Evaluating sample plot imputation techniques as input in forest management planning

Recent advances in the use of data from airborne laser scanners have produced results that are potentially useful for forest-management planning. In this study, the results from recently developed imputation techniques using laser scanner and satellite data were evaluated as input in a timber-oriented forestry planning context. Evaluation comprised a cost plus loss analysis in which the data cost for a specific method is added to the expected loss arising from nonoptimal forestry activities caused by erroneous forest descriptions. Forest data from sample plot imputations based on laser scanner data, satellite data, or a combination of both were available for 64 stands in southern Sweden. For comparison, sample plot field inventories of 5 and 10 plots were simulated for each stand. Different stand areas and real interest rates were tested. The best performing imputation method, using both laser scanner and satellite data, produced the lowest total cost plus loss in the smallest stands when using the highes...

[1]  Harold E. Burkhart,et al.  Allocating inventory resources for multiple-use planning , 1978 .

[2]  Michael L. Clutter,et al.  The value of timber inventory information for management planning , 2008 .

[3]  Tomas Lämås,et al.  The influence of forest data quality on planning processes in forestry , 2006 .

[4]  Albert R. Stage,et al.  Most Similar Neighbor: An Improved Sampling Inference Procedure for Natural Resource Planning , 1995, Forest Science.

[5]  Mikko Inkinen,et al.  A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners , 2001, IEEE Trans. Geosci. Remote. Sens..

[6]  M. Maltamo,et al.  The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs , 2007 .

[7]  Åsa Persson,et al.  Detecting and measuring individual trees using an airborne laser scanner , 2002 .

[8]  Tron Eid,et al.  Use of uncertain inventory data in forestry scenario models and consequential incorrect harvest decisions. , 2000 .

[9]  Bengt Jonsson,et al.  The forest management planning package: theory and application , 1993 .

[10]  Johan Holmgren,et al.  Estimation of forest variables using airborne laser scanning , 2003 .

[11]  Terje Gobakken,et al.  Comparing stand inventories for large areas based on photo-interpretation and laser scanning by means of cost-plus-loss analyses , 2004 .

[12]  Hailemariam Temesgen,et al.  Imputing tree-lists from aerial attributes for complex stands of south-eastern British Columbia , 2003 .

[13]  Frank Yates.  Sampling Methods for Censuses and Surveys , 1950 .

[14]  E. Næsset.  Practical large-scale forest stand inventory using a small-footprint airborne scanning laser , 2004 .

[15]  Markus Holopainen,et al.  Effect of data acquisition accuracy on timing of stand harvests and expected net present value. , 2006 .

[16]  Ljusk Ola Eriksson,et al.  Analysis and planning systems for multiresource, sustainable forestry: the Heureka research programme at SLU , 2003 .

[17]  G. Ståhl,et al.  Cost-Plus-Loss Analyses of Forest Inventory Strategies Based on kNN- Assigned Reference Sample Plot Data , 2003 .

[18]  Terje Gobakken,et al.  Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data , 2005 .

[19]  J. Holmgren.  Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning , 2004 .

[20]  M. Nilsson.  Estimation of forest variables using satellite image data and airborne Lidar , 1997 .

[21]  Jörgen Wallerman,et al.  Estimating field-plot data of forest stands using airborne laser scanning and SPOT HRG data , 2007 .

[22]  Janet L. Ohmann,et al.  Predictive mapping of forest composition and structure with direct gradient analysis and nearest- neighbor imputation in coastal Oregon, U.S.A. , 2002 .

[23]  Juha Hyyppä,et al.  The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve , 2004 .

[24]  D. A. Hill,et al.  Combined high-density lidar and multispectral imagery for individual tree crown analysis , 2003 .

[25]  J. Holmgren,et al.  Estimation of Tree Height and Stem Volume on Plots Using Airborne Laser Scanning , 2003, Forest Science.

[26]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[27]  Göran Ståhl,et al.  Simultaneous Estimations of Forest Parameters using Aerial Photograph Interpreted Data and the k Nearest Neighbour Method , 2001 .

[28]  Juan C. Suárez,et al.  Use of airborne LiDAR and aerial photography in the estimation of individual tree heights in forestry , 2005, Comput. Geosci..

[29]  Åsa Persson,et al.  Identifying species of individual trees using airborne laser scanner , 2004 .

[30]  P. Holmgren,et al.  Satellite remote sensing for forestry planning—A review , 1998 .

[31]  Jing Li,et al.  Accuracy and reliability of map-matched GPS coordinates: the dependence on terrain model resolution and interpolation algorithm , 2005, Comput. Geosci..