气候变化领域集成服务门户(CCPortal): Estimation of forest structural and compositional variables using ALS data and multi-seasonal satellite imagery

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DOI	10.1016/j.jag.2018.10.002
	Estimation of forest structural and compositional variables using ALS data and multi-seasonal satellite imagery
	Shang C.; Treitz P.; Caspersen J.; Jones T.
发表日期	2019
ISSN	15698432
起始页码	360
结束页码	371
卷号	78
英文摘要	Advanced forest resource inventory (FRI) information is of critical importance for sustainable forest management. FRIs are dependent on remote sensing data and processing methods, along with field calibration/validation to generate cost-effective options for modelling forest inventory and biophysical variables over large areas. The objective of this study was to examine the impact of combining multi-seasonal multispectral satellite imagery with airborne laser scanning (ALS) data for estimating basal area, species mixture and stem density for an uneven-aged tolerant hardwood forest in Ontario, Canada. Using random forest (RF) regression as a non-parametric diagnostic technique, three multispectral optical sensors (i.e., Landsat-5 TM, Sentinel-2 A and WorldView-2) were compared to examine the most cost-effective sensor configuration for modelling FRI variables. The contribution of spectral predictors derived from these optical sensors as well as ALS height and intensity metrics were evaluated using RF variable importance. As part of our variable selection framework, all predictor variables were grouped into relatively independent clusters using a hierarchical variable clustering technique, which revealed the distinctiveness between information contained in spectral predictors, height- and intensity-based metrics. This indicates that ALS intensity data carry unique information complementary to passive near-infrared data for forest characterization. ALS data alone did not result in accurate models for basal area and species mixture, but predictive accuracies were improved significantly with the addition of spectral predictors. Compared to single-date images, multi-seasonal imagery proved to be more accurate for modelling FRI variables, especially when combined with ALS data. Despite its limited spatial resolution, Sentinel-2 A was found to be the most cost-effective image source for enhancing ALS-based FRI models. Using variables identified by the variable selection procedure, best subsets regression outperformed the RF models developed for diagnostic analysis, resulting in a suite of accurate and parsimonious predictive models, with coefficients of determination of 0.73, 0.90 and 0.67, for basal area, species mixture, and stem density, respectively. © 2018 Elsevier B.V.
英文关键词	Airborne laser scanning (ALS); Forest resource inventory; Multi-seasonal satellite imagery; Sentinel-2A; Variable selection
语种	英语
scopus关键词	accuracy assessment; airborne sensing; forest inventory; near infrared; satellite data; satellite imagery; spatial resolution; sustainable forestry; Canada; Ontario [Canada]
来源期刊	International Journal of Applied Earth Observation and Geoinformation
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156476
作者单位	Department of Geography and Planning, Queen's University, Kingston, ON K7L 3N6, Canada; Faculty of Forestry, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3, Canada; Forest Research and Monitoring Section, Ontario Ministry of Natural Resources and Forestry, 1235 Queen Street East, Sault Ste. MarieON P6A 2E5, Canada
推荐引用方式 GB/T 7714	Shang C.,Treitz P.,Caspersen J.,et al. Estimation of forest structural and compositional variables using ALS data and multi-seasonal satellite imagery[J],2019,78.
APA	Shang C.,Treitz P.,Caspersen J.,&Jones T..(2019).Estimation of forest structural and compositional variables using ALS data and multi-seasonal satellite imagery.International Journal of Applied Earth Observation and Geoinformation,78.
MLA	Shang C.,et al."Estimation of forest structural and compositional variables using ALS data and multi-seasonal satellite imagery".International Journal of Applied Earth Observation and Geoinformation 78(2019).