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A novel spatiotemporal fractal dimension (STFD) for assessing ecological environment of the large-scale Yangtze River Basin China from 2012 to 2021 remote sensing data

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GIScience & Remote Sensing 2025年第1期62卷

作者： Chang Li Jiao Guo Yongjun Zhang a Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province and College of Urban and Environmental Science Central China Normal University Wuhan China b School of Remote Sensing and Information Engineering Wuhan University Wuhan China

ABSTRACTThe research of remote-sensing-based ecological environment assessment (EEA) faces three major challenges: comprehensive EEA of multiple indexes; impact of time-series fluctuations, i.e. temporal uncertainty; and spatial size selection of buffer zone, i.e. spatial uncertainty. Therefore, this paper proposes a novel spatiotemporal fractal dimension (STFD) to assess ecological environment by treating space and time as a whole, which transforms the problem of spatiotemporal uncertainty of EEA into a deterministic one. The proposed STFD is computed by weighted principal component summation with trend analysis and dendrite-radius fractal dimension from seven remote sensing indexes: normalized difference vegetation index, enhanced vegetation index, leaf area index, gross primary productivity, net primary productivity, land surface temperature, and Wetness. Moreover, it has the advantages of comprehensiveness, objectivity, decorrelation, and control of spatiotemporal uncertainty. For experiments, STFD was used to access the ecological environments of the Yangtze River basin at different river grades in China from 2012 to 2021 by MODIS products. The conclusion is: (1) From 2012 to 2021, the areas with the lowest ecological index scores in the Yangtze River basin are located in the Tibetan Plateau and the eastern coastal areas, but with a trend to become better; (2) The EEAs of primary, secondary, and tertiary rivers based on STFD are 1.22, 1.04, and 1.00 respectively. This indicates an overall trend toward improvement of the EEA in Yangtze River basin; (3) The scale-free interval of STFD ranges from 6.21 to 9.21 with a buffer distance of 500 m to 10,000 m. The coefficients of determination R2are all greater than 0.97, indicating high accuracy and reliability of the fractal dimension. This study not only enriches the theory and methods of controlling spatiotemporal uncertainty of EEA, but also reveals the ecological environmental law in the Yangtze River basin at dif

关键词： Spatiotemporal fractal dimension (STFD) ecological environment assessment (EEA) remote sensing index spatiotemporal uncertainty Yangtze River basin

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

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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 modeling and analysis of the patch size–frequency distribution of forest disturbances in China based on a Landsat forest cover change product

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International Journal of Digital Earth 2021年第2期14卷 181-201页

作者： Dan-Xia Song Chengquan Huang Tao He Joseph O.Sexton Ainong Li Sike Li Hao Wu John R.Townshend College of Urban and Environmental Sciences Central China Normal UniversityWuhanPeople’s Republic of China Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation Central China Normal UniversityWuhanPeople’s Republic of China Department of Geographical Sciences University of MarylandCollege ParkMDUSA School of Remote Sensing and Information Engineering Wuhan UniversityWuhanPeople’s Republic of China terraPulse IncGaithersburgMDUSA Institute of Mountain Hazards and Environment Chinese Academy of SciencesChengduPeople’s Republic of China School of Earth Atmosphere and EnvironmentMonash University–Clayton CampusVictoriaAustralia

Forest disturbances have been altering the ecological properties of ecosystems;meanwhile,disturbance events of varying sizes create different structures and functions for a forest ***,size and frequency are important attributes of disturbances,and their relationship should be *** present a hierarchical method through the modeling of the overall trend of the size–frequency distribution and the characterization of the non-constant variances of disturbance sizes at each frequency *** method was demonstrated to accurately model the sizes as well as the corresponding frequencies;thus,the total disturbed area and number of disturbance patches were both accurately *** applying the method to 13 provinces in China,consistent patterns were revealed by the modeling results and remote-sensing-based product,showing that between 2000 and 2005,forests in most provinces were dominated by moderate disturbances(10 haprovinces contain the largest proportion of small disturbances(size<10 ha),whereas most of the very large disturbances(size>1000 ha)occurred in the northeastern and northwestern *** study concludes that the proposed method can improve the representation of the size–frequency distribution of forest disturbances.

关键词： Forest disturbance patch size remote sensing forestry

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

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

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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

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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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Research on the Integration of Sensing and Communication Based on Fractional-Order Fourier Transform

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Sensors (Basel, Switzerland) 2025年第10期25卷 2956页

作者： Mingyan Qi Yuelong Su Zhaoyi Wang Kun Lu Nanjing Research Institute of Electronics Technology Nanjing 210039 China. The College of Urban & Environmental Sciences Central China Normal University Wuhan 430079 China. Key Laboratory for Geographical Process Analysis & Simulation in Hubei Province Central China Normal University Wuhan 430079 China.

This study investigated the integration of detection and communication techniques. First, the fractional-order Fourier transform (FRFT) is introduced, and the golden section method, parabolic interpolation, and Brent method are applied to search for the optimal fractional-order domain to accurately estimate the parameters of the linear frequency modulation (LFM) signal. Second, the three search algorithms and the performance of the integrated sensing and communication waveform are simulated. The Brent method improves the parameter searching efficiency by approximately 30% compared with the golden section method; the bit error ratio (BER) of the integrated LFM signal can reach 10 with a signal-to-noise ratio (SNR) of 3 dB. The results show that the integrated waveform can realize the detection function with guaranteed communication performance. An anti-frequency sweeping interference method based on the fractional domain matching order was also carried out to optimize the detection performance of the integrated waveform. Through the analysis of the difference-frequency signal under frequency sweeping interference, two methods, direct filtering, and pairwise cancellation filtering, are used to suppress the interference signal and detect the target distance. The simulation evaluated the detection performance of the two methods under different signal-to-interference ratios (SIR) and filter widths. The simulation results show that the pairwise cancellation filtering suppresses the frequency sweeping interference by 4-6 dB more than the direct filtering with an SIR ≤ -15 dB. Both filtering methods can correctly extract the target position information under frequency sweeping interference with a low signal-to-interference ratio (SIR). In conclusion, this study provides an effective solution for parameter estimation optimization and frequency-sweeping interference suppression for FRFT-based sensing communication systems.

关键词： LFM anti-frequency sweeping interference fractional-order fourier transform integration of sensing and communication

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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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Risk Assessment of Geological Hazards Based on Multi-Condition Development Scenarios: A Case Study of Huangshi Town, Guangdong province

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Applied Sciences 2025年第10期15卷

作者： Gonghao Duan Hui Xia Anqi Du Juan Ma Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province Central China Normal University Wuhan 430079 China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan 430205 China Technology Innovation Center for Geo-Hazard Monitoring and Risk Early Warning Ministry of Natural Resources Beijing 100081 China

This study focuses on Huangshi Town, Longchuan County, Guangdong province, as the research area. By utilizing existing data and field surveys, the study identifies geological hazards and risks in the area, deeply analyzes the formation mechanisms and disaster-causing patterns, and systematically summarizes the developmental characteristics and spatial distribution of these hazards. Methodologically, the research combines regular grids and slope units, selecting ten evaluation factors for correlation analysis. The information theory model is used to assess disaster susceptibility. The study further evaluates the impact of three rainfall scenarios—100 mm of rainfall over 24 h, 250 mm of rainfall over 24 h, and 240 mm of effective rainfall over 72 h—on geological disasters in Huangshi Town. As a result, a comprehensive hazard assessment under multiple rainfall scenarios is produced. The findings show that the Receiver Operating Characteristic (ROC) accuracy of the disaster-susceptibility evaluation reaches 0.8739, ensuring high data quality for the geological hazard assessment in Huangshi Town. The zoning results align closely with field survey observations. In conclusion, incorporating rainfall as a triggering factor enhances the accuracy of the susceptibility analysis by better capturing the temporal and spatial patterns of landslide occurrences, thereby offering a more comprehensive understanding of the geological hazard development in the study area. © 2025 by the authors.

关键词： Risk assessment

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"3S" technologies and application for dynamic monitoring soil and water loss in the yangtze river basin, China 24

"3S" technologies and application for dynamic monitoring soi...

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2020 24th ISPRS Congress - Technical Commission III

作者： Li, C. Yao, J. Li, R. Zhu, Y. Yao, H. Zhang, P. Wei, D. Zhao, S. Li, Y. Wu, Y. Key Laboratory for Geographical Process Analysis and Simulation Hubei Province College of Urban and Environmental Science Central China Normal University Wuhan China Changjiang Soil and Water Conservation Monitoring Center CWRC Wuhan China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Vivo Mobile Communication Co. Ltd China

For China, which has many big rivers, there is an urgent need for efficient dynamic monitoring technology of water and soil loss. However, there are some problems in the current 3S (RS, GIS and GPS) technology for dynamic monitoring water and soil loss. This paper takes the Yangtze River Basin as an example to innovate and optimize the key technologies of the remote sensing interpretation of the water and soil loss dynamic monitoring of the Yangtze River Basin, and overcome the major technical difficulties in the remote sensing interpretation of the dynamic monitoring of water and soil loss. The key technologies include: 1) The establishment of a field investigation platform based on Internet and UAV (Unmanned Aerial Vehicle) for remote sensing interpretation;2) Near real-time evaluating key factors of soil and water loss based on UAV photogrammetry and digital terrain analysis;3) Geometric and Radiometric Simultaneous Correction Model (GRSCM) framework for remote sensing images pre-processing;4) An object-oriented land use change update quality control method supported by multi-PC and GIS;and an object-oriented remote sensing image classification system based on random forest, deep learning and transfer learning;5) Improvement of quantitative change detection method for image vegetation and three-dimensional topography. The results have been successfully applied in the remote sensing interpretation of the dynamic monitoring of water and soil loss in the national key prevention and control area of the Yangtze River Basin. They have been provided a scientific reference for the development planning of The Yangtze River Economic Zone. © 2020 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives.

关键词： Geographic information systems

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