Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data Article Swipe
Zongyao Sha
,
Yahya Ali
,
Yuwei Wang
,
Jiangping Chen
,
Xicheng Tan
,
Ruren Li
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi7100407
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/ijgi7100407
Vegetation plays an irreplaceable role for urban ecosystem services. Urban greenness represents all vegetation cover in and around cities. Understanding spatiotemporal patterns of the changes in urban greenness (CUG) provides fundamental clues for urban planning. The impact on CUG can be roughly categorized as being climate-induced and human-induced. Methods for mapping human-induced CUG (H-CUG) are rare. In this paper, a new framework, known as Localized Spatial Association Analysis under Temporal Context (LSAA-TC), was proposed to explore H-CUG. Localized spatial association analysis (LSAA) was performed first to extract local spatial outliers (LSOs), or locations that differ significantly in urban greenness from those located in the neighborhood. LSOs were then analyzed under the temporal context to map their intertemporal variations known as spatiotemporal outliers. We applied LSAA-TC to mapping H-CUG in the Wuhan Metropolitan Area, China during 2000–2015 using the vegetation index from Moderate-resolution Imaging Spectroradiometer (MODIS) 13Q1 as the proxy for urban greenness. The computed H-CUG demonstrated apparent spatiotemporal patterns. The result is consistent with the fact that the traditional downtown area presents the lowest H-CUG, while it is found that the peripheral area in the circular belt within 14–20 km from the urban center demonstrates the most significant H-CUG. We conclude that LSAA-TC can be a widely applicable framework to understand H-CUG patterns and is a promising tool for informative urban planning.
Related Topics
Concepts
Metropolitan area
Context (archaeology)
Remote sensing
Moderate-resolution imaging spectroradiometer
Downtown
Cartography
Vegetation (pathology)
Geography
Urban planning
Outlier
Spatial ecology
Physical geography
Environmental science
Computer science
Ecology
Satellite
Artificial intelligence
Biology
Aerospace engineering
Engineering
Archaeology
Medicine
Pathology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijgi7100407
- https://www.mdpi.com/2220-9964/7/10/407/pdf?version=1539424314
- OA Status
- gold
- Cited By
- 7
- References
- 69
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2895893145
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2895893145Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/ijgi7100407Digital Object Identifier
- Title
-
Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-10-13Full publication date if available
- Authors
-
Zongyao Sha, Yahya Ali, Yuwei Wang, Jiangping Chen, Xicheng Tan, Ruren LiList of authors in order
- Landing page
-
https://doi.org/10.3390/ijgi7100407Publisher landing page
- PDF URL
-
https://www.mdpi.com/2220-9964/7/10/407/pdf?version=1539424314Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2220-9964/7/10/407/pdf?version=1539424314Direct OA link when available
- Concepts
-
Metropolitan area, Context (archaeology), Remote sensing, Moderate-resolution imaging spectroradiometer, Downtown, Cartography, Vegetation (pathology), Geography, Urban planning, Outlier, Spatial ecology, Physical geography, Environmental science, Computer science, Ecology, Satellite, Artificial intelligence, Biology, Aerospace engineering, Engineering, Archaeology, Medicine, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2022: 1, 2021: 2, 2020: 3Per-year citation counts (last 5 years)
- References (count)
-
69Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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