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Remote Sensing in Urban Forestry: Recent Applications and Future Directions

1
Department of Geography, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China
2
Department of Agricultural and Environmental Sciences, University of Bari “Aldo Moro”, Via Amendola 165/A, 70126 Bari, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1144; https://doi.org/10.3390/rs11101144
Received: 22 March 2019 / Revised: 29 April 2019 / Accepted: 8 May 2019 / Published: 14 May 2019
(This article belongs to the Special Issue Remote Sensing of Urban Forests)
Increasing recognition of the importance of urban forest ecosystem services calls for the sustainable management of urban forests, which requires timely and accurate information on the status, trends and interactions between socioeconomic and ecological processes pertaining to urban forests. In this regard, remote sensing, especially with its recent advances in sensors and data processing methods, has emerged as a premier and useful observational and analytical tool. This study summarises recent remote sensing applications in urban forestry from the perspective of three distinctive themes: multi-source, multi-temporal and multi-scale inputs. It reviews how different sources of remotely sensed data offer a fast, replicable and scalable way to quantify urban forest dynamics at varying spatiotemporal scales on a case-by-case basis. Combined optical imagery and LiDAR data results as the most promising among multi-source inputs; in addition, future efforts should focus on enhancing data processing efficiency. For long-term multi-temporal inputs, in the event satellite imagery is the only available data source, future work should improve haze-/cloud-removal techniques for enhancing image quality. Current attention given to multi-scale inputs remains limited; hence, future studies should be more aware of scale effects and cautiously draw conclusions. View Full-Text
Keywords: remote sensing; urban forest; ecosystem services; LiDAR; multi-source data remote sensing; urban forest; ecosystem services; LiDAR; multi-source data
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MDPI and ACS Style

Li, X.; Chen, W.Y.; Sanesi, G.; Lafortezza, R. Remote Sensing in Urban Forestry: Recent Applications and Future Directions. Remote Sens. 2019, 11, 1144. https://doi.org/10.3390/rs11101144

AMA Style

Li X, Chen WY, Sanesi G, Lafortezza R. Remote Sensing in Urban Forestry: Recent Applications and Future Directions. Remote Sensing. 2019; 11(10):1144. https://doi.org/10.3390/rs11101144

Chicago/Turabian Style

Li, Xun, Wendy Y. Chen, Giovanni Sanesi, and Raffaele Lafortezza. 2019. "Remote Sensing in Urban Forestry: Recent Applications and Future Directions" Remote Sensing 11, no. 10: 1144. https://doi.org/10.3390/rs11101144

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