气候变化领域集成服务门户(CCPortal): Modelling canopy gap probability, foliage projective cover and crown projective cover from airborne lidar metrics in Australian forests and woodlands

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DOI	10.1016/j.rse.2019.111520
	Modelling canopy gap probability, foliage projective cover and crown projective cover from airborne lidar metrics in Australian forests and woodlands
	Fisher A.; Armston J.; Goodwin N.; Scarth P.
发表日期	2020
ISSN	00344257
卷号	237
英文摘要	Tree canopy density metrics (TCDM) derived from airborne lidar data are used in a range of crucial environmental monitoring, forestry and natural resource management applications. The derivation of spatially and temporally consistent TCDM, however, typically requires field calibration to account for differences in instrument/survey parameters. Lidar surveys with no coincident field measurements consequently will have an unknown error associated with TCDM limiting their application. In this study, we analysed an extensive set of lidar captures with coincident field data to determine the lidar TCDM that best match the canopy gap probability (Pgap), foliage projective cover (FPC) and crown projective cover (CPC). Furthermore, we developed and evaluated models designed to reduce the bias introduced by variations in lidar instrument and survey acquisition parameters. The dataset incorporated 148 field sites (100 m diameter circular plots) coincident with 13 different lidar surveys between 2008 and 2015, distributed across a range of Australian forests and woodlands. The best lidar metric for 1 − Pgap, achieving a root mean square error (RMSE) of 6.7% with 95% confidence intervals (CI) of 6.1–7.3%, was the proportion of all returns greater than a canopy height threshold (tcanopy) of 1.5 m above ground (dall). The best metric for FPC (RMSE = 6.0%, CI = 5.3–6.7%) used the proportion of returns, weighted as the fraction of the number of returns recorded from each pulse (dweighted), with tcanopy of 1.7 m. The best metric for CPC (RMSE = 7.0%, CI = 6.4–7.7%) was the proportion of 0.5 m pixels greater than 0.8 m above the ground, for an interpolated canopy height model (dinterp). Overall bias for these metrics was low (~1%), however, the bias for individual surveys varied significantly. For example, for one survey dall consistently underestimated 1 − Pgap with a bias of −8.3%, while a different survey consistently overestimated 1 − Pgap with a bias of 3.8%. Elastic net regression models, using instrument, survey and plot parameters as predictor variables, were unable to consistently remove the bias. No relationships could be discerned between lidar parameters and the bias between lidar metrics and field measurements, potentially due to complex interactions between parameters, the spatial scale of the field plots, and uncertainties in field measurements and lidar attributes. Although the bias could not be modelled, the results provide metrics to derive Pgap, FPC and CPC with less than 10% error from lidar surveys captured with similar parameters across Australia (>600,000 km2). © 2019 Elsevier Inc.
英文关键词	Airborne lidar; Canopy density; Vegetation structure
语种	英语
scopus关键词	Environmental management; Errors; Forestry; Information management; Mean square error; Natural resources management; Regression analysis; Surveys; Uncertainty analysis; Acquisition parameters; Airborne LiDAR; Canopy density; Environmental Monitoring; Foliage projective covers; Natural resource management; Root mean square errors; Vegetation structure; Optical radar
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179561
作者单位	Joint Remote Sensing Research Program, School of Earth and Environmental Sciences, University of Queensland, Brisbane, QLD 4072, Australia; Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia; Department of Geographical Sciences, University of Maryland, College Park, 2181 Samuel J. LeFrak Hall, 7251 Preinkert DriveMD 20742, United States; Remote Sensing Centre, Science Delivery, Department of Environment and Science, 41 Boggo RoadQLD 4102, Australia
推荐引用方式 GB/T 7714	Fisher A.,Armston J.,Goodwin N.,et al. Modelling canopy gap probability, foliage projective cover and crown projective cover from airborne lidar metrics in Australian forests and woodlands[J],2020,237.
APA	Fisher A.,Armston J.,Goodwin N.,&Scarth P..(2020).Modelling canopy gap probability, foliage projective cover and crown projective cover from airborne lidar metrics in Australian forests and woodlands.Remote Sensing of Environment,237.
MLA	Fisher A.,et al."Modelling canopy gap probability, foliage projective cover and crown projective cover from airborne lidar metrics in Australian forests and woodlands".Remote Sensing of Environment 237(2020).