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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Dantong Zhong Dan-Xia Song Zihao Wang Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation Central China Normal University Hubei China College of Urban and Environmental Sciences Central China Normal University Hubei China

The Three Parallel Rivers Basin (TPRB) is a global biodiversity hotspot in the southeast of Qinghai-Tibet Plateau. The vegetation ecosystem has undergone significant changes due to global environmental changes, necessitating the long-term and fine-scale monitoring. The Hi-GLASS FVC product, derived from Landsat 8 data, has a fine spatial resolution of 30 m, but spatial gaps caused by clouds and shadows are prevalent. To address this issue, a 16-day, 30 m resolution FVC dataset was generated from 2000 to 2018 for the TPRB by fusing Hi-GLASS FVC and GLASS FVC time-series data. Combining the annual average FVC and growing-season average FVC, trend analysis of vegetation cover was conducted at two different spatial resolutions (30m and 500m) using the Sen's slope and Mann-Kendall test at a 95% confidence level. The results indicated that more areas exhibited significant improvements in vegetation than degradation. The improvements were characterized by the increase of the annual average FVC as well as the growing-season FVC for two decades. This study provides scientific supports for the development of ecological conservation plans in the TPRB region.

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ΩSFormer: Dual-Modal Ω-like Super-Resolution Transformer Network for Cross-scale and High-accuracy Terraced Field Vectorization Extraction

arXiv

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arXiv 2024年

作者： Li, Chang Wang, Yu Zhang, Ce Zhang, Yongjun Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province College of Urban and Environmental Science Central China Normal University Wuhan China Industry and Information Technology Bureau of Lingbao City Henan Province 472500 China School of Remote Sensing and Information Engineering Wuhan University China

Terraced field is a significant engineering practice for soil and water conservation (SWC). Besides ecological benefits, it could increase soil organic matter content, promote carbon fixation and reduce greenhouse gas emissions, which is conducive to accelerating the realization of the "dual-carbon" objective (i.e., carbon peak and carbon neutrality). Terraced field extraction from remotely sensed imagery is the foundation for monitoring and evaluating SWC, whose research is worth paying attention to the following aspects: (1) there is currently no dual-modal (e.g., remotely sensed imagery and DEM) raster-vector dataset (DMRVD) for deep-learning-based terraced field vectorization extraction (TFVE);(2) downsampling in the encoder step of classical semantic segmentation networks leads to poor edge segmentation accuracy;(3) it is necessary to deeply investigate the effect of single or double-branch input method for improving extraction accuracy;(4) the dual-modal input method cannot guarantee a unified resolution or scale, so cross-scale effects remain to be explored;(5) the current pixel-level extraction of remotely sensed imagery is almost a raster result, which does not meet the practical application requirements for lightweight and infinite scalability. To address the issues above, this study is the first to propose a novel dual-modal Ω-like super-resolution Transformer network (ΩSFormer) for intelligent TFVE, offering the following advantages: (1) reducing edge segmentation error from conventional multiscale downsampling encoder, through fusing original high-resolution features with downsampling features at each step of encoder and leveraging a multi-head attention mechanism;(2) improving the accuracy of TFVE by proposing a Ω-like network structure, i.e., dual-modal and dual-branch (D2MB), which fully integrates rich high-level features from both spectral and terrain data to form cross-scale super-resolution features;(3) validating an optimal fusion scheme for cro

关键词： Semantic Segmentation

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Refining the Factors Affecting N_(2)O Emissions from Upland Soils with and without Nitrogen Fertilizer Application at a Global Scale

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Advances in Atmospheric Sciences 2024年第9期41卷 1804-1820页

作者： Wenqian JIANG Siqi LI Yong LI Meihui WANG Bo WANG Ji LIU Jianlin SHEN Xunhua ZHENG State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Key Laboratory for Agro-ecological Processes in Subtropical Regions Institute of Subtropical AgricultureChinese Academy of SciencesChangsha 410125China Changsha Research Station for Agricultural&Environmental Monitoring Chinese Academy of SciencesChangsha 410125China University of Chinese Academy of Sciences Beijing 100049China State Environmental Protection Key Laboratory of Formation and Prevention of the Urban Air Pollution Complex Shanghai Academy of Environment SciencesShanghai 200233China Institute of Carbon Neutrality Qilu ZhongkeJinan 251699China Hubei Province Key Laboratory for Geographical Process Analysis and Simulation Central China Normal UniversityWuhan 430079China Department of Ecohydrology Leibniz Institute of Freshwater Ecology and Inland FisheriesBerlin 12587Germany Key laboratory of Environment Change and Resources Use in Beibu Gulf Ministry of EducationNanning Normal UniversityNanning 530001China Golden Mantis school of Architecture Soochow UniversitySuzhou 215123China

Nitrous oxide(N_(2)O)is a long-lived greenhouse gas that mainly originates from agricultural *** and more studies have explored the sources,influencing factors and effective mitigation measures of N_(2)O in recent ***,the hierarchy of factors influencing N_(2)O emissions from agricultural soils at the global scale remains *** this study,we carry out correlation and structural equation modeling analysis on a global N_(2)O emission dataset to explore the hierarchy of influencing factors affecting N_(2)O emissions from the nitrogen(N)and non-N fertilized upland farming systems,in terms of climatic factors,soil properties,and agricultural *** results show that the average N_(2)O emission intensity in the N fertilized soils(17.83 g N ha^(-1)d^(-1))was significantly greater than that in the non-N fertilized soils(5.34 g N ha^(−1) d^(−1))(p<0.001).Climate factors and agricultural practices are the most important influencing factors on N_(2)O emission in non-N and N fertilized upland soils,*** different climatic zones,without fertilizer,the primary influence factors on soil N_(2)O emissions are soil physical properties in subtropical monsoon zone,whereas climatic factors are key in the temperate *** fertilizer,the primary influence factors for subtropical monsoon and temperate continental zones are soil physical properties,while agricultural measures are the main factors in the temperate monsoon *** enhanced agricultural practices,such as reduced N fertilizer rate combined with the addition of nitrification and urease inhibitors can potentially mitigate N_(2)O emissions by more than 60%in upland farming systems.

关键词： N_(2)O emissions influencing factors nitrogen cycling upland farming systems

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MRIo3DS-Net: A Mutually Reinforcing Images to 3D Surface RNN-like framework for model-adaptation indoor 3D reconstruction

arXiv

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arXiv 2024年

作者： Li, Chang Guo, Jiao Zhao, Yufei Zhang, Yongjun Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province College of Urban and Environmental Science Central China Normal University Wuhan China Hainan Surveying and Mapping Product Quality Supervision and Inspection Station MNR China School of Remote Sensing and Information Engineering Wuhan University China

Currently, deep neural networks (DNNs) have made some progresses in local region feature extraction, image matching under difficult conditions and point cloud surface optimization in photogrammetry and computer vision. However, there have been being no reports on an end-to-end high-accuracy 3D reconstruction network that integrated point cloud generation (i.e., dense image matching) and 3D surface optimization. Moreover, deep-learning-based 3D reconstruction networks are often difficult to apply to new dataset tasks. Therefore, this paper is the first to propose an end-to-end framework of mutually reinforcing images to 3D surface recurrent neural network (RNN)-like (MRIo3DS-Net) for model-adaptation indoor 3D reconstruction, where multi-view dense matching and point cloud surface optimization are mutually reinforced by a RNN-like structure rather than being treated as a separate issue. The characteristics and advantages of this framework are as follows: (1) In the multi-view dense matching module (MVDMM), the model-adaptation strategy is used to fine-tune and optimize a Transformer-based multi-view dense matching DNN, so that it has the higher image feature for matching and detail expression capabilities;(2) In the point cloud surface optimization module (PCSOM), the 3D surface reconstruction network based on 3D implicit field is optimized by using model-adaptation strategy, which solves the problem of point cloud surface optimization without knowing normal vector of 3D surface. Especially, to improve and finely reconstruct 3D surfaces from point cloud, smooth loss function is proposed and added to this module;(3) The proposed MRIo3DS-Net is a RNN-like framework, which utilizes the finely optimized 3D surface obtained by PCSOM to recursively reinforce the differentiable warping for optimizing MVDMM. This refinement leads to achieving better dense matching results, and better dense matching results leads to achieving better 3D surface results recursively and mutually

关键词： Recurrent neural networks

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Optimizing the Spatial Pattern of Land Use in a Prominent Grain- Producing Area: A Sustainable Development Perspective

SSRN

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SSRN 2022年

作者： Jiang, Zhimeng Wu, Hao Lin, Anqi Hu, Qiong Zhu, Wenchao College of Urban and Environmental Sciences Central China Normal University Wuhan430079 China Hubei Province Key Laboratory for Geographical Process Analysis and Simulation Wuhan430079 China

Spatial patterns are essential for examining the sustainability derived from land systems. Constructing spatial patterns for sustainable land development is now high on the global agenda to guarantee human welfare. However, there is as yet no consensus on the technical framework for optimizing the spatial pattern of land development (SPLD) contrapose a prominent grain-producing area (PGPA). To narrow this gap, we propose a synthetic framework to shape a more reasonable SPLD for a sustainable development strategy by measuring the equilibrium between the production–living–ecological space (PLES) functions and the resource and environment carrying capacity (RECC). Taking a prominent grain-producing area (PGPA) as the object, a case study involving the Jianghan Plain (JHP) in China is conducted, leading to the following novel insights. (i) The quality of PLES and RECC in a PGPA is affected by multiple dimensions: agriculture, ecology, environment, and society. The indices of the PLES function and the RECC have significant spatial heterogeneity. SPLD in regions with fragile ecological environments and strong development is often under overload pressure. (ii) Based on the spatial zoning results of SPLD, the five partitions were taken as the optimized objects, including zones of the eco-economic, model-agricultural, core-living, eco-conservation, and coordinated-development. The land function definition of these five types of zoning covers the production-living-ecological function orientation in a PGPA. (iii) The SPLD optimization framework proposed above has strong universality because it comprehensively considers the multi-dimensional spatial functional needs of PGPA. In this study, an optimization decision framework of SPLD based on measurement and zoning was established for a PGPA. Significantly, the introduced framework is applicable and practical for optimizing SPLD from a sustainable equilibrium perspective, and the findings have considerable implications for sustai

关键词： Sustainable development

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High-quality Development of Public Health in Guizhou province, China

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Journal of Resources and Ecology 2024年第4期15卷 925-936页

作者： ZHANG Jisha LUO Jing CHEN Guolei ZHANG Chunyan WANG Jishu LI Lianlian School of Geography and Environmental Science Guizhou Normal UniversityGuiyang 550025China Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation&College of Urban and Environmental Sciences Central China Normal UniversityWuhan 430079China Hubei High-quality Development Research Institute Research Office of Hubei Provincial People’s Government and Central China Normal UniversityWuhan 430079China

People’s health is the basis for the progress of social civilization and an important symbol of national prosperity and national *** the entropy method,spatial autocorrelation,spatio-temporal geographic weighted regression and other methods,we conducted a comprehensive evaluation of the high-quality development level of public health in Guizhou province from 2011 to 2020 and an evolutionary analysis of the spatio-temporal *** results revealed four key aspects of the state of public health in Guizhou province.(1)The overall level of public health is constantly improving,but structural imbalance is prominent.(2)From the spatial dimension,the spatial differences in the level of public health have gradually moved from significant to a state of *** northeastern cities were more strongly driven by the radiation of the peripheral cities,while this effect was weaker in the southwestern *** the time dimension,the spatial pattern shows a clear gradient,with a faster vertical growth rate.(3)The overall spatial correlation of the level of public health is weak,with more cold spots than hot spots,showing a distribution pattern of“strong in the northeast and weak in the southwest”.(4)The new rural cooperative medical care participation rate,(rural)per capita health expenditure,and the electronic health record creation rate have become the most important drivers affecting the high-quality development of public health in Guizhou province.

关键词： public health high-quality development entropy method Guizhou province

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Spatiotemporal-Statistical-Modelling-Based Soil Loss Equation Calibration (Ssmbslec) by Air-Ground Collaboration

SSRN

引用

SSRN 2024年

作者： Li, Chang Yao, Jiayin Ding, Yijia Wu, Yijin Wu, JunZao Wu, Man Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province College of Urban and Environmental Science Central China Normal University Wuhan China Faculty of Resources and Environmental Science Hubei University Wuhan China Water and Soil Conservation Monitoring Station in Caidian District Wuhan China Water and soil conservation and planned water conservation station in Caidian District Wuhan China

The soil erosion module (SEM) calculated by the soil loss equation (SLE) is currently a computer simulation value, which may not be consistent with the actual value of field soil erosion. Therefore, it is necessary to calibrate the SEM to make it more approximate to the actual value. Additionally, due to the existence of spatiotemporal non-stationarity, traditional ordinary least squares (OLS) may not calibrate the deviation between the simulation value and actual value well. To address the aforementioned issues, this paper proposes the first spatiotemporal-statistical-modelling-based calibration method for a soil loss equation (SSMBSLEC) through air-ground collaboration. This method utilises data from unmanned aerial vehicles (UAVs) and ground monitoring stations to quantitatively calibrate the SEM. The experimental results from the Caidian District area demonstrate the effectiveness of this approach. The statistical calibration models were ranked in order of accuracy, from high to low, as follows: The study employed four regression techniques: geographically weighted regression, geographically and temporally weighted regression, temporally weighted regression, and OLS. The results of the hotspot analysis, based on space-time cubes, showed that the spatial law consistency between the actual value of soil erosion and SEM was the strongest, followed by the spatiotemporal law consistency, and the time consistency. These findings suggest that the accuracy of each SSMBSLEC model is affected by these factors. This study could enhance the methods and theory of water and soil conservation, promote interdisciplinary collaboration between it and spatial information science, and provide a research basis for the quantitative estimation of the actual value of SEM. © 2024, The Authors. All rights reserved.

关键词： Erosion

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Soil erosion assessment by RUSLE with improved P factor and its validation:Case study on mountainous and hilly areas of hubei province,China

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International Soil and Water Conservation Research 2021年第3期9卷 433-444页

作者： Pei Tian Zhanliang Zhu Qimeng Yue Yi He Zhaoyi Zhang Fanghua Hao Wenzhao Guo Lin Chen Muxing Liu Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province Central China Normal UniversityWuhan430079PR China School of Geosciences The University of EdinburghEH89XPUK Department of Geography University of MontrealH2V 0B3MontrealQCCanada State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau Institute of Water and Soil ConservationNorthwest A&F UniversityYangling712100Shaanxi PR China

The Revised Universal Soil Loss Equation(RUSLE)is widely used to estimate regional soil ***,quantitative impacts of soil and water conservation(SWC)measures on conservation practice factor(P)of the RUSLE remain largely unclear,especially for the mountainous and hilly *** this study,we improved the RUSLE by considering quantitative impacts of different SWC measures on the P factor *** improved RUSLE was validated against the long-term(2000-2015)soil erosion monitoring data obtained from 96 runoff plots(15—35°)in mountainous and hilly areas of hubei province,China;the result presented a high accuracy with the determination coefficient of *** on the erosion monitoring data of 2018 and 2019,the Root Mean Square Error of the result by the improved RUSLE was 28.0%smaller than that by the original RUSLE with decrement of 19.6%—24.0%in the average P factor values,indicating that the soil erosion modelling accuracy was significantly enhanced by the improved *** low P factor values appeared for farmlands with tillage measures(P<0.53),grasslands with engineering measures(P<0.23),woodlands with biological measures(P<0.28),and other land use types with biological measures(P<0.51).The soil erosion modulus showed a downward trend with the corresponding values of 1681.21,1673.14,1594.70,1482.40 and 1437.50 t km^(-2)a-1 in 2000,2005,2010,2015 and 2019,*** applicability of the improved RUSLE was verified by the measurements in typical mountainous and hilly areas of hubei province,China,and arrangements of SWC measures of this area were proposed.

关键词： Conservation practice factor(P) Soil and water conservation measure Soil erosion Land use Monitoring data

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Measuring and understanding changes in the physical built environment of cities with street view images

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Urban Informatics 2025年第1期4卷 1-23页

作者： Zhou, Yang Thill, Jean-Claude Liu, Xingjian Zhong, Chen Tu, Wei Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province College of Urban and Environmental Sciences Central China Normal University Wuhan China Department of Geography and Earth Sciences University of North Carolina at Charlotte Charlotte USA Department of Urban Planning and Design University of Hong Kong Hong Kong SAR China The Bartlett Centre for Advanced Spatial Analysis (CASA) University College London London UK Key Laboratory for Geo-Environmental Monitoring of Great Bay Area School of Architecture and Urban Planning Shenzhen University Shenzhen China Guangdong Key Laboratory for Urban Informatics Shenzhen University Shenzhen China State Key Laboratory of Subtropical Building and Urban Science Shenzhen China

Street view images (SVIs) may provide information on near-surface urban changes which are not necessarily captured by spaceborne remote sensing data. The application of SVIs in assessing diverse built environment changes at the street level and over time remains challenging. This paper presents a stepwise rule-based method to identify key types of urban built environment changes using multi-year SVIs. In particular, physical/built environment changes along streets are evaluated based on proposed Street Units of analysis (SUA) that account for both street layouts and street view features. The approach employs sharp variations of visual attributes derived from deep learning segmentation model DeepLabv3+. A stepwise rule-based algorithm classifies SUAs. Using panoramic SVIs from 2015–2019 in Wuhan, China, we identify critical types of changes such as those related to highway bridges, sidewalk increases, building increases, road losses, greenness increases, and mixed changes. Identified changes take place on over 50% of roads in the study area. In addition, the robustness of proposed approach is assessed based on results produced by manual labeling and by a fuzzy rough sets analysis. The approach is found to be robust and effective by having an 81.7% agreement with manually labeled analysis and an 80.5% agreement with fuzzy rough sets analysis. Overall, this study contributes to the development of a cost effective and efficient method for detecting physical changes on SUAs, which can be further utilized in studies that link urban changes, space use, and policy interventions.

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From statistics to grids:A two-level model to simulate crop pattern dynamics

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Journal of Integrative Agriculture 2022年第6期21卷 1786-1798页

作者： XIA Tian WU Wen-bin ZHOU Qing-bo Peter HVERBURG YANG Peng HU Qiong YE Li-ming ZHU Xiao-juan Key Laboratory for Geographical Process Analysis&Simulation Hubei Province/College of Urban&Environmental ScienceCentral China Normal UniversityWuhan 430079P.R.China Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijing 100081P.R.China Agricultural Information Institute Chinese Academy of Agricultural SciencesBeijing 100081P.R.China Institute for Environmental Studies VU University AmsterdamAmsterdam 1085The Netherlands Department of Geology Ghent UniversityGhent 9000Belgium Commercial and Economic Law School China University of Political Science and LawBeijing 100088P.R.China

Crop planting patterns are an important component of agricultural land *** patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic ***,the extent of these changes and their possible impacts on the environment,terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting *** fill this gap,this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern *** method features a two-level model that combines a land-use simulation and a crop pattern *** output of the first level is the spatial distribution of the cropland,which is then used as the input for the second level,which allocates crop censuses to individual gridded cells according to certain *** method was tested using data from 2000 to 2019 from Heilongjiang province,China,and was validated using remote sensing *** results show that this method has high accuracy for crop area *** crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.

关键词： crop planting pattern spatialization simulation spatiotemporal change remote sensing

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