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Open AccessArticle

Estimating Urban Vegetation Biomass from Sentinel-2A Image Data

1
School of Environmental Science and Spatial Informatics, China University of Mining and Technology, Daxue Road 1, Xuzhou 221116, China
2
Department of Geography, Earth System Sciences, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
3
Jiangsu Institute of Urban Planning and Design, Caochangmen Avenue 88, Nanjing 210036, China
4
School of Geography, Geomatics, and Planning, Jiangsu Normal University, Shanghai Road 101, Xuzhou 221116, China
*
Author to whom correspondence should be addressed.
Forests 2020, 11(2), 125; https://doi.org/10.3390/f11020125
Received: 18 December 2019 / Revised: 11 January 2020 / Accepted: 19 January 2020 / Published: 21 January 2020
Urban vegetation biomass is a key indicator of the carbon storage and sequestration capacity and ecological effect of an urban ecosystem. Rapid and effective monitoring and measurement of urban vegetation biomass provide not only an understanding of urban carbon circulation and energy flow but also a basis for assessing the ecological function of urban forest and ecology. In this study, field observations and Sentinel-2A image data were used to construct models for estimating urban vegetation biomass in the case study of the east Chinese city of Xuzhou. Results show that (1) Sentinel-2A data can be used for urban vegetation biomass estimation; (2) compared with the Boruta based multiple linear regression models, the stepwise regression models—also multiple linear regression models—achieve better estimations (RMSE = 7.99 t/hm2 for low vegetation, 45.66 t/hm2 for broadleaved forest, and 6.89 t/hm2 for coniferous forest); (3) the models for specific vegetation types are superior to the models for all-type vegetation; and (4) vegetation biomass is generally lowest in September and highest in January and December. Our study demonstrates the potential of the free Sentinel-2A images for urban ecosystem studies and provides useful insights on urban vegetation biomass estimation with such satellite remote sensing data. View Full-Text
Keywords: urban vegetation; biomass estimation; Sentinel-2A; stepwise regression; Xuzhou urban vegetation; biomass estimation; Sentinel-2A; stepwise regression; Xuzhou
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Li, L.; Zhou, X.; Chen, L.; Chen, L.; Zhang, Y.; Liu, Y. Estimating Urban Vegetation Biomass from Sentinel-2A Image Data. Forests 2020, 11, 125.

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