《國際數字地球學報》（International Journal of Digital Earth）是國際數字地球學會依託中國科學院空天信息創新研究院主辦的學術刊物。《學報》於2008年3月創刊，目前已被12個大型國際期刊檢索機構收錄。2020年影響因子為3.097，在全球50個地理類期刊中名列第17位，在30個遙感類期刊中排名第14位。在2019 Scopus CiteScore 引用分數榜中，《學報》在地球與行星科學類187個期刊中排名第11位。
《學報》以傳播數字地球理念為宗旨，致力於數字地球學術交流，促進數字地球技術發展，推動數字地球在經濟和社會可持續發展中的應用，並將在全球氣候變化、自然災害防治與響應、新能源探測、農業與食品安全和城市規劃管理等方面發揮重要作用。該刊得到國內外科學界同行的廣泛認可與高度肯定，成為同領域的主流學術期刊。
Lessons from a Marine Spatial Planning data management process for Ireland
Sarah Flynn , Will Meaney , Adam M. Leadbetter , Jeffrey P. Fisher & CaitrionaNic Aonghusa
Pages: 139-157
Published online: 19 Aug 2020
摘要：
This paper presents a framework containing ten components to deliver a data management process for the storage and management of data used for Marine Spatial Planning (MSP) in Ireland. The work includes a data process flow and a recommended solution architecture. The architecture includes a central data catalogue and a spatial storage system. The components of the process are presented to maximise the reuse potential of any dataset within an MSP context. The terms 『Suitability』 and 『Readiness』 in the MSP context are offered as both formal and considered assessments of data, as is the applicability of a data stewardship maturity matrix. How data contained in such a storage system can be published externally to potential consumers of these data is also explored. The process presents a means of managing data and metadata to ensure data lineage is optimised by carrying information about the origin of and the processing applied to the data; to evaluate the quality and relevance of geospatial datasets for use in MSP decisions in Ireland. The process was piloted in the National Marine Planning Framework for Ireland in the development of draft map products; feedback from the public consultation is ongoing and not presented.
全文連結：
https://doi.org/10.1080/17538947.2020.1808720
Volunteered remote sensing data generation with air passengers as sensors
Chisheng Wang , Yongquan Wang , Leyang Wang , Zhongwen Hu , ShaobiaoZhang , Shuanglong Wang , Wenqun Xiu , Hongxing Cui , Dan Wang &Qingquan Li
Pages: 158-180
Published online: 19 Aug 2020
摘要：
Remote sensing satellites are playing very important roles in diverse earth observation fields. However, long revisit period, high cost and dense cloud cover have been the main limitations of satellite remote sensing for a long time. This paper introduces the novel volunteered passenger aircraft remote sensing (VPARS) concept, which can partly overcome these problems. By obtaining aerial imaging data from passengers using a portable smartphone on a passenger aircraft, it has various advantages including low cost, high revisit, dense coverage, and partial anti-cloud, which can well complement conventional remote sensing data. This paper examines the concept of VPARS and give general data processing framework of VPARS. Several cases were given to validate this processing approach. Two preliminary applications on land cover classification and economic activity monitoring validate the applicability of the VPARS data. Furthermore, we examine the issues about data maintenance, potential applications, limitations and challenges. We conclude the VPARS can benefit both scientific and industrial communities who rely on remote sensing data.
全文連結：
https://doi.org/10.1080/17538947.2020.1808721
Improved modeling and analysis of the patch size–frequency distribution of forest disturbances in China based on a Landsat forest cover change product
Dan-Xia Song , Chengquan Huang , Tao He , Joseph O. Sexton , Ainong Li ,Sike Li , Hao Wu & John R. Townshend
Pages: 181-201
Published online: 03 Sep 2020
摘要：
Forest disturbances have been altering the ecological properties of ecosystems; meanwhile, disturbance events of varying sizes create different structures and functions for a forest landscape. Therefore, size and frequency are important attributes of disturbances, and their relationship should be studied. We present a hierarchical method through the modeling of the overall trend of the size–frequency distribution and the characterization of the non-constant variances of disturbance sizes at each frequency level. This method was demonstrated to accurately model the sizes as well as the corresponding frequencies; thus, the total disturbed area and number of disturbance patches were both accurately estimated. By applying the method to 13 provinces in China, consistent patterns were revealed by the modeling results and remote-sensing-based product, showing that between 2000 and 2005, forests in most provinces were dominated by moderate disturbances (10 ha < size < 100 ha). Southeastern provinces contain the largest proportion of small disturbances (size < 10 ha), whereas most of the very large disturbances (size > 1000 ha) occurred in the northeastern and northwestern provinces. This study concludes that the proposed method can improve the representation of the size–frequency distribution of forest disturbances.
全文連結：
https://doi.org/10.1080/17538947.2020.1810337
Distinguishing different subclasses of water bodies for long-term and large-scale statistics of lakes: a case study of the Yangtze River basin from 2008 to 2018
Jin Luo , Zeqiang Chen & Nengcheng Chen
Pages: 202-230
Published online: 24 Aug 2020
摘要：
Long-term and large-scale lake statistics are meaningful for the study of environment change, but many of the existing methods are labour-intensive and time-consuming. To overcome this problem, a novel method for long-term and large-scale lake extraction by shape-factors- and machine-learning-based water body classification is proposed. An experiment was conducted to extract the lakes in the Yangtze River basin (YRB) from 2008 to 2018 with the Joint Research Centre's Global Surface Water Dataset (JRC GSW) data and OSM data. The results show: 1) The proposed method is automatically and successfully executed. 2) The number of river–lake complexes is between 3008 and 4697, representing 3.56%–5.70% of the total water bodies. 3) The areas of the lakes and rivers in the YRB were obtained, and the accuracy of water classification in each year was stable between 90.2% and 93.6%. Comparing the back propagation neural network, random forest, and support vector machine models, we found that the three machine learning models have similar classification accuracy for the scenario. 4) Fragmented and incomplete small rivers in the JRC GSW data, unchecked training samples, and overlapped shape factors are the three error sources. Future work will focus on addressing these three error sources.
全文連結：
https://doi.org/10.1080/17538947.2020.1810338
Separating the impacts of climate variability, land-use change and large reservoir operations on streamflow in the Yangtze River basin, China, using a hydrological modeling approach
Ning Nie , Wanchang Zhang , Min Liu , Hao Chen & Dengzhong Zhao
Pages: 231-249
Published online: 30 Aug 2020
摘要：
Separating the individual effects of climate variability and human activities on streamflow is more important than just knowing their combined effects. In this paper, using a scenario-based hydrological simulation approach, the streamflow changes caused by climate variability and two different types of human activities (i.e. land-use change and large reservoirs operations) as well as the contribution rates of these three factors over 272 sub-basins in the Yangtze river basin were quantified and compared among 5 different periods (i.e. 1988–1992 (P1), 1993–1997 (P2), 1998–2002 (P3), 2003–2007 (P4) and 2008–2012 (P5)). Results demonstrate that, at the annual scale, climate variability played a leading role in the change in outflow of most sub-basins. With regard to the seasonal variations in discharge at Datong station, climate factors played a predominant role during P1-P2 and P2-P3. Since the Three Gorges Reservoir began operating in 2003, the discharge was enhanced by reservoirs in January-May and reduced by reservoirs in July-December. Reservoir and climate factors codetermined seasonal streamflow change during P3-P4 and P4-P5. Land-use change made the smallest contribution to seasonal discharge fluctuations. This study can support decision-making in regional water resources planning and management.
全文連結：
https://doi.org/10.1080/17538947.2020.1812740
Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data
Ehsan Kamali Maskooni , Hossein Hashemi , Ronny Berndtsson , PeymanDaneshkar Arasteh & Mohammad Kazemi
Pages: 250-270
Published online: 02 Sep 2020
摘要：
Many factors are involved in urban heat island development, such as lack of green spaces, improper choice of building materials, densification, and other human activities. The aim of this research was to quantify the effects of land-use/land-cover (LU/LC) changes on urban land surface temperature (LST) during a 25-year period (1993–2018) for the semiarid Shiraz City in southern Iran using Landsat data (TM, ETM+, and OLI/TIRS) and machine learning algorithms. Five main LU/LC classes, such as orchard, vegetation, bare surface, asphalt cover, and built-up areas, were identified using a support vector machine algorithm. Landsat images were used to retrieve normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The results showed that the mean LST over the entire study domain increased considerably between 1993 and 2018, due to urbanization, decrease of green areas, and increasing industrial areas. Built-up areas increased considerably by 25.8% from 80 to 100.6 km2 between 1993 and 2018, while vegetation cover decreased dramatically by 69.3%. Mean LST increased from 38.4 to 40.2°C during the 25-year period with a significant increase of 3.9°C between 2013 and 2018. In addition, the Urban heat island Ratio Index (URI) displayed a substantial upward trend during the 25-year period.
全文連結：
https://doi.org/10.1080/17538947.2020.1813210
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