Remote sensing of forest pigments using airborne imaging spectrometer and LIDAR imagery.

This study created and tested predictive models developed using airborne imaging spectrometer and light detection and ranging (LIDAR)instruments for estimating the concentrations of photosynthetic pigments in broad-leaved and coniferous forest plantations. Data were acquired using a Compact Airborne Spectrographic Imager (CASI) and an Airborne Laser Terrain Mapping (ALTM) 1020 instrument in midsummer for study sites in the New Forest, England, along with concomitant in situ measurements of canopy properties. The stands used displayed a wide variation in the biophysical and biochemical properties of interest. When employing the imaging spectrometer data alone, there were no relationships between any spectral variables (band reflectance, band ratios, or first derivatives of reflectance) and canopy biophysical and biochemical properties when both broad-leaved and coniferous stands were analysed as a combined data set. However, for the broad-leaved stands alone, curvilinear relationships were found between the wavelength position of the red edge (kRE) and pigment concentrations per unit ground area (e.g., R2=0.88** for chlorophyll a [Chl a]) and per unit leaf mass (e.g., R2=0.76** for Chl a). The predictive value of these models was somewhat limited; for example, the root mean squared error (RMSE) was 300 mg m-2 (27% of the mean) for Chl a concentration per unit ground area and 1.17 mg g-1 (24% of the mean) for Chl a concentration per unit leaf mass. A ratio of a near-infrared and a green band (865 nm/553 nm) was linearly related to leaf area index (LAI) of the broad-leaved stands (R2=0.71**) and the regression model was a reasonable predictor of the LAI for the independent test sites (RMSE=0.88; 18.6% of the mean). Canopy height information derived from the ALTM data was used to mask out canopy gap areas from the CASI imagery of each stand. This process had limited impact on the relationships between spectral and canopy variables for the broad-leaved stands, and kRE remained unrelated to pigment concentrations per unit ground area for the coniferous stands. However, the masking process substantially improved the strength of the relationship between kRE and pigment concentrations per unit leaf mass for the coniferous stands (e.g., for Chl a R2=0.85**; RMSE of prediction=0.84 mg g-1 [22% of the mean]). Therefore, the study demonstrates that for broad-leaved stands, spectral models can be applied to imaging spectrometer data to quantify forest pigments and LAI with moderate accuracy. For coniferous stands, the use of LIDAR data to remove canopy gap areas from the CASI imagery considerably increases the accuracy of spectral predictive models for quantifying pigment concentrations per unit leaf mass