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Quantifying the agreement and accuracy characteristics of four satellite-based LULC products for cropland classification in China

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Journal of Integrative Agriculture 2024年第1期23卷 283-297页

作者： Jie Xue Xianglin Zhang Songchao Chen Bifeng Hu Nan Wang Zhou Shi Department of Land Management Zhejiang UniversityHangzhou 310058China Institute of Applied Remote Sensing and Information Technology College of Environmental and Resource SciencesZhejiang UniversityHangzhou 310058China ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang UniversityHangzhou 311215China Department of Land Resource Management School of Public Finance and Public AdministrationJiangxi University of Finance and EconomicsNanchang 330013China Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and EconomicsNanchang 330013China Key Laboratory of Spectroscopy Sensing Ministry of Agriculture and Rural AffairsHangzhou 310058China

Various land use and land cover(LULC)products have been produced over the past decade with the development of remote sensing *** the differences in LULC classification schemes,there is a lack of research on assessing the accuracy of their application to croplands in a unified ***,this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products(i.e.,MCD12Q1V6,GlobCover2009,FROM-GLC and GlobeLand30)based on the harmonised FAO criterion,and quantified the relationships between four factors(i.e.,slope,elevation,field size and crop system)and cropland classification *** validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency,with overall accuracies of 94.90 and 93.52%,*** FROMGLC showed the worst performance,with an overall accuracy of 83.17%.Overlaying the cropland generated by the four global LULC products,we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification,*** consistency was mainly observed in the Northeast China Plain,the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain,*** contrast,low consistency was detected primarily on the eastern edge of the northern and semiarid region,the Yunnan-Guizhou Plateau and southern *** size was the most important factor for mapping *** area accuracy,compared with China Statistical Yearbook data at the provincial scale,the accuracies of different products in descending order were:GlobeLand30,FROM-GLC,MCD12Q1,and *** cropland classification schemes mainly caused large area deviations among the four products,and they also resulted in the different ranks of spatial accuracy and area accuracy among the four *** results can provide valuable suggestions for selecting cropland products at the national or provinc

关键词： global LULC products cropland mapping accuracy evaluation food security China

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A scaling region identification method for the divider dimension of urban thermal environments 3

A scaling region identification method for the divider dimen...

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2023 3rd International Conference on Computational Modeling, Simulation and Data Analysis, CMSDA 2023

作者： Tao, Sulin Li, Yuhong School of Applied Meteorology Nanjing University of Information Science and Technology Nanjing210044 China Technology Development Centre Nanjing Daqiao Machine Co. Ltd. Nanjing211101 China Liaoning Ecological Meteorology and Satellite Remote Sensing Centre Shenyang110166 China Institute of Atmospheric Environment China Meteorological Administration Shenyang110166 China

A scaling region identification method for divider dimensions using the coefficient of determination is proposed to improve the estimation accuracy of the divider dimension in the study of urban thermal environment differentiation characteristics. The effectiveness of this method was verified by analyzing the urban surface thermal field differentiation characteristics by applying the estimated divider dimensions of Nanjing's thermal environment surfaces and transect lines. The research results show that the urban thermal environment has fractal characteristics only within a certain temperature threshold range. When the constraint of scaling regions is considered, the calculated divider dimension of the surface thermal field surface increases, reflecting the more complex fractal structure of the thermal field. For each temperature grade, the "trough"position of the thermal field divider dimension surface estimated by the line-divider method is near the determination level of the high-Temperature region based on robust statistics, allowing the spatial extent of the urban heat island (UHI) to be determined based on the "trough"feature of the thermal field divider dimension surface. © Published under licence by IOP Publishing Ltd.

关键词： Fractal dimension

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The spectral conjugate gradient method in variational adjoint assimilation for model terrain correction III: Convergence analysis 3

The spectral conjugate gradient method in variational adjoin...

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2023 3rd International Conference on Computational Modeling, Simulation and Data Analysis, CMSDA 2023

作者： Tao, Sulin Li, Yuhong School of Applied Meteorology Nanjing University of Information Science and Technology Nanjing210044 China Technology Development Center Nanjing Daqiao Machine Co. Ltd. Nanjing211101 China Liaoning Ecological Meteorology and Satellite Remote Sensing Center Shenyang110166 China Institute of Atmospheric Environment China Meteorological Administration Shenyang110166 China

In the former parts of this paper, the spectral conjugate gradient (SCG) algorithm was proposed to identify the optimal bottom terrain used for predicting the meteorological fields. In this part, the convergence analysis of the SCG method is presented to verify its validity when solving a typical ill-posed inverse problem. The scaled cost function, the scaled gradient norm, the linear convergence rate, and the Hessian approximation condition number are evaluated for convergence analysis. The SCG method reduces the cost functional value and the gradient norm sufficiently. It always performs linear rates of convergence with a quickly reduced condition number of the Hessian matrix. Sometimes it provides very fast linear convergence rates. The SCG code with optimal step size and Andrei's equation is effective on ill-posed test problems. © Published under licence by IOP Publishing Ltd.

关键词： Cost functions

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Multi-scenario spatial modeling of pltal distribution in indonesia to support clean and affordable energy

Multi-scenario spatial modeling of pltal distribution in ind...

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American Society for Photogrammetry and remote sensing, ASPRS 2021 Annual Conference

作者： Nur Ihsan, Kalingga Titon Sihotang, Elstri Anggraini, Tania Septi Remote Sensing and Geographic Information Science Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Indonesia Center for Remote Sensing Institut Teknologi Bandung Indonesia

Increasing consumption of non-renewable energy has become a topic of global problems today due to its limited availability with a high potential for environmental damage. Seeing these problems, it is necessary to develop renewable energy that is more environmentally friendly and has great potential in Indonesia. One of Indonesia’s potentials is an archipelago country with the second-largest coastline in the world with a coastline of 108,000 km. This causes Indonesia to have quite a large potential for ocean currents, namely around 1.5 m / s and reaching 2.5-3.4 m / s for the Nusa Tenggara region. The energy of ocean currents has the potential to be developed because of its relatively stable and predictable nature. Good planning by observing the potential resources and energy needs spatially is needed in the process of managing and utilizing energy in a maximum and sustainable manner. This study aims to analyze areas that have the potential to generate ocean current energy in Indonesia and utilize spatial and socioeconomic elements to determine the effective and efficient distribution of Ocean Current Power Plants (PLTAL), especially for coastal areas and small islands. This study will integrate multi-dynamic spatial data, such as ocean current data, electrification needs of each region, economic and transportation data, as well as a model of potential land availability based on remote sensing data. The result of this research is in the form of priority analysis of PLTAL development in Indonesia in terms of energy output, spatial aspects, and socioeconomic aspects. It is hoped that the results of this study can be used as considerations and solutions for policymakers in making decisions regarding the development of PLTAL in Indonesia, especially to meet the energy needs of coastal areas and small islands. © 2021 International Society for Photogrammetry and remote sensing. All rights reserved.

关键词： Ocean currents

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Geospatial assessment for planning a smart energy city using rooftop solar photovoltaic in Bandung city, Indonesia

Geospatial assessment for planning a smart energy city using...

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American Society for Photogrammetry and remote sensing, ASPRS 2021 Annual Conference

作者： Nur Ihsan, Kalingga Titon Sakti, Anjar Dimara Wikantika, Ketut Remote Sensing and Geographic Information Science Research Group Faculty of Earth Sciences and Technology Institut Teknologi Bandung Indonesia Center for Remote Sensing Institut Teknologi Bandung Indonesia

Increasing the production of clean and environmentally friendly energy has become one of the world agendas as a strategic effort in dealing with long-term climate change. Seeing the potential of the energy produced, the ease in the installation process, with the small risk of harm generated, solar energy has received significant attention from many countries in the world. The potential for solar energy in Indonesia alone reaches 207 GWp, but only 145.81 MWp has been utilized. Currently, the Indonesian government has set a target to build a Solar Power Plant capacity in 2025 of 6.5 GWh. Urban areas are areas with higher energy demand than rural areas, but the availability of vacant land in urban areas is very minimal for installing solar power plants. Therefore, rooftop solar PV(Photovoltaic) can be a solution in dense areas such as cities. Good planning by looking at the potential resources and energy needs in spatial is needed to manage and utilize energy optimally and sustainably in urban areas. This study aims to develop a geospatial assessment for plan smart energy city that uses rooftop solar PV's potential energy in every building that is effective and efficient. The novelty in the analysis of the distribution of the potential for rooftop solar PV development in urban areas integrates meteorological and spatial aspects and socio-economic aspects. Integration of multi-dynamic spatial data uses in determining the rooftop solar PV construction location, such as meteorological data for solar energy potential, increasing energy needs of each building, and socio-economy data. The data source used comes from statistical data and remote sensing data. The analysis will be carried out temporally (2008, 2013, and 2018) to see the pattern of changes in aspects used in a certain period so that the development plan can be carried out more optimally. This research's output is the formation of a priority analysis of solar PV rooftop construction in urban areas, especially the

关键词： Solar energy

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Heading prediction for Unmanned Surface Vessel based on reservoir computing

Heading prediction for Unmanned Surface Vessel based on rese...

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第42届中国控制会议

作者： Shuo Ding Jiucai Jin Deqing Liu Hongyu Li College of Ocean Science and Engineering Shandong University of Science and Technology Laboratory of Marine Physics and Remote Sensing First Institute of OceanographyMinistry of Natural Resources

Accurate heading prediction is a key premise for path planning and collision avoidance of Unmanned Surface ***,this paper proposes a heading prediction method for USV based on reservoir computing considering environment disturbance and the nonlinear term of the ***,a first-order Nomoto model with nonlinear terms of the model and environmental disturbance is identified by the sea trial data of the ***,the sea trial data and the simulation data are used to train the reservoir computing ***,the prediction results of the reservoir computing model are compared with the model simulation and sea *** practicability of the proposed algorithm is verified by the comparative results.

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Research on Cloud Detection Method of GaoFen-6 Wide Camera Data 7th

Research on Cloud Detection Method of GaoFen-6 Wide Camera D...

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7th China High-resolution Earth Observation Conference, CHREOC 2020

作者： Ke, Shiyun Wang, Mi Cao, Jinshan Long, En State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China School of Computer Science Hubei University of Technology 28 Nanli Road Wuhan430068 China Beijing Institute of Remote Sensing Information 2 Xiaoyingdong Road Beijing100192 China

ISBN: (纸本)9789811657344

Cloud detection is one of the research hotpots in high-resolution remote sensing images, and is an important part of remote sensing image preprocessing. The traditional physical threshold method based on pixel is inefficient when the amount of data is large. It is also easily affected by the noise points in the image, and the appropriate spectral threshold is difficult to determine. In view of the above problems, this paper starts from the GF-6 wide camera data, studies the spectral characteristics of GF-6 wide camera data, and selects the appropriate spectral detection band. Then, in order to improve the universality, using the maximum inter class variance method (OTSU) to adaptively obtain the appropriate cloud detection threshold. Finally, the image is segmented by simple linear iterative clustering algorithm (SLIC). Super pixel is used as the processing unit of subsequent image processing to replace single pixel for cloud detection. It both improves validity and accuracy. In this paper, by doing experiments and quantitative evaluation, it can be proved that the cloud detection algorithm can effectively and accurately detect the cloud region in the image. The accuracy was all higher than 90%, and the average accuracy of the experiment is 94.21%. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cameras

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RailPC: A large-scale railway point cloud semantic segmentation dataset

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1548-1560页

作者： Tengping Jiang Shiwei Li Qinyu Zhang Guangshuai Wang Zequn Zhang Fankun Zeng Peng An Xin Jin Shan Liu Yongjun Wang Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing Normal UniversityNanjingChina Technology Innovation Center for Integrated Applications in Remote Sensing and Navigation Ministry of Natural ResourcesNanjingChina Eastern Institute of Technology(EIT) NingboChina Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-temporal Big Data Technology TianjinChina College of Computer Science and Engineering Northwest Normal UniversityLanzhouChina Mckelvey School of Engineering Washington University in St.LouisSt.LouisMissouriUSA School of Electronic and Information Engineering Ningbo University of TechnologyNingboChina

Semantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value,but its development is severely hindered by the lack of suitable and specific ***,the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non-overlapping special/rare categories,for example,rail track,track bed *** harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation,we introduce RailPC,a new point cloud *** provides a large-scale dataset with rich annotations for semantic segmentation in the railway ***,RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning(MLS)point cloud dataset and is the first railway-specific 3D dataset for semantic *** covers a total of nearly 25 km railway in two different scenes(urban and mountain),with 3 billion points that are finely labelled as 16 most typical classes with respect to railway,and the data acquisition process is completed in China by MLS *** extensive experimentation,we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation *** on our findings,we establish some critical challenges towards railway-scale point cloud semantic *** dataset is available at https://***/NNU-GISA/GISA-RailPC,and we will continuously update it based on community feedback.

关键词： data benchmark MLS point clouds railway scene semantic segmentation

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Wavefront-Coded Image Restoration technology Based on Multi-Scale Deep Neural Networks

SSRN

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SSRN 2024年

作者： Zhen, Zheng Yuan, Yiqiao Zhang, Bo Liang, Zonglin Li, Chenxiao Piao, Mingxu Dong, Keyan Jiang, Huilin School of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China Institute of Space Optoelectronic Technology Changchun University of Science and Technology Changchun130022 China Beijing Institute of Remote Sensing Equipment Beijing100854 China

Optical imaging systems are significantly affected by aerodynamic thermal effects under varying flight conditions, resulting in complex image blurring. To address this challenge, this study proposes a novel wavefront-coded image restoration method based on a Multi-Scale Deep Autoencoder Neural Network (MS-DAE). By modulating blur levels and incorporating a multi-scale loss function with residual attention mechanisms, the proposed method achieves a remarkable improvement in Peak Signal-to-Noise Ratio (PSNR) by 15.52 dB and Structural Similarity Index Measure (SSIM) by 0.3867 compared to Wiener filtering. The approach effectively restores image details, suppresses artifacts, and adapts to diverse flight conditions, demonstrating significant potential for practical applications. © 2024, The Authors. All rights reserved.

关键词： Optical transfer function

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Regional Tropospheric Delay Prediction Model Based on LSTM-Enhanced Encoder Network

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IEEE Journal of Selected Topics in Applied Earth Observations and remote sensing 2025年 18卷 13348-13358页

作者： Peng, Yuanfang Cai, Chenglin Li, Zexian Lv, Kaihui Zhang, Xue Cai, Yihao Xiangtan University Intelligent Navigation and Remote Sensing Research Center School of Automation and Electronic Information Xiangtan411105 China Xiangtan University National Mathematics Application Center School of Automation and Electronic Information Xiangtan411105 China Xiangtan University Intelligent Navigation and Remote Sensing Research Center School of Mathematics and Computational Science Xiangtan411105 China ShanghaiTech University School of Physical Science and Technology Shanghai200031 China

Precise modeling of Zenith Tropospheric Delay (ZTD) is essential for real-time high-precision positioning in global navigation satellite systems (GNSS). Due to the stochastic variability of atmospheric water vapor across different regions, tropospheric delay exhibits strong regional characteristics. Empirical tropospheric delay models built on reanalysis of meteorological data often show significant accuracy discrepancies across regions, failing to meet the needs for precise regional ZTD forecasting. Deep learning methods excel in learning complex patterns and dependencies from time series data. Our study utilized ZTD data from 178 NGL stations in Australia during 2023 as ground truth values and modeled them using an LSTM-Enhanced encoder network. This model incorporated both spatial and temporal information as well as correlations with GPT3 ZTD. Predictions were compared with those from GPT3 ZTD, ERA5 ZTD, ANN ZTD, GRNN ZTD and LSTM ZTD. The results showed that the LSTM-Enhanced encoder ZTD achieved an RMSE of 14.43 mm, a mean bias close to zero, with mean absolute error and mean correlation coefficient of 12.42 mm and 0.95, respectively. The proposed model outperforms the GPT3, ERA5, ANN, GRNN, and LSTM models, with respective RMSE improvements of approximately 62.3%, 12.3%, 61%, 59.9%, and 60%. In addition, we compared the spatial and temporal properties of the proposed model with those of the GPT3 model and the ERA5 model. The discussion section further analyzed the prediction performance of different neural network approaches under different prediction periods. © 2008-2012 IEEE.

关键词： Troposphere

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