DOI: 10.1016/j.foreco.2012.12.019
Scopus记录号: 2-s2.0-84872591926
论文题名: Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory
作者: Junttila V. ; Finley A.O. ; Bradford J.B. ; Kauranne T.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2013
卷: 292 起始页码: 75
结束页码: 85
语种: 英语
英文关键词: Bayesian
; Biomass
; Geostatistical
; LiDAR
; Optimal design
Scopus关键词: Above ground biomass
; Bayesian
; Calibration plot
; Feature space
; Field plot
; Forest inventory
; Forest parameters
; Geostatistical
; Light detection and ranging
; Operational scale
; Optimal design
; Point cloud
; Predictive variables
; Random methods
; Regression model
; Representativity
; Sample sizes
; Sensing modalities
; Biomass
; Forestry
; Regression analysis
; Remote sensing
; Optical radar
; aboveground biomass
; accuracy assessment
; Bayesian analysis
; biomass
; calibration
; ecological modeling
; forest inventory
; geostatistics
; lidar
; remote sensing
; spatial variation
; Automatic Control
; Biomass
; Optical Activity
; Optimization
; Performance
; Radar
; Regression Analysis
; Remote Sensing
; Statistical Analysis
英文摘要: Recently airborne Light Detection And Ranging (LiDAR) has emerged as a highly accurate remote sensing modality to be used in operational scale forest inventories. Inventories conducted with the help of LiDAR are most often model-based, i.e. they use variables derived from LiDAR point clouds as the predictive variables that are to be calibrated using field plots. The measurement of the necessary field plots is a time-consuming and statistically sensitive process. Because of this, current practice often presumes hundreds of plots to be collected. But since these plots are only used to calibrate regression models, it should be possible to minimize the number of plots needed by carefully selecting the plots to be measured. In the current study, we compare several systematic and random methods for calibration plot selection, with the specific aim that they be used in LiDAR based regression models for forest parameters, especially above-ground biomass. The primary criteria compared are based on both spatial representativity as well as on their coverage of the variability of the forest features measured. In the former case, it is important also to take into account spatial auto-correlation between the plots. The results indicate that choosing the plots in a way that ensures ample coverage of both spatial and feature space variability improves the performance of the corresponding models, and that adequate coverage of the variability in the feature space is the most important condition that should be met by the set of plots collected. © 2012 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66734
Appears in Collections: 影响、适应和脆弱性
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作者单位: Department of Applied Mathematics, Lappeenranta University of Technology, Lappeenranta, Finland; Departments of Forestry and Geography, Michigan State University, East Lansing, MI, United States; U.S. Geological Survey, Southwest Biological Science Center, Flagstaff, AZ, United States
Recommended Citation:
Junttila V.,Finley A.O.,Bradford J.B.,et al. Strategies for minimizing sample size for use in airborne LiDAR-based forest inventory[J]. Forest Ecology and Management,2013-01-01,292