《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。2019年影响因子达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。
2020年第13卷第12期论文目次
Challenges and opportunities for the development of MEGACITIES
Deren Li, Jun Ma, Tao Cheng, J. L. van Genderen & Zhenfeng Shao
Pages: 1382-1395
Published online: 26 Aug 2018
摘要:
Urbanization is one of the most important social and economic phenomena in the world today. This paper reviews the formation of megacities and summarizes the main problems, challenges and opportunities faced by the sustainable development of such large megacities. Issues discussed include the problems of land subsidence in megacities, environmental issues, traffic problems and energy supply aspects. The sustainable development of megacities in other parts of the world provided the references and experiences for the countermeasures of megacity planning and development in China. The vision of Digital Earth and Digital Cities can play a major role in the efficient management and sustainable growth of such megacities all around the world.
全文链接:
https://doi.org/10.1080/17538947.2018.1512662
Automating land parcel classification for neighborhood-scale urban analysis
Xinyue Ye, V. Kelly Turner & Bing She
Pages: 1396-1405
Published online: 01 Aug 2018
摘要:
Homeowners’ Associations (HOAs) dictate landscape structure and management through legally enforceable land covenants at the neighborhood scale in the USA. Determining the location and spatial extent of HOAs is critical for examining its influence. However, such analysis is confounded by the lack of spatial data at the appropriate unit for such analysis. The purpose of this paper is to develop and realize an open source implementation to automate land parcel classification, which is an initial step towards the goal of determining the impact of HOAs on urban land management. Using Maricopa County, Arizona as a testbed, we found that parcel merging processes reduce the number of subdivisions from 26,042 to 17,269, such that boundaries better align with neighborhood units to which rule sets like land covenants apply. Moreover, after an initial training period, this process was completed in just over 7 hours. This research is an important first step in enabling a number of analysis including determining the location and spatial extent of HOAs regionally and, eventually, nationally and determining proposed links between HOAs and land management outcomes.
全文链接:
https://doi.org/10.1080/17538947.2018.1502370
A Hierarchical unsupervised method for power line classification from airborne LiDAR data
Yanjun Wang, Qi Chen, Lin Liu & Kai Li
Pages: 1406-1422
Published online: 31 Jul 2018
摘要:
The automatic classification of power lines from airborne light detection and ranging (LiDAR) data is a crucial task for power supply management. The methods for power line classification can be either supervised or unsupervised. Supervised methods might achieve high accuracy for small areas, but it is time consuming to collect training data over areas of different conditions and complexity. Therefore, unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great demand. In this paper, we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points (including the power line corridor direction detection based on a layered Hough transform, connectivity analysis, and Douglas–Peucker simplification algorithm), followed by the extraction of contextual linear and angular features for each candidate laser points, and finally by setting the feature threshold values to identify the power line points. We tested the method over both forest and urban areas and found that the precision, recall and quality rates were up to 96.7%, 88.8% and 78.3%, respectively, for the test datasets and were higher than the ones from a previously developed supervised classification method. Overall, our approach has the advantages of achieving relatively high accuracy and being relatively fast.
全文链接:
https://doi.org/10.1080/17538947.2018.1503740
Evapotranspiration partitioning using an optimality-based ecohydrological model in a semiarid shrubland
Lajiao Chen, Liying Sun, Weijiang Liu, Lizhe Wang, Hui Wu, A-Xing Zhu & Yiqi Luo
Pages: 1423-1440
Published online: 22 Aug 2018
摘要:
Partitioning of evapotranspiration (ET) into biological component transpiration (T) and non-biological component evaporation (E) is crucial in understanding the impact of environmental change on ecosystems and water resources. However, direct measurement of transpiration is still challenging. In this paper, an optimality-based ecohydrological model named Vegetation Optimality Model (VOM) is applied for ET partitioning. The results show that VOM model can reasonably simulate ET and ET components in a semiarid shrubland. Overall, the ratio of transpiration to evapotranspiration is 49% for the whole period. Evaporation and plant transpiration mainly occur in monsoon following the precipitation events. Evaporation responds immediately to precipitation events, while transpiration shows a lagged response of several days to those events. Different years demonstrate different patterns of T/ET ratio dynamic in monsoon. Some of the years show a low T/ET ratio at the beginning of monsoon and slowly increased T/ET ratio. Other years show a high level of T/ET ratio for the whole monsoon. We find out that spring precipitation, especially the size of the precipitation, has a significant influence on the T/ET ratio in monsoon.
全文链接:
https://doi.org/10.1080/17538947.2018.1503741
Lianchong Zhang, Guoqing Li, Chi Zhang, Huanyin Yue & Xiaohan Liao
Pages: 1441-1456
Published online: 02 Aug 2018
摘要:
Earth observation data sharing is an essential part of the data lifecycle and plays a critical role in Earth science research. Existing industry data sharing systems are affected by restrictions in distributed resource management and tightly coupled service interoperability. These systems currently offer no support for facilitating cross-disciplinary exploration and application. The lack of a national data sharing infrastructure has led to reduced international cooperation. These barriers are common and have hindered the development of the Global Earth Observation System of Systems (GEOSS). The China GEOSS Data Sharing Network (China GEOSS DSNet) has been proposed as a part of China’s Plan for Implementing GEOSS (2016–2025) to address the above issues. In this research, we designed a national GEOSS data sharing framework, including resource integration mechanism, sharing-oriented metadata standards, and lightweight interoperability service to coordinate various Earth observation resources. So far, more than 29 million archived satellite metadata records and 200 TB of high-quality satellite datasets have been integrated under this framework. The results were demonstrated in the following applications: domestic satellite archived metadata query service, international Earth observation resource sharing service, and disaster emergency response service.
全文链接:
https://doi.org/10.1080/17538947.2018.1504995
New insight into smart ocean: how is it different from digital ocean?
Xin Zhang, Wanqian Deng & Yuwu Jiang
Pages: 1457-1464
Published online: 11 Feb 2019
摘要:
Oceans occupy approximately seventy-one percent of the earth’s surface, and vast regions of oceans are virtually unexplored, especially the deep parts where humans have never been. Therefore, it is urgent to understand our oceans from a number of perspectives. In this paper, the concept of Smart Ocean (SO) is constructed from the perspectives of environmental protection, sustainable development of the blue economy and marine disaster prevention and mitigation. First, a doublefunnel structure concept model of SO, including ocean observation infrastructure, data, information, application, knowledge and decision support layers, is put forward. Second, the differences between SO and Digital Ocean (DO) are analyzed. Third, taking the Decision Support System for Emergency at Taiwan Strait as an example, a case study, which has assisted in saving more than 200 people, is introduced. Finally, future research agenda is summarized into seven aspects, including new instrumentation and sensor technologies, ocean big data mining and knowledge discovering, prediction, forecast and uncertainty analysis, ocean environment protection technologies and policy, sustainable use of ocean energy and resource, the influence and response to global changes in the ocean, and interdisciplinary collaboration and public service.
全文链接:
https://doi.org/10.1080/17538947.2019.1574317