气候变化领域集成服务门户(CCPortal): A simulation method to infer tree allometry and forest structure from airborne laser scanning and forest inventories

CCPortal

DOI	10.1016/j.rse.2020.112056
	A simulation method to infer tree allometry and forest structure from airborne laser scanning and forest inventories
	Fischer F.J.; Labrière N.; Vincent G.; Hérault B.; Alonso A.; Memiaghe H.; Bissiengou P.; Kenfack D.; Saatchi S.; Chave J.
发表日期	2020
ISSN	00344257
卷号	251
英文摘要	Tropical forests are characterized by large carbon stocks and high biodiversity, but they are increasingly threatened by human activities. Since structure strongly influences the functioning and resilience of forest communities and ecosystems, it is important to quantify it at fine spatial scales. Here, we propose a new simulation-based approach, the “Canopy Constructor”, with which we quantified forest structure and biomass at two tropical forest sites, one in French Guiana, the other in Gabon. In a first step, the Canopy Constructor combines field inventories and airborne lidar scans to create virtual 3D representations of forest canopies that best fit the data. From those, it infers the forests' structure, including crown packing densities and allometric scaling relationships between tree dimensions. In a second step, the results of the first step are extrapolated to create virtual tree inventories over the whole lidar-scanned area. Across the French Guiana and Gabon plots, we reconstructed empirical canopies with a mean absolute error of 3.98 m [95% credibility interval: 3.02, 4.98], or 14.4%, and a small upwards bias of 0.66 m [−0.41, 1.8], or 2.7%. Height-stem diameter allometries were inferred with more precision than crown-stem diameter allometries, with generally larger heights at the Amazonian than the African site, but similar crown-stem diameter allometries. Plot-based aboveground biomass was inferred to be larger in French Guiana with 400.8 t ha−1 [366.2–437.9], compared to 302.2 t ha−1 in Gabon [267.8–336.8] and decreased to 299.8 t ha−1 [275.9–333.9] and 251.8 t ha−1 [206.7–291.7] at the landscape scale, respectively. Predictive accuracy of the extrapolation procedure had an RMSE of 53.7 t ha−1 (14.9%) at the 1 ha scale and 87.6 t ha−1 (24.2%) at the 0.25 ha scale, with a bias of −17.1 t ha−1 (−4.7%). This accuracy was similar to regression-based approaches, but the Canopy Constructor improved the representation of natural heterogeneity considerably, with its range of biomass estimates larger by 54% than regression-based estimates. The Canopy Constructor is a comprehensive inference procedure that provides fine-scale and individual-based reconstructions even in dense tropical forests. It may thus prove vital in the assessment and monitoring of those forests, and has the potential for a wider applicability, for example in the exploration of ecological and physiological relationships in space or the initialisation and calibration of forest growth models. © 2020 Elsevier Inc.
英文关键词	Airborne lidar; Allometry; Approximate Bayesian Computation; Biomass; Canopy space filling; Individual-based modeling; Tropical forest; Vegetation structure
语种	英语
scopus关键词	Biodiversity; Biomass; Ecology; Extrapolation; Optical radar; Physiological models; Tropics; Above ground biomass; Airborne Laser scanning; Assessment and monitoring; Extrapolation procedure; Forest growth model; Mean absolute error; Predictive accuracy; Simulation based approaches; Forestry; airborne survey; allometry; biomass; forest canopy; forest ecosystem; forest inventory; human activity; landscape; laser method; lidar; simulation; tree; tropical forest; Amazonia; French Guiana; Gabon
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/179127
作者单位	Laboratoire Évolution et Diversité Biologique, UMR 5174 (CNRS/IRD/UPS), 118 Route de Narbonne, Toulouse Cedex 9, 31062, France; AMAP, Univ Montpellier, IRD, CIRAD, CNRS, INRAE, Montpellier, France; Cirad, Univ Montpellier, UR Forests & Societies, Montpellier, F-34000, France; INPHB, Institut National Polytechnique Félix Houphouët-Boigny, Yamoussoukro, France; Center for Conservation and Sustainability, Smithsonian Conservation Biology Institute, 1100 Jefferson Drive SW, Suite 3123, Washington, DC 20560-0705, United States; Institut de Recherche en Écologie Tropicale (IRET), Centre National de la Recherche Scientifique et Technologique (CENAREST), Libreville, B.P. 13354, Gabon; Institut de Pharmacopée et de Médecine Traditionnelles (IPHAMETRA)/Herbier National du Gabon, Centre National de la Recherche Scientifique et Technologique (CENAREST), Libreville, B.P. 1165, Gabon; Center for Tropical Forest Science -Forest Global Earth Observatory, Smithsonian Tropical Research Institute, West Loading Dock, 10th a...
推荐引用方式 GB/T 7714	Fischer F.J.,Labrière N.,Vincent G.,et al. A simulation method to infer tree allometry and forest structure from airborne laser scanning and forest inventories[J],2020,251.
APA	Fischer F.J..,Labrière N..,Vincent G..,Hérault B..,Alonso A..,...&Chave J..(2020).A simulation method to infer tree allometry and forest structure from airborne laser scanning and forest inventories.Remote Sensing of Environment,251.
MLA	Fischer F.J.,et al."A simulation method to infer tree allometry and forest structure from airborne laser scanning and forest inventories".Remote Sensing of Environment 251(2020).