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Open AccessArticle

Urban Forest Growth and Gap Dynamics Detected by Yearly Repeated Airborne Light Detection and Ranging (LiDAR): A Case Study of Cheonan, South Korea

1
Interdisciplinary Program in Landscape Architecture, Seoul National University, Seoul 08826, Korea
2
Department of Landscape Architecture, Graduate School of Environmental Studies, Seoul National University, Seoul 08826, Korea
3
Department of Contents, Samah Aerial Survey CO., Goyang 10442, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1551; https://doi.org/10.3390/rs11131551
Received: 29 April 2019 / Revised: 22 June 2019 / Accepted: 27 June 2019 / Published: 29 June 2019
(This article belongs to the Special Issue Remote Sensing of Urban Forests)
Understanding forest dynamics is important for assessing the health of urban forests, which experience various disturbances, both natural (e.g., treefall events) and artificial (e.g., making space for agricultural fields). Therefore, quantifying three-dimensional changes in canopies is a helpful way to manage and understand urban forests better. Multitemporal airborne light detection and ranging (LiDAR) datasets enable us to quantify the vertical and lateral growth of trees across a landscape scale. The goal of this study is to assess the annual changes in the 3-D structures of canopies and forest gaps in an urban forest using annual airborne LiDAR datasets for 2012–2015. The canopies were classified as high canopies and low canopies by a 5 m height threshold. Then, we generated pixel- and plot-level canopy height models and conducted change detection annually. The vertical growth rates and leaf area index showed consistent values year by year in both canopies, while the spatial distributions of the canopy and leaf area profile (e.g., leaf area density) showed inconsistent changes each year in both canopies. In total, high canopies expanded their foliage from 12 m height, while forest gap edge canopies (including low canopies) expanded their canopies from 5 m height. Annual change detection with LiDAR datasets might inform about both steady growth rates and different characteristics in the changes of vertical canopy structures for both high and low canopies in urban forests. View Full-Text
Keywords: airborne LiDAR; annual repeated LiDAR; canopy growth; forest disturbance; canopy structure; change detection airborne LiDAR; annual repeated LiDAR; canopy growth; forest disturbance; canopy structure; change detection
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MDPI and ACS Style

Choi, H.; Song, Y.; Jang, Y. Urban Forest Growth and Gap Dynamics Detected by Yearly Repeated Airborne Light Detection and Ranging (LiDAR): A Case Study of Cheonan, South Korea. Remote Sens. 2019, 11, 1551.

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