Canopy level estimations of chlorophyll and LAI for two tropical species teak and bamboo from Hyperion EO1 data

Current economic development in tropical regions especially in India, China, and Brazil is putting tremendous pressure on tropical forest cover. Some of the dominant and economically important species are planted at large scale in these countries. Teak and bamboo are two important species of tropical regions because of their commercial and conservation values. Accurate estimates of foliar chemistry can help in evaluating the health status of vegetation in these regions. An attempt has been made to derive canopy level estimation of chlorophyll and leaf area index LAI for these species utilizing Hyperion data. Partial least square PLS regression analysis was carried out to identify the correlation between measured parameters chlorophyll and LAI and Hyperion reflectance spectra. PLS regression identified 600–750 nm as a sensitive spectral region for chlorophyll and 1000–1507 nm for LAI. The PLS regression model tested in this study worked well for the estimation of chlorophyll R 2 = 0.90, root mean square error RMSE = 0.182 for teak and R 2 = 0.84, RMSE = 0.113 for bamboo and for the estimation of LAI R 2 = 0.87, RMSE = 0.425. The lower predictive error obtained indicates the robustness of the data set and also of the applicability of the PLS regression analysis. Wavelengths recognized by the PLS regression model were utilized for the development of vegetation indices for estimating chlorophyll and LAI. Predictive performances of the developed simple ratios SRs were evaluated using the cross-validation method. SR 743/692 gave the best results for the prediction of chlorophyll with the leave-one-out cross-validation LOO-CV method R 2 = 0.73, RMSE = 0.28 for teak and R 2 = 0.71, RMSE = 0.15 for bamboo. The normalized difference ratio ND 1457/1084 gave the best results for the prediction of LAI with LOO-CV R 2 = 0.66, RMSE = 0.57. Ratios developed here can be tested for teak and bamboo cover spread in tropical regions with similar environmental conditions.