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    Hyperspectral One-Meter-Resolution Remote Sensing in Yellowstone National Park, Wyoming: II. Biomass

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    Author
    Mirik, Mustafa
    Norland, Jack E.
    Crabtree, Robert L.
    Biondini, Mario E.
    Issue Date
    2005-09-01
    Keywords
    vegetation indices
    grasslands
    riparian
    standing crop
    
    Metadata
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    Citation
    Mirik, M., Norland, J. E., Crabtree, R. L., & Biondini, M. E. (2005). Hyperspectral one-meter-resolution remote sensing in Yellowstone National Park, Wyoming: II. Biomass. Rangeland Ecology & Management, 58(5), 459-465.
    Publisher
    Society for Range Management
    Journal
    Rangeland Ecology & Management
    URI
    http://hdl.handle.net/10150/643365
    DOI
    10.2111/04-18.1
    Additional Links
    https://rangelands.org/
    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.  
    Type
    text
    Article
    Language
    en
    ISSN
    0022-409X
    ae974a485f413a2113503eed53cd6c53
    10.2111/04-18.1
    Scopus Count
    Collections
    Rangeland Ecology & Management, Volume 58, Number 5 (September 2005)

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