Modeling above-ground biomass in tallgrass prairie using ultra-high spatial resolution sUAS imagery

Chuyuan Wang, Kevin P. Price, Deon Van Der Merwe, Nan An, Huan Wang

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

We examined the relationship between tallgrass above-ground biomass (AGB) and NDVI from ultra-high spatial resolution multispectral imagery collected by small unmanned aircraft systems (sUAS). This study was conducted at the Tallgrass Prairie National Preserve in Chase County, Kansas. Results show that NDVI values computed from sUAS imagery explained up to 94 percent of the variance (p <0.01) in AGB measurements. The model coefficient of determination (r2) decreased with increasing aircraft flight altitude suggesting image spatial resolution is a key factor influencing the strength of the relationship. A scaling-up approach from small-scale sUAS imagery to broad-scale, digital aerial imagery collected at 1,200 meters by a piloted aircraft was used to provide AGB model estimates across the entire 4,500 ha of the Preserve. Spectral reflectance data measured by spectroradiometer were also used to identify three optimal regions of the spectrum that have the highest significant correlations with tallgrass AGB.

Original languageEnglish (US)
Pages (from-to)1151-1159
Number of pages9
JournalPhotogrammetric Engineering and Remote Sensing
Volume80
Issue number12
DOIs
StatePublished - Jan 1 2014

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ASJC Scopus subject areas

  • Computers in Earth Sciences

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