检索结果-南通市图书馆

Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

Journal of Integrative Agriculture 2024年第8期23卷 2820-2841页

作者： Xianglin Zhang Jie Xue Songchao Chen Zhiqing Zhuo Zheng Wang Xueyao Chen Yi Xiao Zhou Shi Institute of Applied remote sensing and Information Technology College of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China Department of Land Management Zhejiang UniversityHangzhou 310058China ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang UniversityHangzhou 311215China Institute of Digital Agriculture Zhejiang Academy of Agricultural SciencesHangzhou 310021China Key Laboratory of Spectroscopy sensing Ministry of Agriculture and Rural AffairsHangzhou 310058China

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management *** a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different ***,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human *** overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in *** results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on *** the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to *** recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average *** the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final *** the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping

关键词： cropland soil organic matter digital soil mapping machine learning feature selection model averaging

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Assessment of spongy moth infestation impacts on forest productivity and carbon loss using the Sentinel-2 satellite remote sensing and eddy covariance flux data

引用

Ecological Processes 2024年第2期 231-245页

作者： Nur Hussain Alemu Gonsamo Shusen Wang M.Altaf Arain School of Earth Environment and Society and McMaster Centre for Climate Change Mc Master University Canada Centre for remote sensing Natural Resources Canada

Background Deciduous forests in eastern North America experienced a widespread and intense spongy moth（Lymantria dispar） infestation in 2021. This study quantified the impact of this spongy moth infestation on carbon（C） cycle in forests across the Great Lakes region in Canada, utilizing high-resolution（10 × 10 m2） Sentinel-2 satellite remote sensing images and eddy covariance（EC） flux data. Study results showed a significant reduction in leaf area index（LAI） and gross primary productivity（GPP） values in deciduous and mixed forests in the region in *** remote sensing derived, growing season mean LAI values of deciduous（mixed） forests were 3.66（3.18）, 2.74（2.64）, and 3.53（2.94） m2m-2in 2020, 2021 and 2022, respectively, indicating about 24（14）% reduction in LAI, as compared to pre-and post-infestation years. Similarly, growing season GPP values in deciduous（mixed） forests were 1338（1208）, 868（932）, and 1367（1175） g C m-2, respectively in 2020, 2021 and 2022, showing about 35（22）% reduction in GPP in 2021 as compared to pre-and post-infestation years. This infestation induced reduction in GPP of deciduous and mixed forests, when upscaled to whole study area（178,000 km2）, resulted in 21.1（21.4） Mt of C loss as compared to 2020（2022）, respectively. It shows the large scale of C losses caused by this infestation in Canadian Great Lakes *** The methods developed in this study offer valuable tools to assess and quantify natural disturbance impacts on the regional C balance of forest ecosystems by integrating field observations, high-resolution remote sensing data and models. Study results will also help in developing sustainable forest management practices to achieve net-zero C emission goals through nature-based climate change solutions.

关键词：

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

remote sensing and its applications using GNSS reflected signals:advances and prospects

引用

Satellite Navigation 2024年第1期5卷 10-51页

作者： Shuanggen Jin Adriano Camps Yan Jia Feng Wang Manuel Martin-Neira Feixiong Huang Qingyun Yan Shuangcheng Zhang Zhongyu Li Komi Edokossi Dongkai Yang Zhiyu Xiao Zhongmin Ma Weihua Bai Shanghai Astronomical Observatory Chinese Academy of SciencesShanghai200030China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo454003China Department of Signal Theory and Communications Technical University of CataloniaBarcelona08034Spain Department of Surveying and Geoinformatics Nanjing University of Posts and TelecommunicationsNanjing210023China School of Electronics and Information Engineering Beihang UniversityBeijing100191China European Space Research and Technology Centre(ESTEC) European Space Agency2200-AGNoordwijkThe Netherlands National Space Science Center Chinese Academy of SciencesBeijing100190China School of remote sensing and Geomatics Engineering Nanjing University of Information Science and TechnologyNanjing210044China College of Geological Engineering and Geomatics Chang’an UniversityXi’an710054China University of Electronic Science and Technology of China Chengdu611731China

The Global Navigation Satellite Systems(GNSS),including the US’s GPS,China’s BDS,the European Union’s Galileo,and Russia’s GLONASS,offer real-time,all-weather,any-time,anywhere and high precision observations by transmitting L band signals continuously,which have been widely used for positioning,navigation and *** the development of GNSS technology,it has been found that GNSS-reflected signals can be used to detect Earth’s surface characteristics together with other signals of *** this paper,the current status and latest advances are presented on Global Navigation Satellite System-Reflectometry(GNSS-R)in theory,methods,techniques and *** developments and progresses in GNSS-R instruments,theoretical modeling,and signal processing,ground and space-/air-borne experiments,parameters retrieval(*** speed,sea surface height,soil moisture,ice thickness),sea surface altimetry and applications in the atmosphere,oceans,land,vegetation,and cryosphere are given and reviewed in ***,the challenges in the GNSS-R development of each field are also ***,the future applications and prospects of GNSS-R are discussed,including multi-GNSS reflectometry,new GNSS-R receivers,GNSS-R missions,and emerging applications,such as mesoscale ocean eddies,ocean phytoplankton blooms,microplastics detection,target recognition,river flow,desert studies,natural hazards and landslides monitoring.

关键词： GNSS Reflectometry remote sensing Environmental change

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

remote sensing of quality traits in cereal and arable production systems:A review

引用

The Crop Journal 2024年第1期12卷 45-57页

作者： Zhenhai Li Chengzhi Fan Yu Zhao Xiuliang Jin Raffaele Casa Wenjiang Huang Xiaoyu Song Gerald Blasch Guijun Yang James Taylor Zhenhong Li College of Geodesy and Geomatics Shandong University of Science and TechnologyQingdao 266590ShandongChina Key Laboratory of Quantitative remote sensing in Ministry of Agriculture and Rural Affairs Information Technology Research CenterBeijing Academy of Agriculture and Forestry SciencesBeijing 100097China Institute of Crop Sciences Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and EcologyMinistry of Agriculture and Rural AffairsBeijing 100081China DAFNE Universitàdella TusciaVia San Camillo de Lellis01100 ViterboItaly Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Maize and Wheat Improvement Center(CIMMYT) PO Box 5689Addis AbabaEthiopia ITAP Univ.MontpellierINRAEInstitut AgroMontpellier 34000France College of Geological Engineering and Geomatics Chang’an UniversityXi’an 710054ShaanxiChina

Cereal is an essential source of calories and protein for the global *** predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy *** use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality *** studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing *** this paper,the key quality traits that are of interest to producers and consumers are *** literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality *** review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

关键词： remote sensing Quality traits Grain protein Cereal

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

MFVNet: a deep adaptive fusion network with multiple field-of-views for remote sensing image semantic segmentation

引用

Science China(Information Sciences) 2023年第4期66卷 93-106页

作者： Yansheng LI Wei CHEN Xin HUANG Zhi GAO Siwei LI Tao HE Yongjun ZHANG School of remote sensing and Information Engineering Wuhan University

In recent years, the remote sensing image(RSI) semantic segmentation attracts increasing research interest due to its wide application. RSIs are difficult to be processed holistically on current GPU cards on account of their large field-of-views(FOVs). However, the prevailing practices such as downsampling and cropping will inevitably decrease the quality of semantic segmentation. To address this conflict,this paper proposes a new deep adaptive fusion network with multiple FOVs(MFVNet), which is specially designed for RSI semantic segmentation. Different from existing methods, MFVNet takes into consideration the differences among multiple FOVs. By pyramid sampling the RSI, we first obtain images on different scales with multiple FOVs. Images on the high scale with a large FOV can capture larger spatial contexts and complete object contours, while images on the low scale with a small FOV can keep the higher spatial resolution and more detailed information. Then scale-specific models are chosen to make the best predictions for all scales. Next, the output feature maps and score maps are aligned through the scale alignment module to overcome spatial misregistration among scales. Finally, the aligned score maps are fused with the help of adaptive weight maps generated by the adaptive fusion module, producing the fused prediction. The performance of MFVNet surpasses the previous state-of-the-art semantic segmentation models on three typical RSI datasets, demonstrating the effectiveness of the proposed MFVNet.

关键词： semantic segmentation remote sensing image (RSI) field-of-view (FOV) adaptive fusion convolutional neural network

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Livability assessment of 101,630 communities in China's major cities: A remote sensing perspective

引用

Science China Earth Sciences 2022年第6期65卷 1073-1087页

作者： Xin HUANG Yue LIU School of remote sensing and Information Engineering Wuhan UniversityWuhan 430079China State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhan 430079China

Some of China's major cities have entered the middle and late stages of urbanization, and the development focus of these cities has gradually shifted from outward expansion to inward renewal. The community, as the basic unit of a city, is undoubtedly the main object of urban renewal. In order to efficiently and effectively address the problems in current community construction, it is necessary to conduct a large-scale in-depth assessment of the community livability, which can directly imply the satisfaction of residents with their quality of life. This study achieved the first comprehensive livability assessment at the individual community scale for 42 major cities of China from the perspective of remotely sensed and crowd-sourced geographic information. Specifically, we produced abundant fine-grained datasets for 42 cities, including high-resolution land cover maps interpreted from Ziyuan-3 satellites(ZY-3, 2.1 m), building height, point-of-interest, and boundaries of 101,630 *** designed in our proposed framework, the community livability was evaluated by 5 level-1 indicators, 27 level-2 indicators and an integrated community livability index(CLI). A number of interesting findings were obtained from this assessment:(1)According to the expert questionnaires, living comfort was considered as the most important livability factor for residents with the highest weight, while the building environment was rated the least. The negative factors(e.g., the factories around the community) impacted more on livability than the positive ones.(2) Most communities in major Chinese cities were characterized by dense buildings and sparse green spaces.(3) In these cities, community security construction was severely insufficient,particularly in less developed regions.(4) Imbalanced community livability development was prevalent across cities, and simultaneously, the CLI distribution within cities also exhibited significant spatial aggregation and heterogeneity. This resear

关键词： Community Livability Living comfort Building environment Security remote sensing

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

SRS-Net: Training object detectors from scratch for remote sensing images without pretraining

引用

Chinese Journal of Aeronautics 2023年第8期36卷 269-283页

作者： Haining WANG Yang LI Yuqiang FANG Yurong LIAO Bitao JIANG Xitao ZHANG Shuyan NI Space Engineering University Beijing 101416China Beijing Institute of remote sensing Information Beijing 100192China

Most of the current object detection algorithms use pretrained models that are trained on ImageNet and then fine-tuned in the network,which can achieve good performance in terms of general object ***,in the field of remote sensing image object detection,as pretrained models are significantly different from remote sensing data,it is meaningful to explore a train-fromscratch technique for remote sensing *** paper proposes an object detection framework trained from scratch,SRS-Net,and describes the design of a densely connected backbone network to provide integrated hidden layer supervision for the convolution ***,two necessary improvement principles are proposed:studying the role of normalization in the network structure,and improving data augmentation methods for remote sensing *** evaluate the proposed framework,we performed many ablation experiments on the DIOR,DOTA,and AS *** results show that whether using the improved backbone network,the normalization method or training data enhancement strategy,the performance of the object detection network trained from scratch *** principles compensate for the lack of pretrained ***,we found that SRS-Net could achieve similar to or slightly better performance than baseline methods,and surpassed most advanced general detectors.

关键词： Denseconnection Object detection Pretraining remote sensing image Trainfrom scratch

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

remote sensing data classification using tolerant rough set and neural networks

引用

Science China Earth Sciences 2005年第12期48卷 2251-2259页

作者： MA Jianwen HASI Bagan State Key Laboratory of remote sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China State Key Laboratory of Remote Sensing Science Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing 100101 China

BP algorithm of neural net is used more in remote sensing data classification. One of drawbacks of BP algorithm is the overall low function when the net is training. To avoid this kind of problem, the paper introduces the tolerant rough set for classification-preprocessing the training data to reduce the influence elements of the training convergence in order to improve the net training successful rate. ETM+ data of Beijing in May 2003 is selected in the study. ETM+ data before and after classification preprocessing, respectively, are used for BP (Back propaga-tion) training. The result shows that such a preprocessing not only compensates the drawback of BP algorithm when processing ETM+ data but also improves classification accuracy.

关键词： tolerant rough set, neural network, BP algorithm, classification.

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

GIS based integration of multi-spectral remote sensing and morphostructural analysis for delineating potential areas of hydrocarbon exploration in Western Vindhyan Basin,India

引用

Energy Geoscience 2022年第2期3卷 191-201页

作者： Subhobroto Mazumde Ashish Misra remote sensing&Geomatics KDM Institute of Petroleum ExplorationOil and Natural Gas Corporation LimitedIndia

The Chambal Valley forms the western part of the Proterozoic Vindhyan Basin that has been less studied and poorly explored from the hydrocarbon perspective than its eastern counterpartdthe Son Valley *** the present manuscript,attempts have been made to use remote sensing and GIS(geographic information system)based analysis to focus on the northern part of Chambal Valley to decipher its tectonics and structural configuration,its tectonic relation with the Son Valley Vindhyans,Bundelkhand Massif and Ganga *** study also assumes significance as it attempts to delineate potential lead areas for hydrocarbon exploration in Chambal *** part of the study,a detailed spectral analysis based on image data sets is carried out to identify areas of microseeps as proxies of potential hydrocarbon ***,a neotectonics-based geomorphic analysis is carried out to delineate a fault network and geomorphic highs that act as aliases to subsurface faults and structural *** results are subsequently validated by available geophysical data and extensive fieldwork,and suggest four structural trends in the Chambal Valley as striking NE-SW,E-W,NW-SE and NeS,of which the NW-SE trend is the oldest,followed by the NE-SW,E-W and NeS trends.A GIS-based analysis of the E-W trends indicates that these are probably manifestations of the westward continuity of E-W-trending shear zones developed in Bundelkhand massif within the basement of the Chambal *** remote sensing studies of the shear zone network exposed in the Bundelkhand massif indicate the pattern to be replicated on a larger scale in the morphostructural fault layout in the adjacent Ganga Basin,implying these shear zones may be continuing in the basement of Ganga Basin and controlling structures developed later,and consequently the different petroleum system elements in the basin.A dip data based structural analysis of the Chambal Valley describes a regional synform attributed with an almost

关键词： Chambal Vindhyans Hydrocarbon remote sensing Shear zone Syncline

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Land cover classification from remote sensing images based on multi-scale fully convolutional network

引用

Geo-Spatial Information Science 2022年第2期25卷 278-294页

作者： Rui Li Shunyi Zheng Chenxi Duan Libo Wang Ce Zhang School of remote sensing and Information Engineering Wuhan UniversityWuhanChina Faculty of Geo-Information Science and Earth Observation(ITC) University of TwenteEnschedeThe Netherlands The State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina Lancaster Environment Centre Lancaster UniversityLancasterUK UK Centre for Ecology&Hydrology LancasterUK

Although the Convolutional Neural Network(CNN)has shown great potential for land cover classification,the frequently used single-scale convolution kernel limits the scope of informa-tion ***,we propose a Multi-Scale Fully Convolutional Network(MSFCN)with a multi-scale convolutional kernel as well as a Channel Attention Block(CAB)and a Global Pooling Module(GPM)in this paper to exploit discriminative representations from two-dimensional(2D)satellite ***,to explore the ability of the proposed MSFCN for spatio-temporal images,we expand our MSFCN to three-dimension using three-dimensional(3D)CNN,capable of harnessing each land cover category’s time series interac-tion from the reshaped spatio-temporal remote sensing *** verify the effectiveness of the proposed MSFCN,we conduct experiments on two spatial datasets and two spatio-temporal *** proposed MSFCN achieves 60.366%on the WHDLD dataset and 75.127%on the GID dataset in terms of mIoU index while the figures for two spatio-temporal datasets are 87.753%and 77.156%.Extensive comparative experiments and abla-tion studies demonstrate the effectiveness of the proposed MSFCN.

关键词： Spatio-temporal remote sensing images Multi-Scale Fully Convolutional Network land cover classification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：