TY - JOUR
T1 - Quantitative mapping of hydrodynamic vegetation density of floodplain forests under leaf-off conditions using airborne laser scanning
AU - Straatsma, M.
PY - 2008
Y1 - 2008
N2 - In this paper a method is presented to extract hydrodynamic vegetation density from airborne laser scanner data, relevant for exceedance levels of embankments of lowland areas. Two indices to predict vegetation density from the laser data were considered: (a) Percentage Index (PI) of points in the height interval inundated by the water, and (b) the Vegetation Area Index (VAI) that corrects for occlusion from the crown area. A computer simulation, using a digital forest model, showed a sensitivity of the indices for laser pulses that were sent out, but not detected by the laser receiver. The locations of these invalid points were therefore reconstructed. Two different assumptions were tested to assign new coordinates to these so-called invalid points. Percentage Index, with the invalid points reconstructed by means of thresholding the point density ratio, proved the best predictor (R2 = 0.66) of vegetation density of deciduous floodplain forests under winter conditions.
AB - In this paper a method is presented to extract hydrodynamic vegetation density from airborne laser scanner data, relevant for exceedance levels of embankments of lowland areas. Two indices to predict vegetation density from the laser data were considered: (a) Percentage Index (PI) of points in the height interval inundated by the water, and (b) the Vegetation Area Index (VAI) that corrects for occlusion from the crown area. A computer simulation, using a digital forest model, showed a sensitivity of the indices for laser pulses that were sent out, but not detected by the laser receiver. The locations of these invalid points were therefore reconstructed. Two different assumptions were tested to assign new coordinates to these so-called invalid points. Percentage Index, with the invalid points reconstructed by means of thresholding the point density ratio, proved the best predictor (R2 = 0.66) of vegetation density of deciduous floodplain forests under winter conditions.
KW - ADLIB-ART-2702
KW - ESA
U2 - 10.14358/PERS.74.8.987
DO - 10.14358/PERS.74.8.987
M3 - Article
SN - 0099-1112
VL - 74
SP - 987
EP - 998
JO - Photogrammetric engineering and remote sensing
JF - Photogrammetric engineering and remote sensing
IS - 8
ER -