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Land Surface and Cryosphere remote sensing V 2024

作者： Pham, Chi Cuong Lin, Tang-Huang International Master of Science Program in Remote Sensing Science and Technology Center for Space and Remote Sensing Research National Central University Taoyuan32001 Taiwan Center for Space and Remote Sensing Research National Central University Taoyuan32001 Taiwan

ISBN: (数字)9781510682696

ISBN: (纸本)9781510682689

In the context of increasing climate change and rapid urban expansion in Ho Chi Minh City, rising temperatures pose significant heat stress and health risks to residents. Assessing heat vulnerability is crucial for improving thermal comfort and achieving sustainable urban development. However, there is limited research on heat vulnerability within Local Climate Zones (LCZs), which are essential for understanding the relationship between urban form and heat exposure. This study integrates the Heat Vulnerability Index (HVI) with LCZ analysis to evaluate heat vulnerability across the city. Data were sourced from Landsat 8 to calculate Land Surface Temperature (LST), the Enhanced Normalized Difference Impervious Surfaces Index (ENDISI), the Modified Normalized Difference Water Index (MNDWI), and for LCZ classification using a random forest algorithm;LandScan for population density;OpenStreetMap for road and medical facility density;and VIIRS for nighttime lighting. The HVI model combines exposure (represented by Land Surface Temperature), sensitivity (calculated as an average of population density, road density, and ENDISI), and adaptability (calculated as an average of nighttime lighting, medical facilities, and MNDWI). The results show that built LCZs have significantly higher thermal exposure, sensitivity, and adaptability compared to natural LCZs. Greater vulnerability was observed in urbanized areas, with LCZ1 and LCZ2 having the highest HVI scores, and over 90% of their areas classified as highly vulnerable. Additionally, 158 out of 315 wards had HVI values exceeding 60%, with most located in urban areas. Notably, rural wards such as Pham Van Coi, Cu Chi, and Hoa Phu were identified as particularly vulnerable and in need of immediate attention. In conclusion, integrating LCZ and HVI provides key insights into heat vulnerability patterns, helping future studies optimize the spatial layouts of local climate zones to mitigate heat impacts and improve the urban therma

关键词： Health risks

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DEEP LEARNING-BASED APPROACH FOR ANNUAL CHANGE DETECTION AND OPEN PIT COAL MINE DETECTION USING SATELLITE IMAGERY 44

DEEP LEARNING-BASED APPROACH FOR ANNUAL CHANGE DETECTION AND...

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44th Asian Conference on remote sensing, ACRS 2023

作者： Prasetya, Koni Dwi Tsai, Fuan International PhD Program in Environmental Science and Technology National Central University 300 Jhongda Rd. Jhongli Taoyuan32001 Taiwan Center for Space and Remote Sensing Research National Central University 300 Jhongda Rd. Jhongli Taoyuan32001 Taiwan

ISBN: (纸本)9781713893646

This study introduces a novel deep learning-based method for detecting open pit coal mines in medium high (MH)resolution satellite imagery and analyzing annual variations in coal mining areas. The study aims to monitor and precisely identify coal mines, given their vital function as a primary energy source and a significant contributor to greenhouse gas emissions. The proposed method employs the U-Net architecture and ResNet 34 as its backbone for accurate detection and classification. The applied dataset consists of multispectral imagery from Sentinel-2 and synthetic aperture radar (SAR) imagery from Sentinel-1. Manual labelling of known coal mine locations using mining data as a reference, subdividing Sentinel image patches for training U-Net convolutional neural networks (CNNs) to classify coal mine and non-coal mine areas, and training and testing U-Net architecture and ResNet as a backbone deep learning model are the three essential steps involved in the process. The classification accuracy of the coal mining detection deep learning model is 97%, and the kappa value is 0.91. Preliminary results indicate that the investigation demonstrates the evolution of coal mining from 2016 to 2021, with an increase of over 40% in coal mining area in 2019. In 2017 and 2020, the area mined for coal will decrease. Variations in annual coal mining variations emphasize the significance of replanting efforts. The proposed method uses deep learning and satellite imagery to provide an accurate and efficient solution for detecting and monitoring open-pit coal mines. Incorporating artificial intelligence into dynamic coal mining activities yields valuable insights that aid in making informed decisions for sustainability. © 2023 ACRS. All Rights Reserved.

关键词： Artificial Intelligence Coal Mine Deep Learning Monitoring Multispectral SAR Sentinel Supervised Classification U-Net

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INTEGRATING SENTINEL-1 SYNTHETIC APERTURE RADAR AND CHIRPS DATA FOR UNDERSTANDING HEAVY PRECIPITATION AND FLOODING IN AN GIANG PROVINCE, VIET NAM 44

INTEGRATING SENTINEL-1 SYNTHETIC APERTURE RADAR AND CHIRPS D...

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44th Asian Conference on remote sensing, ACRS 2023

作者： Minh, Ton Binh Chiang, Shou-Hao International Ph.D Program in Environmental Science and Technology University System of Taiwan No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Center for Space and Remote Sensing Research National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan

ISBN: (纸本)9781713893646

Understanding the mechanisms of heavy precipitation and flooding is essential for effective climate change adaptation and response. The cloud-penetrating ability of microwaves makes Synthetic Aperture Radar (SAR) an ideal tool for providing weather-independent images for flood mapping. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a land-only rainfall dataset that incorporates 0.05° resolution satellite imagery with in-situ station data to create gridded rainfall time series for various analysis applications. This study employs intensity images from Sentinel-1 satellites to generate flood maps and utilizes CHIRPS to derive heavy rainfall frequency. The segmentation technique is applied to distinguish between pre-existing standing water, recently flooded areas, and non-flooded regions within the study area. By combining SAR and CHIRPS data, this research yields valuable insights into the relationship between heavy rainfall and flood occurrences in An Giang province, Viet Nam. The escalating frequency of heavy precipitation correlates with an augmentation in local rainfall amounts, and a positive correlation emerges between heavy rainfall and the extension of flood-prone areas. While recognizing that flooding influences water flow from the upstream of the Mekong River, flooding discharges from neighboring areas, and tidal levels, the accelerated trend of urbanization has also played a significant role. The expansion of impervious surfaces contributes to the severity of deep floods and elevates flood potential in the region. Furthermore, this research affirms the effectiveness of SAR intensity image mapping for flood detection, and the integration of SAR and CHIRPS data furthers our understanding of heavy precipitation mechanisms. As the implications of climate change persist, the findings are crucial for developing sustainable flood management strategies, as well as enhancing resilience to climate-induced challenges in the Mekong River Del

关键词： CHIRPS climate change Flood Mekong River Delta Sentinel-1

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POST-CLASSIFICATION ENHANCEMENT IN THE RESULT OF DEEP LEARNING LAND COVER CLASSIFICATION USING VERY-HIGH RESOLUTION SATELLITE IMAGERY 44

POST-CLASSIFICATION ENHANCEMENT IN THE RESULT OF DEEP LEARNI...

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44th Asian Conference on remote sensing, ACRS 2023

作者： Hakim, Yofri Furqani Tsai, Fuan International Ph.D. Program in Environmental Science and Technology National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan Center for Space and Remote Sensing Research National Central University No. 300 Zhongda Rd. Zhongli District Taoyuan City32001 Taiwan

ISBN: (纸本)9781713893646

Land cover is one of the fundamental data utilized in spatial analysis for a range of applications, including climate, environment, natural resources, agriculture, forestry, planning, health, and even social issues. The demand for land cover data is diverse, ranging from global scale, regional scale, and detailed scale. Furthermore, data updates also become a necessity for users. The current trend is the requirement for more accurate and up-to-date land cover data. The growing demand for accurate and up-to-date land cover data is in sync with improvements in satellite imagery data acquisition technology, which now offers improved spatial resolution and more effective satellite imagery data processing. Satellite imagery data processing technology is also growing rapidly with the advance of artificial intelligence for semantic classification or segmentation. This research uses a deep learning approach to classify land cover on Pleiades very high-resolution satellite imagery. A post-classification enhancement is also carried out to improve the consistency and accuracy of the deep learning classification results. The preliminary research results show that post-classification enhancement with the algorithms proposed in this study can increase the accuracy of classification results using deep learning-based approaches by approximately 2%. The original Overall Accuracy and Kappa of the deep learning classification results were 0.84 and 0.79. After the post-classification enhancement process, the Overall Accuracy and Kappa values increased to 0.86 and 0.81. © 2023 ACRS. All Rights Reserved.

关键词： Deep Learning Image Morphology Land Cover Classification Post-Classification Enhancement Semantic Segmentation U-Net

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Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave remote-sensing Datasets from 2013 to 2019

Journal of Remote Sensing

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Journal of remote sensing 2023年第1期3卷 19-34页

作者： Zhongbing Chang Lei Fan Jean-Pierre Wigneron Ying-Ping Wang Philippe Ciais Jérôme Chave Rasmus Fensholt Jing MChen Wenping Yuan Weimin Ju Xin Li Fei Jiang Mousong Wu Xiuzhi Chen Yuanwei Qin Frédéric Frappart Xiaojun Li Mengjia Wang Xiangzhuo Liu Xuli Tang Sanaa Hobeichi Mengxiao Yu Mingguo Ma Jianguang Wen Qing Xiao Weiyu Shi Dexin Liu Junhua Yan Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems South China Botanical GardenChinese Academy of SciencesGuangzhou 510650China University of Chinese Academy of Sciences Beijing 100049China Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouthwest UniversityChongqing 400715China INRAE UMR1391 ISPAUniversite de BordeauxF-33140 Villenave d’OrnonFrance CSIRO Oceans and Atmosphere AspendaleVIC 3195Australia Laboratoire des Sciences du Climat et de l’Environnement CEA/CNRS/UVSQ/Universite Paris SaclayGif-sur-YvetteFrance Laboratoire Evolution et Diversite Biologique Universite Paul SabatierToulouseFrance Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagenDenmark Department of Geography and Program in Planning University of TorontoTorontoON M5S 3G3Canada College of Geographical Science Fujian Normal UniversityFuzhou 3500007FujianChina School of Atmospheric Sciences Sun Yat-sen UniversityZhuhai 519082GuangdongChina Southern Marine Science and Engineering Guangdong Laboratory Zhuhai 519000GuangdongChina Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Institute of Tibetan Plateau Research Chinese Academy of SciencesBeijing 100101China CAS Center for Excellence in Tibetan Plateau Earth Sciences Chinese Academy of SciencesBeijing 100101China Department of Microbiology and Plant Biology Center for Spatial AnalysisUniversity of OklahomaNormanOKUSA LEGOS Universite de ToulouseCNESCNRSIRDUPS-14 avenue Edouard Belin31400 ToulouseFrance State Key Laboratory of Remote Sensing Science Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Climate Change Research Centre University of N

Over the past 2 to 3 decades,Chinese forests are estimated to act as a large carbon sink,yet the magnitude and spatial patterns of this sink differ considerably among *** 3 microwave(L-and X-band vegetation optical depth[VOD])and 3 optical(normalized difference vegetation index,leaf area index,and tree cover)remote-sensing vegetation products,this study compared the estimated live woody aboveground biomass carbon(AGC)dynamics over China between 2013 and *** results showed that tree cover has the highest spatial consistency with 3 published AGC maps(mean correlation value R=0.84),followed by L-VOD(R=0.83),which outperform the other *** AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to *** performance of the AGC estimation model was good(root mean square error=0.05 Pg C and R^(2)=0.90 with a mean relative uncertainty of 9.8% at pixel scale[0.25°]).Results of the AGC estimation model showed that carbon uptake by the forests in China was about+0.17 Pg C year^(-1) from 2013 to *** the regional level,provinces in southwest China including Guizhou(+22.35 Tg C year^(-1)),Sichuan(+14.49 Tg C year^(-1)),and Hunan(+11.42 Tg C year^(-1))provinces had the highest carbon sink rates during 2013 to *** of the carbon-sink regions have been afforested recently,implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

关键词： estimation remote microwave

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Towards seamless environmental prediction-development of Pan-Eurasian EXperiment(PEEX)modelling platform

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Big Earth Data 2024年第2期8卷 189-230页

作者： Alexander Mahura Alexander Baklanov Risto Makkonen Michael Boy Tuukka Petäjä Hanna KLappalainen Roman Nuterman Veli-Matti Kerminen Stephen R.Arnold Markus Jochum Anatoly Shvidenko Igor Esau Mikhail Sofiev Andreas Stohl Tuula Aalto Jianhui Bai Chuchu Chen Yafang Cheng Oxana Drofa Mei Huang Leena Järvi Harri Kokkola Rostislav Kouznetsov Tingting Li Piero Malguzzi Sarah Monks Mads Bruun Poulsen Steffen M.Noe Yuliia Palamarchuk Benjamin Foreback Petri Clusiu Till Andreas Soya Rasmussen Jun She Jens Havskov Sørensen Dominick Spracklen Hang Su Juha Tonttila Siwen Wang Jiandong Wang Tobias Wolf-Grosse Yongqiang Yu Qing Zhang Wei Zhang Wen Zhang Xunhua Zheng Siqi Li Yong Li Putian Zhou Markku Kulmala Faculty of Science Institute for Atmospheric and Earth System Research(INAR)/PhysicsUniversity of Helsinki(UHEL)HelsinkiFinland Science and Innovation Department World Meteorological Organization(WMO)GenevaSwitzerland Niels Bohr Institute University of CopenhagenCopenhagenDenmark Climate System Research Finnish Meteorological Institute(CSR-FMI)HelsinkiFinland School of Engineering Sciences Lappeenranta University of TechnologyLappeenrantaFinland Institute for Climate and Atmospheric Science School of Earth and EnvironmentUniversity of LeedsLeedsUK Biodiversity and Natural Resources Program International Institute for Applied Systems Analysis(IIASA)LaxenburgAustria Department of Physics and Technology UiT-The Arctic University of NorwayTromsoNorway Nansen Environmental and Remote Sensing Center(NERSC) BergenNorway Atmospheric Composition Finnish Meteorological Institute(AC-FMI)HelsinkiFinland Norwegian Institute for Air Research(NILU) KjellerNorway LAGEO Institute of Atmospheric PhysicsChinese Academy of Sciences(IAP-CAS)BeijingChina Max Planck Institute for Chemistry(MPI-Chem) MainzGermany Institute of Atmospheric Sciences and Climate National Research Council of Italy(CNR-15SAC)BolognaItaly Institute of Geographic Sciences and Natural Resources Research(IGSNRR) Chinese Academy of Sciences(CAS)BeijingChina Faculty of Science Helsinki Institute of Sustainability Science(HELSUS)University of Helsinki(UHEL)HelsinkiFinland Atmospheric Research Centre of Eastern Finland Finnish Meteorological Institute(ARCEF-FMI)KuopioFinland Institute of Atmospheric Physics Chinese Academy of Sciences(IAP-CAS)BeijingChina Department of Plant Physiology Estonian University of Life Sciences(EULS)Institute of Agricultural and Environmental Sciences(IAES)TartuEstonia Danish Meteorological Institute(DMI) CopenhagenDenmark State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry(LAPC) Institute of Atmospheric Physics(IAP)Chinese Academy of Sciences(CAS)BeijingChina Uni

The Pan-Eurasian Experiment Modelling Platform(PEEX-MP)is one of the key blocks of the PEEX Research *** PEEX MP has more than 30 models and is directed towards seamless envir-onmental *** main focus area is the Arctic-boreal regions and *** models used in PEEX-MP cover several main components of the Earth’s system,such as the atmosphere,hydrosphere,pedosphere and biosphere,and resolve the physicalchemicalbiological processes at different spatial and temporal scales and *** paper introduces and discusses PEEX MP multi-scale modelling concept for the Earth system,online integrated,forward/inverse,and socioeconomical modelling,and other approaches with a particular focus on applications in the PEEX geographical *** employed high-performance com-puting facilities,capabilities,and PEEX dataflow for modelling results are *** virtual research platforms(PEEXView,Virtual Research Environment,Web-based Atlas)for handling PEEX modelling and observational results are *** over-all approach allows us to understand better physical-chemicalbiological processes,Earth’s system interactions and feedbacks and to provide valuable information for assessment studies on evaluating risks,impact,consequences,*** population,envir-onment and climate in the PEEX *** work was also one of the last projects of *** Zilitinkevich,who passed away on 15 February *** the finalization took time,the paper was actually submitted in 2023 and we could not argue that the final paper text was agreed with him.

关键词： Multi-scale and-processes modelling concept seamless coupling high-performance computing data infrastructure virtual research platforms

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Study of area change and rehabilitation of mangrove ecosystems in Karimunjawa National Park

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AIP Conference Proceedings 2023年第1期2683卷

作者： M. Arief Rahman Halim Tri Retnaningsih Soeprobowati Hadiyanto Hadiyanto Luri Nurlaila Syahid Master Program of Environmental Science School of Postgraduate Studies Universitas Diponegoro Semarang Indonesia Department of Biology Faculty of Science and Mathematics Universitas Diponegoro Semarang Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Diponegoro Semarang Indonesia Department of Geodesy and Geomatics Remote Sensing and Geographic Information Science Research Group Faculty of Earth Science and Technology Institut Teknologi Bandung Bandung Indonesia

The mangrove ecosystem is one of the unique and distinctive ecosystems because it is a compelling natural resource. However, in the current era, many people are greedy, resulting in the conversion of mangrove land, which causes a decrease in the area of the mangrove ecosystem. This study aims to determine changes in the extent of the mangrove ecosystem in Karimunjawa National Park and the appropriate rehabilitation efforts there. The method used in this research is a case study with descriptive analysis. Data collection techniques used literature studies from several related research sources whose research locations were in the Karimunjawa National Park. The collected data were then analyzed using the SWOT method. The main priority to be chosen as the primary strategic plan to repair damage and rehabilitate mangroves in Karimunjawa National Park is to establish synergistic cooperation between the implementation of government programs and the wishes of the local community through revitalizing coastal areas due to abrasion by replanting mangrove trees. This alternative strategy is a Strength-Threats (ST) strategy, where strength is maximized to overcome threats.

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Quantifying material stocks in long-lived products: Challenges and improvements for informing sustainable resource use strategies

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Resources, Conservation and Recycling 2025年 221卷

作者： Jan Streeck André Baumgart Helmut Haberl Fridolin Krausmann Bowen Cai Tomer Fishman Maud Lanau Peter Berrill Zhi Cao Sebastiaan Deetman David Frantz Volker Krey Alessio Mastrucci Alessio Miatto Stefan Pauliuk Lola Sylvie Annie Rousseau Shoshanna Saxe Danielle Densley Tingley Gamze Ünlü Dominik Wiedenhofer BOKU University Institute of Social Ecology Department of Economics and Social Sciences Schottenfeldgasse 29 1070 Vienna Austria Wuhan University School of Remote Sensing and Information Engineering Wuhan Hubei Province 430079 China Leiden University Institute of Environmental Sciences (CML) 2333 CC Leiden Netherlands Chalmers University of Technology Department of Architecture and Civil Engineering Research Area Sustainable Built Environments Gothenburg 412 96 Sweden Universität Berlin Sustainability Economics of Human Settlements Berlin 10623 Germany College of Environmental Science and Engineering Nankai University Tianjin 300350 China School of Earth System Science Tianjin University Tianjin 300072 China Trier University Geoinformatics -Spatial Data Science Trier Rhineland-Palatinate 54296 Germany International Institute for Applied Systems Analysis (IIASA) Energy Climate and Environment (ECE) Program Schlossplatz 1 2361 Laxenburg Austria Commonwealth Scientific and Industrial Research Organisation (CSIRO) Canberra 2601 ACT Australia University of Freiburg Faculty of Environment and Natural Resources Freiburg Baden-Wuerttemberg D-79102 Germany Norwegian University of Science and Technology (NTNU) Industrial Ecology Programme Department of Energy and Process Engineering Trondheim 7034 Norway University of Toronto Department of Civil and Mineral Engineering Toronto Ontario M5S 1A1 Canada University of Sheffield School of Mechanical Aerospace and Civil Engineering Mappin Street Sheffield S1 3JD United Kingdom

Material stocks in long-lived products require over half of the annual global resource extraction for their construction and maintenance, and lock in energy use through their technical and geospatial characteristics. A thorough understanding of material stocks is therefore essential to inform sustainable resource use strategies. However, despite substantial advances in material stock research in recent decades, their robust quantification remains challenging and bears considerable uncertainties. We assess the (dis)agreement of material stock estimates from 32 recent studies across global, national, and urban scales, and propose recommendations for future work. Overall, we observe medium to high divergences between studies estimating the same material stocks. For end-use categories that aggregate multiple material stocks (e.g., buildings), most global-level estimates show divergences within 140 %. At the national level, most estimates for the USA diverge by <210 %, while those for China by <550 %. At the urban level, most estimates for Beijing fall within 90 %, and for Vienna, within 70 %. For low-income countries, non-residential buildings, and individual materials, the differences are often substantially higher, highlighting the need for an improved scientific basis for policy and planning. These disparities arise from differences in system boundaries, methodology, data sources, definitions, and lack of data to capture the diversity of material stock types. To robustly inform sustainable resource use strategies, the scientific community and practitioners should systematically assess and report sensitivity and uncertainty, and reduce the latter through transparent documentation, model intercomparisons, consensus and open-access databases, enhanced data collection, and comprehensive quantification of material stocks.

关键词： Material flow analysis Sustainable resource management Uncertainty analysis Socioeconomic metabolism Material stocks

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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