DOI: 10.1016/j.foreco.2015.08.001
Scopus记录号: 2-s2.0-84939149795
论文题名: Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning
作者: Watt M.S. ; Dash J.P. ; Bhandari S. ; Watt P.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2015
卷: 357 起始页码: 1
结束页码: 9
语种: 英语
英文关键词: Airborne laser scanning
; Enhanced Vegetation Index
; LiDAR
; Normalised Difference Vegetation Index
; Plantation forestry
; Productivity surfaces
; RapidEye
Scopus关键词: Forestry
; Laser applications
; Optical radar
; Productivity
; Satellites
; Statistical methods
; Surface analysis
; Tracking (position)
; Vegetation
; Airborne Laser scanning
; Enhanced vegetation index
; Normalised difference vegetation index
; Plantation forestry
; Productivity surfaces
; Rapideye
; Satellite imagery
; airborne survey
; comparative study
; coniferous tree
; data assimilation
; data set
; environmental monitoring
; field survey
; forest management
; forest product
; laser method
; lidar
; model validation
; NDVI
; plantation forestry
; precision
; satellite imagery
; site index
; vegetation index
; Coefficients
; Forests
; Pinus Radiata
; Plantations
; Productivity
; Remote Sensing
; Satellites
; New Zealand
; North Island
; Pinus radiata
; Radiata
英文摘要: Site Index (. SI) is one of the main measures of forest productivity used throughout the world. For even-age plantations Site Index is defined as the height of dominant trees at a given reference age. Site Index is normally determined from field measurements and expressed from these measurements at the resolution of the stand. Development of fine resolution spatial surfaces describing variation in productivity across broad landscapes would be of considerable use in improving stand management. Using data obtained from a large Pinus radiata D. Don forest located in the central North Island, New Zealand, the objective of this study was to compare the precision of parametric and non-parametric models of Site Index that included explanatory variables extracted from aerially acquired Light Detection and Ranging (LiDAR), satellite imagery or environmental surfaces and combinations of these three data sources. Models were constructed both with and without age as an explanatory variable as managers may not always have access to stand age. A total of 32 models (16 data sources. ×. two model methods) were constructed using data from 484 plots. Validation methods used to examine precision and bias of these models included leave one out cross validation and k-fold analysis.For all but one of the 16 data sources parametric models were found to be more precise than non-parametric models. Inclusion of stand age as an explanatory variable improved the precision of all but one model. For parametric models that included stand age, the R2 and RMSE (in brackets) for models with (i) all metrics derived from satellite imagery, (ii) environmental surface variables, (iii) variables derived from satellite imagery and environmental surfaces, (iv) LiDAR metrics and (v) all available variables were, respectively, 0.237 (2.850m), 0.613 (2.267m), 0.716 (2.025m), 0.883 (1.378m) and 0.801 (1.672m). These results show that LiDAR was the most useful data source for precise and unbiased prediction of Site Index. The parametric model created using variables derived from environmental surfaces and satellite imagery was also very precise showing that, in combination, these datasets may provide a useful alternative for predictions of Site Index when LiDAR data are not available. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/65267
Appears in Collections: 影响、适应和脆弱性
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作者单位: Scion, PO Box 29237, Fendalton, Christchurch, New Zealand; Scion, PO Box 3020, Rotorua, New Zealand; Indufor Asia-Pacific Ltd, PO Box 105039, Auckland, New Zealand
Recommended Citation:
Watt M.S.,Dash J.P.,Bhandari S.,et al. Comparing parametric and non-parametric methods of predicting Site Index for radiata pine using combinations of data derived from environmental surfaces, satellite imagery and airborne laser scanning[J]. Forest Ecology and Management,2015-01-01,357