TY - JOUR
T1 - Landscape-scale parameterization of a tree-level forest growth model
T2 - A k-nearest neighbor imputation approach incorporating LiDAR data
AU - Falkowski, Michael J.
AU - Hudak, Andrew T.
AU - Crookston, Nicholas L.
AU - Gessler, Paul E.
AU - Uebler, Edward H.
AU - Smith, Alistair M.S.
PY - 2010/2
Y1 - 2010/2
N2 - Sustainable forest management requires timely, detailed forest inventory data across large areas, which is difficult to obtain via traditional forest inventory techniques. This study evaluated k-nearest neighbor imputation models incorporating LiDAR data to predict tree-level inventory data (individual tree height, diameter at breast height, and species) across a 12 100 ha study area in northeastern Oregon, USA. The primary objective was to provide spatially explicit data to parameterize the Forest Vegetation Simulator, a tree-level forest growth model. The final imputation model utilized LiDAR-derived height measurements and topographic variables to spatially predict tree-level forest inventory data. When compared with an independent data set, the accuracy of forest inventory metrics was high; the root mean square difference of imputed basal area and stem volume estimates were 5 m2·ha-1 and 16 m3·ha-1, respectively. However, the error of imputed forest inventory metrics incorporating small trees (e.g., quadratic mean diameter, tree density) was considerably higher. Forest Vegetation Simulator growth projections based upon imputed forest inventory data follow trends similar to growth projections based upon independent inventory data. This study represents a significant improvement in our capabilities to predict detailed, tree-level forest inventory data across large areas, which could ultimately lead to more informed forest management practices and policies.
AB - Sustainable forest management requires timely, detailed forest inventory data across large areas, which is difficult to obtain via traditional forest inventory techniques. This study evaluated k-nearest neighbor imputation models incorporating LiDAR data to predict tree-level inventory data (individual tree height, diameter at breast height, and species) across a 12 100 ha study area in northeastern Oregon, USA. The primary objective was to provide spatially explicit data to parameterize the Forest Vegetation Simulator, a tree-level forest growth model. The final imputation model utilized LiDAR-derived height measurements and topographic variables to spatially predict tree-level forest inventory data. When compared with an independent data set, the accuracy of forest inventory metrics was high; the root mean square difference of imputed basal area and stem volume estimates were 5 m2·ha-1 and 16 m3·ha-1, respectively. However, the error of imputed forest inventory metrics incorporating small trees (e.g., quadratic mean diameter, tree density) was considerably higher. Forest Vegetation Simulator growth projections based upon imputed forest inventory data follow trends similar to growth projections based upon independent inventory data. This study represents a significant improvement in our capabilities to predict detailed, tree-level forest inventory data across large areas, which could ultimately lead to more informed forest management practices and policies.
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U2 - 10.1139/X09-183
DO - 10.1139/X09-183
M3 - Article
AN - SCOPUS:77049116290
SN - 0045-5067
VL - 40
SP - 184
EP - 199
JO - Canadian Journal of Forest Research
JF - Canadian Journal of Forest Research
IS - 2
ER -