Skip to main content

A method for predicting fresh green leaf nitrogen concentrations from shortwave infrared reflectance spectra acquired at the canopy level that requires no in situ nitrogen data

Buy Article:

$63.00 plus tax (Refund Policy)

Abstract:

This letter presents a new method for predicting foliar nitrogen concentration from shortwave infrared reflectance data that involves the following steps: (1) reduce the spectral resolution of the data from 2 to 10 nm by averaging, (2) convert from reflectance to absorption using Beer's law, (3) in the spectral region between 2.01 and 2.22 ┬Ám, transform the data by subtracting the average of the two neighbouring absorption values from the absorption at the wavelength of interest, and (4) use stepwise multiple linear regression to create a model relating the transformed values to nitrogen concentration. Results using data from the Accelerated Canopy Chemistry Program (ACCP) showed that models constructed using dried foliage from eastern US forests could be used to predict accurately nitrogen concentrations of fresh whole seedlings measured at the canopy level. The seedlings belonged to two western US species, bigleaf maple (Acer macrophyllum) and Douglas fir (Pseudotsuga menziesii). The relationship between actual and predicted nitrogen concentration was not 1:1, but appears to be similar for both species and to be relatively unaffected by leaf water concentration.

Document Type: Research Article

DOI: https://doi.org/10.1080/01431160304993

Affiliations: 1: Department of Forestry, Virginia Polytechnic Institute and State University, 319 Cheatham Hall, Blacksburg, VA 24061, USA; e-mail: zbortolo@vt.edu 2: Department of Forestry, Virginia Polytechnic Institute and State University, 319 Cheatham Hall, Blacksburg, VA 24061, USA; e-mail: wynne@vt.edu

Publication date: 2003-02-01

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more