Correcting attenuation effects caused by interactions in the forest canopy in full-waveform airborne laser scanner data
Full-waveform airborne laser scanning offers a great potential for various forestry applications. Especially applications requiring information on the vertical structure of the lower canopy parts benefit from the great amount of information contained in waveform data. To enable the derivation of vertical forest canopy structure, the development of suitable voxel based data analysis methods is straightforward. Beyond extracting additional 3D points, it is very promising to derive the voxel attributes from the digitized waveform directly. For this purpose, the differential backscatter cross sections have to be projected into a Cartesian voxel structure. Thereby the voxel entries represent amplitudes of the cross section and can be interpreted as a local measure for the amount of pulse reflecting matter. However, the "history" of each laser echo pulse is characterized by attenuation effects caused by reflections in higher regions of the crown. As a result, the received waveform signals within the canopy have a lower amplitude than it would be observed for an identical structure without the previous canopy structure interactions (Romanczyk et al., 2012). If the biophysical structure is determined from the raw waveform data, material in the lower parts of the canopy is thus under-represented.
To achieve a radiometrically correct voxel space representation the loss of signal strength caused by partial reflections on the path of a laser pulse through the canopy has to be compensated. In this paper, we present an integral approach correcting the waveform at each recorded sample. The basic idea of the procedure is to enhance the waveform intensity values in lower parts of the canopy for portions of the pulse intensity, which have been reflected (and thus blocked) in higher parts of the canopy. The paper will discuss the developed correction method and show results from a validation both with synthetic and real world data.