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Spatial Distribution of High-temperature Risk with a Return Period of Different Years in the Yangtze River Delta Urban Agglomeration

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Chinese Geographical science 2022年第6期32卷 963-978页

作者： ZHANG Guixin WANG Shisheng ZHU Shanyou XU Yongming School of Geographical Sciences Nanjing University of Information Science&TechnologyNanjing 210044China Academy of Environmental Planning&Design CO. LTD.Nanjing UniversityNanjing 210093China School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044China

Against the background of global warming,research on the spatial distribution of high-temperature risk is of great significance to effectively prevent the adverse effects of high *** using air temperature data from 1951 to 2018 measured by meteorological stations located in the Yangtze River Delta urban agglomeration,the daily maximum air temperature distribution is interpolated at a resolution of 1 km based on the local thin disk smooth spline function;the high-temperature threshold for return periods of 5,10,20 and 30 yr are then calculated by using the generalized extreme value *** yearly average high-temperature intensity and high-temperature days are finally calculated as high-temperature danger *** statistical data and remotely sensed image data in 2018 are used as the background data to calculate the spatial distribution of high-temperature vulnerability factors and prevention capacity factors,which are then used to compute the high-temperature risk index during different recurrence periods in the Yangtze River Delta urban *** results show that the spatial distribution features of high-temperature risk in different return periods are *** high-temperature risk index gradually increases from northeast to southwest and from east coast to inland,which has obvious latitude variation characteristics and a relationship with the comprehensive influence of the underlying surface and urban *** terms of time variation,the high-temperature risk index and its spatial distribution difference gradually decreases with increasing return *** different cities,the high-temperature risk in the central area of the city is generally higher than that in the surrounding suburban ***,Hangzhou of Zhejiang Province and Xuancheng of Anhui Province are the top three cities with high-temperature risk in the study area.

关键词： high-temperature risk generalized extreme value method recurrence period remote sensing spatialization

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A New Algorithm of Rain Type Classification for GPM Dual-Frequency Precipitation Radar in Summer Tibetan Plateau

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Advances in Atmospheric sciences 2024年第11期41卷 2093-2111页

作者： Yunfei FU Liu YANG Zhenhao WU Peng ZHANG Songyan GU Lin CHEN Sun NAN School of Earth and Space Sciences CMA-USTC Laboratory of Fengyun Remote SensingUniversity of Science and Technology of ChinaHefei 230026China Hubei Key Laboratory for Heavy Rain Monitoring and Warning Research Institute of Heavy RainChina Meteorological AdministrationWuhan 430205China National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081China

In this study, a new rain type classification algorithm for the Dual-Frequency Precipitation Radar(DPR) suitable over the Tibetan Plateau(TP) was proposed by analyzing Global Precipitation Measurement(GPM) DPR Level-2 data in summer from 2014 to 2020. It was found that the DPR rain type classification algorithm(simply called DPR algorithm) has mis-identification problems in two aspects in summer TP. In the new algorithm of rain type classification in summer TP,four rain types are classified by using new thresholds, such as the maximum reflectivity factor, the difference between the maximum reflectivity factor and the background maximum reflectivity factor, and the echo top height. In the threshold of the maximum reflectivity factors, 30 d BZ and 18 d BZ are both thresholds to separate strong convective precipitation, weak convective precipitation and weak precipitation. The results illustrate obvious differences of radar reflectivity factor and vertical velocity among the three rain types in summer TP, such as the reflectivity factor of most strong convective precipitation distributes from 15 d BZ to near 35 d BZ from 4 km to 13 km, and increases almost linearly with the decrease in height. For most weak convective precipitation, the reflectivity factor distributes from 15 d BZ to 28 d BZ with the height from 4 km to 9 km. For weak precipitation, the reflectivity factor mainly distributes in range of 15–25 d BZ with height within 4–10 km. It is also shows that weak precipitation is the dominant rain type in summer TP, accounting for 40%–80%,followed by weak convective precipitation(25%–40%), and strong convective precipitation has the least proportion(less than 30%).

关键词： satellite precipitation radar rain type classification method Tibetan Plateau strong convective precipitation

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Variations of soil moisture and its influencing factors in arid and semi-arid areas,China

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Journal of Arid Land 2025年第5期17卷 624-643页

作者： NIU Jiqiang LIU Zijian CHEN Feiyan LIU Gangjun ZHOU Junli ZHOU Peng LI Hongrui LI Mengyang School of Geographic Sciences Xinyang Normal UniversityXinyang 464000China Henan Key Technology Engineering Research Center of Microwave Remote Sensing and Resource Environment Monitoring Xinyang Normal UniversityXinyang 464000China School of Science STEM CollegeRoyal Melbourne Institute of Technology(RMIT)UniversityMelbourne 3001Australia Henan Remote Sensing Institute Zhengzhou 450000China Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education Beijing Normal UniversityBeijing 100875China Institute of Disaster Risk Science Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China

Soil moisture(SM)is a critical variable in terrestrial ecosystems,especially in arid and semi-arid areas where water sources are *** its importance,understanding the spatiotemporal variations and influencing factors of SM in these areas remains *** study investigated the spatiotemporal variations and influencing factors of SM in arid and semi-arid areas of China by utilizing the extended triple collation(ETC),Mann-Kendall test,Theil-Sen estimator,ridge regression analysis,and other relevant *** following findings were obtained:(1)at the pixel scale,the long-term monthly SM data from the European Space Agency Climate Change Initiative(ESA CCI)exhibited the highest correlation coefficient of 0.794 and the lowest root mean square error(RMSE)of 0.014 m^(3)/m^(3);(2)from 2000 to 2022,the study area experienced significant increase in annual average SM,with a rate of 0.408×10^(-3)m^(3)/(m^(3)•a).Moreover,higher altitudes showed a notable upward trend,with SM increasing rates at 0.210×10^(-3)m^(3)/(m^(3)•a)between 1000 and 2000 m,0.530×10^(-3)m^(3)/(m^(3)•a)between 2000 and 4000 m,and 0.760×10^(-3)m^(3)/(m^(3)•a)at altitudes above 4000 m;(3)land surface temperature(LST),root zone soil moisture(RSM)(10-40 cm depth),and normalized difference vegetation index(NDVI)were identified as the primary factors influencing annual average SM,which accounted for 34.37%,24.16%,and 22.64%relative contributions,respectively;and(4)absolute contribution of LST was more significant in subareas at higher altitudes,with average absolute contributions of 0.800×10^(-3)m^(3)/(m^(3)•a)between 2000 and 4000 m and 0.500×10^(-2) m^(3)/(m^(3)•a)above 4000 *** study reveals the spatiotemporal variations and main influencing factors of SM in Chinese arid and semi-arid areas,highlighting the more pronounced absolute contribution of LST to SM in high-altitude areas,providing valuable insights for ecological research and water resource management in these areas.

关键词： soil moisture arid and semi-arid areas remote sensing extended triple collation ridge regression analysis

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Deep unsupervised adaptive attention network for hyperspectral image cross-region reconstruction

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Journal of Applied remote sensing 2025年第1期19卷

作者： Jia, Jia Yu, Haoyang Wang, Chengjun Zheng, Ke Liu, Yao Hu, Jiaochan Dalian Maritime University Information Science and Technology College Center of Hyperspectral Imaging in Remote Sensing Dalian China Liaocheng University College of Geography and Environment Liaocheng China Ministry of Natural Resources of China Land Satellite Remote Sensing Application Center Beijing China Chinese Academy of Sciences Aerospace Information Research Institute State Key Laboratory of Remote Sensing Science Beijing China

Due to the limitations of hyperspectral satellite imaging systems, hyperspectral images (HSIs) have a low spatial resolution and spatial missing regions, which hinders their full potential for land cover classification. Through collaboration with the auxiliary high-resolution multispectral image (MSI), the high-resolution HSI (HHSI) can be obtained. Although image fusion deep learning methods have emerged as the predominant approach for enhancing the spatial resolution of HSI, few studies have solved the problem of spatial missing. Moreover, the reliance on the spectral response function and the point spread function limits the applicability and performance of these methods. This study proposes an HSI cross-region reconstruction framework, which can not only fuse the HSI and MSI in overlapping regions but also be used to reconstruct the missing information of HSI in non-overlapping regions. The core of this framework is an unsupervised deep end-to-end fusion network with adaptive attention based on the conception of endmember and abundance. Specifically, a multi-layer attention mechanism with an interaction module is designed to enhance the extraction of endmember and abundance features. In addition, the attached module addresses the dependency on the prior information about degradation models. To verify the practicability of the proposed framework, we conduct evaluations using both simulation datasets and real on-board data for experiments involving both overlapping and non-overlapping regions. The experimental results demonstrate that the proposed method is effective and the reconstructed HHSI is reliable. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Optical transfer function

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A UAV-Based remote sensing Image Automatic Monitoring System Empowered by Artificial Intelligence

A UAV-Based Remote Sensing Image Automatic Monitoring System...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Liu, Tao Zhang, Bohan College of Computer Science and Technology Heilongjiang Institute of Technology Harbin China School of Information Engineering China University of Geosciences Wuhan Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (纸本)9798350360660

In this study, an automatic monitoring system based on convolutional neural network (CNN) is proposed to address the automation and accuracy of remote sensing image processing. With the maturity and wide application of remote sensing and UAV technologies, efficient and accurate image analysis methods have become particularly important. We developed this system through in-depth research on multiple stages of data preprocessing, feature extraction and model training. In the experimental stage, through the comparison experiments with Support Vector Machine (SVM) and Random Forest (RF) models, it is found that the CNN model has significant advantages in processing speed, anti-interference ability and accuracy. Especially in the processing of urban remote sensing images, CNN exhibits up to 90% accuracy, showing its wide applicability and excellent cross-domain application capability. Overall, this study not only successfully develops an efficient and accurate automatic monitoring system for remote sensing images, but also provides strong theoretical and experimental support for future optimization of CNN architecture in natural environments, improvement of real-time data processing capability, and extension to practical applications such as disaster monitoring and environmental protection. © 2024 IEEE.

关键词： Environmental monitoring

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Estimation of Sea Ice Thickness Using FY-3E Data Based on Random Forest Method

Estimation of Sea Ice Thickness Using FY-3E Data Based on Ra...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Hongying Li Qingyun Yan Yinqing Zhen School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

Sea ice is a fundamental component of the cryosphere, necessitating a comprehensive understanding of its parameters. Notably, sea ice thickness (SIT) poses a significant challenge for accurate measurement. In this study, we employ a machine learning approach, specifically the Random Forest (RF) method, to estimate SIT by combining data from the Fengyun-3E (FY-3E) satellite and Soil Moisture Ocean Salinity (SMOS) datasets. This method relies on four input parameters: the incidence angle (θ), reflectivity (Γ), sea ice salinity (S), and sea ice temperature (T). The former two parameters are derived from FY-3E data, while the latter two are extracted from SMOS data. Moreover, FY-3E has the capability to receive signals reflected from both the Global Positioning System (GPS) and the Beidou Navigation Satellite System (BDS). Our investigation is centered on the Arctic polar region. An evaluation spanning from October 2022 to April 2023 demonstrates that the RF model trained on GPS data achieves a high coefficient of determination (r) of 0.98 for the training set, accompanied by a mean absolute error (MAE) of 0.021 m. For the test set, r is 0.95, and the MAE is 0.033 m. The RF model trained on BDS data exhibits a coefficient r of 0.98, with a corresponding MAE of 0.024 m. For the test set, r is 0.94, and the MAE is 0.038 m. These results underscore the efficacy of machine learning methodologies and the benefits of integrating FY-3E data with SMOS sea ice parameters for SIT estimation.

关键词： Radio frequency Training Satellites Accuracy Estimation Data models Random forests

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Variability of Hydro-Meteorological Fluxes in North West Himalayan Basins for Hydrological and Sustainability Studies

Variability of Hydro-Meteorological Fluxes in North West Him...

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2024 Symposium on Beyond the Canopy: Technologies and Applications of remote sensing

作者： Rachit Masiwal, Abhay Singla, Sanyam Singhal, Gaurish Thakur, Praveen K. Garg, Vaibhav Romshoo, Shakil Indian Institute of Remote Sensing ISRO 248001 4-Kalidas Road Uttarakhand Dehradun India Islamic University of Science and Technology Jammu and Kashmir Pulwama India University of Kashmir Hazratbal Jammu & Kashmir Srinagar190006 India

The Himalayas are a crucial source of food, water, and habitat for a wide range of ecosystems in the northern Indian subcontinent. This research focuses on utilizing remote sensing data to analyze hydrological and meteorological factors-such as precipitation, temperature, runoff, evapotranspiration (ET), soil moisture and snow depth-for nine watersheds in the North West Himalayas (NWH): Jhelum, Tawi, Beas, Parbati, Suketi, Gangotri, Aglar, Asan, and Henval. The study employs near real-time (~1-day latency) hydrological flux data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) at a spatial resolution of 0.01°. To gain further insights, the trend in snow cover area (SCA) for the NWH region is analyzed using Interactive Multisensor Snow and Ice Mapping System (IMS) data from the United States National Ice Center (USNIC), disseminated by the National Oceanic and Atmospheric Administration (NOAA) via the National Snow and Ice Data Center (NSIDC). The analysis covers water balance components for these basins from 2001 to 2023. Monthly and annual averages are computed to reveal overall watershed behavior and distribution, which is closely linked to the land use and land cover (LULC) in the area. For instance, the Gangotri basin, with over 50% snow coverage, shows low ET throughout the year, a trend reflected in the long-term annual average ET values. Similarly, basins with higher SCA exhibit lower ET percentages, resulting in increased runoff and a strong correlation between rainfall and runoff patterns. A regional analysis of snow cover from 2014 to 2024, encompassing the union territories of Jammu and Kashmir, Ladakh, and the states of Himachal Pradesh and Uttarakhand, indicates a significant decrease in relative SCA. For the peak winter months of December and January, SCA dropped by about 50% from January 2023 (~2,94,635 km2) to January 2024 (~1,48,225 km2). Additionally, the Indian Meteorological Department's (IMD) daily r

关键词： Soil moisture

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C++ Teaching Reform and Exploration Based on ACM/ICPC and Live Code 5

C++ Teaching Reform and Exploration Based on ACM/ICPC and Li...

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5th International Conference on Education technology Management, ICETM 2022

作者： Zheng, Yunping Sarem, Mudar School of Computer Science and Engineering South China University of Technology Guangzhou China General Organization of Remote Sensing Damascus Syria

ISBN: (纸本)9781450398015

How to further increase the programming interest of the C++ course for all freshmen is still a very challenging problem for the time being. The International Collegiate programming Contest (ICPC) is sponsored by the Association for Computing Machinery (ACM). It is an annual competition designed to showcase college students' innovative ability, team spirit and ability to write programs, and analyze and solve problems under pressure. Aiming at the main problems existing in traditional C++ curriculum teaching, a live code teaching method and an ACM/ICPC innovation model are proposed in this paper that highlights the "student-centered"teaching concept. By taking the "problem-solving"as the main line of teaching, the "live code"as the teaching method, and the informatization teaching as the means, the ACM/ICPC competition promotes the integration of the students' theory and practice. The results of more than ten years of practical teaching show that the proposed live code pedagogy and the ACM/ICPC-based reform have effectively cultivated the students' programming interest and computational thinking, and significantly improved their programming and innovation abilities. At present, the live code teaching method and the ACM/ICPC innovation model have been effectively promoted and applied in the three campuses of our university, as they have a certain leading and exemplary role in cultivating strong professional programming skills, computational thinking, global vision and the all-round development of top-notch innovative talents in the computer field. © 2022 ACM.

关键词： Teaching

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A Novel ACM/ICPC-Based C++ Paperless Examination System 2

A Novel ACM/ICPC-Based C++ Paperless Examination System

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2nd IEEE International Conference on Educational technology, ICET 2022

作者： Zheng, Yunping Sarem, Mudar School of Computer Science and Engineering South China University of Technology Guangzhou China General Organization of Remote Sensing Damascus Syria

ISBN: (数字)9781665485531

ISBN: (纸本)9781665485531

The ACM/ICPC is recognized as the largest and highest level international collegiate programming contest in the world, and it is one of the most important ways to train and select top students in computer science worldwide. Currently, C++ is the most popular and practical computer advanced programming language in the world. It is an important professional main course and one of the main tools for teaching programming and computer software development. It has become a programming course for universities and colleges. With further deepening of education reform, the paperless examination has become an important sign of the college examination reform, and its advantages have become more and more obvious. Also, the paperless examination has gradually become a trend of various examinations. Although there have been some paperless examination systems, but these systems usually can only automatically score four types of objective questions, including true and false questions, fill-in-the-blank questions, single-choice questions and multiple-choice questions. These paperless examination systems are still unable to conduct fair and impartial automatic scoring for programming questions which are however important subjective questions. In response to this problem, based on the ACM/ICPC platform, we propose a new design method for the C++ paperless examination system, which realizes the automatic scoring of C++ examinations. The system can greatly improve the flexibility, fairness, impartiality, credibility and work efficiency of the examination. Our design system can be an important platform for deepening the teaching reform of colleges and universities, promoting the innovation of C++ teaching, and improving the efficiency of college examinations. Therefore, it has a certain promotion and demonstration value. © 2022 IEEE.

关键词： Software design

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GIS-Based Surface Runoff Modeling Using Empirical Technique For A River Basin In South India

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Nature Environment and Pollution technology 2021年第5期20卷 2117-2123页

作者： Batvari, B. Prabhu Dass Nagamani, K. Centre for Earth and Atmospheric Sciences Sathyabama Institute of Science and Technology Chennai600119 India Centre for Remote Sensing and Geoinformatics Sathyabama Institute of Science and Technology Chennai600119 India

Precipitation is the primary source of fresh water in the world. Surface runoff will happen when the amount of rainfall is greater than the soil’s infiltration capacity. In most water resource applications, runoff is the most important hydrological variable. Aside from these rainfall characteristics, there are a number of catchment-specific elements that have a direct impact on runoff amount and volume. This research focuses on estimating surface runoff over the lower Vellar basin, a river basin in the southern part of India, by integrating Soil Conservation Service-Curve Number (SCS-CN) method with GIS. This technique is one of the most common methods used by hydrologists for estimating surface runoff. Curve Number (CN) is an index established by the Natural Resource Conservation Service (NRCS) to denote the potential for stormwater runoff. The nature of the watershed is explored first by creating land use and land cover pattern followed by the preparation of slope, drainage, and location maps. The area taken for this study is the lower Vellar basin situated in the Cuddalore District of Tamil Nadu, India. The curve number is analyzed using the rainfall data of 15 years (2001-2015) and the runoff is being calculated. The watershed pattern of the study area is also explored being analyzed and executed. Preservation of the runoff water is also discussed. © 2021 Technoscience Publications. All rights reserved.

关键词： Land use

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