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science Bulletin 2024年第1期 114-124页

作者：夏晓圣任培阳王旭辉刘丹陈修治丹利何斌何洪林居为民梁敏琪陆星劼彭静覃章才夏江周郑博魏静乐旭于贵瑞朴世龙袁文平 jiangsu key Laboratory of Atmospheric Environment Monitoring and Pollution Control Collaborative Innovation Center of Atmospheric Environment and Equipment Technology School of Environmental Science and Engineering Nanjing University of Information Science & Technology School of Atmospheric sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Sino-French Institute for Earth System science College of Urban and Environmental Sciences Peking University State key Laboratory of Tibetan Plateau Earth System Resources and Environment Institute of Tibetan Plateau Research Chinese Academy of Sciences key Laboratory of Regional Climate-Environment for Temperate East Asia Institute of Atmospheric Physics Chinese Academy of Sciences State key Laboratory of Earth Surface Processes and Resource Ecology College of Global Change and Earth System Science Beijing Normal University Synthesis Research Center of China's Ecosystem Research Network Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences International Institute for Earth System science Nanjing University Tianjin key Laboratory of Water Resources and Environment Tianjin Normal University Institute of Environment and Ecology Tsinghua Shenzhen International Graduate School Tsinghua University

As one of the world’s largest emitters of greenhouse gases,China has set itself the ambitious goal of achieving carbon peaking and carbon neutrality.Therefore,it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide(CO2),and to monitor China’s progress toward these goals.Using state-of-the-art datasets and models,this study comprehensively estimated the anthropogenic CO2emissions from energy,industrial processes and product use,and waste along with natural sources and sinks of CO2for all of China during 1980-2021.To recognize the differences among various methods of estimating greenhouse emissions,the estimates are compared with China’s National Greenhouse Gas Inventories(NGHGIs) for 1994,2005,2010,2012,and 2014.Anthropogenic CO2emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO2a-1in 2021.While benefiting from ecological projects(e.g., Three Norths Shelter Forest System Project),the land carbon sink in China has reached 1.65 Gt CO2a-1averaged through 2010-2021,which is almost 15.81 times that of the carbon sink in the 1980s.On average,China’s terrestrial ecosystems offset 14.69%±2.49% of anthropogenic CO2emissions through 2010-2021.Two provincial-level administrative regions of China,Xizang and Qinghai,have achieved carbon neutrality according to our estimates,but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10% of anthropogenic CO2emissions.This study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO2emissions,but found notable differences for land carbon sinks.Future estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

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A labor-free index-guided semantic segmentation approach for urban vegetation mapping from high-resolution true color imagery

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International Journal of Digital Earth 2023年第1期16卷 1640-1660页

作者： Peng Zhang Cong Lin Shanchuan Guo Wei Zhang Hong Fang Peijun Du jiangsu provincial key Laboratory of geographic information science and technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjingPeople’s Republic of China jiangsu Center for Collaborative Innovation in geographical information Resource Development and Application NanjingPeople’s Republic of China c Nanjing Research Institute of Surveying Mapping&Geotechnical InvestigationCo.LtdNanjingPeople’s Republic of China

Accurate and timely information on urban vegetation(UV)can be used as an important indicator to estimate the health of cities.Due to the low cost of RGB cameras,true color imagery(TCI)has been widely used for high spatial resolution UV mapping.However,the current index-based and classifier-based UV mapping approaches face problems of the poor ability to accurately distinguish UV and the high reliance on massive annotated samples,respectively.To address this issue,an index-guided semantic segmentation(IGSS)framework is proposed in this paper.Firstly,a novel cross-scale vegetation index(CSVI)is calculated by the combination of TCI and Sentinel-2 images,and the index value can be used to provide an initial UV map.Secondly,reliable UV and non-UV samples are automatically generated for training the semantic segmentation model,and then the refined UV map can be produced.The experimental results show that the proposed CSVI outperformed the existingfive RGB vegetation indices in highlighting UV cover and suppressing complex backgrounds,and the proposed IGSS workflow achieved satisfactory results with an OA of 87.72%∼88.16%and an F1 score of 87.73%∼88.37%,which is comparable with the fully-supervised method.

关键词： Urban vegetation mapping Sustainable Development Goals(SDGs) cross-scale vegetation index(CSVI) semantic segmentation high-resolution true color imagery(TCI)

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Simulation of China’s potential rice yields by coupling land system evolution and climate change

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science China Earth sciences 2023年第8期66卷 1776-1788页

作者： Manchun LI Cong DU Penghui JIANG Wenbo LUAN Dengshuai CHEN jiangsu provincial key Laboratory of geographic information science and technology Key Laboratory for Land Satellite Remote SensingApplications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjing 210023China Collaborative Innovation Center of South China Sea Studies Nanjing 210023China School of Public Management Nanjing Agricultural UniversityNanjing 210095China China Resources&Environment and Development Academy Nanjing 210095China

Land systems and climate,which are the key elements of agricultural production and key drivers of crop yields,affect the quality of arable land.However,a quantitative model to reveal the mechanism of how potential grain yields are affected by macro-scale arable land evolution and climate change has not yet been developed.In this study,we constructed a Grey Prediction Model-Future Land Use Simulation(GM-FLUS),which combined land system evolution with climate change data,to simulate changes in China’s land system over the next 40 years.We improved the Global Agro-Ecological Zone(GAEZ)model,estimated China’s potential rice yields and their spatial distribution in the next 40 years under four scenarios(shared socioeconomic pathway SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5)of the Sixth International Coupling Model Intercomparison Project,analysed the spatiotemporal variations in the potential rice yields and their drivers,and provided appropriate suggestions for increasing rice yields.The simulation results indicated an increase in China’s potential rice yields during 2020–2060 under the SSP1-2.6 and SSP3-7.0 scenarios and a decrease under the SSP2-4.5 and SSP5-8.5 scenarios.Moreover,China’s development strategy of“achieving carbon peaking by 2030 and carbon neutrality by 2060”was similar to the SSP1-2.6 scenario,under which rice yields were relatively stable.Furthermore,under China’s arable land protection policy,China’s paddy field area will change slightly during 2020–2060,and potential rice yields will be influenced by climate.Under the four climate change scenarios,air temperature increased and was negatively correlated with potential rice yields in main rice-producing regions.Additionally,potential rice yields were positively correlated with precipitation,which increased stably under the SSP1-2.6 and SSP3-7.0scenarios and decreased under the SSP2-4.5 and SSP5-8.5 scenarios.These results suggest that the development of heat-resistant rice varieties and the implementation of measures that will mitigate the impacts o

关键词： Land system Shared socioeconomic pathways Global Agro-Ecological Zone model Potential rice yield Grain security

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Analysis of jiangsu Tidal Flats Reclamation from 1974 to 2012 Using Remote Sensing

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China Ocean Engineering 2015年第1期29卷 143-154页

作者：赵赛帅刘永学李满春孙超周旻曦张荷霞 jiangsu provincial key Laboratory of geographic information science and technology Nanjing University Collaborative Innovation Center for the South China Sea Studies Department of Geographic Information ScienceNanjing University jiangsu Center for Collaborative Innovation in geographical information Resource Development and Application Nanjing University

jiangsu has the most abundant tidal flat resources among China＇s coastal provinces. To ease the conflict between the growing population and shrinking usable land and to promote regional sustainable development, large-scale coastal reclamation development activities have been performed in jiangsu Province since 1949. The present study has integrated multi-source remote sensing images during 1974 to 2012 by using a Multi-point Fast Marching Method （MFMM） to extract the jiangsu coastal reclamation areas for different time periods. The temporal and spatial patterns of the extent and elevation of the reclamation areas were analysed in order to determine the unused potential for future reclamation. It will provide information necessary to support the development and construction of tidal flats in jiangsu. Results show that：（1） the reclaimed tidal fiats along the jiangsu coast cover more than 19.86×10^4 hm^2, of which 13.97×10^4 hm^2 is located in Yancheng, 4.84×10^4 hm^2 in Nantong, and 1.05×10^4 hm^2 in Lianyungang; （2） the average elevation of the reclaimed jiangsu tidal flats has gradually decreased over the last 40 years, while those in Dongtai and Rudong have showed particularly accelerated decrease since the 1990s; and （3） in 2012, very few unused tidal flats two meters above the sea level are left along the jiangsu coast, and mainly concentrated in Yancheng. As there are still reserving some coastal land resources between 0-2 m, providing us with a potential for future development. All of these findings may be useful for researchers and local authorities for the development and utilization of tidal flat resources in jiangsu.

关键词： tidalflats remote sensing jiangsu coast developmentpotential reclamation

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The status of carbon neutrality of the world’s top 5 CO_(2) emitters as seen by carbon satellites

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Fundamental Research 2022年第3期2卷 357-366页

作者： Fei Jian Wei He Weimin Ju Hengmao Wang Mousong Wu Jun Wang Shuzhuang Feng Lingyu Zhang Jing MChen jiangsu provincial key Laboratory of geographic information science and technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023China jiangsu Center for Collaborative Innovation in geographical information Resource Development and Application Nanjing 210023China Department of Geography University of TorontoTorontoOntario M5S3G3Canada Frontiers science Center for Critical Earth Material Cycling Nanjing UniversityNanjing 210023China

China,the Unite States(US),the European Union(EU),India,and Russia are the world’s top 5 fossil fuel and cement CO_(2)(FFC)emitting countries or regions(CRs).It is very important to understand their status of carbon neutrality,and to monitor their future changes of net carbon fluxes(NCFs).In this study,we implemented a well-established global carbon assimilation system(GCAS,Version 2)to infer global surface carbon fluxes from May 2009 to December 2019 using both GOSAT and OCO-2 XCO_(2)retrievals.The reductions of flux uncertainty and XCO_(2)bias,and the evaluation of posterior flux show that GCAS has comparable and good performance in the 5 CRs.The results suggest that Russia has achieved carbon neutrality,but the other 4 are still far from being carbon neutral,especially China.The mean annual NCFs in China,the US,the EU,India,and Russia are 2.33±0.29,0.82±0.20,0.42±0.16,0.50±0.12,and-0.33±0.23 PgC yr^(−1),respectively.From 2010 to 2019,the NCFs showed an increasing trend in the US and India,a slight downward trend after 2013 in China,and were stable in the EU.The changes of land sinks in China and the US might be the main reason for their trends.India’s trend was mainly due to the increase of FFC emission.The relative contributions of NCFs to the global land net carbon emission of China and the EU have decreased,while those of the US and India have increased,implying the US and India must take more active measures to control carbon emissions or increase their sinks.This study indicates that satellite XCO_(2)could be successfully used to monitor the changes of regional NCFs,which is of great significance for major countries to achieve greenhouse gas control goals.

关键词： Net carbon flux GOSAT OCO-2 XCO_(2) Atmospheric inversion

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Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model： A Case Study in Nanjing, China

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Chinese geographical science 2018年第6期28卷 1048-1060页

作者： WANG Jiechen WU Jiayi NI Jianhua CHEN Jie XI Changbai Department of geographic information science Nanjing University Nanjing 210023 China jiangsu provincial key Laboratory of geographic information science and technology Nanjing 210023 China jiangsu Center for Collaborative Innovation in geographical information Resource Development and Application Nanjing 210023 China

With the increasing number of vehicles in large-and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands.

关键词：道路等级道路交通聚类模型特征时间特征和城市南京案例

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A new vegetation index combination for leaf carotenoid-tochlorophyll ratio: minimizing the effect of their correlation

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International Journal of Digital Earth 2023年第1期16卷 272-288页

作者： Chunmei He Jia Sun Yuwen Chen Lunche Wang Shuo Shi Feng Qiu Shaoqiang Wang Torbern Tagesson Hubei key Laboratory of Regional Ecology and Environmental Change School of Geography and Information EngineeringChina University of GeosciencesWuhanPeople’s Republic of China Department of Physical Geography and Ecosystem science Lund UniversityLundSweden State key Laboratory of information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhanPeople’s Republic of China International Institute for Earth System science Jiangsu Provincial Key Laboratory of Geographic Information Science and TechnologyNanjing UniversityNanjingPeople’s Republic of China Department of Geosciences and Natural Resource Management University of CopenhagenCopenhagenDenmark

The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio.This study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and sites.VIs sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT model.The VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent datasets.The ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability.Compared with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield datasets.This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.

关键词： Leaf carotenoids content leaf chlorophyll content PROSPECT model ratio of Car to Chl vegetation index

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Shallow water bathymetry based on a back propagation neural network and ensemble learning using multispectral satellite imagery

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Acta Oceanologica Sinica 2023年第5期42卷 154-165页

作者： Sensen Chu Liang Cheng Jian Cheng Xuedong Zhang Jie Zhang Jiabing Chen Jinming Liu jiangsu provincial key Laboratory of geographic information science and technology Nanjing UniversityNanjing 210023China Collaborative Innovation Center of South China Sea Studies Nanjing UniversityNanjing 210093China School of Geography and Ocean science Nanjing UniversityNanjing 210023China jiangsu Center for Collaborative Innovation in Novel Software technology and Industrialization Nanjing 210023China Zhejiang Institute of Hydraulics and Estuary Hangzhou 310020China Institute of Defense Engineering Academy of Military SciencesBeijing 100036China

The back propagation(BP)neural network method is widely used in bathymetry based on multispectral satellite imagery.However,the classical BP neural network method faces a potential problem because it easily falls into a local minimum,leading to model training failure.This study confirmed that the local minimum problem of the BP neural network method exists in the bathymetry field and cannot be ignored.Furthermore,to solve the local minimum problem of the BP neural network method,a bathymetry method based on a BP neural network and ensemble learning(BPEL)is proposed.First,the remote sensing imagery and training sample were used as input datasets,and the BP method was used as the base learner to produce multiple water depth inversion results.Then,a new ensemble strategy,namely the minimum outlying degree method,was proposed and used to integrate the water depth inversion results.Finally,an ensemble bathymetric map was acquired.Anda Reef,northeastern Jiuzhang Atoll,and Pingtan coastal zone were selected as test cases to validate the proposed method.Compared with the BP neural network method,the root-mean-square error and the average relative error of the BPEL method can reduce by 0.65–2.84 m and 16%–46%in the three test cases at most.The results showed that the proposed BPEL method could solve the local minimum problem of the BP neural network method and obtain highly robust and accurate bathymetric maps.

关键词： bathymetry back propagation neural network ensemble learning local minimum problem multispectral satellite imagery

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Relationship between settlements and topographical factors:An example from Sichuan Province,China

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Journal of Mountain science 2018年第9期15卷 2043-2054页

作者： XI Chang-bai QIAN Tian-lu CHI Yao CHEN Jie WANG Jie-chen Department of geographic information science Nanjing UniversityNanjing 210023China jiangsu provincial key Laboratory of geographic information science and technology Nanjing UniversityNanjing 210023China jiangsu Center for Collaborative Innovation in geographical information Resource Development and Application Nanjing 210023China

Terrain can influence the spatial distribution of settlements. Studies on the terrain characteristics of settlements can help to understand the effects of the environment on human activities. This paper provides a quantitative analysis of the relationship between settlements and topographical factors. A statistically significant sample of residential locations and ASTER GDEM V2 were used to investigate terrain traits and settlements distributions. We selected eight topographical factors and introduced a practical concept, distributive entropy, into assessing the aggregation extent of the settlements' spatial distribution. The study showed that topography varies within the study area, and distributive entropy indicates that settlements have distinctive distribution tendency in statistic approach. According to the results of this study, mountain inhabitants prefer to settle in valleys. Additionally, with distributive entropy, residential suitability was divided to three levels: suitable, normal, and unsuited. The results showed that suitable area is small in Sichuan Province, accounting for 8.2%～29.9%; however, unsuited area is large, accounting for 33%～63.3%.

关键词：地形学四川省个例中国定量分析分发地面显示解

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A Self-calibration Bundle Adjustment Algorithm Based on Block Matrix Cholesky Decomposition technology

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Journal of Geodesy and Geoinformation science 2023年第1期6卷 11-30页

作者： Huasheng SUN Yuan ZHANG Shandong provincial key Laboratory of Water and Soil Conversation and Environmental Protection School of Resources and EnvironmentLinyi UniversityLinyi 276000China key Laboratory of geographic information science East China Normal UniversityShanghai 200062China

In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innovation points are reflected in the following aspects:①The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear;②The complexities of time and space tend to O(n)in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly;③The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes;④Some measures are employed to improve the optimization effects.Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth.This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.

关键词： bundle adjustment self-calibration block matrix Cholesky decomposition

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