outlines a few major hypotheses to assess the usefulness of simulated data in training DL models. The hypotheses are designed to assess the role of simulated data alone or in combination with real data or by strategic boosting of minor classes in simulated data. Several experiments are carried out to test these hypotheses. An experiment involves training a DL model, PointCNN in this case, using various combinations of simulated and real LiDAR data and measuring its performance to segment the test data. Results show that training using simulated data alone can produce an overall accuracy (OA) of 89% and the weighted-averaged F1 score of 88.81%. It is further observed that training using a combination of simulated and real data can achieve accuracies comparable to when only a large quantity of real data is employed. Strategic boosting of minor classes in simulated data improves the accuracies of minor classes by up to 23% compared to only real data. Training a DL model using simulated data, due to the ease in its generation and positive impact on segmentation accuracy, can be highly beneficial in the use of DL for LiDAR data. The use of simulated data for training has the potential to minimize the resource-intensive exercise of developing labeled real data.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"4 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505026","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}

引用次数: 0

Vegetation Dynamics Assessment: Remote Sensing and Statistical Approaches to Determine the Contributions of Driving Factors 植被动态评估：确定驱动因素贡献的遥感和统计方法

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-26 DOI: 10.1007/s12524-024-01917-y

Pouyan Dehghan Rahimabadi, Mahsa Abdolshahnejad, Esmail Heydari Alamdarloo, Hossein Azarnivand

{"title":"Vegetation Dynamics Assessment: Remote Sensing and Statistical Approaches to Determine the Contributions of Driving Factors","authors":"Pouyan Dehghan Rahimabadi, Mahsa Abdolshahnejad, Esmail Heydari Alamdarloo, Hossein Azarnivand","doi":"10.1007/s12524-024-01917-y","DOIUrl":"https://doi.org/10.1007/s12524-024-01917-y","url":null,"abstract":"To properly manage the terrestrial ecosystem, it is essential to understand the vegetation sensitivity to climate variations and human actions. The main target of this survey was to evaluate the spatiotemporal variation in vegetation cover, and its relationship with climate variations and to calculate the contributions of driving factors in Namak Lake basin, Iran, during 2001–2019. To this end, Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI) in 3, 6, 9, and 12-month time scales were used to assess vegetation dynamics and its reactions to climate variations based on coefficient of determination (R2) and Linear Regression (LR). The results presented that vegetation cover had an improving trend in 87.78% and a decreasing trend in 12.19% of the basin, while it was stable in 0.03% of areas. The correlation between VHI and different time scales of SPEI indicated that coverage was mainly affected by 3-month SPEI in more than half of the basin (53.74%). High correlations between VHI and SPEI were found in upland areas in the northeast and some areas in the east of the basin. These areas also had the highest slope of VHI changes in relation to climate factors. Climate variability affected about four-fifths (79.22%) of coverage, while 16.36% was influenced by human actions, and 4.42% by both factors. Moreover, more than 99% of the significant improvements and degradations in coverage were related to climate variations and mankind’s actions, respectively. The outcomes can serve as a foundation for initiating vegetation growth and protection in the Namak Lake basin.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"353 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516536","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}

引用次数: 0

Construction of Remote Sensing Quantitative Model for Biomass of Deciduous Broad-Leaved Forest in Mazongling Nature Reserve Based on Machine Learning 基于机器学习的马宗岭自然保护区落叶阔叶林生物量遥感定量模型构建

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-26 DOI: 10.1007/s12524-024-01901-6

Xuehai Tang, Dagui Yu, Haiyan Lv, Qiangxin Ou, Meiqin Xie, Peng Fan, Qingfeng Huang

{"title":"Construction of Remote Sensing Quantitative Model for Biomass of Deciduous Broad-Leaved Forest in Mazongling Nature Reserve Based on Machine Learning","authors":"Xuehai Tang, Dagui Yu, Haiyan Lv, Qiangxin Ou, Meiqin Xie, Peng Fan, Qingfeng Huang","doi":"10.1007/s12524-024-01901-6","DOIUrl":"https://doi.org/10.1007/s12524-024-01901-6","url":null,"abstract":"As an important forest type, deciduous broad-leaved forest is crucial for estimating forest carbon sequestration capacity and evaluating forest carbon balance. This study focuses on the natural deciduous broad-leaved forest of Mazongling Nature Reserve in Jinzhai County of China. WorldView-2 images were selected as data source. 36 candidate factors including vegetation indices, texture features, and topographic factors were used for modelling. Three machine learning algorithms (i.e., random forest, k-nearest neighbor, and artificial neural network) were used to establish the optimal quantitative retrieval model for natural deciduous broad-leaved biomass. Results showed that the ANN model was the best predictor with R2 = 0.69 and RMSE = 31.53 (Mg·ha−1). Combining the ANN model with the complete spatial coverage of remote sensing data, we developed a distribution map of natural deciduous broad-leaved biomass in the Mazongling forest farm. The estimated average biomass of the study area was 90.34 ± 47.96 Mg·ha−1. In addition, the influence of light saturation on model accuracy is also discussed. This study confirms that remote sensing data in temporal and spatial space can improve the model estimation accuracy.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"77 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505027","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}

引用次数: 0

Scrutinizing Diurnal Scale Rainfall Variability Over Himachal Pradesh Using High Resolution Satellite-Based GPM-IMERG Product 利用基于高分辨率卫星的 GPM-IMERG 产品研究喜马偕尔邦昼夜降雨量的可变性

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-26 DOI: 10.1007/s12524-024-01923-0

V. S. Anjana, Charu Singh

{"title":"Scrutinizing Diurnal Scale Rainfall Variability Over Himachal Pradesh Using High Resolution Satellite-Based GPM-IMERG Product","authors":"V. S. Anjana, Charu Singh","doi":"10.1007/s12524-024-01923-0","DOIUrl":"https://doi.org/10.1007/s12524-024-01923-0","url":null,"abstract":"The pattern of monsoon rainfall has been explored at diurnal scale level using the precipitation dataset GPM-3IMERGHH-06 over the state of Himachal Pradesh from 2004 to 2020. The present study identified the spatio-temporal pattern of rainfall peaks and found the trend of rainfall peaks (5% significance level) during different times over selected locations. One-sample student’s t-test has been employed to find the significance of the trend. Intense rainfall peaks (0.7–1 mm/hour) are found at late night (0200 IST to 0230 IST) as well as morning (0600 IST TO 0630 IST) hours over the mountain foot regions and less intense peaks (0.4–0.6 mm/hour) are found during afternoon or evening time (1430 IST to 1700 IST) over southern parts of the state. Late-night peaks of rainfall show a significant increasing trend over the regions Kullu, Mandi, and Sirmaur, while morning peaks of rainfall show a decreasing trend over the same regions. Significant increasing trends have been found over Hamirpur and Mandi during evening hours. The state is characterized by undulating terrain and is prone to extreme rainfall events during the monsoon season. Diurnal pattern of rainfall gives a glimpse into the physical mechanism behind such sudden unexpected events. Trend analysis helps to understand the future risk over different regions which is important for implementing any kind of mitigative actions.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"31 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516496","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}

引用次数: 0

Atmospheric Wind Estimation Using Adaptive Block James–Stein Technique for Higher Range Coverage in MST Radar 利用自适应块状詹姆斯-斯泰因技术估算大气风速，提高 MST 雷达的覆盖范围

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-25 DOI: 10.1007/s12524-024-01916-z

Manas Ranjan Padhy, Srinivasan Vigneshwari, M. Venkat Ratnam

{"title":"Atmospheric Wind Estimation Using Adaptive Block James–Stein Technique for Higher Range Coverage in MST Radar","authors":"Manas Ranjan Padhy, Srinivasan Vigneshwari, M. Venkat Ratnam","doi":"10.1007/s12524-024-01916-z","DOIUrl":"https://doi.org/10.1007/s12524-024-01916-z","url":null,"abstract":"Measuring atmospheric winds over longer ranges using VHF-MST radar is extremely useful for studying stratosphere-troposphere exchange. The present study uses an adaptive block technique (ABlockJS), a mixed model of a parametric technique, and a few non-parametric techniques to address this aspect. The signal estimates are substantiated with five moments and six quality-related parameters while deriving three wind components along with horizontal wind speed and direction. The parametric part of the technique improves the signal, while the non-parametric part lowers noise variance. This technique is established using NARL MST Radar experimental data. The computed wind components derived from this technique are verified with the independent wind components acquired from the concurrent GPS radiosonde in-situ observations. It is observed that this analytical technique can deliver wind components more precisely and consistently, covering longer ranges of 25.20 km. It enhances the benchmark range coverage of 21.45 km attained using Fourier-based estimators on the MST dataset. The complete procedure is developed in C# from scratch without using any standard routine from available packages, thus, it fits acquisition-time application needs fine. It benefits various atmospheric research which demands higher range coverage using VHF radar.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"191 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516497","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}

引用次数: 0

Integrated Remote Sensing and Petrographic Guide to Delineate the Hydrothermal Alteration Zones Along the Phyllites of the Main Zawar Fold, Rajasthan, India 印度拉贾斯坦邦扎瓦尔主褶皱辉绿岩热液蜕变带的综合遥感和岩石学划分指南

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-24 DOI: 10.1007/s12524-024-01924-z

Sima Gorai, Nisha Rani, T. Vijaya Kumar, Bulusu Sreenivas

{"title":"Integrated Remote Sensing and Petrographic Guide to Delineate the Hydrothermal Alteration Zones Along the Phyllites of the Main Zawar Fold, Rajasthan, India","authors":"Sima Gorai, Nisha Rani, T. Vijaya Kumar, Bulusu Sreenivas","doi":"10.1007/s12524-024-01924-z","DOIUrl":"https://doi.org/10.1007/s12524-024-01924-z","url":null,"abstract":"This study integrates Remote Sensing data, field investigation, and petrography to analyze the Zawar Pb–Zn sulfide deposits, in the Paleoproterozoic Aravalli Supergroup rocks of NW India. Structural features of the study area are delineated using Remote Sensing and Shuttle Radar Topography Mission (SRTM) data. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, is used to distinguish the major rock types and alteration zones. Our findings reveal that the Zawar belt is composed of phyllite, quartzite, carbonate, and, greywacke. Phyllites from the hinge of the Main Zawar Fold (MZF) provide critical insights into the distribution of monazite veins, and, support the evidence of hydrothermal alteration over the hinge area of the MZF. Textural evidence investigated by Scanning Electron Microscopic study (SEM) suggests that the monazite is of epigenetic hydrothermal origin, formed subsequently after the formation of the primary host rock. Energy Dispersive X-ray spectroscopic (EDS) study indicates that these monazites have an average composition, P2O5(17.85 wt.%), Ce2O3 14.49, La2O3 6.98, Nd2O3 5.39 and ThO2 1.60 wt.%, suggesting its hydrothermal origin.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"3 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141505028","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}

引用次数: 0

Drought Monitoring Using MODIS Derived Indices and Google Earth Engine Platform for Vadodara District, Gujarat 利用 MODIS 衍生指数和谷歌地球引擎平台对古吉拉特邦瓦多达拉地区进行干旱监测

IF 2.5 4区地球科学

Journal of the Indian Society of Remote Sensing Pub Date : 2024-06-21 DOI: 10.1007/s12524-024-01922-1

Sharmistha Bhowmik, Bindu Bhatt

{"title":"Drought Monitoring Using MODIS Derived Indices and Google Earth Engine Platform for Vadodara District, Gujarat","authors":"Sharmistha Bhowmik, Bindu Bhatt","doi":"10.1007/s12524-024-01922-1","DOIUrl":"https://doi.org/10.1007/s12524-024-01922-1","url":null,"abstract":"Drought is considered to be the most complex but least understood of all natural hazards, affecting more people. Its reappearance in drought-prone areas every few years is almost certain. Also, they lack sudden and easily identified onsets and terminations. Under the background of global climate change, the impact from drought exhibits the characteristics of complexity and multi-process. It has a significant impact on the water resources, agriculture, society, and economy hence needs attention. Vegetation Condition Index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the Temperature Condition Index (TCI) is used for studying the temperature change. Dryness or wetness of soil is a major indicator for agriculture and a comprehensive assessment of vegetation and temperature stress is achieved from MODIS satellite data in Google Earth Engine (GEE) platform for pre and post monsoon season from 2008 to 2022 (15- year period). Vegetation Condition Index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the Temperature Condition Index (TCI) is used for studying the temperature change. The research also incorporates precipitation data from WorldClim to investigate its influence on the Vegetation Health Index (VHI). Mann Kendall trend analysis is employed to examine spatio-temporal variations in drought severity, for both pre-monsoon and post-monsoon seasons. The results emphasize the sensitivity of VHI to shifts in rainfall patterns, providing valuable insights for drought monitoring and management. In essence, this study enhances understanding of drought dynamics and emphasizes the significance of Remote Sensing data and climate information for effective drought assessment and mitigation strategies.","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"14 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516502","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}

引用次数: 0