《国际数字地球学报》(International Journal of Digital Earth)是国际数字地球学会依托中国科学院空天信息创新研究院主办的学术刊物。《学报》于2008年3月创刊,目前已被12个大型国际期刊检索机构收录。2020年影响因子为3.097,在全球50个地理类期刊中名列第17位,在30个遥感类期刊中排名第14位。在2019 Scopus CiteScore 引用分数榜中,《学报》在地球与行星科学类187个期刊中排名第11位。
《学报》以传播数字地球理念为宗旨,致力于数字地球学术交流,促进数字地球技术发展,推动数字地球在经济和社会可持续发展中的应用,并将在全球气候变化、自然灾害防治与响应、新能源探测、农业与食品安全和城市规划管理等方面发挥重要作用。该刊得到国内外科学界同行的广泛认可与高度肯定,成为同领域的主流学术期刊。
2021年第14卷第3期论文目次
Building health monitoring in the old town of Madrid: applicability of SAR Imagery to the monitoring of underground works through classification indexes
Adrian Jesus Garcia, Beatriz González-Rodrigo, Sara Martinez, Ruben Martinez & Miguel Marchamalo
Pages: 271-287
Published online: 08 Sep 2020
摘要:
The old city centers of many major cities represent a great challenge from a constructive point of view since the foundations of the buildings consist of an aggregation of previous constructions. This endorses thorough monitoring activities during any underground construction. The capabilities of the persistent scatterer interferometry (PSI) can be exploited to cope with these monitoring needs. However, the old city centers represent a very challenging urban scenario since the rooftops are usually filled with air conditioning systems and due to the fact that the streets are usually crowded. This work assesses the applicability of the PSI technique under this challenging scenario. Moreover, it proposes a set of novel classification indexes that allow an objective assessment of the building’s health and the impact derived from any activity. They can be applied in deformation monitoring and risk evaluation in urban areas. The applicability of the technique and the proposed indexes is validated in the monitoring of underground construction works in the old city center of Madrid comparing the results against on-ground measurements and identifying the potential and the limitation of the technique.
全文链接:
https://doi.org/10.1080/17538947.2020.1815878
Interactive data styling and multifocal visualization for a multigrid web-based Digital Earth
M. J. Sherlock, M. Hasan & F. F. Samavati
Pages: 288-310
Published online: 08 Sep 2020
摘要:
Globe-based Digital Earth (DE) is a promising system that uses 3D models of the Earth for integration, organization, processing, and visualization of vast multiscale geospatial datasets. The growing size and scale of geospatial datasets present significant obstacles to interactive viewing and meaningful visualizations of these DE systems. To address these challenges, we present a novel web-based multiresolution DE system using a hierarchical discretization of the globe on both server and client sides. The presented web-based system makes use of a novel data encoding technique for rendering large multiscale geospatial datasets, with the additional capability of displaying multiple simultaneous viewpoints. Only the data needed for the current views and scales are encoded and processed. We leverage the power of GPU acceleration on the client-side to perform real-time data rendering and dynamic styling. Efficient rendering of multiple views allows us to support multilevel focus+context visualization, an effective approach to navigate through large multiscale global datasets. The client–server interaction as well as the data encoding, rendering, styling, and visualization techniques utilized by our presented system contribute toward making DE more accessible and informative.
全文链接:
https://doi.org/10.1080/17538947.2020.1822452
Research agenda for the Russian Far East and utilization of multi-platform comprehensive environmental observations
Tuukka Petäjä, Kirill S. Ganzei, Hanna K. Lappalainen, Ksenia Tabakova, Risto Makkonen, Jouni Räisänen, Sergey Chalov, Markku Kulmala, Sergej Zilitinkevich, Petr Ya Baklanov, Renat B. Shakirov, Natalia V. Mishina, Evgeny G. Egidarev & Igor I. Kondrat’ev
Pages: 311-337
Published online: 29 Sep 2020
摘要:
The Russian Far East is a region between China and the Russian Arctic with a diverse climatological, geophysical, oceanic, and economical characteristic. The southern region is located in the Far East monsoon sector, while the northern parts are affected by the Arctic Ocean and cold air masses penetrating far to the south. Growing economic activities and traffic connected to the China Belt and Road Initiative together with climate change are placing an increased pressure upon the Russian Far East environment. There is an urgent need to improve the capacity to measure the atmospheric and environmental pollution and analyze their sources and to quantify the relative roles of local and transported pollution emissions in the region. In the paper, we characterize the current environmental and socio-economical landscape of the Russian Far East and summarize the future climate scenarios and identify the key regional research questions. We discuss the research infrastructure concept, which is needed to answer the identified research questions. The integrated observations, filling in the critical observational gap at the Northern Eurasian context, are required to provide state-of-the-art observations and enable follow-up procedures that support local, regional, and global decision making in the environmental context.
全文链接:
https://doi.org/10.1080/17538947.2020.1826589
Laura Zepner, Pierre Karrasch, Felix Wiemann & Lars Bernard
Pages: 338-356
Published online: 07 Oct 2020
摘要:
This article describes the conception and implementation of a web platform which uses special charts and maps for climate monitoring and analysis. At first it gives an overview of related web applications and their advantages and limitations. This is followed by a basic introduction of current technologies and methods for working with climate data, geospatial web services and visualization techniques. Finally, the implementation based on prior defined requirements is presented and its strengths and limitations are discussed. The application provides several basic charts for climate analysis, as well as a more complex one which uses a standardized visualization concept which is suitable for comparing different local climates (Walter-Lieth-Standard). The charts are based on different interpolated datasets with global coverage as well as data from the Global Historical Climate Network (GHCN). Overall, the application enables users to generate individual historical climate charts from the beginning of the twentieth century until present day.
全文链接:
https://doi.org/10.1080/17538947.2020.1829112
Shouji Du, Shihong Du, Bo Liu & Xiuyuan Zhang
Pages: 357-378
Published online: 09 Oct 2020
摘要:
Semantic segmentation of remote sensing images is an important but unsolved problem in the remote sensing society. Advanced image semantic segmentation models, such as DeepLabv3+, have achieved astonishing performance for semantically labeling very high resolution (VHR) remote sensing images. However, it is difficult for these models to capture the precise outlines of ground objects and explore the context information that revealing relationships among image objects for optimizing segmentation results. Consequently, this study proposes a semantic segmentation method for VHR images by incorporating deep learning semantic segmentation model (DeepLabv3+) and object-based image analysis (OBIA), wherein DSM is employed to provide geometric information to enhance the interpretation of VHR images. The proposed method first obtains two initial probabilistic labeling predictions using a DeepLabv3+ network on spectral image and a random forest (RF) classifier on hand-crafted features, respectively. These two predictions are then integrated by Dempster-Shafer (D-S) evidence theory to be fed into an object-constrained higher-order conditional random field (CRF) framework to estimate the final semantic labeling results with the consideration of the spatial contextual information. The proposed method is applied to the ISPRS 2D semantic labeling benchmark, and competitive overall accuracies of 90.6% and 85.0% are achieved for Vaihingen and Potsdam datasets, respectively.
全文链接:
https://doi.org/10.1080/17538947.2020.1831087
Monitoring travel patterns in German city regions with the help of mobile phone network data
Stefan Fina , Jigeeshu Joshi & Dirk Wittowsky
Pages: 379-399
Published online: 22 Oct 2020
摘要:
This paper discusses the possibility to use mobile phone network data to monitor spatial policies in land use and transport planning. Monitoring requires robust time series and reproducible concepts linking spatial policies to monitoring outcomes, a requirement differing from current literature where mobile phone data analysis is exemplified in selected areas with privileged data access. Concepts need to serve the evaluation of policy objectives, for example in regional or local area plans. In this study, we, therefore, extend the application of mobile phone network data to monitoring applications comparing urban settlement types and their characteristic mobility patterns. To accomplish this, we link mobile phone records with urban classifications and transport network data, using both visual and computational approaches to mine the data. The article presents comparisons of travel patterns for selected monocentric and polycentric city regions in Germany, testing hypotheses of transit-oriented regional development, as well as testing for congestion risks in the transport network. The results help us to gain a more detailed understanding of spatial and temporal patterns in mobility for different urban types and assess future potentials for monitoring spatial policies with mobile phone network data.
全文链接:
https://doi.org/10.1080/17538947.2020.1836048
编辑/排版:林之叶
审校:刘珍
终审:王长林
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