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analysis of city centrality based on entropy weight TOPSIS and population mobility:A case study of cities in the Yangtze River Economic Belt

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Journal of geographical Sciences 2020年第4期30卷 515-534页

作者： LUO Jing CHEN Siyun SUN Xuan ZHU Yuanyuan ZENG Juxin CHEN Guangping Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province Central China Normal UniversityWuhan 430079China The College of Urban&Environmental Sciences Central China Normal UniversityWuhan 430079China Academy of Wuhan Metropolitan Area Hubei Provincial Development and Reform Commission/Central China Normal UniversityWuhan 430079China Institute of Geographic Sciences and Natural Resources Research CASBeijing 100101China School of Earth Sciences Zhejiang UniversityHangzhou 310027China

Based on statistical data and population flow data for 2016,and using entropy weight TOPSIS and the obstacle degree model,the centrality of cities in the Yangtze River Economic Belt(YREB)together with the factors influencing centrality were *** addition,data for the population flow were used to analyze the relationships between cities and to verify *** results showed that:(1)The pattern of centrality conforms closely to the pole-axis theory and the central geography *** axes,corresponding to the Yangtze River and the Shanghai-Kunming railway line,interconnect cities of different *** the whole,the downstream cities have higher centrality,well-defined gradients and better development of city infrastructure compared with cities in the middle and upper reaches.(2)The economic scale and size of the population play a fundamental role in the centrality of cities,and other factors reflect differences due to different city *** most of the coastal cities or the capital cities in the central and western regions,factors that require long-term development such as industrial facilities,consumption,research and education provide the main competitive *** cities that are lagging behind in development,transportation facilities,construction of infrastructure and fixed asset investment have become the main methods to achieve development and enhance competitiveness.(3)The mobility of city populations has a significant correlation with the centrality score,the correlation coefficients for the relationships between population mobility and centrality are all greater than 0.86(P<0.01).The population flow is mainly between high-class cities,or high-class and low-class cities,reflecting the high centrality and huge radiating effects of high-class ***,the cities in the YREB are closely linked to Guangdong and Beijing,reflecting the dominant economic status of Guangdong with its geographical proximity to the YREB and Beijing's e

关键词： city centrality entropy weight TOPSIS population mobility Yangtze River Economic Belt obstacle degree model

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Measuring Network Configuration of the Yangtze River Middle Reaches Urban Agglomeration:Based on Modified Radiation Model

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Chinese geographical Science 2020年第4期30卷 677-694页

作者： ZHENG Wensheng KUANG Aiping WANG Xiaofang CHEN Jing Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province Central China Normal UniversityWuhan 430079China Academy of Wuhan Metropolitan Area Hubei Development and Reform Commission&Central China Normal UniversityWuhan 430079China Hubei Institute of Economic and Social Development Central China Normal UniversityWuhan 430079China Wuhan Branch of China Tourism Academy Central China Normal UniversityWuhan 430079China

The objective of this study is to develop a framework for re-examining and re-defining the classical concepts of spatial interaction and reorganization in the urban *** introduce a modified radiation model for spatial interactions,coupled with migration big data,transport accessibility algorithm,and city competitiveness assessment for efficient distribution of the inter-city flow through the *** Yangtze River Middle Reaches(YRMR)urban agglomeration(UA)is chosen as the case study region to systematically identify and measure its spatial configuration and to gain insights for other UAs‘sustainable development in *** results are also compared with those computed by the classical gravity model to systematically discuss the applicability of spatial interaction laws and models,and related practical policies for regional sustainable development are discussed based on the findings as *** conclusions are highlighted below:1)Combining with the?city network paradigm‘and?central place theory‘can better express the spatial configurations of city systems in the context of?space of flows‘;2)The results validate the potentialities of a multi-analysis framework to assess the spatial configurations of city network based on the improved radiation model and network analysis tools;3)The applications of spatial interaction models should be considered according to the specific geographical entity and its spatial scale.

关键词： spatial interaction city network radiation model gravity model urban agglomeration

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Tracking Autumn Photosynthetic Phenology of Alpine Grassland on the Tibetan Plateau with Green-Red Vegetation Index

SSRN

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SSRN 2022年

作者： Li, Wangchao Yang, Yajie Chen, Rui Ma, Dujuan Wang, Changjing Wang, Cong Chen, Huai Yin, Gaofei Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu610031 China Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province School of Urban and Environmental Sciences Central China Normal University Wuhan430079 China Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization & Ecological Restoration Biodiversity Conservation Key Laboratory of Sichuan Province Chengdu Institute of Biology Chinese Academy of Sciences Chengdu610041 China

Accurate monitoring autumn photosynthetic phenology is essential for understanding carbon cycles. The broadband green-red vegetation index (GRVI) derived from broadband red and green reflectance has been increasingly used in this field. However, the performance of GRVI in large areas remains unclear. We evaluated the performance of the normalized vegetation index (NDVI), normalized difference greenness index (NDGI), solar-induced chlorophyll fluorescence (SIF) and GRVI in tracking autumn photosynthetic phenology of alpine grassland at flux sites and the entire Tibetan Plateau (TP). The results revealed that GRVI (R2 = 0.42, RMSE = 7.32 d and Bias = 4.39 d) performed comparable with SIF (R2 = 0.46, RMSE = 5.16 d and Bias = 0.71 d) in extracting the end of the photosynthetically active season (EOS) with eddy covariance flux measurements as reference. On contrary, a systematically later EOS was estimated by NDVI (Bias = 20.72 d) and NDGI (Bias = 13.62 d). The application example on the TP collaborated these findings. The divergent performances between indicators were rooted in the photosynthesis downregulation in autumn which is jointly controlled by canopy structure and physiology. Our study highlights the unique advantage of GRVI over existing satellite indicators for estimating autumn photosynthetic phenology with high resolution and long-time span. We suggest revisiting the dynamics of autumn photosynthetic phenology using GRVI, which has significant implications on carbon uptake studies. © 2022, The Authors. All rights reserved.

关键词： Vegetation

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Change features of time-series climate variables from 1962 to 2016 in Inner Mongolia,China

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Journal of Arid Land 2020年第1期12卷 58-72页

作者： XU Lili YU Guangming ZHANG Wenjie TU Zhenfa TAN Wenxia Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province Central China Normal UniversityWuhan 430079China College of Urban and Environmental Sciences Central China Normal UniversityWuhan 430079China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchBeijing 100101China Plant Functional Biology and Climate Change Cluster(C3) University of Technology SydneyNSW 2007Australia

Detecting change features of climate variables in arid/semi-arid areas is essential for understanding related climate change patterns and the driving and evolution mechanism between climate and arid/semi-arid *** paper takes Inner Mongolia of China,a unique arid/semi-arid ecosystem,as the study *** first detected trend features of climate variables using the linear trend analysis method and then detected their trend-shift features using the breaks for additive seasonal and trend method based on the time-series of monthly precipitation and monthly mean temperature datasets from 1962 to *** analyzed the different change features of precipitation and temperature on a regional scale and in different ecological zones to discover the spatial heterogeneity of change *** results showed that Inner Mongolia has become warmer-wetter during the past 54 *** regional annual mean temperature increased 0.4°C per decade with a change rate of 56.2%.The regional annual precipitation increased 0.07 mm per decade with a slightly change rate of about 1.7%,but the trend was not statistically *** warmer trend was contributed by the same positive trend in each season,while the wetter trend was contributed by the negative trend of the summer precipitation and the positive trend of the other three *** regional monthly precipitation series had a trend-shift pattern with a structural breakpoint in the year 1999,while the regional monthly mean temperature series showed an increasing trend without a periodical *** the year 2000,the warmer-wetter trend of the climate in Inner Mongolia was *** late 20th century was a key period,because the acceleration of the wetter trend in some local zones(I and II)and the alleviation of the warmer trend in some local zones(Ⅶ,Ⅷand IX)occurred ***,the change features had a strong spatial heterogeneity,the southeastern and southwestern of Inner Mongolia went through

关键词： temperature precipitation trend feature trend-shift feature arid/semi-arid area

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A process-Based Deep Learning Hydrological Model for Daily Rainfall-Runoff simulation

SSRN

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SSRN 2023年

作者： Li, Heng Zhang, Chunxiao Chu, Wenhao Shen, Dingtao School of Information Engineering China University of Geosciences in Beijing No. 29 Xueyuan Road Haidian District Beijing100083 China Observation and Research Station of Beijing Fangshan Comprehensive Exploration Ministy of Natural Resources Beijing100083 China Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province Central China Normal University Wuhan430079 China College of Urban and Environmental Sciences Central China Normal University Wuhan430079 China

Rainfall–runoff simulation is an old but still core task of hydrology. Deep learning (DL) models, in particular long-short-term memory (LSTM) neural networks, demonstrate unprecedented accuracy in rainfall-runoff estimation. However, the problem currently is that DL offers few physical explanation beyond its fitting capability. In this study, we developed a process-based DL model. This model implants a process-based ordinary differential equations (ODE) hydrological model into the DL architecture to enable neural networks to obtain process understanding and description of rainfall-runoff similar to process-based models. To implement the model, a simple bucket hydrological model (EXP-HYDRO) is implanted into a common Recurrent Neural Network (RNN) cell and an Entity-Aware LSTM (EA-LSTM) cell is added as a post-processor layer, namely process-based RNN-EA-LSTM (PRNN-EA-LSTM). In PRNN-EA-LSTM, the network structure is represented by the ODE of EXP-HYDRO. simulation accuracy of PRNN-EA-LSTM on the 531 catchments from the Catchment Attributes and Meteorology for Large-sample Studies dataset is almost as good as pure DL models, while maintaining the interpretable model structure like process-based hydrological models. Specifically, the median Nash-Sutcliffe efficiency (NSE) in local and regional rainfall-runoff simulation is 0.70 and 0.72 respectively. In addition, it is also found that PRNN-EA-LSTM surprisingly improves the low-value flows simulations in the runoff hydrograph and reduces the number of catchments with negative NSE, which is difficult to achieve by pure DL models. Therefore, adding physics knowledge to DL models is able to improve simulations, which could be a feasible research direction in the furture research to cognize and simulate hydrology. © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Improved Weed Mapping in Corn Fields by Combining Uav-Based Spectral, Textural, Structural, and Thermal Measurements

SSRN

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SSRN 2022年

作者： Xu, Binyuan Meng, Ran Chen, Gengshen Liang, Linlin Lv, Zhengang Zhou, Longfei Sun, Rui Zhao, Feng Yang, Wanneng Macro Agriculture Research Institute Interdisciplinary Sciences Research Institute College of Resources and Environment Huazhong Agricultural University Wuhan430070 China Hubei Hongshan Laboratory Huazhong Agricultural University Wuhan430070 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Geographical Process Analysis & Simulation of Hubei Province College of Urban and Environmental Sciences Central China Normal University Wuhan430079 China

Weed is one of the most important stresses in corn production globally. Spatially explicit mapping of weeds in corn fields is critical for the precise removal of weeds to minimize herbicide use and ensure food security. The development of unmanned aerial vehicle (UAV) based remote sensing presents an unprecedented opportunity for weed mapping with centimeter-level spatial resolution and simultaneous multi-source data measurements. In most cases, spectral, textural, and structural measurements have been used for weed mapping, while thermal measurements (e.g., canopy temperature, CT) were seldom considered and used. CT can potentially capture the differences in evapotranspiration rates and canopy structure of weeds and crops to improve the accuracy of weed mapping. However, little is known about the performance of integrating thermal measurements with spectral, textural, and structural measurements for weed mapping. In this study, UAV-based RGB, multispectral, and thermal imagery were used to quantify the optimal combination of spectral (i.e., vegetation Indices), textural (i.e., grey level co-occurrence matrix), structural (i.e., Plant Height), and CT measurements for more accurate and efficient weed mapping. In addition, the performances of three machine learning algorithms, random forest (RF), support vector machine (SVM), and Naïve-Bayes classifier (NBC), were compared. The results showed that: (1) CT improved weed mapping accuracies as complementary information for spectral, textural, and structural features (up to 5% and 0.051 improvements in OA and Marco-F1, respectively);(2) the fusion of textural, structural, and thermal features achieved the best performance in weed mapping (OA = 96.4%, Marco-F1 = 0.964), followed by the fusion of structural and thermal features (OA = 93.6%, Marco-F1 = 0.936);(3) SVM-based model achieved the best performance in weed mapping, with 3.5% and 7.1% improvement in OA and 0.036 and 0.071 in Marco-F1 respectively, compared to the be

关键词： Remote sensing

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Industrial Source Emission Estimations of Pharmaceutical and Personal Care Products (PPCPs) in China

SSRN

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SSRN 2022年

作者： Wang, Rui Lu, Yonglong Song, Shuai Yang, Shengjie Wu, Yanqi Cui, Haotian State Key Laboratory of Urban and Regional Ecology Research Centre for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China State Key Laboratory of Marine Environmental Science and Key Laboratory The Ministry of Education for Coastal Wetland Ecosystems College of the Environment and Ecology Xiamen University Fujian361102 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province College of Urban and Environmental Sciences Central China Normal University Wuhan430079 China

Pharmaceuticals and personal care products (PPCPs) are widely used for both humans and animals, but their negative effects on ecosystems and human health are of great concern. The potential harm suffered by aquatic ecosystems and human health through long-term exposure and consumption of PPCP-contaminated water has attracted worldwide attention. Industrial production sources refer to locations from which PPCPs are discharged from pharmaceutical factories or sewage treatment plants to environmental waters during the PPCP production process. There is a lack of information regarding the emission inventories and environmental fate of PPCPs in China. Moreover, due sampling difficulties, a full understanding of large-scale PPCP emissions is also lacking. In this paper, we developed a methodology for estimating industrial PPCP source emissions in China. In general, the emission rate, density and intensity of the eastern coastal provinces, the most industrialized regions in China, are significantly higher than those of the western and northern provinces. The existing PPCP concentration monitoring data collected in surface waters also show a similar distribution pattern;that is, as the population density and economic development increase, the concentrations of various PPCPs in natural waters also show increasing trends, thus confirming that manufacturing and industrial uses are some of the main sources of PPCP pollution and pose significant risks to the environment. © 2022, The Authors. All rights reserved.

关键词： Aquatic ecosystems

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Climatic Limitations on Grassland Photosynthesis Over the Tibetan Plateau Shifted from Temperature to Water

SSRN

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SSRN 2023年

作者： Xie, Jiangliu Yin, Gaofei Ma, Dujuan Chen, Rui Zhao, Wei Xie, Qiaoyun Wang, Cong Lin, Shangrong Yuan, Wenping Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu610031 China Institute of Mountain Hazards and Environment Chinese Academy of Sciences Chengdu610041 China School of Engineering The University of Western Australia PerthWA6009 Australia Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province School of Urban and Environmental Sciences Central China Normal University Wuhan430079 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Zhuhai China

Plant photosynthesis plays an essential role in regulating the global carbon cycle. Therefore, it is essential to understand the limitations imposed by climate on plant photosynthesis to comprehend the impacts of climate change on land carbon dynamics. In this study, taking gross primary productivity as a direct representation of photosynthesis, we employed a light use efficiency paradigm (i.e., the revised EC-LUE) and factorial analysis method to quantify the spatiotemporal variation of temperature- and water-limitations on plant photosynthesis over the Tibetan Plateau (TP) grasslands during growing season (May to October) in 1983-2018. Results revealed a clear spatiotemporal pattern of the temperature- and water-limitations: temperature is the primary climatic limiting factor in the eastern TP, while water is the primary climatic limiting factor in the western TP;the water- and temperature-limitations prevail in summer and spring/autumn, respectively. The water- and temperature-limitations intensified and alleviated during 1983 through 2018. There also was a widespread shift from temperature-limitation to water-limitation in the TP, particularly in midsummer (August). Our findings demonstrated the shifting relative importance of climatic limitations on plant photosynthesis under changing climate, which is crucial for predicting future terrestrial carbon cycle dynamics. © 2023, The Authors. All rights reserved.

关键词： Photosynthesis

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Preferential flow influences the profile partition patterns of rainwater infiltration in a steep forested headwater catchment

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Journal of Hydrology 2025年 661卷

作者： Lu, Shiguo Liu, Muxing Yi, Jun Yang, Qian Ding, Feng Key Laboratory of Geographical Process Analysis & Simulation of Hubei Province Central China Normal University Wuhan430079 China College of Urban and Environmental Sciences Central China Normal University Wuhan430079 China Yichang Three Gorges Dalaoling Nature Reserve Administration Yichang443000 China

Infiltration is a critical component of the hydrologic cycle, affecting key behaviors such as runoff generation, plant available water, and groundwater recharge. However, it remains uncertain how and to what extent preferential flow (PF) influences infiltration and its partition patterns with depth. Here, we performed an event-based study using a four-year soil moisture (SM) monitoring dataset collected at multiple depths (0–80 cm) across a steep forested headwater catchment. The infiltration partitioning index (i.e., the percentage of soil water storage increment in each layer relative to the total soil profile) was calculated to quantify the influence of controlling factors on infiltration processes. We found that approximately 79.3 % of infiltrated water was retained in the upper three layers (0–40 cm). A larger rainfall and a lower SM condition permitted rainwater infiltration into the deeper layers of a soil profile. PF significantly facilitated water infiltration in most cases. However, under heavy and continuous rainfall conditions (mean amount ≥60.7 mm), or under wet SM conditions (>36 %), no significant (p > 0.05) difference in infiltration effects was observed between PF events and MF (matrix flow) events in shallow layers. In contrast, the water transport of PF events was significantly (p © 2025 Elsevier B.V.

关键词： Soil moisture

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Fusing multi-source data to map spatio-temporal dynamics of winter rape on the Jianghan Plain and Dongting Lake Plain, China

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Journal of Integrative Agriculture 2019年第10期18卷 2393-2407页

作者： TAO Jian-bin LIU Wen-bin TAN Wen-xia KONG Xiang-bing XU Meng Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/School of Urban and Environmental Sciences Central China Normal UniversityWuhan 430079P.R.China Key Laboratory of the Loess Plateau Soil Erosion and Water Loss Process and Control Ministry of Water Resources/Yellow River Institute of Hydraulic ResearchZhengzhou 450003P.R.China

Mapping crop distribution with remote sensing data is of great importance for agricultural production, food security and agricultural sustainability. Winter rape is an important oil crop, which plays an important role in the cooking oil market of China. The Jianghan Plain and Dongting Lake Plain (JPDLP) are major agricultural production areas in China. Essential changes in winter rape distribution have taken place in this area during the 21st century. However, the pattern of these changes remains unknown. In this study, the spatial and temporal dynamics of winter rape from 2000 to 2017 on the JPDLP were analyzed. An artificial neural network (ANN)-based classification method was proposed to map fractional winter rape distribution by fusing moderate resolution imaging spectrometer (MODIS) data and high-resolution imagery. The results are as follows:(1) The total winter rape acreages on the JPDLP dropped significantly, especially on the Jianghan Plain with a decline of about 45% during 2000 and 2017.(2) The winter rape abundance keeps changing with about 20–30% croplands changing their abundance drastically in every two consecutive observation years.(3) The winter rape has obvious regional differentiation for the trend of its change at the county level, and the decreasing trend was observed more strongly in the traditionally dominant agricultural counties.

关键词： winter rape,spatio-temporal dynamics,time-series MODIS data artificial neural network

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