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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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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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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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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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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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Biome reconstruction on the Tibetan Plateau since the Last Glacial Maximum using a machine learning method

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Science China Earth Sciences 2022年第3期65卷 518-535页

作者： Feng QIN Yan ZHAO Xianyong CAO Key Laboratory ofLand Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Alpine Paleoecology and Human Adaptation(ALPHA)Group State Key Laboratory of Tibetan Plateau Earth System Science(LATPES)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Historical biome changes on the Tibetan Plateau provide important information that improves our understanding of the alpine vegetation responses to climate ***,a comprehensively quantitative reconstruction of the historical Tibetan Plateau biomes is not possible due to the lack of quantitative methods that enable appropriate classification of alpine biomes based on proxy data such as fossil pollen *** this study,a pollen-based biome classification model was developed by applying a random forest algorithm(a supervised machine learning method)based on modern pollen assemblages on and around the Tibetan Plateau,and its robustness was assessed by comparing its results with the predictions of the biomisation *** results indicated that modern biome distributions reconstructed using the random forest model based on modern pollen data generally concurred with the observed zonal *** random forest model had a significantly higher accuracy than the biomisation method,indicating the former is a more suitable tool for reconstructing alpine biome changes on the Tibetan *** random forest model was then applied to reconstruct the Tibetan Plateau biome changes from 22 ka BP to the present based on 51 fossil pollen *** reconstructed biome distribution changes on the Tibetan Plateau generally corresponded to global climate changes and Asian monsoon *** the Last Glacial Maximum,the Tibetan Plateau was mainly desert with subtropical forests distributed in the *** the last deglaciation,the alpine steppe began expanding and gradually became zonal vegetation in the central and eastern *** meadow occupied the eastern and southeastern areas of the Tibetan Plateau since the early Holocene,and the forest-meadow-steppe-desert pattern running southeast to northwest on the Tibetan Plateau was established *** the mid-Holocene,subtropical forests extended north,which reflected the“optimum”***

关键词： Biome reconstruction Random forest algorithm Biomisation method Pollen data Last Glacial Maximum Tibetan Plateau

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Climate Regulates the Effect of Abrupt Vegetation Shifts on Soil Moisture on the Loess Plateau, China

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SSRN 2024年

作者： Xiao, Guoan Xin, Liangjie Wang, Xue Li, Xiubin Tan, Minghong Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Science Beijing China College of Resources and Environment University of Chinese Academy of Sciences Beijing China

Abrupt vegetation shifts indicate the occurrence of sudden and significant changes in ecosystem structure and function. However, it has not yet been clarified whether abrupt vegetation shifts exacerbate soil moisture deficits. In the context of revegetation on the Loess Plateau is approaching sustainable water resource limits, knowing the mechanisms and characteristics of this impact is vital for ecological restoration and water resource management. Here, we used the multi-period difference-in-differences method to find that positive abrupt vegetation shifts prevailed (29% for grasslands and 25% for forests) on the Chinese Loess Plateau from 2000 to 2020. Most of the positive abrupt grassland shifts (89%) favored soil moisture accumulation, whereas positive abrupt forest shifts had positive (51%) impacts on soil moisture in "hot zones" (characterized by higher temperatures and higher solar radiation) but negative impacts (47%) in "wet zones" (characterized as cloudy and rainy). The results show that a full understanding of how abrupt vegetation shifts affect soil moisture is critical for managing ecosystem sustainability. © 2024, The Authors. All rights reserved.

关键词： Vegetation

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Risk assessment of typhoon storm surge based on a simulated annealing algorithm and the least squares method: a case study in Guangdong Province, China

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Natural Hazards Research 2022年第3期2卷 249-258页

作者： Guo, Tengjiao Li, Guosheng He, Lei National Science Library Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China School of Tourism and Urban Management Jiangxi University of Finance and Economics Nanchang 330013 China

Under global warming, the risk level of storm surge disasters in coastal areas has substantially increased. Guangdong Province is among the provinces most affected by typhoon storm surges in China. The typhoon risk characteristics of this region play an important role in regional development and disaster prevention and mitigation. Based on the theory of disaster risk, this study constructs a risk assessment model based on the regional characteristics of Guangdong Province and an indicator system selected in previous work. It optimizes the model using a simulated annealing algorithm and the least squares method. The optimization model was used to assess the hazard, vulnerability, disaster resilience, and risk capability of cities in Guangdong Province from 2001 to 2014. The results indicate that the hazard is related to the typhoon landing area for a single typhoon storm surge disaster. The hazard in the typhoon landing areas is undoubtedly higher than in the regions far away from the typhoon, but their risk capability is not proportional to the risk level. For many years of average typhoon storm surge disasters, cities' hazard, vulnerability, and risk capability levels have increased from inland to coastal areas. The level of resilience is not related to the location but to the city's development indicators. Zhanjiang city has the highest risk level. © 2022 National Institute of Natural Hazards, Ministry of Emergency Management of China

关键词： Guangdong Province Least squares Risk assessment Simulated annealing algorithm Typhoon storm surge

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Impact of spatial transfer of farmland on the food-water-GHG nexus in China during 2000–2020

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Resources, Conservation and Recycling 2025年 221卷

作者： Liu, Yunxi Wang, Xue Tan, Minghong Zhang, Fuyao Li, Xiubin Key Laboratory of Land Surface Pattern and Simulation Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing100049 China

Analyzing the impact of spatial transfer of farmland (STF) on the food-water-greenhouse gases (FWG) nexus through a coupled perspective holds significant scientific value for guiding sustainable farmland management policies. This study developed an integrated assessment framework to quantify FWG-nexus dynamics induced by STF, systematically evaluating changes in crop yields, irrigation water consumption, and GHG emissions across China from 2000 to 2020. The coupling coordination degree (CCD) of the FWG system was further examined. The results show that between 2000 and 2020, the area of farmland in China decreased by 2.63 × 104km2, and the center of gravity of farmland shifted approximately 56.90 km towards the northwest. Nationwide, STF led to an increase in average crop yields by 58.54 kg/ha;at the same time, the average irrigation water consumption increased by 1.93 mm, and the average GHG emissions also rose by 23.63 kgCO2-eq/ha. During the study period, the FWG-nexus exhibited a tendency toward decoupling, as indicated by a 0.21 % decrease in the FWG CCD. The increasing consumption for irrigation water and rising GHG emissions have consistently been the primary constraints to the harmonization of the FWG-nexus at the national level, with notable regional variations of these constraints. These insights help deepen the understanding of the impact of STF on the FWG-nexus, providing scientific support for sustainable land use in China and the achievement of the United Nations Sustainable Development Goals (SDGs). © 2025 Elsevier B.V.

关键词： Greenhouse gas emissions

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