Abstract
This study was designed to determine the utility of a 1-m-resolution hyperspectral sensor to estimate total and live biomass along with the individual biomass of litter, grasses, forbs, sedges, sagebrush, and willow from grassland and riparian communities in Yellowstone National Park, Wyoming. A large number of simple ratio-type vegetation indices (SRTVI) and normalized difference- type vegetation indices (NDTVI) were developed from the hyperspectral data and regressed against ground-collected biomass. Results showed the following: 1) Strong relationships were found between SRTVI or NDTVI and total (R2 = 0.87), live (R2 = 0.84), sedge (R2 = 0.77), and willow (R2 = 0.66) biomass. 2) Weak relationships were found between SRTVI or NDTVI and grass (R2 = 0.39), forb (R2 = 0.16), and litter (R2 = 0.51) biomass, possibly caused by the mixture of spectral signatures with grasses, sedges, and willows along with the variable effect of the litter spectral signature. 3) A weak relationship was found between sagebrush biomass and SRTVI or NDTSI (R2 = 0.3) that was related to interference from sagebrush photosynthetic or nonphotosynthetic branch and twig material, and from the indeterminate spectral signature of sagebrush. This study has shown that hyperspectral imagery at 1-m resolution can result in high correlations and low error estimates for a variety of biomass components in rangelands. This methodology can thus become a very useful tool to estimate rangeland biomass over large areas.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.