DOI: 10.1016/j.foreco.2014.03.016
Scopus记录号: 2-s2.0-84899476190
论文题名: Application of Metabolic Scaling Theory to reduce error in local maxima tree segmentation from aerial LiDAR
作者: Swetnam T.L. ; Falk D.A.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2014
卷: 323 起始页码: 158
结束页码: 167
语种: 英语
英文关键词: Allometry
; Forest structure
; LiDAR
; Local maxima
; Segmentation
; Tree size
Scopus关键词: Forecasting
; Image segmentation
; Metabolism
; Optical radar
; Above ground biomass
; Allometry
; Forest structure
; Individual tree inventories
; Local maximum
; Normalization constants
; Tree size
; Two-parameter models
; Forestry
; aboveground biomass
; algorithm
; allometry
; coniferous forest
; data set
; error correction
; forest canopy
; forest inventory
; lidar
; semiarid region
; Forecasts
; Forestry
; Image Analysis
; Metabolism
; Radar
; Segmentation
; Arizona
; New Mexico
; United States
; Coniferophyta
英文摘要: Identifying individual trees across large forested landscapes is an important benefit of an aerial LiDAR collection. However, current approaches toward individual tree segmentation of aerial LiDAR data do not always reflect how the allometry of tree canopies change with height, age, or competition for limiting space and resources. We developed a variable-area local maxima (VLM) algorithm that incorporates predictions of the Metabolic Scaling Theory (MST) to reduce the frequency of commission error in a local maxima individual tree inventory derived from aerial LiDAR. By comparing the MST prediction to 663 species of North American champion-sized trees (which include the tallest and the largest trees on the planet), and 610 measured trees in semi-arid conifer forests in Arizona and New Mexico we show the MST canopy radius model rcan=βhα where β is the normalization constant, h is height, and α is a dynamic exponent predicted by MST to be α = 1, can be applied as a general model in many water-limited conifer forests. MST also informs the estimate of individual tree bole diameter d bole (which aerial LiDAR does not measure directly) based on two primary size measures easily obtained from the aerial LiDAR: height h and canopy diameter dcan. A two parameter model βh√d can is shown to better predict bole diameter (r2=0.811, RMSE=7.66cm) than a single parameter model of either canopy diameter or height alone: βdcanα (r2=0.51 RMSE=12. 4cm) or βhα (r2=0.753, RMSE=8.94cm). By improving methods to identify individual trees and more accurately predict bole diameter, estimates of total forest stand density, structural diversity, above ground biomass and carbon over large landscapes will likewise be improved. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/65965
Appears in Collections: 影响、适应和脆弱性
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作者单位: The University of Arizona, School of Natural Resources and the Environment, United States
Recommended Citation:
Swetnam T.L.,Falk D.A.. Application of Metabolic Scaling Theory to reduce error in local maxima tree segmentation from aerial LiDAR[J]. Forest Ecology and Management,2014-01-01,323