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Analysis of differentiation effect of surface thermal environment in intensive mining areas based on NDVI-DFI model

Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 2020年第3期36卷 160-168页

作者： Hou, Chunhua Li, Fuping Feng, Yifan Gu, Haihong Yuan, Xuetao Song, Wen Liu, Huixin College of Mining Engineering North China University of Science and Technology Tangshan063210 China Hebei Key Laboratory of Mining Developmeng and Security Technology Tangshan063210 China Hebei Industrial Technology Institute of Mine Ecological Remediation Tangshan063210 China Co. Ltd. Qingdao266109 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China North China University of Science and Technology Library Tangshan063210 China

Currently, the dimidiate pixel model is used in most studies to discuss vegetation fractional coverage (VFC) and explore its relationship with urban heat islands. Few studies have used the three-component pixel model to invert vegetation fractional coverage (VFC) and discuss the relationship between VFC and the differentiation effect of the surface thermal environment in mining-intensive areas. In this study, VFC was inverted based on the normalized difference vegetation index (NDVI)-dry fuel index (DFI) three-component pixel model using landsat satellite remote sensing images from 2000 to 2018. Mixed pixels were decomposed into photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and bare soil (BS). The radiative transfer equation (RTE) was used to invert the surface temperature. The basic principle of this method is to remove the atmospheric influence and use the atmospheric radiation transfer equation;surface heat radiation intensity observed by the satellite is converted to the corresponding surface temperature. The parameters involved are mainly atmospheric profile parameters and surface specific emissivity. Heat-island index (HI) was used to grade the mean LST of the 2000-2018 study area. The relationship between the spatial-temporal changes in VFC and the differentiation effect of the surface thermal environment in the study area was analyzed using hierarchical statistics, a red-blue difference image method, correlation analysis, and regression analysis. On this basis, the influence mechanism of spatial-temporal variation in the VFC on the differentiation effect of surface thermal environment was studied. The results showed that the NDVI-DFI feature space of the study area conformed to the basic assumption of the three-component pixel model. The correlation matrix showed that there was a strong correlation between the changes in land surface temperature (LST) and VFC (the fractional cover of PV, fractional cover of NPV, and fractional cover of

关键词： Remote sensing

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Evaluating the accuracy of Chinese pasture data in global historical land use datasets

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Science China Earth Sciences 2018年第11期61卷 1685-1696页

作者： Fanneng HE Shicheng LI Fan YANG Meijiao LI Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China Department of Land Resource Management School of Public Administration China University of Geosciences Wuhan 430074 China University of Chinese Academy of Sciences Beijing 100049 China

Global historical land use datasets are widely used in global environmental change studies; however, uncertainties in the included pasture data have not been evaluated. In this study, using trend, relative difference ratio(RDR), and spatial comparisons, we evaluated the accuracy of China pasture data in the HYDE(versions 3.1 and 3.2), SAGE, and PJ datasets using historical document-based reconstructions, including the land use datasets for China covering 1935–1997(hereafter Ge-dataset)and Northeast China covering the 1700 s–2000(hereafter Ye-dataset), and the satellite-based China's land-Use/cover Datasets(CLUDs) for 1980–2015. Four important results were obtained.(1) China pasture area in the HYDE, SAGE, and PJ datasets and grassland area in the Ge-dataset, Ye-dataset, and CLUDs show both disparate trends and large differences in absolute values.(2)Spatially, 50.9%, 52.8%, and 63.0% pasture/grassland grids in 2000 had RDRs greater than 60% between HYDE3.1, HYDE3.2,and SAGE datasets, and CLUDs, respectively. The percentage of grids with RDRs less than 20% were 24.9%, 26.7%, and 16.0%,respectively.(3) Based on HYDE3.2, the spatial distribution of pasture in Northeast China over the last 300 year has expanded,which is in contrast to the results from the Ye-dataset, which shows the spatial distribution of grassland shrinking because of human reclamation activities.(4) The large contrasts between the global datasets and Chinese native datasets are due to differences in pasture/grassland definitions, land use practices, and spatial reconstruction methods.

关键词： Historical pasture/grassland Global datasets Accuracy evaluation China

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Reliable unmanned aerial vehicle-based thermal infrared target detection method for monitoring Procapra przewalskii in Qinghai

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Ecological Informatics 2025年 90卷

作者： Guoqing Zhang Wei Luo Yongxiang Zhao Quanqin Shao Lin Li keyu Mei Guohong Li North China Institute of Aerospace Engineering Langfang 065000 China National & Regional Joint Engineering Research Center for Aerospace Remote Sensing Application Technology Langfang 065000 China Aerospace Remote Sensing Information Processing and Application Collaborative Innovation Center of Hebei Province Langfang 065000 China Hebei Aerospace Remote Sensing Information Technology Innovation Center Langfang 065000 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China

Procapra przewalskii plays a vital role in maintaining ecological balance; however, it faces considerable threats due to habitat degradation and illegal poaching. Monitoring this species using unmanned aerial vehicles (UAVs) has proven to be an effective conservation strategy. A major challenge in UAV-based surveillance of Procapra przewalskii is conducting observations at night or under conditions of poor visible light. To address this issue, this paper presents a thermal infrared (TIR) target monitoring technique using UAVs. This technique employs YOLOv8s as the base model and proposes a multi-frame processing (MFP) method (YOLO-MFP). This method uses the current frame as the primary input and combines optical flow–processed images and background-suppressed images as auxiliary inputs. Background-suppressed images can effectively minimize most background pixels, while regions with high vector values in optical flow–processed images indicate object positions. The model extracts raw feature data, object details, and movement information from these inputs to improve detection performance. Additionally, a small target detection layer is added to reduce missed detections of smaller targets in TIR images while enhancing the overall detection accuracy. Furthermore, the VoVGSCSP module refines the model's neck architecture by effectively merging the feature maps across various stages, reducing computational demands without sacrificing detection precision. Finally, through numerous comparative experiments on our proposed TIR- Procapra przewalskii dataset, YOLO-MFP reaches a mean average precision ( [email protected] ) value of 96.4 %, precision value of 92.6 %, and recall of 97.0 %, making it superior to the current state-of-the-art models. The importance of this study lies in its enhanced monitoring capabilities for Procapra przewalskii , providing valuable insights for future UAV-based wildlife observation efforts.

关键词： Procapra przewalskii monitoring UAV Thermal infrared image YOLOv8s Multi-frame processing

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Potential range expansion of the red imported fire ant(Solenopsis invicta) in China under climate change

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Journal of Geographical Sciences 2018年第12期28卷 1965-1974页

作者：王焕炯王慧陶泽兴葛全胜 Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research CAS University of Chinese Academy of Sciences

The red imported fire ant（RIFA, Solenopsis invicta）, a notorious invasive insect, has received considerable attention owing to its impacts on native biodiversity, agriculture, and human health. Under global warming, the inhabitable area of the RIFA may be enlarged. However, few studies have focused on the potential range expansion of the RIFA in East Asia. Using a process-based physiological model, we simulated the potential range of the RIFA in China based on gridded temperature datasets for the current（2004–2012） and future（2090–2100） climates under Representative Concentration Pathway（RCP） 4.5 and RCP 8.5. It was found that the southeastern part of China（below 32°N） is suitable for RIFA proliferation. The present distribution area of the RIFA corresponds well with the potential range simulated by the model. In the RCP 4.5 and RCP 8.5 warming scenarios, the inhabitable area of the RIFA along the northern boundary would on average extend 101.3±85.7（mean±SD） and 701.2±156.9 km, respectively, by the end of the 21 st century. Therefore, future climate change would significantly affect the inhabitable area of the RIFA. Our results provide the basis for local quarantine officials to curtail accidental introductions of this insect, especially in the certain and possible infestation zones.

关键词： biological invasions distribution climate change red imported fire ant Solenopsis invicta

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Monitoring Intra-annual Spatiotemporal Changes in Urban Heat Islands in 1449 Cities in China Based on Remote Sensing

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Chinese Geographical Science 2019年第6期29卷 905-916页

作者： LI Yuanzheng WANG Lan ZHANG Liping LIU Min ZHAO Guosong School of Resources and Environment Henan University of Economics and LawZhengzhou 450046China Academician Laboratory for Urban and Rural Spatial Data Mining of Henan Province Henan University of Economics and LawZhengzhou 450046China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China Key Laboratory of Urban Environment and Health Institute of Urban EnvironmentChinese Academy of SciencesXiamen 361021China Center for Environmental Zoning Chinese Academy for Environmental PlanningMinistry of Environmental Protection of ChinaBeijing 100012China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101

This study aimed to accurately study the intra-annual spatiotemporal variation in the surface urban heat island intensities(SUHIIs) in 1449 cities in ***, China was divided into five environmental ***, the SUHIIs were accurately calculated based on the modified definitions of the city extents and their corresponding nearby rural ***, we explored the spatiotemporal variation of the mean, maximum, and minimum values, and ranges of SUHIIs from several *** results showed that larger annual mean daytime SUHIIs occurred in hot-humid South China and cold-humid northeastern China, and the smallest occurred in arid and semiarid west *** seasonal order of the SUHIIs was summer > spring > autumn > winter in all the temperate regions except west *** SUHIIs were obviously larger during the rainy season than the dry season in the tropical ***, significant differences were not observed between the two seasons within the rainy or dry *** the daytime, the maximum SUHIIs mostly occurred in summer in each region, while the minimum occurred in winter.A few cold island phenomena existed during the *** maximum SUHIIs were generally significantly positively correlated with the minimum SUHIIs during the daytime, nighttime and all-day in all environmental regions throughout the year and the four ***, significant correlation scarcely existed between the daytime and nighttime ranges of the *** addition, the daytime SUHIIs were also insignificantly correlated with the nighttime SUHIIs in half of the cases.

关键词： surface urban heat island intensities(SUHIIs) land surface temperature(LST) seasonal changes maximum and minimum SUHII cold island China

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Urban land expansion and its driving factors of mountain cities in China during 1990–2015

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Journal of Geographical Sciences 2018年第8期28卷 1152-1166页

作者：王自威吕昌河 Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research CAS College of Resources and Environment University of Chinese Academy of Sciences

land expansion of mountain cities in China is not systematically studied yet. This study identified 55 major mountain cities at and above prefecture level, and analyzed the land expansion characteristics and driving forces, based on visually interpreted data from TM images in 1990, 2000, 2010 and 2015. From 1990 to 2015, total built-up land area of the mountain cities increased by 3.87 times, 5.56% per year. The urban land growth was apparently accelerated after 2000, from 4.35% per year during 1990-2000 increased to 6.47% during 2000-2010 and 6.2% during 2010-2015. Compared to the urban population growth, the urban land expansion rate was 44% higher. As a result, the urban land area per capita increased, but it was still within the government control target, and also was much lower than the average of all cities in China. Urban development policy, changes to administrative divisions, GDP and population growth, and road construction were identified as the major driving forces of land expansion. Terrain conditions were not found a relevance to the urban land expansion rate during 1990-2015, but had a significant impact on the layout and shape, and also probably on the urban land efficiency.

关键词： mountain cities urban land expansion driving forces elastic coefficient city compactness

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The 5.5 cal ka BP climate event, population growth, circumscription and the emergence of the earliest complex societies in China

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Science China Earth Sciences 2018年第2期61卷 134-148页

作者： WU WenXiang ZHENG HongBo HOU Mei GE QuanSheng Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China Research Center for Earth System Science Yunnan University Kunming 650091 China University of Chinese Academy of Sciences Beijing 100049 China

The emergence of complex society is a milestone in the history of human society evolution. China is one of the few regions in the world where the earliest complex society appeared; however, its driving mechanisms remain unresolved. On the base of available evidence from both archaeology and Holocene climate, in combination with agency theory, this study attempts to address the driving mechanisms for the simultaneous emergence of complex societies in multiple areas of China around 5.5 cal ka BP. It is hypothesized that three factors, including climate change, population growth, and circumscription, jointly act and cause regional population-resource imbalance and trigger inter-group conflicts and wars. Such competitions provide the opportunity for some power-pursuing agents to break the restriction of social leveling mechanism and to become the centralized decision-making leaders, which further lead to the emergence of incipient large-scale complex societies. Increase in extreme climate events during 6.0–5.0 cal ka BP cooling period causes frequent occurrence of resource stress and increase in the frequency of inter-group competitions, which creates conditions for the legitimation, institutionalization, and persistence of centralized leadership, and finally leads to the formation of persistent institutionalized inequity. Our research result can explain not only the process and mechanism of complex society formation, but also two phenomena which cannot be reasonably explained by previous theories, that are, why the earliest complex societies in China emerge around 5.5 cal ka BP, and why they appear simultaneously in multiple regions.

关键词： 5.5 cal ka BP climate event Population growth Circumscription Complex society Driving mechanisms

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Corrigendum to "Hair-biomonitoring assessment of rare-earth-element exposure in residents of the largest rare-earth mining and smelting area of China" [Environ. Int. 179 (2023) 108177]

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Environment international 2024年 194卷 109161页

作者： Lijun Dai Jinsong Ge Lingqing Wang Xiaoming Wan Guanghui Guo Tao Liang Nanthi Bolan Thilo Rennert Jörg Rinklebe Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Ecological Environment Planning and Environmental Protection Technology Center of Qinghai Province Xining 810007 China. Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China Ecological Environment Planning and Environmental Protection Technology Center of Qinghai Province Xining 810007 China University of Wuppertal School of Architecture and Civil Engineering Institute of Foundation Engineering Water- and Waste-Management Soil- and Groundwater-Management Pauluskirchstraße 7 42285 Wuppertal Germany. Electronic address: wanglq@***. UWA School of Agriculture and Environment The UWA Institute of Agriculture The University of Western Australia Perth WA 6001 Australia. University of Hohenheim Institute of Soil Science and Land Evaluation Department of Soil Chemistry and Pedology 70593 Stuttgart Germany. University of Wuppertal School of Architecture and Civil Engineering Institute of Foundation Engineering Water- and Waste-Management Soil- and Groundwater-Management Pauluskirchstraße 7 42285 Wuppertal Germany. Electronic address: rinklebe@uni-wuppertal.de.

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Global observation of urban expansion and land-cover dynamics usingsatellite big-data

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Science Bulletin 2021年第4期66卷 297-300页

作者： Wenhui Kuang Guoming Du Dengsheng Lu Yinyin Dou Xiaoyong Lid Shu Zhang Wenfeng Chi Jinwei Dong Guangsheng Chen Zherui Yin Tao Pan Rafiq Hamd Yali Hou Chunyang Chen Han Li Chen Miao Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China School of Public Administration and Law Northeast Agricultural UniversityHarbin 150030China School of Geographical Sciences Fujian Normal UniversityFuzhou 350007China University of Chinese Academy of Sciences Beijing J00049China State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China School of Resources and Environmental Economics Inner Mongolia University of Finance and EconomicsHohhot 010017China School of Environmental&Resource Sciences Zhejiang Agriculture and Forestry UniversityHangzhou 311300China Xinjiang Institute of Ecology and Geography Chinese Academy of SciencesUrumqi 830011China Royal Meteorological Institute of Belgium Brussels 1180Belgium National Remote Sensing Center of China Beijing 100036China

Humans deeply influence the urbanizing of earth’s surface system in an exacerbating manner across space and time[1].Around the globe,urban land-use/cover changes reflect the intensities of human activities and land s... 详细信息

关键词： dominated expansion dynamics

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Near-real-time global gridded daily CO2 emissions 2021

arXiv

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arXiv 2022年

作者： Dou, Xinyu Hong, Jinpyo Ciais, Philippe Chevallier, Frédéric Yan, Feifan Yu, Ying Hu, Yifan Huo, Da Sun, Yun Wang, Yilong Davis, Steven J. Crippa, Monica Janssens-Maenhout, Greet Guizzardi, Diego Solazzo, Efisio Lin, Xiaojuan Song, Xuanren Zhu, Biqing Cui, Duo Ke, Piyu Wang, Hengqi Zhou, Wenwen Huang, Xia Deng, Zhu Liu, Zhu Department of Earth System Science Tsinghua University Beijing100084 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Laboratoire des Sciences du Climat et de l'Environnement LSCE/IPSL CEA-CNRS-UVSQ UniversitéParis-Saclay Gif-sur-Yvette France Key Laboratory of Marine Environment and Ecology Frontiers Science Center for Deep Ocean Multispheres and Earth System Ministry of Education Ocean University of China Qingdao266100 China Environmental Sciences and Engineering University of North Carolina at Chapel Hill Chapel Hill United States Key Laboratory of Sustainable Forest Ecosystem Management Northeast Forestry University Harbin150040 China School of Environmental Science and Engineering Tianjin University Tianjin300072 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Department of Earth System Science University of California IrvineCA United States Ispra Italy Product and Solution & Website Business Unit Alibaba Cloud Hangzhou311121 China

We present a near-real-time global gridded daily CO2 emissions dataset (GRACED) throughout 2021. GRACED provides gridded CO2 emissions at a 0.1°×0.1°spatial resolution and 1-day temporal resolution from cement production and fossil fuel combustion over seven sectors, including industry, power, residential consumption, ground transportation, international aviation, domestic aviation, and international shipping. GRACED is prepared from a near-real-time daily national CO2 emissions estimates (Carbon Monitor), multi-source spatial activity data emissions and satellite NO2 data for time variations of those spatial activity data. GRACED provides the most timely overview of emissions distribution changes, which enables more accurate and timely identification of when and where fossil CO2 emissions have rebounded and decreased. Uncertainty analysis of GRACED gives a grid-level two-sigma uncertainty of value of ±19.9% in 2021, indicating the reliability of GRACED was not sacrificed for the sake of higher spatiotemporal resolution that GRACED provides. Continuing to update GRACED in a timely manner could help policymakers monitor energy and climate policies’ effectiveness and make adjustments quickly. © 2022, CC BY.

关键词： Uncertainty analysis

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