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arXiv 2018年

作者： Li, Tongwen Shen, Huanfeng Yuan, Qiangqiang Zhang, Liangpei School of Resource and Environmental Sciences Wuhan University Wuhan430079 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Key Laboratory of Geographic Information System Ministry of Education Wuhan University Wuhan430079 China School of Geodesy and Geomatics Wuhan University Wuhan430079 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China

Satellite-derived aerosol optical depth (AOD) has been increasingly employed for the estimation of ground-level PM2.5, which is often achieved by modeling the relationship between AOD and PM2.5. To evaluate the accuracy of PM2.5 estimation, the cross-validation (CV) technique has been widely used. There have been several CV-based validation approaches applied for the AOD-PM2.5 models. However, the applicable conditions of these validation approaches still remain unclear. Additionally, is there space to develop better validation approaches for the AOD-PM2.5 models? The contributions of this study can be summarized as two aspects. Firstly, we comprehensively analyze and assess the existing validation approaches, and give the suggestions for applicable conditions of them. Then, the existing validation approaches do not take the distance to monitoring station into consideration. A new validation approach considering the distance to monitoring station is proposed in this study. Among the existing validation approaches, the sample-based CV is used to reflect the overall prediction ability;the site-based CV and the region-based CV have the potentials to evaluate spatial prediction performance;the time-based CV and the historical validation are capable of evaluating temporal prediction accuracy. In addition, the validation results indicate that the proposed validation approach has shown great potentials to better evaluate the accuracy of PM2.5 estimation. This study provides application implications and new perspectives for the validation of AOD-PM2.5 models. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Estimation of Grassland Production in Central and Eastern Mongolia from 2006 to 2015 via Remote Sensing

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Journal of Resources and Ecology 2019年第6期10卷 676-684页

作者： LI Ge WANG Juanle WANG Yanjie WEI Haishuo State key Laboratory of Resources and Environment Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China School of Civil and Architectural Engineering Shandong University of TechnologyZibo 255049ShandongChina College of Geoscience and Surveying Engineering China University of Mining&TechnologyBeijing 100083China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China

Mongolia is an important part of the Belt and Road Initiative"China-Mongolia-Russia Economic Corridor"and a region that has been severely affected by global climate *** in grassland production have had a profound impact on the sustainable development of the *** study explored an optimal model for estimating grassland production in Mongolia and discovered its temporal and spatial *** estimation models were established using a statistical analysis method based on EVI,MSAVI,NDVI,and PsnNet from Moderate Resolution Imaging Spectroradiometer(MODIS)remote sensing data and measured data.A model evaluation and accuracy comparison showed that an exponential model based on MSAVI was the best simulation(model accuracy 78%).This was selected to estimate the grassland production in central and eastern Mongolia from 2006 to *** results show that the grassland production in the study area had a significantly fluctuating trend for the decade study;a slight overall increasing trend was *** the first five years,the grassland production decreased slowly,whereas in the latter five years,significant fluctuations were *** grassland production(per unit yield)gradually increased from the southwest to *** most provinces of the study area,the production was above 1000 kg ha with the largest production in Hentiy,at 3944.35 kg *** grassland production(total yield)varied greatly among the provinces,with Kent showing the highest production,2341.76x1〇4 *** also indicate that the trend in grassland production along the China-Mongolia railway was generally consistent with that of the six provinces studied.

关键词： grassland production MODIS remote sensing estimation model Mongolia

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A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests

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The Innovation 2021年第4期2卷 27-34页

作者： Xueqin Yang Jianping Wu Xiuzhi Chen Philippe Ciais Fabienne Maignan Wenping Yuan Shilong Piao Song Yang Fanxi Gong Yongxian Su Yuhang Dai Liyang Liu Haicheng Zhang Damien Bonal Hui Liu Guixing Chen Haibo Lu Shengbiao Wu Lei Fan Pierre Gentine SJoseph Wright Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies School of Atmospheric SciencesSun Yat-sen University&Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and ApplicationGuangzhou Institute of GeographyGuangdong Academy of SciencesGuangzhou 510070China Laboratoire des Sciences du Climat et de l’Environnement IPSLCEA-CNRS-UVSQUniversite Paris-Saclay91191 Gif sur YvetteFrance Sino-French Institute for Earth System Science College of Urban and Environmental SciencesPeking UniversityBeijing 100871China College of Earth Sciences Chengdu University of TechnologyChengdu 610000China College of Forestry and Landscape Architecture South China Agricultural UniversityGuangzhou 510000China Department of Geoscience Environment&SocietyUniversite Libre de BruxellesBruxellesBelgium INRA UMR“Ecologie et Ecophysiologie Forestieres”Universite de Lorraine-INRA54280 ChampenouxFrance South China Botanical Garden Chinese Academy of SciencesGuangzhou 510650China School of Biological Sciences The University of Hong KongPokfulamHong Kong Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station School of Geographical SciencesSouthwest UniversityChongqing 400715China Department of Earth&Environmental Engineering Columbia UniversityNew YorkNY 10027USA Smithsonian Tropical Research Institute Apartado 0843-03092BalboaPanama

Relationships among productivity,leaf phenology,and seasonal variation in moisture and light availability are poorly understood for evergreen broadleaved tropical/subtropical forests,which contribute 25% of terrestrial *** the one hand,as moisture availability declines,trees shed leaves to reduce transpiration and the risk of hydraulic *** the other hand,increases in light availability promote the replacement of senescent leaves to increase ***,we provide a comprehensive framework that relates the seasonality of climate,leaf abscission,and leaf productivity across the evergreen broadleaved tropical/subtropical forest *** seasonal correlation between rainfall and light availability varies from strongly negative to strongly positive across the tropics and maps onto the seasonal correlation between litterfall mass and productivity for 68 *** rainfall and light covary positively,litterfall and productivity also covary positively and are always greater in the wetter sunnier *** rainfall and light covary negatively,litterfall and productivity are always greater in the drier and sunnier season if moisture supplies remain adequate;otherwise productivity is smaller in the drier sunnier *** framework will improve the representation of tropical/subtropical forests in Earth system models and suggests how phenology and productivity will change as climate change alters the seasonality of cloud cover and rainfall across tropical/subtropical forests.

关键词： tropical forest leaf abscission and productivity plant adaptive strategy climate and phenology regime climatic driver

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

arXiv

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arXiv 2022年

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA algorithm are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5

arXiv

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arXiv 2018年

The integration of satellite-derived aerosol optical depth (AOD) and station-measured PM2.5 provides a promising approach for obtaining spatial PM2.5 data. Several spatiotemporal models, which considered spatial and temporal heterogeneities of AOD-PM2.5 relationship, have been widely adopted for PM2.5 estimation. However, they generally described the complex AODPM2.5 relationship based on a linear hypothesis. Previous machine learning models yielded great superiorities for fitting the nonlinear AOD-PM2.5 relationship, but seldom allowed for its spatiotemporal variations. To simultaneously consider the nonlinearity and spatiotemporal heterogeneities of AOD-PM2.5 relationship, geographically and temporally weighted neural networks (GTWNNs) were developed for satellite-based estimation of ground-level PM2.5 in this study. Using satellite AOD products, NDVI data, and meteorological factors over China as input, GTWNNs were set up with station PM2.5 measurements. Then the spatial PM2.5 data of those locations with no ground stations could be obtained. The proposed GTWNNs have achieved a better performance compared with previous spatiotemporal models, i.e., daily geographically weighted regression and geographically and temporally weighted regression. The sample-based and site-based cross-validation R2 values of GTWNNs are 0.80 and 0.79, respectively. On this basis, the spatial PM2.5 data with a resolution of 0.1 degree were generated in China. This study implemented the combination of geographical law and neural networks, and improved the accuracy of satellite-based PM2.5 estimation. Copyright © 2018, The Authors. All rights reserved.

关键词： Neural networks

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A Hybrid Blockchain Scheme for Tracing Manufacturing Processes and Trading Transactions

A Hybrid Blockchain Scheme for Tracing Manufacturing Process...

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International Symposium on Parallel and Distributed Processing with applications, ISPA

作者： Ye Yuan Mingxing Yang Ruoting Xiong Yani Sun Xianchao Zhang Wei Ren HuBei Land Resources Vocational College China Gemological Institute China University of Geosciences Wuhan China School of Computing Science University of East Anglia UK Yunnan Key Laboratory of Blockchain Application Technology Yunnan Innovation Institute of Beihang University Kunming China Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Jiaxing University Jiaxing China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Beijing China Ministry of Education Key Laboratory of Data Protection and Intelligent Management (Sichuan University) China School of Computer Science China University of Geosciences Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Blockchain can be envisioned as an enabling framework in terms of cryptography and distributed computing to construct trusted data records among un-trusted users. It can be applied in two typical scenarios - historical process provenance and transaction tracing, by immutably sequentialized blocks. In the former there exists no tokens; but in the latter token handover among accounts should be cryptographically guaranteed. In some situations, above two applications may be both required. E.g., in the manufacture of a jewel, processing flow by different operators should be recorded for quality guaranteeing, and in its trading stage, the ownership handover should be recorded for value confirmation. To tackle above two requirements, two chains are usually thus demanded. In this paper, we propose a single chain solution to avoid cross-chain burden, by a hybrid block structure. The process data can be imported from the legacy (non-anonymous) information processing system, and transaction data stems from anonymous payment demand for ownership handover. We formally describe and prove token handover model with cryptographical authenticity. We also propose several security enhancements in the design for shortening the length of the chain, and protecting the data confidentiality in the chain. Our design is general and not limited to single application. The experimental results and extensive analysis justified that the performance and security of our proposed scheme is efficient.

关键词： Distributed processing Data privacy Manufacturing processes information processing Handover Throughput Data structures Blockchains Delays Periodic structures

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Deep learning-based air temperature mapping byfusing remote sensing, station, simulation and socioeconomic data

arXiv

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arXiv 2020年

作者： Shen, Huanfeng Jiang, Yun Li, Tongwen Cheng, Qing Zeng, Chao Zhang, Liangpei School of Resource and Environmental Sciences Wuhan University Wuhan430079 China School of Urban Design Wuhan University Wuhan430079 China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China Collaborative Innovation Center of Geospatial Technology Wuhan430079 China Key Laboratory of Geographic Information System Ministry of Education Wuhan University Wuhan430079 China

Air temperature(Ta)isanessential climatologicalcomponentthatcontrolsand influences variousearth surface *** this study, we makethe first attempttoemploy deep learning for Ta mapping mainly based on space remote sensing and ground station *** that Ta varies greatly in space and time and is sensitive tomany factors, assimilation dataand socioeconomicdata are also included for a multi-source data fusion based ***, a5-layers structured deep belief network(DBN) isemployed to better capturethe complicatedand non-linear relationships between Taand different ***-wise pre-trainingprocess foressentialfeaturesextraction and fine-tuningprocess forweight parametersoptimization ensure the robust prediction of Ta spatio-temporal distribution. The DBN model was implemented for 0.01°daily maximum Ta mapping across China. The ten-fold cross-validation resultsindicate that the DBN model achieves promisingresults with the RMSEof1.996°C, MAEof1.539°C, and Rof 0.986at the national *** with multiple linear regression (MLR), back-propagation neural network (BPNN) and random forest (RF)method, the DBN modelreduces the MAE values by 1.340°C, 0.387°Cand0.222°C,*** analysis on spatialdistribution and temporal tendency of predictionerrors both validate the great potentials of DBN inTa estimation. Copyright © 2020, The Authors. All rights reserved.

关键词： Atmospheric temperature

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Robust fault tolerant manoeuvring and rudder control of marine vehicles with state saturation

Robust fault tolerant manoeuvring and rudder control of mari...

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IEEE International Conference on Industrial technology (ICIT)

作者： Xiaozheng Jin Qiuhong Liu Yu Kang Jiahu Qin School of Electrical Engineering and Automation HeFei University of Technology HeFei AnHui China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467380768

In this paper, the problem of rudder-roll damping control for a class of cruise keeping ships subject to both actuator fault and state saturation is investigated. A manoeuvring and rudder control model of marine vessels is firstly reviewed based on the existing literature. Actuator faults which including partial loss of rudder effectiveness and actuator-bias faults are considered in the model. Then a switching control strategy is developed to compensate for the actuator faults and to guarantee the stability of the rudder-roll damping control system. Moreover, some relationship regarding state saturation bounds, actuator fault limits and control parameters are analyzed in this paper. Finally, simulation results are given to illustrate the proposed procedures and their effectiveness.

关键词： Actuators Vehicles Damping Mathematical model Marine vehicles Vehicle dynamics

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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature

Decentralized and Lightweight Cross-Chain Transaction Scheme...

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Huiying Zou Jia Duan Xi Liu Wei Ren Tao Li Xianghan Zheng Kim-Kwang Raymond Choo School of Computer Science China University of Geosciences Wuhan China The Third Surveying and Mapping Institute of Hunan Province Hunan Engineering Research Center of Geographic Information Security and Application Hunan China Key Laboratory of Data Protection and Intelligent Management Sichuan University Ministry of Education China State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR CASM Beijing China College of Computer and Big Data Fuzhou University School of Information and Intelligent Engineering Sanya College China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach’s security.

关键词： Privacy systematics Costs Scalability Blockchains Security

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HDEC-TFA: An unsupervised learning approach for discovering physical scattering properties of single-polarized SAR image

TechRxiv

引用

TechRxiv 2020年

作者： Huang, Zhongling Datcu, Mihai Pan, Zongxu Qiu, Xiaolan Lei, Bin Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Huairou District Beijing101408 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Wessling82234 Germany

Knowing the physical properties and scattering mechanisms contributes to Synthetic Aperture Radar (SAR) image interpretation. For single-polarized SAR data, however, it is difficult to extract the physical scattering mechanisms due to lack of polarimetric information. Time-frequency analysis (TFA) on complex-valued SAR image provides extra information in frequency perspective beyond the’image’ domain. Based on TFA theory, we propose to generate the sub-band scattering pattern for every object in complex-valued SAR image as the physical property representation, which reveals backscattering variations along slant-range and azimuth directions. In order to discover the inherent patterns and generate a scattering classification map from single-polarized SAR image, an unsupervised hierarchical deep embedding clustering algorithm based on time-frequency analysis (HDEC-TFA) is proposed to learn the embedded features and cluster centers simultaneously and hierarchically. The polarimetric analysis result for quad-pol SAR images is applied as reference data of physical scattering mechanisms. In order to compare the scattering classification map obtained from single-polarized SAR data with the physical scattering mechanism result from full-polarized SAR, and to explore the relationship and similarity between them in a quantitative way, an information theory based evaluation method is proposed. We take Gaofen-3 quad-polarized SAR data for experiments and the results and discussions demonstrate that the proposed method is able to learn valuable scattering properties from single-polarization complex-valued SAR data, and to extract some specific targets as well as polarimetric analysis. At last, we give a promising prospect to future applications. © 2020, CC BY.

关键词： Synthetic aperture radar

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