https://orcid.org/0000-0002-2094-6480
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ABSTRACT
Remote sensing provides us with an approach for the rapid identification and monitoring of spatiotemporal changes in the urban ecological environment at different scales. This study aimed to construct a remote sensing assessment index for urban ecological livability with continuous fine spatiotemporal resolution data from Landsat and MODIS to overcome the dilemma of single image-based, single-factor analysis, due to the limitations of atmospheric conditions or the revisit period of satellite platforms. The proposed Ecological Livability Index (ELI) covers five primary ecological indicators – greenness, temperature, dryness, water-wetness, and atmospheric turbidity – which are geometrically aggregated by non-equal weights based on an entropy method. Considering multisource time-series data of each indicator, the ELI can quickly and comprehensively reflect the characteristics of the Ecological Livability Quality (ELQ) and is also comparable at different time scales. Based on the proposed ELI, the urban ecological livability in the central urban area of Wuhan, China, from 2002 to 2017, in the different seasons was analyzed every 5 years. The ELQ of Wuhan was found to be generally at the medium level (ELI ≈0.6) and showed an initial trend of degradation but then improved. Moreover, the ecological livability in spring and autumn and near rivers and lakes was found to be better, whereas urban expansion has led to the outward ecological degradation of Wuhan, but urban afforestation has enhanced the environment. In general, this paper demonstrates that the ELI has an exemplary embodiment in urban ecological research, which will support urban ecological protection planning and construction.
Acknowledgements
Special thanks are given to the editor and referees for their suggestions.
Data availability statement
The raw Landsat land surface reflectance products can be obtained from the United States Geological Survey (USGS, https://earthexplorer.usgs.gov/). Meanwhile, all the standard MODIS land product can be downloaded from the National Aeronautics and Space Administration (NASA, https://ladsweb.modaps.eosdis.nasa.gov/search/).
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Junbo Yu
Junbo Yu is currently a PhD candidate at Wuhan University. His research interests include image processing, remote sensing application, multi-scale change detection of map data.
Xinghua Li
Xinghua Li is currently an associate professor at Wuhan University. He received his BSc degree in geographical information system and his PhD degree in cartography and geographical information engineering from Wuhan University in 2011 and 2016, respectively. His research interests include multi-temporal remote sensing analysis and application, remote sensing image processing, deep learning, and sparse representation.
Xiaobin Guan
Xiaobin Guan is currently a postdoctoral research assistant at Wuhan University. He received the BSc and PhD degrees in geographical information system at Wuhan University in 2013 and 2018, respectively. His research interests include the processing of multi-source remote sensing images and its application in the terrestrial ecosystem and global change.
Huanfeng Shen
Huanfeng Shen is currently a professor at Wuhan University. He received a BSc degree in surveying and mapping engineering and a PhD degree in photogrammetry and remote sensing from Wuhan University in 2002 and 2007, respectively. His research interests include remote sensing image processing, multi-source data fusion, intelligent environmental sensing, and regional and global environmental changes.