Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass
This article aims at finding efficient hyperspectral indices for the estimation of forest sun leaf chlorophyll content (CHL, [mu]g cm-2leaf), sun leaf mass per area (LMA, gdry ratter m-2leaf), canopy leaf area index (LAI, m2leaf m-2soil) and leaf canopy biomass (Bleaf, gdry matter m-2soil). These parameters are useful inputs for forest ecosystem simulations at landscape scale. The method is based on the determination of the best vegetation indices (index form and wavelengths) using the radiative transfer model PROSAIL (formed by the newly-calibrated leaf reflectance model PROSPECT coupled with the multi-layer version of the canopy radiative transfer model SAIL). The results are tested on experimental measurements at both leaf and canopy scales. At the leaf scale, it is possible to estimate CHL with high precision using a two wavelength vegetation index after a simulation based calibration. At the leaf scale, the LMA is more difficult to estimate with indices. At the canopy scale, efficient indices were determined on a generic simulated database to estimate CHL, LMA, LAI and Bleaf in a general way. These indices were then applied to two Hyperion images (50 plots) on the Fontainebleau and Fougères forests and portable spectroradiometer measurements. They showed good results with an RMSE of 8.2 [mu]g cm-2 for CHL, 9.1 g m-2 for LMA, 1.7 m2 m-2 for LAI and 50.6 g m-2 for Bleaf. However, at the canopy scale, even if the wavelengths of the calibrated indices were accurately determined with the simulated database, the regressions between the indices and the biophysical characteristics still had to be calibrated on measurements. At the canopy scale, the best indices were: for leaf chlorophyll content: NDchl=([rô]925 - [rô]710)/([rô]925 + [rô]710), for leaf mass per area: NDLMA=([rô]2260 - [rô]1490)/([rô]2260+[rô]1490), for leaf area index: DLAI=[rô]1725 - [rô]970, and for canopy leaf biomass: NDBleaf=([rô]2160 - [rô]1540)/([rô]2160 + [rô]1540).
| Summary: | This article aims at finding efficient hyperspectral indices for the estimation of forest sun leaf chlorophyll content (CHL, [mu]g cm-2leaf), sun leaf mass per area (LMA, gdry ratter m-2leaf), canopy leaf area index (LAI, m2leaf m-2soil) and leaf canopy biomass (Bleaf, gdry matter m-2soil). These parameters are useful inputs for forest ecosystem simulations at landscape scale. The method is based on the determination of the best vegetation indices (index form and wavelengths) using the radiative transfer model PROSAIL (formed by the newly-calibrated leaf reflectance model PROSPECT coupled with the multi-layer version of the canopy radiative transfer model SAIL). The results are tested on experimental measurements at both leaf and canopy scales. At the leaf scale, it is possible to estimate CHL with high precision using a two wavelength vegetation index after a simulation based calibration. At the leaf scale, the LMA is more difficult to estimate with indices. At the canopy scale, efficient indices were determined on a generic simulated database to estimate CHL, LMA, LAI and Bleaf in a general way. These indices were then applied to two Hyperion images (50 plots) on the Fontainebleau and Fougères forests and portable spectroradiometer measurements. They showed good results with an RMSE of 8.2 [mu]g cm-2 for CHL, 9.1 g m-2 for LMA, 1.7 m2 m-2 for LAI and 50.6 g m-2 for Bleaf. However, at the canopy scale, even if the wavelengths of the calibrated indices were accurately determined with the simulated database, the regressions between the indices and the biophysical characteristics still had to be calibrated on measurements. At the canopy scale, the best indices were: for leaf chlorophyll content: NDchl=([rô]925 - [rô]710)/([rô]925 + [rô]710), for leaf mass per area: NDLMA=([rô]2260 - [rô]1490)/([rô]2260+[rô]1490), for leaf area index: DLAI=[rô]1725 - [rô]970, and for canopy leaf biomass: NDBleaf=([rô]2160 - [rô]1540)/([rô]2160 + [rô]1540). |
|---|