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A new snow depth retrieval method by improved hybrid DEM differencing and coherence amplitude algorithm for PolInSAR

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JOURNAL OF HYDROLOGY 2024年 628卷

作者： Qiao, Haiwei Zhang, Ping Li, Zhen Huang, Lei Gao, Shuo Liu, Chang Wu, Zhipeng Liang, Shuang Zhou, Jianmin Sun, Wei Wang, Jian Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Natl Engn Lab Satellite Remote Sensing Applicat Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Anhui Univ Sci & Technol Sch Spatial Informat & Geomatics Engn Huainan 230001 Peoples R China

Snow depth is a critical parameter for measuring the variations of the snowpack, and its measurement also in-fluences the accuracy of hydrological and climate models. Polarimetric SAR interferometry (PolInSAR) tech-nology combines the capabilities of Polarimetric SAR (PolSAR) and interferometric SAR (InSAR), and has significant potential for retrieving snow depth. The hybrid DEM differencing and coherence amplitude algorithm (HDCA) is a robust algorithm for PolInSAR data, but encounters such problems as uncertainty in finding the ground scattering phase center, impure volume decorrelation, and the lack of consideration of the dense medium characteristic of snow. In this study, the HDCA is improved by optimizing the phase and decorrelation param-eters and by considering the dense medium characteristic of snow. First, the Freeman-Duren decomposition method is put forward to extract the ground and volume scattering phase centers from the snow. Second, the coherence region boundary estimation method is proposed to optimize the volume decorrelation. Following this, the parameter assumed by the original method in free space is adjusted by considering the dense medium characteristic of snow. Finally, the Ku band UAV SAR data were applied to validate the new method, and in-situ data were used to assess its accuracy. The correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the proposed method are 0.93, 3.59 cm, and 2.98 cm, respectively, and were signifi-cantly superior to those of the original HDCA method of 0.74, 7.44 cm, and 6.04 cm, respectively.

关键词： PolInSAR Snow Depth IHDCA UAV-borne Ku-band

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RETRACTED: Remote sensing image landmark segmentation algorithm based on improved GSA and PCNN combination (Retracted Article)

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intERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION 2020年第1-Sup期60卷 665-679页

作者： Liu, Zilong Chen, Guobin Haikou Univ Econ Haikou Hainan Peoples R China Chongqing Technol & Business Univ Chongqing Key Lab Spatial Informat Data Min & Big Rongzhi Coll Chongqing 401320 Peoples R China

Application of Remote Sensing Technology in the Development of Urban Functions is presented. Remote sensing image processing for ecological protection and monitoring, this paper proposes a remote sensing image landmark segmentation algorithm based on the IGSA and PCNN. Because the GSA algorithm has the disadvantages of premature convergence and is easy to fall into the local optimal solution, the improved IGSA algorithm is used to extract the ratio of the image entropy and energy to IGSA, and the entropy change value is used as the IGSA algorithm. Based on the global search ability of IGSA, the optimal value of the key parameters affecting the segmentation effect in PCNN model is found. Finally, through experimental comparison, the proposed method has strong advantages in segmentation effect, real-time and robustness

关键词： Remote sensing technology remote sensing image IGSA PCNN

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Multilayer network analysis of idiosyncratic volatility connectedness: Evidence from China

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PACIFIC-BASIN FINANCE JOURNAL 2024年 88卷

作者： Zhou, Xuewei Ouyang, Zisheng Lu, min Ouyang, Zhongzhe Shanghai Univ Finance & Econ Sch Finance Shanghai 200433 Peoples R China Shanghai Inst Int Finance & Econ Shanghai 200433 Peoples R China Hunan Normal Univ Business Sch Changsha 410081 Hunan Peoples R China Hunan Key Lab Macroecon Big Data Min & its Applica Changsha 410081 Hunan Peoples R China Guangzhou Univ Sch Econ & Stat Guangzhou 510006 Guangdong Peoples R China

This paper proposes multilayer networks, including lagged, contemporaneous, and long-run networks, to examine the idiosyncratic connectedness among Chinese financial institutions. We explore the topology of multilayer networks through static and dynamic analysis to capture the information transmission mechanism of idiosyncratic risks. Moreover, we investigate the drivers influencing idiosyncratic connectedness across financial institutions. We find that idiosyncratic risk contagion among financial institutions is stronger in the long-run network. At the same time, we note that the three networks contain different connection structures, which means that the information transmission mechanism of idiosyncratic risks is heterogeneous in multilayer networks. In addition, when the financial system is under stress, we observe that the contemporaneous effect of idiosyncratic connectedness rises rapidly. Institutional level analysis shows that diversified financial institutions play active roles in the lagged network, securities institutions are transmitters of contemporaneous information spillovers, and banking institutions are the main drivers of the long-run network. Finally, our study shows that size is the main factor driving idiosyncratic risk spillovers among financial institutions.

关键词： Idiosyncratic connectedness Financial institutions Multilayer networks Risk spillovers

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Application of Grey System Model to Forecast the Natural Gas Imports in China

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MATHEMATICAL PROBLEMS IN ENGINEERING 2021年第1期2021卷

作者： Shi, Zhuan-Zhuan Gou, Xiao-Yi Zeng, Bo Chongqing Finance & Econ Coll Int Cooperat & Exchange Off Chongqing 401320 Peoples R China Chongqing Technol & Business Univ Sch Management Sci & Engn Chongqing 400067 Peoples R China Chongqing Finance & Econ Coll Chongqing Key Lab Spatial Data Min & Big Data Int Chongqing 401320 Peoples R China

China's natural gas imports will keep an upward trend in the future due to its increasing demands. A comparatively accurate prediction of natural gas imports will help the Chinese government make appropriate decisions when formulating energy policies. In this paper, a new grey predication model, GPM_NGI model, was proposed to forecast China's natural gas imports. Compared with GM (1, 1) and DGM (1, 1) model, the proposed new model performed better in the simulation process and bore smaller mean relative percentage error when used in simulating China's natural gas imports from 2011 to 2019. Then, the new model was employed to forecast China's natural gas imports from 2020 to 2022. The results showed that China's natural gas imports would continue to grow rapidly over the next three years. Therefore, in order to strike a balance between the natural gas supply and demand in the future and avoid overdependence on imports, the Chinese government should take effective measures from both the supply and demand ends, such as making better use of shale gas, wind, and solar energy as well as reducing the industrial dependence on natural gas.

关键词： Forecasting

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A Novel Method for Snow Depth Retrieval Using Improved Dense Medium RVoG Model

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 1874-1884页

作者： Qiao, Haiwei Zhang, Ping Li, Zhen Huang, Lei Wu, Zhipeng Gao, Shuo Liu, Chang Liang, Shuang Zhou, Jianmin Sun, Wei Wang, Jian Chinese Acad Sci Aerosp Informat Res Inst Key Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Natl Engn Lab Satellite Remote Sensing Applicat Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Anhui Univ Sci & Technol Sch Spatial Informat & Geomatics Engn Huainan 230001 Peoples R China

Snow depth is a fundamental parameter in hydrological and climate models, which is crucial for studying climate change, hydrological cycles, and ecosystem changes. Polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the most promising snow depth retrieval methods, which is sensitive to the shape, direction, and vertical distribution of targets. The dense medium random-volume-over-ground (DM-RVoG) model for PolInSAR has been shown to be workable for snow depth retrieval, it still suffers from the inaccuracy of the parameters representing the phase center and decorrelation. In this study, based on the backscattering mechanism of snow, a novel snow depth retrieval method is proposed to improve the DM-RVoG model using polarization decomposition and decorrelation optimization. First, the polarization decomposition is extended to obtain the ground scattering phase. Then, the coherence region boundary estimation method is put forward to obtain pure volume decorrelation. Finally, the proposed method is validated using Ku-band UAV SAR data, and the accuracy is assessed using in situ data. The correlation coefficient, root mean square error, and mean absolute error of the proposed method are 0.88, 4.98, and 4.08 cm, respectively, demonstrating significant improvements compared with the original method.

关键词： Climate change Climatology Snow Hydrologic measurements Modeling Weather forecasting Weather modification Measurement Improved dense medium random-volume-over-ground (RVoG) model interferometry Ku-band polarimetric synthetic aperture radar interferometry (PolInSAR) snow depth UAV synthetic aperture radar (SAR)

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GF-5 hyperspectral inversion of soil parameters using a VAE style-based spectral fusion model

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 233卷

作者： Ou, Depin Li, Jie Wu, Zhifeng Tan, Kun Ma, Weibo Wang, Xue Zhu, Yueqin Minist Emergency Management Natl Inst Nat Hazards Beijing 100085 Peoples R China Minist Ecol & Environm Peoples Republ China South China Inst Environm Sci Guangzhou 510655 Peoples R China Guangzhou Univ Sch Geog & Remote Sensing Guangzhou 510006 Peoples R China East China Normal Univ Key Lab Geog Informat Sci Minist Educ Shanghai 200241 Peoples R China East China Normal Univ Key Lab Spatial Temporal Big Data Anal & Applicat Minist Nat Resources Shanghai 200241 Peoples R China Minist Ecol & Environm Nanjing Inst Environm Sci Nanjing 210042 Peoples R China China Univ Min & Technol Engn Res Ctr Minist Educ Mine Ecol Restorat Xuzhou 221116 Jiangsu Peoples R China

Inverting soil parameters through hyperspectral techniques is currently one of the highly popular research topics and the major challenges in quantitative remote sensing. To date, indoor spectral data-based inversion models cannot be directly applied to satellite-based hyperspectral data, due to the weak model migration capability caused by the large differences between the two spectral data. Therefore, the present study aims to improve the inversion soil parameter accuracies using satellite-based GF-5 hyperspectral remote sensing data by merging multiple hyperspectral data. First, indoor Analytical Spectral Devices (ASD) hyperspectral and pre-processed GF5 data of soil samples were used to develop a variational auto-encoder (VAE)-based spectral fusion model capable of transforming GF-5 spectra into indoor spectra. Second, traditional machine learning regression algorithms, namely Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR), were used to build an inversion model using the mixed spectra data to determine the spatial distributions of soil organic matter (SOM), arsenic (As) and copper (Cu) contents across a large study area. The results demonstrated the effectiveness of the VAE-based spectral fusion model in removing substantial noise information while preserving the spectral features from the GF-5 data. The optimal inversion accuracies of the SOM, As, and Cu contents showed coefficients of determination (R2) of 0.87, 0.88, and 0.85, which are 38%, 55%, and 28% higher than those obtained using the original GF-5 data-derived model, respectively. In addition, the spatial distributions of the SOM, As, and Cu contents demonstrated that the GF-5 satellite data are more intuitive and effective for large-scale soil composition analysis.

关键词： GF-5 hyperspectral image Spectra data fusion Soil organic matter Soil heavy metals VAE

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Satellite-Based Water Quality Assessment of the Beijing Section of the Grand Canal: Implications for SDG11.4 Evaluation

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REMOTE SENSING 2024年第5期16卷 909页

作者： Xie, Ya Zhou, Qing Xiao, Xiao Chen, Fulong Huang, Yingchun Kang, Jinlong Wang, Shenglei Zhang, Fangfang Gao, min Du, Yichen Shen, Wei Li, Junsheng China Univ Geosci Sch Earth Sci & Resources Beijing 100083 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Beijing Inst Surveying & Mapping Beijing 100038 Peoples R China Beijing Key Lab Urban Spatial Informat Engn Beijing 100038 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Shaanxi Inst Surveying & Mapping Land Ltd Xian 710054 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

The Beijing-Hangzhou Grand Canal in China became a World Cultural Heritage Site in 2014, and the water quality of this ancient man-made canal has increasingly attracted societal attention. This study focuses on monitoring the water quality of the Beijing section of the Grand Canal (BGC) using remote sensing technology. Analysis of the comprehensive trophic level index (TLI) indicates that the water in the Canal was predominantly light eutrophic from 2016 to 2022. The annual average results of the TLI reveal that the water quality in the Kunming Lake and North Canal of BGC is generally good, characterized by some mesotrophic waters, and others are in light eutrophication. The TLI for the entire BGC water body decreased from 64.7 in 2016 to 60.3 in 2022, indicating an improvement trend in water quality. Notably, the proportion of good water with TLI less than 60 increased from 50% in 2016 to 83% in 2022. This improvement of water quality may be attributed to the reduced use of fertilizers and pesticides and the implementation of various environmental policies by Beijing Municipal government. These findings offer valuable insights for the management and protection of the water resources of the BGC, and further contribute to the evaluation of the United Nations Sustainable Development Goal (SDG) 11.4.

关键词： grand canal water quality eutrophic comprehensive trophic level index SDG

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Object Tracking by the Least Spatiotemporal Searches

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IEEE intERNET OF THINGS JOURNAL 2021年第16期8卷 12934-12946页

作者： Yu, Zhiyong Han, Lei Chen, Chao Guo, Wenzhong Yu, Zhiwen Fuzhou Univ Coll Math & Comp Sci Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou 350108 Peoples R China Fuzhou Univ Fujian Key Lab Network Comp & Intelligent Informa Fuzhou 350108 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Chongqing Univ Sch Comp Sci Chongqing 400044 Peoples R China

Tracking a suspicious car or a person in a city efficiently is crucial in urban safety management. But how can we complete the task with the minimal number of spatiotemporal searches when massive camera records are involved? To this end, this study proposes a strategy named intermediate searching at heuristic moments (IHMs). At each step, we determine which moment is the best one for the search based on a heuristic indicator. Then, at that moment, locations are searched one by one in descending order of predicted appearing probabilities until a search hit is obtained. We iterate this step until we derive the object's current location. Five searching strategies are compared via experiments. Among these strategies, the IHMs strategy is validated as the most efficient. IHMs can save up to 1/3 of the total cost. This result provides evidence that "searching at intermediate moments can save cost."

关键词： Search problems Spatiotemporal phenomena Automobiles Trajectory Cameras Prediction algorithms Law enforcement Heuristic algorithms Markov decision process (MDP) mobility prediction spatiotemporal searches trajectory analysis ubiquitous computing

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Quantifying landscape connectivity gaps between protected area and natural habitat

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JOURNAL OF CLEANER PRODUCTION 2024年 437卷

作者： Mu, Haowei Guo, Shanchuan Li, Xuecao Zhou, Yuyu Lu, Yihe Du, Xiaoping Huang, Jianxi Ma, Chaode Zhang, Xingang Xia, Zilong Fang, Hong Du, Peijun Nanjing Univ Sch Geog & Ocean Sci Jiangsu Prov Key Lab Geog Informat Sci & Technol Key Lab Land Satellite Remote Sensing ApplicatMin Nanjing 210023 Jiangsu Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Resou Nanjing 210023 Jiangsu Peoples R China China Agr Univ Coll Land Sci & Technol Beijing 100083 Peoples R China Univ Hong Kong Dept Geog Hong Kong 999077 Peoples R China Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China Aerosp Informat Res Inst Chinese Acad Sci Key Lab Digital Earth Sci Beijing 100094 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China UN Dev Programme China Beijing 100600 Peoples R China

Protected areas are crucial for safeguarding biodiversity and mitigating significant threats. However, the effectiveness of protected areas has been debated, particularly regarding their anthropogenic protection's impact on species migration within the current protected area network. To address this issue, we calculated the connectivity between landscape elements (e.g., Core, Patch, and Connector) in China based on the resistance theory, taking the Core with high habitat quality as a source. We found that the landscape connectivity of protected areas in China is 44% of that of the natural habitats (i.e., Core), indicating that the effectiveness of the connected network of protected areas in China requires improvement. Nevertheless, the planning of protected areas in the Yellow River Basin makes landscape connectivity higher than that of natural habitats. In addition, the species abundance in protected areas is lower than that of natural habitats, overall, species abundance increases with habitat quality and landscape connectivity. Our results indicate that the previously protected area planning mainly focused on mammals while overlooking reptiles but reducing landscape connectivity gaps can enhance species abundance. The findings underscore the importance of incorporating landscape connectivity into the effectiveness evaluation system of protected area networks.

关键词： Landscape connectivity Species migration Habitat quality Biodiversity conservation Ecological corridor Protected areas

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ROAD EXTRACTION BASED ON DEEP LEARNING USING SDGSAT-1 NIGHTTIME LIGHT data

ROAD EXTRACTION BASED ON DEEP LEARNING USING SDGSAT-1 NIGHTT...

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IEEE international Geoscience and Remote Sensing Symposium (IGARSS)

作者： Xie, Qiyuan Li, Hui Jing, Linhai Zhang, Kongwen Fuzhou Univ Zijin Sch Geol & Min Fuzhou 350108 Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing 100094 Peoples R China Hainan Aerosp Informat Res Inst Hainan Key Lab Earth Observat Wenchang 571399 Peoples R China Univ Fraser Valley Sch Comp 33844 King Rd Abbotsford BC V2S 7M7 Canada

ISBN: (纸本)9798350360332;9798350360325

Previous road extraction research based on remote sensing data mainly used traditional optical remote sensing imagery acquired during the daytime, but some roads on the images were affected by problems such as building shadows and similar spectral features of road with other materials, which reduced the accuracy of road extraction. The Glimmer Imager (GI) equipped on the Sustainable Development Science Satellite 1 (SDGSAT-1) provides nighttime light (NTL) images with relatively high spatial resolution (RGB: 40m;PAN: 10m), which show detail of road networks. In order to explore the potential of the NTL data obtained from SDGSAT-1 on road extraction,a deep learning framework combining ResUnet and Dynamic Snake Convolution (DSC) module was proposed in this study. The experimental results demonstrated the effectiveness of SDGSAT-1 NTL on road extraction tasks and the extracted roads can be used for road dataset updating.

关键词： Road extraction SDGSAT-1 Nighttime light data Deep learning OSM

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