Title of article :
Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure
Author/Authors :
Vincent، نويسنده , , G. and Sabatier، نويسنده , , D. and Blanc، نويسنده , , L. and Chave، نويسنده , , J. and Weissenbacher، نويسنده , , E. and Pélissier، نويسنده , , R. and Fonty، نويسنده , , E. and Molino، نويسنده , , J.-F. and Couteron، نويسنده , , P.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2012
Pages :
11
From page :
23
To page :
33
Abstract :
We predict stand basal area (BA) from small footprint LiDAR data in 129 one-ha tropical forest plots across four sites in French Guiana and encompassing a great diversity of forest structures resulting from natural (soil and geological substrate) and anthropogenic effects (unlogged and logged forests). We use predictors extracted from the Canopy Height Model to compare models of varying complexity: single or multiple regressions and nested models that predict BA by independent estimates of stem density and quadratic mean diameter. Direct multiple regression was the most accurate, giving a 9.6% Root Mean Squared Error of Prediction (RMSEP). The magnitude of the various errors introduced during the data collection stage is evaluated and their contribution to MSEP is analyzed. It was found that these errors accounted for less than 10% of model MSEP, suggesting that there is considerable scope for model improvement. Although site-specific models showed lower MSEP than global models, stratification by site may not be the optimal solution. The key to future improvement would appear to lie in a stratification that captures variations in relations between LiDAR and forest structure.
Keywords :
LIDAR , Basal area , Tropical moist forest
Journal title :
Remote Sensing of Environment
Serial Year :
2012
Journal title :
Remote Sensing of Environment
Record number :
1632547
Link To Document :
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