Estimating attributes of deciduous forest cover of a sanctuary in India utilizing Hyperion data and PLS analysis

Continuous and comprehensive evaluation of biochemical and biophysical attributes of forest ecosystems is a key aspect for monitoring their health status in the current global change scenario. Traditional methods of monitoring forest cover such as inventorying are time consuming, cost intensive, and untimely in delivering the output. The present study was carried out to monitor three important deciduous forest covers of India (teak, bamboo, and mixed), utilizing Hyperion (EO1) data of two seasons and partial least squares regression analysis. Attributes measured were canopy chlorophyll, nitrogen, cellulose, lignin, and biomass of tree trunks. Measured attributes showed a wider range, indicating variation in the growth phase of the covers. PLS models developed in this study showed higher R ² values (0.63–0.90 for chlorophyll and nitrogen, 0.52–0.80 for cellulose and lignin, 0.80–0.86 for bole biomass). From the spectral data analysis we conclude that PLS regression with selected bands is better for the computation of specific biochemical parameters. For parameters such as bole biomass, reflectance spectra of 165 bands worked better. Developed models are advantageous for monitoring two important tropical covers (teak and bamboo) by utilizing space-borne data. A PLS model developed for teak-cover biomass worked well with mixed species cover (tested as an independent data set), indicating the applicability of the model across similar tropical covers.