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Bibliometric analysis of fires on wildland-urban interfaces

Natural Hazards Research 2022年第3期2卷 147-153页

作者： Zong, Xuezheng Tian, Xiaorui Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Ecology and Nature Conservation Institute Chinese Academy of Forestry Key Laboratory of Forest Protection of National Forestry and Grassland Administration Beijing China

Most wildland-urban interface (WUI) areas in the world will face severe wildfire risks due to climate warming and rapid urbanization. Mitigating the damage caused by WUI fires has become a worthy topic for fire researchers and managers. In recent years, WUI fires have occurred in China and caused significant property damage, and even threatened some communities. To understand the research status and promote WUI fire management in China, this paper collected all research articles published from 1991 to 2021 on the topic. We performed a bibliometric analysis for the year, author, institution, country, journal, and keywords. The results show that 717 research articles were published in the field of WUI fire management over this period. The annual number of published articles has exceeded 40 since 2013 and is expected to reach 80 in 2022. The United States has the most active research institutions and groups working in the field of WUI fire management, with 507 published articles over the past three decades, accounting for 70.7% of the total articles. Ninety percent of the top ten authors or institutions come from the United States. The International Journal of Wildland Fire, an important journal in the field of wildfire, has published 96 research articles on the topic over the same period, accounting for 13.4% of the total. Research focused on WUI fire occurrence and its influencing factors, fire behavior simulation, fire risk assessment, and fuel treatment. Future research on WUI fire management will explore the impacts of climate change on the WUI fire regime, improve risk assessment methods, and enhance fire mitigation strategies. © 2022 National Institute of Natural Hazards, Ministry of Emergency Management of China

关键词： Bibliometric analysis Fire management Mitigation strategy Wildland fire Wildland-urban interface

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Understanding the Efficiency and Uncertainty of Water Supply Service Assessment Based on the Budyko Framework: A Case Study of the Yellow River Basin, China

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SSRN 2025年

作者： Zhang, Tingjing Shao, Quanqin Huang, Haibo Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China

Freshwater ecosystem services (ESs) analyses are increasingly employed to address water resource management challenges. However, few studies have systematically examined the efficiency and uncertainty of such assessments, limiting their applicability for decision-making. In this study, the InVEST water yield model was applied to assess water supply service in the Yellow River Basin (YRB) from 2000 to 2022. We evaluated the model's sensitivity to climate variables and the parameter ω. Six sets of data/parameter input combinations were constructed to drive the model independently. Spatiotemporal trends were compared with observed data from 33 hydrological stations along the Yellow River mainstem and tributaries to assess the model performance and uncertainties. Finally, the response of water supply to climate change and vegetation dynamics was further discussed. The results showed that precipitation exhibited the highest sensitivity, and errors in precipitation inputs were the primary source of data input uncertainties. Compared to the raster-scale ω-value calculation method, the method combined with the lumped model delivered the most robust simulation results (R2, RMSE, and MAE for mainstream basins: 0.91, 50.08 mm, and 38.85 mm;for tributary basins: 0.89, 6.43 mm, and 4.04 mm, respectively). Climate change, particularly changes in precipitation, emerged as a key factor driving water supply service dynamics. These findings enhance the understanding of efficiency and uncertainty in water-related ESs assessments and offer valuable insights for applications in other regions. © 2025, The Authors. All rights reserved.

关键词： Rivers

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2000-2019年中国重大生态工程生态效益遥感评估

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Journal of Geographical Sciences 2023年第8期33卷 1587-1613页

作者：邵全琴刘树超宁佳刘国波杨帆张雄一牛丽楠黄海波樊江文刘纪远 Key Laboratory of Terrestrial Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China School of Economics and Management Zhejiang Ocean UniversityZhoushan 316022ZhejiangChina

We propose a theoretical framework for assessing the ecological benefits provided by key national ecological projects in China over the past 20 years.A dataset consisting of six primary indicators and nine secondary indicators of ecosystem structure,ecosystem quality,and ecosystem services for 2000-2019 was generated using ground survey and remote sensing *** benefits were quantitatively evaluated following the implementation of these projects in *** with medium,relatively high,and high degrees of ecological restoration accounted for 24.1%,11.9%,and 1.7% of the national land,*** of ecological restoration were higher in areas with greater numbers of ecological *** with relatively and absolutely high degrees of ecological restoration were mainly concentrated in the Loess Plateau,the farming-pastoral zone of northern China,the Northeast China Plain,and an area spanning the borders of Sichuan,Yunnan,Guizhou,Chongqing,and *** relative contributions of climatic factors and ecological projects to changes in vegetation net primary productivity were 85.4% and 14.6%,respectively,and the relative contributions of climatic factors and ecological projects to changes in water erosion modulus were 69.5% and 30.5%,*** restoration potential of national vegetation coverage was 20%,and the restoration potential percentage of forest and grassland vegetation coverage was 6.4% and 23%,*** conditions can inhibit ecological *** with relatively high and high degrees of ecological restoration were mainly distributed in areas with an average annual temperature greater than 0℃ and annual precipitation greater than 300 ***,the limitations associated with climate conditions require consideration during the implementation of national ecological *** implementation of combined measures should be emphasized,and the benefits of ecological investment funds should be maximized.

关键词： ecological projects ecosystem ecological restoration degree ecological restoration potential

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Intensified risk to ecosystem productivity under climate change in the arid/humid transition zone in northern China

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Journal of Geographical Sciences 2021年第9期31卷 1261-1282页

作者： YIN Yunhe DENG Haoyu MA Danyang WU Shaohong Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China Henan Province Development and Reform Commission Zhengzhou 450018China University of Chinese Academy of Sciences Beijing 100049China

Assessing the climate change risk faced by the ecosystems in the arid/humid transition zone(AHTZ)in northern China holds scientific significance to climate change *** simulated the net primary productivity(NPP)for four representative concentration pathways(RCPs)using an improved Lund-Potsdam-Jena *** a method was established based on the NPP to identify the climate change risk *** the midterm period(2041–2070)to the long-term period(2071–2099),the risks indicated by the negative anomaly and the downward trend of the NPP gradually extended and *** higher the scenario emissions,the more serious the *** particular,under the RCP8.5 scenario,during 2071–2099,the total risk area would be 81.85%,that of the high-risk area would reach 54.71%.In this high-risk area,the NPP anomaly would reach–96.00±46.95 gC·m-2·a-1,and the rate of change of the NPP would reach–3.56±3.40 gC·m-2·*** eastern plain of the AHTZ and the eastern grasslands of Inner Mongolia are expected to become the main risk concentration *** results indicated that the management of future climate change risks requires the consideration of the synergistic effects of warming and intensified drying on the ecosystem.

关键词： climate change risk assessment arid/humid transition zone NPP ecosystem

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Mapping Cropland Soil Salinity Based on Multi-Cycle Classification to Mitigate Retrieval Error

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SSRN 2024年

作者： Gao, Liaoran Xu, Erqi Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China

Soil salinization is one of the primary factors limiting agricultural development in arid regions. Accurately predicting soil salinity in croplands based remote sensing is advantageous for guiding precise agricultural management practices and achieving sustainable agricultural development. However, due to the poor separability in remote sensing image features between non- and mild-salinized croplands, coupled with potential confounding factors such as natural environmental conditions and agricultural practices, the existing research methods exhibit poor accuracy in predicting cropland soil salinity. In response to these challenges, this study introduces a two-step strategy involving classification and retrieval. Initially, this study establishes an innovative multi-cycle classification method, which divides the original samples into a rested sample set obtained through multiple sample deletion cycles, and a deleted sample set composed of excluded samples. Separate classifiers are trained on rested and deleted sample sets, and the rules are generated to distinguish the areas where the classifiers are applicable. Subsequently, soil salinity in salinized croplands is predicted. The optimal remote sensing retrieval features are obtained through Pearson correlation coefficients and backward feature elimination within the classified salinized croplands. Machine learning techniques are then applied to predict the soil salinity. The results indicate that the proposed multi-cycle classification method achieves superior classification accuracy for salinized cropland (Fsalinized=0.82, Fnon-salinized=0.87) compared with traditional methods such as Random Forest (RF) (Fsalinized=0.74, Fnon-salinized=0.64) and 1D-CNN (Fsalinized=0.76, Fnon-salinized=0.65). The R2 of soil salinity retrieval based on Gradient Boosting Decision Trees (GBDT) in salinized cropland areas is 0.66, significantly outperforming the R2 of salinity retrieval results without classification, which is 0.23. The

关键词： Remote sensing

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Developing Goethite Modified Reed-Straw Biochar for Remediation of Metal(Loids) Co-Contamination

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SSRN 2024年

作者： Feng, Hanxiao Yang, Fen Wei, Chaoyang Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China

Arsenic and cadmium contamination, along with their associated environmental risks, have become a global concern worldwide. However, the synergistic stabilization of metals and metalloids presenting as cations and oxyanions in contaminated environments poses a formidable challenge. To tackle this issue, in this study, goethite-modified biochar (BC-Gt) was synthesized and investigated for its remediation potential in co-contamination of cadmium (Cd) and arsenic (As), in comparison to pristine biochar (reed-straw biochar, BC) and goethite (Gt). Among the three materials, BC-Gt exhibited the highest adsorption capacity, followed by BC and Gt, with maximum adsorption capacities 126.02 mg·g-1, 28.90 mg·g-1 and 16.84 mg·g-1 for As, and 125.12 mg·g-1, 68.85 mg·g-1 and 21.31 mg·g-1 for Cd, respectively, in the binary As and Cd solution. The analysis of BET-SSA, SEM-EDS, XRD, FTIR and XPS revealed that the co-adsorption of As and Cd onto BC-Gt were involved in ion exchange, electrostatic interaction, surface precipitation and complexation, while As and Cd might occupy different surface functional sites. Moreover, the specific adsorption of Cd(II) involved cation-π interaction, while limited oxidation-reduction of As(V) occurred during adsorption. In As, Cd and lead (Pb) co-contaminated soils, BC-Gt displayed profound stabilization effects, as seen with maximum removal efficiencies of 63.98%, 83.67% and 83.92% for available concentrations of As, Cd and Pb, respectively. Besides, BC-Gt outperformed BC and Gt in transforming the exchangeable fraction (Exc) into reducible (Red), oxidizable (Oxi) and residual (Res) fractions of As, Cd and Pb. This study provides evidence in the potential of goethite-modified biochar (BC-Gt) for remediating co-contamination of As and Cd in water, as well as As, Cd and Pb in soil. © 2024, The Authors. All rights reserved.

关键词： Precipitation (chemical)

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Digital Mapping and Scenario Prediction of Soil Salinity in Coastal Lands Based on Multi-Source Data Combined with Machine Learning Algorithms

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SSRN 2024年

作者： Zhou, Mengge Li, Yonghua Liao, Xiaoyong Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China

Soil salinization is a major soil degradation process threatening ecosystems and posing a great challenge to sustainable agriculture and food security worldwide. This study aimed to evaluate the potential of state-of-the-art machine learning algorithms in soil salinity (EC1: 5) mapping. Further, we predicted the distribution patterns of soil salinity under different future scenarios in the Yellow River Delta. A geodatabase comprising 201 soil samples and 19 conditioning factors was used to compare the predictive performance of the ordinary kriging, inverse distance weighting regression, random forest, and CatBoost models. The CatBoost model exhibited the highest performance with both training (MAE=0.383, RMSE = 0.601) and testing datasets (MAE=0.403, RMSE = 0.670). Among explanatory factors, soil Na2O was the most important for predicting EC1:5, followed by the normalized difference vegetation index and soil organic carbon. Soil EC1:5 predictions suggested that the Yellow River Delta region faces severe salinization, particularly in coastal zones. Among three scenarios with increases in soil organic carbon content (1, 2, and 3 g/kg), the 2 g/kg scenario resulted in the best improvement effect on saline-alkali soils with EC1:5 > 2 ds/m. Our results provide valuable insights for policymakers to improve saline-alkali land quality and plan regional agricultural development. © 2024, The Authors. All rights reserved.

关键词： Soils

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Urban construction land demand prediction and spatial pattern simulation under carbon peak and neutrality goals:A case study of Guangzhou,China

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Journal of Geographical Sciences 2022年第11期32卷 2251-2270页

作者： HU Xintao LI Zhihui CAI Yumei WU Feng Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchCASBeijing 100101China College of Land Science and Technology China Agricultural UniversityBeijing 100193China University of Chinese Academy of Sciences Beijing 100049China Research Centre for Territorial Spatial Planning Ministry of Natural Resources of the People’s Republic of ChinaBeijing 100812China

Urban construction land has relatively high human activity and high carbon *** on urban construction land prediction under carbon peak and neutrality goals(hereafter“dual carbon”goals)is important for territorial spatial *** study analyzed quantitative relationships between carbon emissions and urban construction land,and then modified the construction land demand prediction ***,an integrated model for urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals was developed,where urban construction land suitability was modified based on carbon source and sink capacity of different land-use *** Guangzhou as a case study,the integrated model was validated and applied to simulate the spatiotemporal dynamics of its urban construction land during 2030–2060 under baseline development and“dual carbon”goals *** simulation results showed that Guangzhou’s urban construction land expanded rapidly until 2030,with the spatial pattern not showing an intensive development ***’s urban construction land expansion slowed during 2030–2060,with an average annual growth rate of 0.2%,and a centralized spatial pattern *** the“dual carbon”goal scenario,Guangzhou’s urban construction land evolved into a polycentric development pattern in *** with the baseline development scenario,urban construction land expansion in Guangzhou during 2030–2060 is slower,with an average annual growth rate of only 0.1%,and the polycentric development pattern of urban construction land was more ***,land maintenance and growth,that is,a carbon sink,is more obvious under the“dual carbon”goals scenario,with the forest land area nearly 10.6%higher than that under the baseline development *** study of urban construction land demand prediction and spatial pattern simulation under“dual carbon”goals provides a scientific decision-making support tool for territorial spati

关键词： carbon peak and neutrality goals urban construction land demand prediction spatial pattern simulation Guangzhou

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Yield Estimation Window Constrains the Accuracy of Soybean Yield Estimation in the Global Black Soil Regions

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SSRN 2024年

作者： Huang, Shuyuan Liu, Yujie Chen, Jiahao Zhang, Ermei Pan, Tao Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China

The lack of detailed phenological information is a significant factor contributing to uncertainty in crop yield simulation studies. Previous research has predominantly relied on static, fixed crop calendars, while leaving the response of yield estimation accuracy to external environmental changes during different growth stage windows has largely remained unexplored. This study compares three methods for selecting yield estimation windows: fixed, rule-based, and sliding windows. Employing models constructed from multi-source data, physical rules, and machine learning techniques, we aim to uncover the underlying mechanisms driving changes in soybean yield estimation accuracy within the black soil regions of China and the United States of America (USA) under the influence of climate change. Our results indicate that, due to the cyclical variation in climate and crops, climate and remote sensing variables display distinct regular variations across different windows. In dynamic windows (both rule-based and sliding windows), there is a possibility of 46.3% and 70.8% to improve soybean yield estimation accuracy in China and the USA, respectively. Notably, the LSTM demonstrates superior estimation capabilities and a heightened ability to capture variations between different windows and models compared to RF and XGBoost. County-level analyses based on the LSTM model reveal that rule-based windows increase estimation accuracy by 28.0% and 32.1%, while sliding windows estimation accuracy improvements of 53.7% and 45.2% in China and the USA, respectively. It is estimated that windows contribute to approximately 9% of the uncertainty in soybean yield estimation. This study underscores the critical importance of selecting the appropriate yield estimation windows to enhance accuracy. Furthermore, it proposes a novel yield estimation approach that combines physical rule constraints with machine learning models, which provides a more precise capture of soybean yield variation in the

关键词： Crops

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Ecological environmental quality evaluation and driving factor analysis of the Lijiang River Basin,based on Google Earth Engine

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中国生态农业学报（中英文） 2024年第9期32卷 1592-1608页

作者： WEI Xi YANG Dazhi CAI Xiangwen SHAO Ya TANG Xiangling College of Earth Sciences Guilin University of TechnologyGuilin 541006China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China

For regional ecological management,it is important to evaluate the quality of ecosystems and analyze the underlying causes of ecological *** the Google Earth Engine(GEE)platform,the remote sensing ecological index(RSEI)was calculated for the Lijiang River Basin in Guangxi Zhuang Autonomous Region for 1991,2001,2011,and *** autocorrelation analysis was employed to investigate spatiotemporal variations in the ecological environmental quality of the Lijiang River ***,geographic detectors were used to quantitatively analyze influencing factors and their interaction effects on ecological environmental *** results verified that:1)From 1991 to 2021,the ecological environmental quality of the Lijiang River Basin demonstrated significant *** area with good and excellent ecological environmental quality in proportion increased by 19.69%(3406.57 km^(2)),while the area with fair and poor ecological environmental quality in proportion decreased by 10.76%(1860.36 km^(2)).2)Spatially,the ecological environmental quality of the Lijiang River Basin exhibited a pattern of low quality in the central region and high quality in the ***,poor ecological environmental quality characterized the Guilin urban area,Pingle County,and Lingchuan County.3)From 1991 to 2021,a significant positive spatial correlation was observed in ecological environmental quality of the Lijiang River *** with high-high agglomeration were predominantly forests and grasslands,indicating good ecological environmental quality,whereas areas with low-low agglomeration were dominated by cultivated land and construction land,indicating poor ecological environmental quality.4)Annual average precipitation and temperature exerted the most significant influence on the ecological environmental quality of the basin,and their interactions with other factors had the great *** study aimed to enhance understanding of the evolution of the ecol

关键词： Ecological environmental quality Remote sensing ecological index Driving factor Google Earth Engine Lijiang River Basin

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