兰州大学机构库
Impact of spatial and temporal scales of remote sensing data on the spatiotemporal change in geographic elements
Wan, Changjun1; Wu, Xiaodan1; Lin, Xingwen2
Indexed ByEI
2019
Source PublicationYaogan Xuebao/Journal of Remote Sensing
ISSN20959494
Volume23Issue:6Pages:1064-1077
AbstractThe detection of the changes in surface parameters is important to understand the regularity of the Earth's surface. Traditional data sources, such as in situ stations and field observations, have been extensively used to analyze the spatiotemporal changes in geographic parameters. However, this kind of data only provides information at " point scales," which cannot comprehensively reflect the spatiotemporal change information of land surfaces. Remote sensing provides a practical means of spatially and continuously obtaining the land surface information at regular intervals. However, the achievable information remains limited by specific spatial and temporal resolutions. Furthermore, results of change detection are typically inconsistent when different remote sensing data are used because of the heterogeneities and seasonal change in land surfaces. This study reviewed the causes and effects of spatial and temporal scales on the change detection of land surface parameters. The methods for reducing the uncertainty of change detection results are summarized. Spatial scale involves the size of the spatial extent and spatial resolution. Similarly, temporal scale partially refers to the length of time range and temporal resolution. The features of the surface landscape are complexity, heterogeneity, and fragmentation. Consequently, the geographic entity exists at a specific spatial and temporal frame. Thus, the completeness of the acquired surface information depends on the spatial and temporal scales of remote sensing data. The geographic elements, which are homogeneous on a scale, may be heterogeneous on another scale. Generally, the remote sensing data at large pixel scales frequently combine the detailed information contained in small pixel scales. Low temporal resolution can result in the inability to capture the rapid changes in land surfaces. For the uncertainty caused by spatial scale, the methods for reducing such kind of effects include multisource remote sensing collaborative observation and inversion, scale conversion, and spatial modeling. For the temporal scale, the methods for reducing such kind of effect are mainly focused on combining multitemporal remote sensing data. The advantages and disadvantages of these methods in practical applications were analyzed in this study. Although many scholars have developed methods for reducing the influence of spatiotemporal scale on the change detection of geographic elements, a universal method is difficult to establish given the contradiction between the spatially and temporally varying characteristics of geographic elements and the nature of remote sensing data. However, these impacts can be minimized by selecting appropriate observation scales and improving the algorithms based on the characteristics of surface parameters. With the diversity of high-quality satellite data and the improvement of the algorithms, the uncertainties of change detection results can be reduced.
© 2019, Science Press. All right reserved.
DOI10.11834/jrs.20198327
Funding ProjectNational Natural Science Foundation of China[41801226] ; Fundamental Research Funds for the Central Universities[lzujbky-2018-45]
PublisherScience Press
EI Accession Number20195107877570
EI KeywordsPixels ; Surface measurement ; Uncertainty analysis
EI Classification NumberProbability Theory:922.1 ; Mechanical Variables Measurements:943.2
Original Document TypeJournal article (JA)
Citation statistics
Document Type期刊论文
Identifierhttp://ir.lzu.edu.cn/handle/262010/405008
Collection兰州大学
Corresponding AuthorWu, Xiaodan
Affiliation1.College of Earth and Environmental Sciences, Lanzhou University, Lanzhou; 730000, China
2.College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua; 321004, China
First Author AffilicationLanzhou University
Corresponding Author AffilicationLanzhou University
Recommended Citation
GB/T 7714
Wan, Changjun,Wu, Xiaodan,Lin, Xingwen. Impact of spatial and temporal scales of remote sensing data on the spatiotemporal change in geographic elements[J]. Yaogan Xuebao/Journal of Remote Sensing,2019,23(6):1064-1077.
APA Wan, Changjun,Wu, Xiaodan,&Lin, Xingwen.(2019).Impact of spatial and temporal scales of remote sensing data on the spatiotemporal change in geographic elements.Yaogan Xuebao/Journal of Remote Sensing,23(6),1064-1077.
MLA Wan, Changjun,et al."Impact of spatial and temporal scales of remote sensing data on the spatiotemporal change in geographic elements".Yaogan Xuebao/Journal of Remote Sensing 23.6(2019):1064-1077.
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