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Vegetation Changes from 2014 to 2023 in the Mongolian Plateau Permafrost Region under Climate Change

Journal of Resources and Ecology 2024年第5期15卷 1147-1159页

作者： LI Fengjiao WANG Juanle LI Pengfei Davaadorj DAVAASUREN College of Geomatics Xi’an University of Science and TechnologyXi’an 710054China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China Department of Geography School of the Art and SciencesNational University of MongoliaUlaanbaatar 14201Mongolia

The permafrost region is one of the most sensitive areas to climate *** global warming,the Mongolian Plateau permafrost is rapidly degrading,and its vegetation ecosystem is seriously *** address this challenge,it is essential to understand the impact of climate change on vegetation at different permafrost degradation stages on the Mongolian *** on the general permafrost distribution,in this study,we divided different permafrost regions and explored the response of vegetation to climate change at different stages of permafrost degradation by the idea of“space instead of time”from 2014 to *** results of the study showed that:(1)Normalized difference vegetation index(NDVI)values showed a decreasing trend,and the proportion of the decreasing region was in the order of sporadic permafrost region>isolated and sparse permafrost region>continuous and discontinuous permafrost regions.(2)The main controlling factors of vegetation growth in permafrost regions are different,air temperature is the main controlling factor of vegetation growth in isolated and sparse permafrost region(r=-0.736)and sporadic permafrost regions(r=-0.522),and precipitation is the main controlling factor of vegetation growth in continuous and discontinuous permafrost region(r=-0.498).(3)The response of NDVI to climate change varies at different stages of permafrost *** the early stages of permafrost degradation,increased land surface temperature(LST)and air temperature favored vegetation growth and increased vegetation cover,whereas increased precipitation impeded vegetation growth;as the permafrost degraded,increased LST and air temperature impeded vegetation growth,whereas increased precipitation promoted vegetation growth.

关键词： Mongolian Plateau MODIS NDVI permafrost vegetation changes

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Modeling Soybean Cultivation Suitability in China and its Future Trends in Climate Change Scenarios

SSRN

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SSRN 2023年

作者： Zhu, Qinyan Wang, Fumin Yi, Qiuxiang Zhang, Xiaoyang Chen, Siting Zheng, Jueyi Li, Jiale Xu, Tianyue Peng, Dailiang Institute of Applied Remote Sensing & Information Technology Zhejiang University Hangzhou310058 China Key Laboratory of Agricultural Remote Sensing and Information System Zhejiang University Hangzhou310058 China School of Geomatics and Municipal Engineering Zhejiang University of Water Resources and Electric Power Hangzhou311228 China Geospatial Sciences Center of Excellence Department of Geography & Geospatial Sciences South Dakota State University BrookingsSD57007 United States Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health Zhejiang University Hangzhou310058 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Soybean is an important source of oil and vegetable protein and plays a key role in agricultural production and economy. A suitability evaluation of soybean cultivation is important for identifying potential soybean planting areas. Based on the raster data of soybean harvest ratio (FSHA) and climate-soil-topographic environmental factors, we used MaxEnt to simulate the soybean planting suitability and potential distribution in China and the future trends of soybean cultivation under climate change. The result shows that the suitability of soybean cultivation was primarily influenced by elevation, precipitation of warmest quarter, capacity of the clay fraction, slope, mean diurnal temperature range, accumulated temperature ≥10℃, solar radiation and topsoil gravel content. High-suitability and moderate-suitability area are respectively 44.61 Mha and 71.12 Mha in China. High-suitability areas for soybean are mainly concentrated in the Northeast Plain, the North China Plain and the northern parts of the middle and lower Yangtze River plain. There were many provinces with high soybean planting potential but low development degrees, including Hebei, Henan, Shandong, Tianjin, Jilin, Liaoning, Jiangsu, Hubei and Shaanxi. Under future climate scenarios (SSP126, SSP245 and SSP585) of multiple global circulation models in CMIP6, the area highly or moderately suitable for soybean cultivation is generally stable, while low suitability area shows growth evidently. The total increase of three suitability area is the most obvious under SSP245 during 2021-2040, accounting for more than 5% in the base of historical data. In the future, most parts of Northeast China (eastern Inner Mongolia, Heilongjiang and eastern and northern Jilin), parts of north-central China (northern Hebei, northern Shanxi and Shaanxi) and parts of southwest China (eastern Sichuan, Chongqing, northern Guizhou and Yunnan) may be more suitable for soybean cultivation. This study provides a practical reference for

关键词： Climate change

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Spectral Characteristics and Model Inversion of Common Greening Plants in Guangzhou Under Dust Pollution 7th

Spectral Characteristics and Model Inversion of Common Green...

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7th International Conference on Geoinformatics in Sustainable Ecosystem and Society, GSES 2019, and 1st International Conference on geospatial Artificial Intelligence for Urban Computing, GeoAI 2019

作者： Zhang, Chen Zhou, Xia Li, Yong Yang, Ji Yang, Chuanxun Wen, Kaixiang Chen, Yuchan Yang, Libo Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou510070 China Guangzhou511458 China State Grid Hunan Maintenance Company Hengyang421000 China

ISBN: (纸本)9789811561054

Hyperspectral remote sensing provides a highly efficient, convenient, and non-destructive technical means for the quantitative study of the dust retention content (DRC) of plants. Based on the common greening plants in Guangzhou, this study used hyperspectral technology to explore the spectral characteristics of different plant leaves under dust pollution, and used the Pearson correlation method to construct the DRC estimation models of plants. The results show that the spectral characteristics of the dust-retention effect of plants are mainly concentrated in the visible and near-infrared wavelengths (350–1360 nm), which are important band intervals for the study of spectral characteristics and the inversion band selection. The first derivative of spectral reflectance is more suitable for constructing the DRC estimation model than the spectral reflectance, and CF has a good fitting effect, and the deviation between the Model estimated DRC and the measured DRC is small, and its R2 is 0.55 and the RMSE is 0.300 g/m2. Through this research, the application of hyperspectral remote sensing technology in the dust retention effect research of plants can be effectively promoted, which can help to quickly and non-destructively evaluate the DRC of plants, and provide a scientific basis for rational selection of green plant types and urban air quality monitoring, and lay a foundation for large-scale DRC estimation. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Reflection

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A Comparative Study of Single-polarized and Polarimetric SAR Images Applied on Unsupervised Classification

A Comparative Study of Single-polarized and Polarimetric SAR...

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IEEE International Conference on Radar

作者： Junrong Qu Xiaolan Qiu Key Laboratory of Technology in Geo-spatial Information and Application System Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information and Application System Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665468893

SAR image understanding is a significant but also challenging issue in practice. In order to detect the difference between single-polarized and polarimetric SAR (PolSAR) data and explore more information from the pre one, we use GF3 data for experiments and adopt the Deep Clustering with Convolutional Autoencoders (DCEC) algorithm for single-polarized SAR image unsupervised classification. Using both the Kappa coefficient and mutual information derived from the confusion matrix, the results of single-polarized SAR data and PolSAR data are compared. It shows that the classification results of single-polarized SAR data match with the polarimetric ones to some extent, and their results are comparable in some specific applications. This paper presents the potential of information mining from single-polarized SAR images, as well as gives some references for the selection and trade-off between single- and full-polarized SAR data.

关键词： Image resolution Correlation Clustering algorithms Scattering Radar imaging Radar polarimetry Classification algorithms

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NewsGPT: ChatGPT Integration for Robot-Reporter

arXiv

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arXiv 2023年

作者： Hireche, Abdelhadi Belkacem, Abdelkader Nasreddine Jamil, Sadia Chen, Chao College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates School of International Communications University of Nottingham Ningbo China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin China

The integration of large language models (LLMs) with social robots has emerged as a promising avenue for enhancing human–robot interactions at a time when news reports generated by artificial intelligence (AI) are gaining in credibility. This integration is expected to intensify and become a more productive resource for journalism, media, communication, and education. In this paper a novel system is proposed that integrates AI’s generative pretrained transformer (GPT) model with the Pepper robot, with the aim of improving the robot’s natural language understanding and response generation capabilities for enhanced social interactions. By leveraging GPT’s powerful language processing capabilities, this system offers a comprehensive pipeline that incorporates voice input recording, speech-to-text transcription, context analysis, and text-to-speech synthesis action generation. The Pepper robot is enabled to comprehend user queries, generate informative responses with general knowledge, maintain contextually relevant conversations, and act as a more domain-oriented news reporter. It is also linked with a news resource and powered with a Google search capability. To evaluate the performance of the framework, experiments were conducted involving a set of diverse questions. The robot’s responses were assessed on the basis of eight criteria, including relevance, context, and fluency. Despite some identified limitations, this system contributes to the field of journalism and human–robot interaction by showcasing the potential of integrating LLMs with social robots. The proposed framework opens up opportunities for improving the conversational capabilities of robots, enabling interactions that are smoother, more engaging, and more context aware. © 2023, CC0.

关键词： Human robot interaction

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GRIDLESS TOMOGRAPHIC SAR IMAGING BASED ON ACCELERATED ATOMIC NORM MINIMIZATION WITH EFFICIENCY

arXiv

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arXiv 2022年

作者： Gao, Silin Zhang, Zhe Zhang, Bingchen Wu, Yirong Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China

Synthetic aperture radar (SAR) tomography (TomoSAR) enables the reconstruction and three-dimensional (3D) localization of targets based on multiple two-dimensional (2D) observations of the same scene. The resolving along the elevation direction can be treated as a line spectrum estimation problem. However, traditional super-resolution spectrum estimation algorithms require multiple snapshots and uncorrelated targets. Meanwhile, as the most popular TomoSAR imaging method in modern years, compressed sensing (CS) based methods suffer from the gridding mismatch effect which markedly degrades the imaging performance. As a gridless CS approach, atomic norm minimization can avoid the gridding effect but requires enormous computing resources. Addressing the above issues, this paper proposes an improved fast ANM algorithm to TomoSAR elevation focusing by introducing the IVDST-ANM algorithm, which reduces the huge computational complexity of the conventional time-consuming semi-positive definite programming (SDP) by the iterative Vandermonde decomposition and shrinkage-thresholding (IVDST) approach, and retains the benefits of ANM in terms of gridless imaging and single snapshot recovery. We conducted experiments using simulated data to evaluate the performance of the proposed method, and reconstruction results of an urban area from the SARMV3D-Imaging 1.0 dataset are also presented. Copyright © 2022, The Authors. All rights reserved.

关键词： Compressed sensing

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GDMD: A Disaster Data Hub Fabric the Open Data Network for Disaster Risk Reduction

引用

Data Science Journal 2025年 24卷

作者： Wang, Juanle Bu, Kun Min, Xiaodong Wang, Qiuyuan Yuan, Yuelei Sun, Feiran State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences Natural Resources Research Chinese Academy of Sciences Beijing100101 China China-Pakistan Earth Science Research Center Islamabad45320 Pakistan Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing210023 China Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun130102 China School of Civil Engineering and Geomatics Shandong University of Technology Zibo255049 China

Disaster risk reduction is a priority in the Open Science recommendations proposed by UNESCO. Diverse types of scattered disaster data are the basis for disaster prevention and mitigation. The realization of these interconnections and links remains challenging. This study proposes an open directory mechanism that facilitates the interoperability of global disaster data resources, named Global Disaster Data Master Directory (GDMD). Based on open standards and open-source technology, the prototype GDMD system was developed and deployed in the Disaster Risk Reduction Knowledge Service (DRRKS) system at the UNESCO Category 2 centre. The GDMD realizes three types of disaster data resource aggregation: local resources built by DRRKS, manual cataloging from open disaster platforms worldwide, and protocol harvesting based on interoperable technology. Two types of retrieval services are implemented for users through local and federal searches. The master directory has interconnected over 1,400 resources. The open model of GDMD can realize Findable, Accessible, Interoperable, and Reusable data governance in disaster risk reduction, further pushing the Open Science mechanism and innovative applications of resources. © 2025 The Author(s).

关键词： Data consistency

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A novel TomoSAR imaging method with few observations based on nested array

arXiv

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arXiv 2022年

作者： Jiang, Pengyu Zhang, Zhe Zhang, Bingchen Xu, Zhongqiu Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Suzhou Aerospace Information Research Institute Suzhou215000 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou21500 China

Synthetic aperture radar tomography (TomoSAR) baseline optimization technique is capable of reducing system complexity and improving the temporal coherence of data, which has become an important research in the field of TomoSAR. In this paper, we propose a nested TomoSAR technique, which introduces the nested array into TomoSAR as the baseline configuration. This technique obtains uniform and continuous difference co-array through nested array to increase the degrees of freedom (DoF) of the system and expands the virtual aperture along the elevation direction. In order to make full use of the difference co-array, covariance matrix of the echo needs to be obtained. Therefore, we propose a TomoSAR sparse reconstruction algorithm based on nested array, which uses adaptive covariance matrix estimation to improve the estimation performance in complex scenes. We demonstrate the effectiveness of the proposed method through simulated and real data experiments. Compared with traditional TomoSAR and coprime TomoSAR, the imaging results of our proposed method have a better anti-noise performance and retain more image information. Copyright © 2022, The Authors. All rights reserved.

关键词： Covariance matrix

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A gravity-inspired model integrating geospatial and socioeconomic distances for truck origin–destination flows prediction

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International Journal of Applied Earth Observation and Geoinformation 2025年 136卷

作者： Zhao, Yibo Cheng, Shifen Gao, Song Lu, Feng State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China Geospatial Data Science Lab Department of Geography University of Wisconsin-Madison Madison 53706 WI United States The Academy of Digital China Fuzhou University Fuzhou 350002 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023 China

Accurately predicting truck origin–destination (OD) flows is essential for optimizing logistics systems and promoting coordinated regional development. Existing methods typically assume a monotonic decrease in truck OD flows with increasing geospatial distance, which oversimplifies the complex non-monotonic distribution patterns observed in practice. Moreover, these methods overlook interregional socioeconomic distances and their interaction with geospatial distances, thereby limiting the prediction accuracy and reliability. This study introduces a gravity-inspired model that integrates both geospatial and socioeconomic distances (GSD-DG) to explicitly represent their combined influence on truck OD flows. Specifically, we 1) develop a geospatial distance relation graph using the Weibull function to model the complex spatial distribution patterns of truck OD flows with varying geospatial distances;2) propose a gravity-inspired representation learning method based on graph attention mechanism to quantify the influence of socioeconomic distance on truck OD flows;and 3) construct a deep gravity model that integrates these distances and their interactions to capture their non-linear relationship with truck OD flows. Extensive experiments on four datasets with varying spatial scale and economic development levels demonstrate that the GSD-DG model improves the robustness and prediction accuracy across diverse spatial distribution patterns, reducing RMSE by 14.2%–85.8% and MSE by 23.5%–92.5% compared to the six baseline models. Incorporating socioeconomic distance and its interaction with geospatial distance further reduces RMSE by 8.5%–36.0%. Additionally, explainable artificial intelligence techniques highlight how these distances affect truck OD flows, providing valuable policy insights for logistics planning and coordinated regional development. © 2024 The Authors

关键词： geospatial distance Gravity-inspired model Heavy trucks Origin–destination flows Socioeconomic distance Spatial interaction

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Satellite-Based Water Depth Estimation: A Review 7th

Satellite-Based Water Depth Estimation: A Review

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7th International Conference on Geoinformatics in Sustainable Ecosystem and Society, GSES 2019, and 1st International Conference on geospatial Artificial Intelligence for Urban Computing, GeoAI 2019

作者： Wen, Kaixiang Li, Yong Wang, Hua Jing, Wenlong Yang, Ji Zhang, Chen Wang, Zhou Guangdong University of Technology Guangzhou510070 China Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System Guangdong Open Laboratory of Geospatial Information Technology and Application Guangzhou Institute of Geography Guangzhou510070 China State Grid Panzhihua Power Supply Company Panzhihua617000 China

ISBN: (纸本)9789811561054

This paper reviews the advances in water depth estimations from satellite images. According to the sensor type, current satellite-based water depth estimations are divided into two categories: optical-based and SAR-based (synthetic aperture radar) methods. By analyzing and discussing on the advantages and disadvantages of various methods. The following points can be summarized: First, the accuracy of optical remote sensing water depth detection method is higher than that of SAR-based models. Besides, optical-based method is simpler and less restricted than the SAR-based methods. Second, hyperspectral-based approach performed better than the multispectral optical remote sensing approaches. Since hyperspectral images provide richer spectral information than multispectral, which significantly matter in the water depth detection. Third, remote sensing water depth detection method is the best way to obtain the underwater topographic features of the inaccessible waters. Finally, on the basis of summarizing the previous water depth estimation methods, the development prospects of shallow seawater deep inversion technology are discussed and analyzed. For example, the establishment of an inversion model by fusing multiple remote sensing data, and the use of artificial intelligence technology for water depth inversion. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Synthetic aperture radar

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