最新刊期
卷 25 ， 期 2 ， 2021
-
摘要：Mapping as a human cognition behavior has experienced four stages: illustrative mapping, surveying and mapping, mapping with remote sensing, and now entering into intelligent mapping age - iMap. In this note, I argue that the next generation iMap needs at least five elements. These include flexible and cross-walkable purpose of mapping cutting across spatio-temporal scales, powerful knowledge transfer that harness digital library and knowledge extracted from the cyberspace, a diverse sources of data including social big data, an ensembled use of multiple algorithms for pattern recognition (i.e., Earth surface type labelling and target recognition), and a mapping platform that is easy to use by laypersons.关键词：AI and mapping;knowledge transfer;cross-walkable classification system;big data- 899
- |
- 225
- |
- 6
发布时间：2021-03-03
Scholar's View Point
-
摘要：With ongoing and accelerating global climate change, temperate glaciers are very sensitive to variations in the temperature and precipitation, and thus are in fact regarded as natural indicators of climate change. Glacier velocity, which is a combination of ice deformation, bed deformation, and glacier sliding, is an important parameter to better study the dynamics of glaciers and their interplay with climate changes in the region. In situ observations serve as one of the most accurate methods for measuring glacier velocity, but the remote areas where glaciers develop have prevented frequent visitation by people. Remote sensing is more effective in glacier monitoring and has been applied to study glacier velocity in many regions of the Tibetan Plateau, such as in Karakoram, Himalaya, West Kunlun, and other areas. In recent years, the Chinese high-resolution optical remote sensing images has gradually increased, but there was not much use of Chinese produced satellite remote sensing images for monitoring glacier flow parameters in mountain regions. In view of this situation, this study tried to apply the domestic “GaoFen-1” satellite images (GF-1) to the extraction of Yanong Glacier flow in southeast Tibet. By preprocessing and applying feature tracking on all available pairs within a defined period to derive the velocity, the reliable glacier velocities can be obtained by selecting stable ground control points from the ice-free areas to register these GF-1 data and the offset can be computed. The accuracy of the glacier flow velocity derived from GF-1 data was assessed by the residual displacements in non-glacial stable regions and glacier flow velocity along the longitudinal profile of the Yanong Glacier compared to that of Landsat-8 data in the same resolution and at the same Periods. The evaluation results showed that: The average deviation of GF-1 data in non-glacial stable regions was 7.48- 766
- |
- 303
- |
- 6
发布时间：2021-03-03 -
摘要：The high-spatial-resolution GF-6 satellite has been successfully launched for less than one year, and the application of its imagery has just started. GF-6 is a satellite of a high-resolution earth observation system, which is a major scientific and technological project in China. The Wide Field-of-View (WFV) images of the GF-6 wide-format camera add two red-edged bands in comparison with similar images of the GF-1; thus, the monitoring capacity of agriculture, forestry, and grassland is improved. To analyze the ability of GF-6 WFV imagery in plantation classification and promote the further application of GF-6 data in forestry field, this study provides a hierarchical classification method to extract plantation types, such as eucalyptus and fir, by using the latest WFV images of GF-6 in the Nanning forest farm in Guangxi Province. Furthermore, the classification process was combined with ground measured data.The random forest classification method was adopted, and the steps are as follows: First, the six vegetation indices were calculated and optimized using the random forest feature selection method, and then the classification scheme of the four datasets was determined. The schemes are as follows: (1) first four bands of WFV image without red edge, (2) eight bands with red edge, (3) eight bands plus unoptimized vegetation index features, and (4) eight bands plus optimized vegetation index features. Then, the random forest classifier was utilized in four datasets. Random forest is an effective classification method that uses the classification regression tree algorithm to generate classification trees and combines the advantages of bagging and the random selection of feature variables. Lastly, the classification results of four schemes were compared; in addition, accuracy assessment was performed in accordance with field survey and forestry inventory data.Results showed that Scheme 2 has an accuracy of 4.99% higher than that of Scheme 1, and the kappa coefficient increased by 0.058. These results indicate that the accuracy of eight-band data with red edges is significantly improved in comparison with four-band data.The classification accuracy of the eight bands plus optimized vegetation index features was the highest among the schemes, reaching 85.38%. Compared with the bands with and without red edge, the accuracy improved by 3.98% and 8.97%, respectively; moreover, the kappa coefficient increased by 0.046 and 0.104.WFV images are added with the red-edge index, thereby enhancing vegetation information. This dataset can accurately reflect the differences of plantation type characteristics and significantly improve the classification accuracy of the plantation. Therefore, this study can effectively improve the information extraction effect of plantation types in Guangxi Province and provide a scientific reference for the evaluation of GF-6 image quality and its forestry application potential.关键词：high spatial resolution GF-6 satellite;planted forest types classification;random forests;hierarchical classification method;red-edge vegetation index- 1114
- |
- 263
- |
- 11
发布时间：2021-03-03 -
摘要：The GF-6 WFV image is the first remote sensing image of the 8-band multi-spectral satellite with medium and high resolution in China. 4 spectrum bands including two red-edge bands are added to the image based on the conventional red, green, blue and near-infrared band. As a vegetation sensitive band, the red-edge band is one of the methods used for crop classification and identification in remote sensing images. Research on the impact of GF-6 WFV image and its red-edge bands on crop classification is urgently needed.This research uses GF-6 WFV images as the data source. The main work is (1) proposes a convolutional neural network (RE-CNN) remote sensing image crop classification model suitable for GF-6 WFV red-edge bands; (2) conducts crop classification research about GF-6 WFV imagery and its red-edge bands and evaluates effectiveness of red-edge bands due to the lack of relevant research, (3) uses the strategy of combining object-oriented and deep learning for crop classification. The core idea of this research: multi-scale segmentation is used in order to avoid the influence of salt and pepper phenomenon on image classification, and image segmentation is completed by selecting the best segmentation parameters with ESP tools and ROC-LV. Object-oriented classification by CART decision tree can extract vegetation area while eliminating salt and pepper noise, and convert it into input data of convolutional neural network. The network structure of Inception was introduced to extract the multi-scale features of the image and then a convolutional neural network model (RE-CNN) for GF-6 WFV imagery was constructed for crop classification. A control experimental group with or without red-edge bands was set up and the RE-CNN model was used for crop classification and accuracy verification. The effect of the newly added red-edge bands on crop classification is studied, and the effectiveness and sensitivity of the red-edge band are evaluated in crop classification by GF-6 WFV imagery.The experimental results of this study show that: (1) Object-oriented CART decision tree classification effectively eliminates salt and pepper noise in vegetation area extraction, and the classification strategy of combining with deep learning achieve better classification results in remote sensing image crop classification. (2) The RE-CNN model proposed in this paper can be used for GF-6 WFV remote sensing image crop classification. The classification accuracy of the experimental group in the group of red-edge bands is as high as 94.38%, and the Kappa coefficient is 0.92. (3) The newly added red-edge bands in GF-6 WFV images can effectively improve the crop classification accuracy of remote sensing image. Compared with the group without red-edge bands, the classification accuracy is increased by 2.83%, which verifies the effectiveness and sensitivity that the newly-added red-edge bands in GF-6 WFV images improves the classification accuracy. Moreover, it provides a reference for the research of GF-6 WFV image and its red-edge band for crop classification.关键词：remote sensing;object-oriented;classification;GF-6;Red-Edge band;convolutional neural network- 873
- |
- 374
- |
- 9
发布时间：2021-03-03
Chinese Satellites
-
摘要：The Three-Dimensional (3D) computer simulation model is an important part of remote sensing studies, especially for complex surfaces including mixed vegetated scenes, urban area, and mountain area. After 20 years of development, the 3D computer simulation has made remarkable progress, and has been widely used in the analysis of surface radiative transfer process, the validation of simplified models/algorithms and retrieved remote sensing products. Recently, there has been a surge of interest in the high-resolution remote sensing data obtained from both satellite- and Unmanned Aerial Vehicle (UAV)-board sensors, which have heightened the need of 3D models in remote sensing simulation and inversion researches at fine scales. However, few studies have focused on the difference among models and further modifications. In order to fully understand the development of 3D remote sensing models and to explore how to better serve applications in different fields using these models, this paper reviews the research of 3D remote sensing models in optical remote sensing, which wavelength range encompasses the visible, near infrared and thermal infrared bands. In this paper, the principle, application, and development trend of models are discussed. Firstly, the modelling strategies based on ray tracing /flux tracing and radiosity theories are introduced and then some existing models are briefly compared. Then, the typical applications of 3D computer simulation models in optical remote sensing are summarized. A lot of literature has published on Bidirectional Reflectance Distribution Function (BRDF), Directional Brightness Temperatures (DBT), Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC), in which the 3D model serves as not only a static tool as data generator in validation and analysis processes but also a dynamic tool for remote sensing inversion directly. Finally, this paper provides some insights for the future development trend of the model. Three perspectives can be performed in the future: (1) all 3D models suffer from slow operation speed, thereby models should be improved associated with operation efficiency such as using new Graphics Processing Unit (GPU) devices; (2) The main advantage of 3D models lies in its accurate simulation. Then, refine the simulation and replacing the empirical or semi-empirical processes by those physical-based must be performed. At the same time, the evaluation of 3D models should be further promoted based on multiple types of measurements from field and UAV-based experiments. In addition, (3) since multiple-source remote sensing data can be obtained, a comprehensive model based on not only radiative transfer but also interdisciplinary theories associated with evapotranspiration and fluorescence would promote its ability to explain the surface phenomena. With the in-depth study of remote sensing modeling for complex surfaces, the 3D computer model will play a more vital role in the research and application of remote sensing in the future.关键词：3D computer model;optics remote sensing;ray tracing;flux tracing;radiosity- 925
- |
- 326
- |
- 5
发布时间：2021-03-03 -
摘要：Global warming is a hot topic in recent years as it can lead to temperature increasing, more frequent extreme weather, etc. Temperature is an important parameter to characterize the thermodynamic state of the atmosphere. The distribution of temperature affects the radiation flux of long-wave and short-wave, which in turn plays a significant role in the balance of global energy radiation budget. Therefore, understanding the spatial and temporal distribution of atmospheric temperature and its long-term variation comprehensively are essential for our research on weather forecast and climate change research.Presently, many ground-based and space borne sensors have been developed for temperature detection. Space borne sensors observe temperature by nadir viewing, occultation detection, or limb sounding. Space borne sensors have a high spatial and temporal resolution, which can provide sufficient data for scientific research. In this paper, seven foreign hyperspectral sensors (IMG, AIRS, IASI, HALOE, HALOE, TES, MIPAS, ACE-FTS) and domestic sensors (the series of FY and GF) are introduced in terms of parameters, performance, and application. These sensors acquire the information of atmospheric parameters through the radiation in infrared band. Occultation and limb observation are less affected by the underlying surface and have high vertical resolution and sensitivity, such as HALOE, MIPAS, ACE-FTS, TES, etc. So far, the accuracy of temperature products of nadir observation (AIRS and IASI) is less than 1K with a vertical direction of 1 km, and the accuracy of temperature products in the troposphere by TES limb observation can reach 0.5 K. All of them meet the requirement of Numerical Weather Prediction and can be used widely in weather forecasting and climate change research.The accuracy of temperature profiles of the Feng Yun series is comparable to that of IASI, but the spatial and temporal resolution are needed to be improved. The radiation transfer is the basis for trace gas inversion. Firstly, we introduced the radiation transfer equations of nadir viewing, occultation detection, or limb sounding. The radiation transfer process of limb observation is similar to that of occultation detection, in which line-of-sight reaching a down-looking sensor is entirely above the ground. Then, three retrieval methods (statistical regression, physical inversion, and artificial neural network inversion) of temperature profile are described in terms of principles, characteristics, and development history. The advantages and disadvantages of them are also compared. The statistical regression method is simple and efficient, but the accuracy is relatively lower, which is usually used as the a priori profiles for the physical algorithm. The accuracy of physical inversion is improved obviously, but the retrieval process of is complex and the a priori profiles are needed in physical inversion. The artificial neural network method can acquire the temperature profile accurately and efficiently, but a variety of samples are needed for training. Meanwhile, the key problems that the influence of the cloud, aerosol, and surface reflectivity on temperature retrieval are described, and the possible solutions are then given, respectively. The generation and propagation of error (smoothness, model parameter, and measurement error) in temperature retrieval are summarized. Finally, the problems existing in atmospheric temperature inversion are proposed.关键词：satellite-borne infrared hyperspectral sensor;atmospheric temperature profile;radiation transmission;key issues;error analysis- 816
- |
- 408
- |
- 5
发布时间：2021-03-03 -
摘要：The Planetary Data System (PDS) is an online platform where deep space mission data can be archived and released, and it is used as the basis for planetary research. Different deep space exploration missions, including Chinese “Chang ’E project” and the upcoming “Tianwen series,” have disadvantages such as complex data format conversion, inconvenient data processing, and strong professionalism; these issues hinder their application. A planetary data system (named SDU-PDS) is designed and developed in this study to manage and find the aforementioned data. Meanwhile, we attempt to design a data model to archive these planetary data in SDU-PDS.On the basis of the three-layer distribution structure of WebGIS and the object-oriented spatiotemporal data model, SDU-PDS is developed. Specifically, this system is divided into two versions: website or mobile devices. One is an online system, called “web version,” which is based on Web Graphics Library and developed by JavaScript. The other one, called mobile version, is developed on Android system through Unity3D platform. In addition, planetary data standard in this system uses Keyhole Markup Language, and the object-oriented spatiotemporal model is consistent with PDS4.Three layers of architecture, namely, data, intermediate application, and customer layers, are present in SDU-PDS. Different versions have different data transfer processes and functions in spite of same architecture. The web version can effectively store, publish, manage, and even analyze Chinese deep space exploration data. It provides a convenient data management and processing platform for researchers. The mobile version is for the general public and astrophile that integrates planetary data browsing, stacking, and 3D visualization.In this study, we made two further attempts in planetary data processing on the basis of functions on SDU-PDS website. The lunar geological map is the first attempt and is mapped out after some sophisticated steps. Various jobs, such as inversing planetary composition, devising geological unit, extracting structural objects, dating lunar basalt unit, stacking layers, and fusing information, are all indispensable. Consequently, exploring a large amount of information in this geological map, such as the lithofacies, lithology, geological structure and ages, and magmatic activity, is possible. Meanwhile, another attempt is to estimate the lunar surficial safety. We can understand the surficial relief on a smaller scale, such as meter scale, through remote sensing images. Then, factors such as terrain and distance are integrated to estimate the safety of the Chang ’E-4 landing zone and even design the rover’s path.Clearly, SDU-PDS not only can provide data services and analysis functions for scientific researchers but also can popularize knowledge of planetary science to the public to improve basic scientific literacy of citizens. By combining and optimizing the basic functions of SDU-PDS, we realize the mining and fusion of planetary data. In general, the system can meet the application requirements of Chinese deep space exploration missions to a certain extent by conducting scientific studies such as lunar geological mapping and pre-selection of “Chang ’E-4” safe landing areas. However, this planetary data system should be continuously developed and perfected, and the interactivity between two versions should be enhanced to explore the potential of planetary data further.关键词：remote sensing;Planetary Data System;SDU-PDS;WebGIS;data fusion;data mining- 696
- |
- 210
- |
- 1
发布时间：2021-03-03 -
摘要：InSAR technology, one of the important earth observation technologies, has been widely used and explored in the field of surface deformation monitoring, such as city, mine, and geological disaster, especially in landslide deformation monitoring. This study systematically expounded and summarized the relevant progress worldwide in recent years from three aspects of InSAR methods, thematic fields, and existing problems. This task is conducted to fully and accurately understand the frontier scientific problems of the InSAR technology in landslide disaster application and sort out its limitations, challenges, and future development trend. Moreover, this work is carried out to better serve the landslide disaster control and monitoring. The specific content includes the following aspects:(1) Based on the overview of the main InSAR methods used in landslide monitoring, our research comprehensively reviewed and summarized the application scope, advantages, and disadvantages and internal relations of various InSAR methods. A reasonable understanding of the characteristics of various methods is an important part of the scientific design of the InSAR landslide application monitoring scheme.(2) We analyzed the four relevant topics in recent years regarding InSAR landslide early identification and detection, deformation monitoring of different magnitudes, activity patterns, and 3D information acquisition, and coupling of deformation and inducement. This work focused on the cases of major outstanding innovations in the existing applications and summarized the deficiencies and challenges of the corresponding topic content in the current research. First, starting from the early identification of InSAR landslide, a research hotspot, comparative analysis, and discussion are made on its research scenes by country and situation. In view of the different characteristics of the deformation variables at various stages of landslides, this research focused on the effective monitoring and acquisition of landslide deformation information, landslide movement patterns, and 3D landslide information. This work discussed in detail the progress made in the past and current problems. Then, this work demonstrated the application boundaries and effective auxiliary methods of different InSAR technologies in landslide monitoring and made a comprehensive and in-depth analysis of the development of InSAR technologies. This work focused on the analysis of the current InSAR technology and data on the progress of landslide activity mode information acquisition and landslide 3D deformation research. Moreover, this work summarized the advantages and disadvantages of various methods that can obtain 3D landslide information. Finally, this work briefly discussed the current progress and inadequacies related to the coupling of InSAR deformation and incentives and the multisource/metalandslide monitoring cases with InSAR as the main supplement to other remote sensing technologies.(3) The limitations of the InSAR technology system and the characteristics of landslide disasters were summarized according to the research progress made under the existing conditions. We analyzed the problems of geometric distortion, dense vegetation coverage, atmospheric interference, 3D deformation information acquisition, accuracy evaluation, complexity, and nonlinearity of landslide deformation in InSAR landslide monitoring. This work also provided concrete and feasible solutions and recommended measures for solving the corresponding problems in this research.(4) From the perspective of the construction of the InSAR landslide industry system, we combined artificial intelligence, machine learning, UAV remote sensing, seismic network in the field of geosciences, and other observation technologies in our analysis. In view of data processing and integration with other new technologies, the future research of InSAR in landslide applications was summarized and prospected.关键词：remote sensing;InSAR;landslides;geological hazards;deformation monitoring;research progress- 1809
- |
- 943
- |
- 29
发布时间：2021-03-03
Review
-
摘要：Image registration is a process of geometric alignment of two or more images acquired at different time, different sensors or under different conditions (weather, illumination, camera position and angle, etc.). Remote sensing image registration is an important prerequisite for subsequent processing, such as image fusion, image stitching, long time sequence analysis etc., and it is one of spotlights in the field of remote sensing information processing. High-precision registration of multi-temporal remote sensing images covering complex-terrain region is always a problem to break through. The conventional registration algorithms guiding by the transformation model, is enable to take the pixel-level geometric distortion into consideration, which means that the displacements of a pair of corresponding pixels is different from that of the other pair.Under this circumstance, the global or local mapping function could not describe the geometric deformation between two images covering the complex-terrain region. Optical flow estimation calculates per-pixel displacements considering the very local distortions, even the pixel-level deformation in the computer vision field, providing a feasible and creative solution. It estimates displacement in x- and y-directions for a pair of corresponding pixels under the intensity and gradient consistency constraints, with resistant to the change of illumination. However, it is sensitive to land cover changes, which often lead to abnormal optical flow field and further affect the registered image after the coordinate transformation and resampling. To this end, a registration algorithm based on the optical flow modification for multi-temporal remote sensing images covering the complex-terrain region is proposed. On the preliminary optical flow field, Laplace of Gaussian operator is employed to detect the abnormal optical flow in Munsell color system. With the mask of abnormal optical flow based on the detection results, the Delaunay triangle curved surface interpolation is utilized to correct them, which is calculated by the around accurate pixel displacements. The coordinates in the sensed image are transformed, and the new pixel value is put on the corresponding pixel with the specified resampling method. Ultimately, the aligned image is generated. Experiments based on multi-temporal remote sensing images covering the complex-terrain region with land cover changes demonstrate that the proposed method achieves high-fidelity and high-precision registration compared with the results of the conventional methods.Nevertheless, for registration of the image with sub-meter spatial resolution or image registration of different sensors, the difference of imaging angle, imaging mechanism, noise type etc. have an impact on the accuracy of the proposed algorithm. These remote sensing images are important data guarantee for fine research of earth surface and disaster assessment under poor imaging conditions in disaster region. How to realize high fidelity and high efficiency registration of the ultra-high resolution image or the multi-model image, is a problem that needs an in-depth study for us. In our future work, based on the proposed algorithm in this paper, research will be carried out specifically for the aforementioned problem. The aligned complex topographic region images will be applied to disaster monitoring, assessment, land use change analysis and other fields for an assessment to further improve our proposed method.关键词：remote sensing;complex-terrain region;optical flow algorithm;Laplace of Gaussian;registration;Delaunay triangle curved surface interpolation- 681
- |
- 190
- |
- 4
发布时间：2021-03-03 -
摘要：Super Resolution Mapping (SRM) technology can effectively handle mixed pixels in remote sensing image and obtain the accurate distribution information of land-cover class. SRM technology is currently successfully applied to flood inundation mapping for multispectral image, which is called Super Resolution Flood Inundation Mapping (SRFIM). However, the existing SRFIM methods are often based on pixel-scale spatial correlation. This method considers the spatial relationship between pixels in the set rectangular window, but the shape of the inundation area or the non-inundation area is irregular in reality. Thus, the pixel-scale spatial correlation is insufficiently accurate, which affects the final accuracy of flood inundation mapping. Super-resolution flood inundation mapping of multispectral image based on super-pixel scale spatial correlation (SSSC-SRFIM) is proposed to solve the abovementioned problem.In SSSC-SRFIM, the original coarse multispectral image is first improved by bicubic interpolation to obtain the improved image, and the fractional image with the proportion value of each subpixel belonging to inundation area is obtained by unmixing the improved image. The first principal component of the improved image is then extracted by principal component analysis, and the image segmentation based on multi-resolution is used to segment the first principal component to obtain the super-pixels with irregular shape. Next, the fractional image and super-pixels are integrated, and the random walk algorithm is introduced to calculate the super-pixel-scale spatial correlation. Finally, according to the super-pixel-scale spatial correlation, the label of the inundation area or the non-inundation area is assigned to each sub-pixel by the class allocation method based on the unit of object. Thus, the final result of flood inundation mapping is produced.Two Landsat 8 OLI multispectral images are used to evaluate the method. The proposed SSSC-SRFIM method has better performance than the traditional methods.In the proposed SSSC-SRFIM, the super-pixel-scale spatial correlation is more accurate than pixel-scale spatial correlation because the irregular distribution shape of the actual inundation and non-inundation areas is considered. Therefore, better flood inundation mapping result can be obtained by the proposed SSSC-SRFIM.关键词：remote sensing;multispectral image;flood inundation;super-resolution mapping;super-pixel;image segmentation;random walk algorithm- 788
- |
- 233
- |
- 0
发布时间：2021-03-03 -
摘要：In order to accurately locate the deposit, the ASTER data of Weiya area in eastern Tianshan Mountain of Xinjiang is selected to study the extraction method of mineralization alteration information. To improve the accuracy of ASTER data mineralization alteration information extraction method, a method based on Principal Component Analysis (PCA), multilevel segment method, and Support Vector Machine (SVM) is proposed in this study. First, the special band of alteration information is selected after analyzing the ASTER data, and the principal component image is acquired by PCA. Then, the mean image is obtained after the principal component image is segmented. Subsequently, the training samples are trained by SVM after the training samples are extracted. Moreover, the optimal model is constructed using the optimal kernel parameters and flabby variable obtained by repeated testing. Finally, the optimal model is used to accomplish the extraction of alteration information from ASTER data. The abnormal ferric contamination is extracted using 1, 2, 3, and 4 bands, the alteration anomalies with AL-OH groups are extracted from 1, 4, 6, and 7 bands, and the alteration anomalies with OH, CO32- groups are extracted by 1, 2, 8, and 9 bands. SMO is adopted to improve operation efficiency. Thus, the speed is increased by 12%. A comparison with band ratio method, PCA method, spectral angle mapper and SVM method is conducted. The degree of the abnormal ferric contamination, the alteration anomalies with AL-OH groups, and the alteration anomalies with OH and CO32- groups are 87.98%, 90.01%, and 88.93%, respectively. The corresponding Kappa coefficients are 0.8011, 0.8134, and 0.8023. The extraction results of anomaly information are consistent with metallogenic belt, the known mineralization points, and the mineralization characteristics of different geological conditions.关键词：remote sensing;ASTER;mineralization alteration information extraction;multilevel segment method;Principal Component Analysis (PCA);Support Vector Machine(SVM);Sequential Minimum Optimization(SMO)- 690
- |
- 202
- |
- 3
发布时间：2021-03-03
Technologies and Methodologies
-
摘要：The Guangdong-Hong Kong-Macao Greater Bay Area (the greater bay area) is the key strategic area in our country, and the cognition of the collaborative structure of the Greater Bay Area is an important research content for it to build a world-class bay area. As an important model of urban agglomerations, the Greater Bay Area has a complex structural relationship, which is fully reflected in the characteristics of the flow of people between cities and towns, yet inter-city job-housing migration behavior is an intuitive and stable manifestation of the regional population mobility. Therefore, it is significant to develop the cognition of the bay area collaborative structure based on high precision inter-city job-housing data.Based on the summary of the research in Bay Areas around the world, the application research on job-housing spatiotemporal big data, the current status of the relationship between the Greater Bay Area, this paper carried out the research and practice of the collaborative cognitive methods in the Greater Bay Area based on inter-city job-housing spatiotemporal data which is identified by Baidu map. The study built an inter-city job-housing exchange network, using the statistical units as the network nodes and the migration flow as the connection weight, and recognize the inter-city job-housing relationship from three aspects, including the proportion of people moving in and out, weighted degree centrality, and average distance of migration. The study further carries out a cluster analysis on each unit combines with economic data and summarize the units into six types, including exchange center units, dominant units and their special cases, units to be developed, output units, and input units.The research results found that the Greater Bay Area has constructed three complex groups with different exchange structures including Guangzhou and Foshan, Zhongshan and Zhuhai, Shenzhen Dongguan and Huizhou, with obvious multi-center structure. Also, the problem of unbalanced regional coordination in the Bay Area still exists. The spatial distribution of all units’ type shows a pronounced circle structure, and the exchange relationship between the east and west banks is obviously different. Finally, combined with the analysis of spatial structural of relevant policies, the paper preliminarily expounds the development status, development problems and future direction of the urban agglomeration structure, and pointed out that the future development of the Greater Bay Area needs to strengthen the advantages of the multi-center development structure and needs to solve the structural problems in the region, such as the “strong east and weak west”, the lagging development of periphery, and the northward shift of the exchange core. By sorting out the cooperation modes between various units, the Greater Bay Area needs to strengthen the contribution of the dominant units, consolidate the participation of the exchange center units, and avoid the formation of one-way input and output between the polar cores and the surrounding area. Also, the Greater Bay Area needs to make full use of the vast hinterland area to promote the complementarity of all kinds of functions, in order to provide support for the collaborative development of the urban agglomerations.关键词：remote sensing;the Greater Bay Area;urban agglomerations structure;inter-city job-housing;spatiotemporal big data- 736
- |
- 200
- |
- 3
发布时间：2021-03-03 -
摘要：The Bailong River Basin is located in the southeast of Gansu Province and situated at the intersection of the Qinghai–Tibet Plateau, the Loess Plateau, and the Sichuan Basin. Geohazards, such as landslides and debris flows, have high frequency and wide distribution due to the impact of rainfall, tectonic activity, and earthquakes. These phenomena pose a serious threat to the safety of life and property of the local people. Investigating a new method to detect potential landslide and study its characteristics is important to provide key supports for local disaster prevention and mitigation.In this study, an InSAR technique called Small Baseline Subset was selected to process 60 Sentinel-1A SAR images acquired from March 2018 to March 2019. Moreover, the study area was clipped into 8 blocks to improve the efficiency of data processing and minimize the errors caused by the complex terrain of the region.On the basis of the abovementioned method, the mean surface displacement rates ranging from -158 and 100 mm/year along the line-of-sight direction were detected during March 2018 and March 2019. A total of 114 potential landslides were investigated and identified in the Bailong River Basin based on optical image interpretation and field survey. Statistical analysis of their basic information shows that most of the potential landslides tend to develop in the S, SSW, and SSE-faced slope with a gradient of 20°—40°. The elevation difference of potential landslides is less than 150 m. The slope material is mostly composed of slope deposits and heavy weathered rocks, such as phyllite. The majority of potential landslides have an area less than 5×104 m2.Yahuokou landslide, which was investigated as a potential landslide with displacement rates > 38 mm/year, broke and ran into Min River from 19 July 2019. On the basis of the analysis of landslide pre-cursory deformation and geomorphology, the landslide was divided into three sections: source, propagation, and accumulation areas. The successful identification of potential landslide demonstrates the applicability and efficiency of InSAR technique in landslide investigation and identification. This research provides foundation and scientific support for landslide mapping and disaster prevention in Bailong River Basin.关键词：Bailong River Basin;potential landslide;InSAR;ground deformation;early identification;characteristics;Yahuokou landslide- 884
- |
- 382
- |
- 13
发布时间：2021-03-03
Remote Sensing Applications
- Postal code：100190
- Tel：010-58887052 Email：nrsb@aircas.ac.cn
- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备20021838号-9
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
0