The effect of the understory on the estimation of coniferous forest leaf area index (LAI) based on remotely sensed data

The SAIL model, a canopy reflectance model, was used to simulate narrow-band reflectance of overstory/background compositions to study the effect of the background on the estimation of the coniferous forest LAI based on remotely sensed data. We have simulated several mixed targets with a pine tree canopy and different backgrounds, including understory vegetation, soil and litter. For each type of mixed target we have modelled the reflectance for several LAI. The modelled data were used to evaluate the performance of the broad and narrow band NDVI for predicting the LAI. Results show that, for low LAI, the type of background contributes strongly to the reflectance of the mixed targets. Furthermore, the way how the understory affects the mixed signal depends significantly on the vegetation species. The sensitivity of the NDVI for estimating the pine canopy LAI depends on the type of background and it was verified that mixed targets with non-vegetative backgrounds have larger sensitivity than the ones with vegetative backgrounds. The results show that the NDVI, calculated with broad or narrow bands, is not adequate to predict the LAT of open pine stands, when one does not know the type of background that is underneath the pine canopy.