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Remote Sens. 2016, 8(4), 333; doi:10.3390/rs8040333

Trends in Automatic Individual Tree Crown Detection and Delineation—Evolution of LiDAR Data

1
Department of Forest Management, School of Forestry, Northeast Forestry University, Harbin 150040, China
2
Department of Environmental Resource Engineering, College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA
3
Department of Forest and Natural Resources Management, College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Anu Swatantran, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 30 January 2016 / Revised: 30 March 2016 / Accepted: 12 April 2016 / Published: 15 April 2016
View Full-Text   |   Download PDF [2092 KB, uploaded 15 April 2016]   |  

Abstract

Automated individual tree crown detection and delineation (ITCD) using remotely sensed data plays an increasingly significant role in efficiently, accurately, and completely monitoring forests. This paper reviews trends in ITCD research from 1990–2015 from several perspectives—data/forest type, method applied, accuracy assessment and research objective—with a focus on studies using LiDAR data. This review shows that active sources are becoming more prominent in ITCD studies. Studies using active data—LiDAR in particular—accounted for 80% of the total increase over the entire time period, those using passive data or fusion of passive and active data comprised relatively small proportions of the total increase (8% and 12%, respectively). Additionally, ITCD research has moved from incremental adaptations of algorithms developed for passive data sources to innovative approaches that take advantage of the novel characteristics of active datasets like LiDAR. These improvements make it possible to explore more complex forest conditions (e.g., closed hardwood forests, suburban/urban forests) rather than a single forest type although most published ITCD studies still focused on closed softwood (41%) or mixed forest (22%). Approximately one-third of studies applied individual tree level (30%) assessment, with only a quarter reporting more comprehensive multi-level assessment (23%). Almost one-third of studies (32%) that concentrated on forest parameter estimation based on ITCD results had no ITCD-specific evaluation. Comparison of methods continues to be complicated by both choice of reference data and assessment metric; it is imperative to establish a standardized two-level assessment framework to evaluate and compare ITCD algorithms in order to provide specific recommendations about suitable applications of particular algorithms. However, the evolution of active remotely sensed data and novel platforms implies that automated ITCD will continue to be a promising technology and an attractive research topic for both the forestry and remote sensing communities. View Full-Text
Keywords: tree detection; crown delineation; remotely sensed data; ITCD algorithm; forest type; accuracy assessment tree detection; crown delineation; remotely sensed data; ITCD algorithm; forest type; accuracy assessment
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MDPI and ACS Style

Zhen, Z.; Quackenbush, L.J.; Zhang, L. Trends in Automatic Individual Tree Crown Detection and Delineation—Evolution of LiDAR Data. Remote Sens. 2016, 8, 333.

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