Skip to page content
USDA Forest Service
  
Treesearch

Research & Development Treesearch

 
Treesearch Home
About Treesearch
Contact Us
Research & Development
Forest Products Lab
International Institute of Tropical Forestry
Northern
Pacific Northwest
Pacific Southwest
Rocky Mountain
Southern Research Station
Help
 

Science.gov - We Participate


USA.gov  Government Made Easy


Global Forest Information Service

US Forest Service
P.O. Box 96090
Washington, D.C.
20090-6090

(202) 205-8333

You are here: Home / Search / Publication Information
Bookmark and Share

Publication Information

View PDF (649 KB)

Title: Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty

Author: White, Katharine; Pontius, Jennifer; Schaberg, Paul.;

Date: 2014

Source: Remote Sensing of Environment. 148: 97-107.

Publication Series: Scientific Journal (JRNL)

Description: Current remote sensing studies of phenology have been limited to coarse spatial or temporal resolution and often lack a direct link to field measurements. To address this gap, we compared remote sensing methodologies using Landsat Thematic Mapper (TM) imagery to extensive field measurements in a mixed northern hardwood forest. Five vegetation indices, five mathematical fits to model a continuous temporal response, and a suite of threshold estimates for "start of spring/season" (SOS) assessments were compared to field measurements of bud burst stage and hemispherical photo derived canopy structural metrics (transparency, leaf area index, greenness). Results indicated that a four-parameter logistic model based on at least five spring coverages of the Enhanced Vegetation Index (EVI) and a SOS threshold of 0.3 was most closely related to field metrics and most accurate in predicting the date of full leaf out. Plot level SOS was predicted with a mean absolute error of 11 days for all species and elevation combinations, but improved to 9 days for hardwood dominated plots and 7 days for sugar maple dominated plots. Mean absolute error was improved to 8 days when forest type (mixed, conifer hardwood) was used to refine predictions. The consistency of prediction errors across forest types indicates that while overall accuracy across pixels may be low, inter-annual comparisons of changes in phenology on a pixel basis may provide accurate assessments of changes in phenology over time. This was confirmed by application to seven years of independent phenology data predicted with 12 days of mean absolute error. However, image availability will be a limiting factor in areas of frequent cloud cover.

Keywords: Landsat, Green leaf phenology, Vegetation indices, Field to sensor scaling, EVI, NDVI

Publication Notes:

  • 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.
  • This publication may be available in hard copy. Check the Northern Research Station web site to request a printed copy of this publication.
  • Our on-line publications are scanned and captured using Adobe Acrobat. During the capture process some typographical errors may occur. Please contact Sharon Hobrla, shobrla@fs.fed.us if you notice any errors which make this publication unusable.

XML: View XML

Citation:


White, Katharine; Pontius, Jennifer; Schaberg, Paul. 2014. Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty. Remote Sensing of Environment. 148: 97-107.

 


 [ Get Acrobat ]  Get the latest version of the Adobe Acrobat reader or Acrobat Reader for Windows with Search and Accessibility

USDA logo which links to the department's national site. Forest Service logo which links to the agency's national site.