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Prediction of forest fire occurrence in China under climate change scenarios

Journal of Forestry Research 2023年第5期34卷 1217-1228页

作者： Yakui Shao Guangpeng Fan Zhongke Feng Linhao Sun Xuanhan Yang Tiantian Ma XuSheng Li Hening Fu Aiai Wang Precision Forestry Key Laboratory of Beijing Beijing Forestry UniversityBeijing 100083People’s Republic of China School of Information Science and Technology Beijing Forestry UniversityBeijing 100083People’s Republic of China College of Forestry Hainan UniversityHaikou 570228People’s Republic of China National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology Beijing Re-Search Institute of Uranium GeologyBeijing 100029People’s Republic of China School of Geographical Sciences Harbin Normal UniversityHarbin 150028People’s Republic of China

Climate change has an impact on forest fire *** the context of global warming,it is important to study the possible effects of climate change on forest fires,carbon emission reductions,carbon sink effects,forest fire management,and sustainable development of forest *** study is based on MODIS active fire data from 2001-2020 and the influence of climate,topography,vegetation,and social factors were *** and precipitation information from different scenarios of the BCC-CSM2-MR climate model were used as future climate *** climate change scenarios of a sustainable low development path and a high conventional development path,the extreme gradient boosting model predicted the spatial distribution of forest fire occurrence in China in the 2030s(2021-2040),2050s(2041-2060),2070s(2061-2080),and2090s(2081-2100).Probability maps were generated and tested using ROC *** results show that:(1)the area under the ROC curve of training data(70%)and validation data(30%)were 0.8465 and 0.8171,respectively,indicating that the model can reasonably predict the occurrence of forest fire in the study area;(2)temperature,elevation,and precipitation were strongly correlated with fire occurrence,while land type,slope,distance from settlements and roads,and slope direction were less strongly correlated;and,(3)based on future climate change scenarios,the probability of forest fire occurrence will tend to shift from the south to the center of the *** with the current climate(2001-2020),the occurrence of forest fires in 2021-2040,2041-2060,2061-2080,and 2081-2100 will increase significantly in Henan Province(Luoyang,Nanyang,S anmenxia),Shaanxi Province(Shangluo,Ankang),Sichuan Province(Mianyang,Guangyuan,Ganzi),Tibet Autonomous Region(Shannan,Linzhi,Changdu),Liaoning Province(Liaoyang,Fushun,Dandong).

关键词： Climate change Scenarios XGBoost model Forest fires China

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Evaluation of C/N0 raw observation positioning by smartphone based on indoor asynchronous pseudolite

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Urban Informatics 2024年第1期3卷 1-7页

作者： Lu, Xiangchen Chen, Liang Shen, Nan Jiang, Jiahui Dai, Yue Chen, Ruizhi State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan People’s Republic of China School of Geomatics Science and Technology Nanjing Tech University Nanjing People’s Republic of China

With the rapid development of the economy and information services, there is an increasing demand for indoor positioning. However, Global Navigation Satellite System (GNSS) signals face difficulties in penetrating buildings, making indoor positioning challenging. As a complementary solution to outdoor GNSS signals, pseudolites have shown promising results in various scenarios, and have become one of the research hotspots in the field of indoor positioning. Previous studies on indoor positioning with pseudolites have focused on issues such as initial position determination and carrier phase ambiguity resolution. However, most of these studies have remained in the research stage due to data post-processing and complex calculations. To overcome the limitations associated with the aforementioned factors, this paper proposes a real-time indoor positioning method that utilizes only a smartphone equipped with a built-in GNSS chip. By effectively obtaining pseudolite signal information, the smartphone establishes a simple model based on the distance and received power between the pseudolite and the smartphone. Kalman filtering is employed to mitigate the effects of multipath and occlusion. This simple and low-complexity model enables fast estimation of the user's distance from the pseudolite, thereby facilitating indoor positioning services. In this paper, the effectiveness and low complexity of the method are verified through data collection and experiments in the actual scene of the underground garage. The valuation results show that the root mean square error (RMSE) of its navigation and positioning is better than 3 m, which can meet the needs of daily life applications.

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AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based remote sensing

AutoLCZ: Towards Automatized Local Climate Zone Mapping from...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Chenying Liu Hunsoo Song Anamika Shreevastava Conrad M Albrecht Data Science in Earth Observation Technical University of Munich Germany Remote Sensing Technology Institute German Aerospace Center Germany Lyles School of Civil Engineering Purdue University USA Environmental Science and Engineering Caltech USA

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Local climate zones (LCZs) established a standard classification system to categorize the landscape universe for improved urban climate studies. Existing LCZ mapping is guided by human interaction with geographic information systems (GIS) or modelled from remote sensing (RS) data. GIS-based methods do not scale to large areas. However, RS-based methods leverage machine learning techniques to automatize LCZ classification from RS. Yet, RS-based methods require huge amounts of manual labels for training. We propose a novel LCZ mapping framework, termed AutoLCZ, to extract the LCZ classification parameters from high-resolution RS modalities. We study the definition of numerical rules designed to mimic the LCZ definitions. Those rules model geometric and surface cover parameters from LiDAR data. Correspondingly, we enable LCZ classification from RS data in a GIS-based scheme. The proposed AutoLCZ method has potential to reduce the human labor to acquire accurate metadata. At the same time, AutoLCZ sheds light on the physical interpretability of RS-based methods. In a proof-of-concept for New York City (NYC) we leverage airborne LiDAR surveys to model four LCZ parameters to distinguish eight LCZ types. The results indicate the potential of AutoLCZ as a promising avenue for large-scale LCZ mapping from RS data.

关键词： Training Surveys Laser radar Accuracy Urban areas Surface roughness Thermal pollution

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Point Cloud Classification and Accuracy Analysis Based on Feature Fusion

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Journal of Geodesy and Geoinformation science 2021年第3期4卷 38-48页

作者： Xiaochen WANG Hongchao MA Liang ZHANG Zhan CAI Haichi MA School of Remote Sensing and Information Engineering Wuhan UniversityWuhan 430079Shool of Environmental Science and EngineeringChina Faculty of Resources and Environmental Science Hubei UniversityWuhan 430062China School of Resources Environment Science and Technology Hubei University of Science and TechnologyXianning 437000China

A method for land-cover classification was proposed based on the fusion of features generated from waveform data and point cloud *** aims to partially overcome the ineffectiveness of many traditional classifiers caused by the fact that point cloud is lacking spectral *** whole flowchart of the method is as follows:Firstly,Gaussian decomposition was applied to fit an echo *** parameters associated with the Gaussian function were optimized by LM(Levenberg-Marquard)*** and thirteen features were generated to describe the waveform characteristics and the local geometry of point cloud,***,a random forest was selected as the classifier to which the generated features were ***-F was used to rank the weights of all the features ***,features were input to the classifier one by one according to the weights calculated from feature ranking,where classification accuracies were *** experimental results show that the effectiveness of the fusion of features generated from waveform and point cloud for LiDAR data classification,with 95.4%overall accuracy,0.90 kappa coefficient,which outperform the results obtained by a single class of features,no matter whether they were generated from point cloud or waveform data.

关键词： airborne LiDAR full-waveform data feature fusion land-cover classification

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REMM:Rotation-Equivariant Framework for End-to-End Multimodal Image Matching

arXiv

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arXiv 2024年

作者： Nie, Han Luo, Bin Liu, Jun Fu, Zhitao Liu, Weixing Su, Xin The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China The School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China The Faculty of Land Resources Engineering Kunming University of Science and Technology Kunming650031 China

We present REMM, a rotation-equivariant framework for end-to-end multimodal image matching, which fully encodes rotational differences of descriptors in the whole matching pipeline. Previous learning-based methods mainly focus on extracting modal-invariant descriptors, while consistently ignoring the rotational invariance. In this paper, we demonstrate that our REMM is very useful for multimodal image matching, including multimodal feature learning module and cyclic shift module. We first learn modal-invariant features through the multimodal feature learning module. Then, we design the cyclic shift module to rotationally encode the descriptors, greatly improving the performance of rotation-equivariant matching, which makes them robust to any angle. To validate our method, we establish a comprehensive rotation and scale-matching benchmark for evaluating the anti-rotation performance of multimodal images, which contains a combination of multi-angle and multi-scale transformations from four publicly available datasets. Extensive experiments show that our method outperforms existing methods in benchmarking and generalizes well to independent datasets. Additionally, we conducted an in-depth analysis of the key components of the REMM to validate the improvements brought about by the cyclic shift module. Code and dataset at https://***/HanNieWHU/REMM. Copyright © 2024, The Authors. All rights reserved.

关键词： Image matching

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Semi-supervised segmentation of individual buildings from SAR imagery

Semi-supervised segmentation of individual buildings from SA...

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Urban remote sensing Joint Event

作者： Qingyu Li Yao Sun Lichao Mou Yilei Shi Xiao Xiang Zhu Data Science in Earth Observation Technical University of Munich (TUM) Munich Germany Data Science in Earth Observation Technical University of Munich (TUM) Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR) Munich Germany Chair of Remote Sensing Technology Technical University of Munich (TUM) Munich Germany

Buildings are essential geo-targets that contribute to the monitoring of urban development. Synthetic aperture radar (SAR) provides excellent opportunities for building segmentation as it is insensitive to sunlight illumination and weather conditions. Nevertheless, the majority of existing approaches that exploit convolutional neural networks (CNNs), need to collect an enormous quantity of annotations for network training. Therefore, we propose an innovative semi-supervised method for individual building segmentation from SAR imagery. Our approach has three modules: a weights-shared encoder, a main decoder as well as an auxiliary decoder. For unlabeled samples, given the perturbation added to the encoder’s output, we enforce the consistency between the feature and output of the auxiliary decoder and those of the main decoder. This allows for the use of abundant unlabeled samples to make up for a lack of supervisory information. The experiments are carried out on a SAR dataset that is collected from the city of Berlin, Germany. Quantitative and qualitative results suggest that our approach is superior to other competitors.

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THE DEPLOYMENT OF A WI-FI POSITIONING SYSTEM VIA CROWDSOURCING 7

THE DEPLOYMENT OF A WI-FI POSITIONING SYSTEM VIA CROWDSOURCI...

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7th International Conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services, UPINLBS 2022

作者： Cao, X. Chen, G. Zhuang, Y. Wang, X. Yang, X. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China School of Environment Science and Spatial Informatics China University of Mining and Technology Xuzhou China Wuhan Institute of Quantum Technology Wuhan China

Wi-Fi fingerprint positioning is widely used because of its ready hardware and high accuracy. However, its application is considerably restricted by time-consuming and labor-intensive works of offline collection and irregular fluctuation of signals. To address the above problems, we proposed a novel method to deploy the Wi-Fi fingerprint database based on implicit crowdsourcing and improved the weighted k-nearest neighbor (WKNN) algorithm to eliminate the influence of neighbor mismatching and device heterogeneity. First, ordinary users continuously gather Wi-Fi information instead of collecting one point after another. Meanwhile, video surveillance cameras record users' trajectories without any intervention and use monocular vision based on plane constraints to obtain users' location at the moment of each scanning. At the localization phase, the morphology similarity distance instead of the Euclidean distance is used to measure the similarity of signals to solve the problem of device heterogeneity. Outlier detection is also utilized for a secondary selection of neighbor points. Finally, geometric and signal morphology similarity distances are used to determine the combined weight of all neighbors after the dimensionless treatment. Results of the experiments conducted in a real indoor environment show that the proposed strategy improves the efficiency of fingerprint collection and achieves higher positioning accuracy. © Copyright:

关键词： combined weight device heterogeneity morphology similarity distance outlier detection Wi-Fi fingerprint indoor positioning

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Study on the correction factors in detection efficiency measurements based on spontaneous parametric down-conversion

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Optics Express 2025年第13期33卷 27358-27370页

作者： Zhihan Jin Bing Yu Junwei Chu Jianjun Li Xiaobing Zheng Anhui Province Key Laboratory of Optical Quantitative Remote Sensing Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei Anhui Province 230031 China University of Science and Technology of China Hefei Anhui Province 230026 China Xi'an Institute of Applied Optics Xi'an Shanxi Province 710065 China

This study discusses the correction factors in detection efficiency measurements based on spontaneous parametric down-conversion (SPDC) and coincidence counting. To address the coincidence loss induced by relative delays between two detection channels, this study proposes an accurate calculation analysis based on counting probability statistics derived from coincidence events. Additionally, an approximate method utilizing histograms and the photon count rate of detectors is proposed, which can effectively reduce the errors in coincidence loss corrections. Furthermore, the study discusses the dead time of single-photon detectors (SPDs) and proposes a weighted-average method applicable for correcting the influence of dead time.

关键词： Calibration Detectors Parametric down conversion Photodetectors Photon counting Single-photon avalanche diodes

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Improving the UNet with channel attention mechanism for Taihu Lake water body recognition

Improving the UNet with channel attention mechanism for Taih...

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2024 International Conference on remote sensing and Global Positioning Algorithm, RSGPA 2024

作者： Nie, Shiliang Zhao, Limin Jiang, Mingyong Li, Zhiliang Liu, Shumin Li, Jiaguo Wan, Wei Sun, Yuan School of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Space Information Space Engineering University Beijing101416 China Institute of Remote Sensing and GIS School of Earth and Space Sciences Peking University Beijing100871 China

Accurate recognition of surface water bodies is important for disaster management and resource planning. To explore a higher-precision water body recognition method, propose an improved channel attention mechanism UNet (ECA-UNet) water body recognition model based on Sentinel-2A remote sensing images. Utilizing ECA-UNet, UNet, PSPnet, and DeeplabV3, as well as the traditional normalized water body index (NDWI), recognize the water body in the Taihu Lake area. Through comparative analysis, the following main conclusions are drawn: (1) The recognition accuracy of the deep learning network model is better than the traditional NDWI method, among which the ECA-UNet network model has the highest accuracy of 98.67%, and the lowest recognition accuracy of NDWI is 89.06%. (2) The ECA-UNet network model can effectively extract water body recognition targets, has a better recognition effect on local features of water bodies, and can improve the water body extraction effect. The above results show that the improved channel attention mechanism ECA-UNet has the highest accuracy in water body recognition in Taihu Lake, which provides a reference for subsequent research and applications in this field. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Lakes

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China Land Carbon Budget(CLCB1.0):a comprehensive estimate of the land carbon budget in China

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National science Review 2025年第4期12卷 15-18页

作者： Jiangzhou Xia Xiaosheng Xia Xuhui Wang Weimin Ju Zhengyang Lin Zhangcai Qin Yuxing Sang Yanzi Yan Wenping Yuan Xu Yue Haicheng Zhang Hao Zhou Qiuan Zhu Tianjin Key Laboratory of Water Resources and Environment Tianjin Normal University School of Atmospheric Sciences Sun Yat-sen University Institute of Carbon Neutrality Sino-French Institute for Earth System ScienceCollege of Urban and Environmental SciencesPeking University International Institute for Earth System Sciences Nanjing University Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application School of Atmospheric Sciences Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Key Laboratory of Tropical Atmosphere-Ocean System (Ministry of Education) Sun Yat-sen University Department of Soil and Environment Swedish University of Agricultural Sciences 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 (NUIST) Carbon-Water Research Station in Karst Regions of Northern Guangdong School of Geography and Planning Sun Yat-sen University College of Meteorology and Oceanography National University of Defense Technology College of Geography and Remote Sensing Hohai University

The terrestrial ecosystem plays an important role in regulating the regional carbon balance, in particular for China, where ecological projects during past decades [1,2] have led to large increases in China's land carbon sinks, partially offsetting fossil fuel emissions. Therefore, accurate evaluation of land carbon fluxes in China is critical for improving our understanding of the magnitude of China's carbon budget, and projecting its future changes.

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