DOI: 10.1016/j.foreco.2015.09.003
Scopus记录号: 2-s2.0-84941074284
论文题名: Forest canopy-structure characterization: A data-driven approach
作者: Leiterer R. ; Furrer R. ; Schaepman M.E. ; Morsdorf F.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2015
卷: 358 起始页码: 48
结束页码: 61
语种: 英语
英文关键词: ALS
; Functional diversity
; LiDAR
; Multi-scale
; Remote sensing
; Vegetation
Scopus关键词: Calibration
; Forestry
; Optical radar
; Remote sensing
; Turpentine
; Vegetation
; Airborne Laser scanning
; ALS
; Automatic determination
; Bio-physical variables
; Classification approach
; Forest canopy structure
; Functional diversity
; Multi-scale
; Classification (of information)
; airborne sensor
; automation
; Bayesian analysis
; biophysics
; calibration
; canopy architecture
; efficiency measurement
; energy flux
; feasibility study
; forest canopy
; functional change
; in situ measurement
; laser method
; lidar
; quantitative analysis
; remote sensing
; vegetation dynamics
英文摘要: Forest canopy structure influences and partitions the energy fluxes between the atmosphere and vegetation. It serves as an indicator of a variety of biophysical variables and ecosystem goods and services. Airborne laser scanning (ALS) can simultaneously provide horizontal and vertical information on canopy structure. Existing approaches to assess canopy structure often focus on in situ collected structural variables and require a substantial set of prior information about stand characteristics. They also rely on pre-defined spatial units and are usually dependent on site-specific model calibrations. We propose a method to provide quantitative canopy-structure descriptors on different scales, retrieved from ALS data. The approach includes (i) a sensitivity assessment and a quantification of ALS-derived canopy-structure information dependent on ALS data properties, (ii) an automatic determination of the most feasible spatial unit for canopy-structure characterization, and (iii) the derivation of canopy-structure types (CSTs) using a hierarchical, multi-scale classification approach based on Bayesian robust mixture models (BRMM), satisfying structurally homogenous criteria without the use of in situ calibration information. The CSTs resulted in retrievals of canopy layering (single-, two-, and multi-layered canopies) and canopy types (deciduous or evergreen canopies). Retrievals classified seven CSTs with accuracies ranging from 52% to 82% user accuracy (canopy layering) and 89-99% user accuracy (canopy type). The method supports a data-driven approach, allowing for an efficient monitoring of canopy structure. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/65242
Appears in Collections: 影响、适应和脆弱性
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作者单位: Remote Sensing Laboratories, Department of Geography, University of Zurich, Winterthurerstrasse 190, Zurich, Switzerland; Institute of Mathematics, University of Zurich, Winterthurerstrasse 190, Zurich, Switzerland
Recommended Citation:
Leiterer R.,Furrer R.,Schaepman M.E.,et al. Forest canopy-structure characterization: A data-driven approach[J]. Forest Ecology and Management,2015-01-01,358