Skip to Main Content
Due to a lapse in federal funding, this USDA website will not be actively updated. Once funding has been reestablished, online operations will continue.
Quantifying forest ground flora biomass using proximal sensingAuthor(s): Paul F. Doruska; Robert C. Weih; Matthew D. Lane; Don C. Bragg
Source: Journal of the Arkansas Academy of Science 57: 37-43
Publication Series: Miscellaneous Publication
PDF: View PDF (569 KB)
DescriptionCurrent focus on forest conservation and forest sustainability has increased the level of attention pen to measures of pund flora in Sorest ecosystems. Traditionally, such data are collected via time- and resource-intensive methods of field identification, clipping, and weighing. With increased focus on community composition and structure measures of forest ground flora, the manner in which these data are collected must change. This project uses color and color infrared digital cameras to proximally sense forest ground flora and to develop regression models to predict green and dry biomass g/m2) from the proximally sensed data. Traditional vegetative indices such as the Normalized Difference Vegetative Index (NDVI) and the Average Visible Reflectance Index (AVR) explained 35-45% of the variation in forest ground flora biomass. Adding individual color band variables, especially the red and near infrared bands, to the regression model allowed the model to explain 66% and 58% of the variation in green and dry biomass, respectively, present.
- You may send email to firstname.lastname@example.org to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationDoruska, Paul F.; Weih, Robert C., Jr.; Lane, Matthew D.; Bragg, Don C. 2003. Quantifying forest ground flora biomass using proximal sensing. Journal of the Arkansas Academy of Science 57: 37-43
- Quantifying Forest Ground Flora Biomass Using Close-range Remote Sensing
- Remote estimation of a managed pine forest evapotranspiration with geospatial technology
- Application of two regression-based methods to estimate the effects harvest on forest structure using Landsat data
XML: View XML