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 (358 KB)

Title: Remote Sensing of Forest Health Indicators for Assessing Change in Forest Health

Author: Crosby, Michael K.; Fan, Zhaofei; Spetich, Martin A.; Leininger, Theodor D.;

Date: 2012

Source: In: Merry, K.; Bettinger, P.; Lowe, T.; Nibbelink, N.; Siry, J., eds. Proceedings of the 8th Southern Forestry and Natural Resources GIS Conference (2012). Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA.

Publication Series: Paper (invited, offered, keynote)

Description: Oak decline poses a substantial threat to forest health in the Ozark Highlands of northern Arkansas and southern Missouri, where coupled with diseases and insect infestations, it has damaged large tracts of forest lands. Forest Health Monitoring (FHM) crown health indicators (e.g. crown dieback, etc.), collected by the U.S. Forest Service’s Forest Inventory and Analysis (FIA) program, provide a method of assessing forest health. These data were obtained for the Ozark Highlands for the years 2003-2007; and levels of red oak crown dieback were calculated at the plot level along with basal area and age. Also, calculations of Normalized Difference Moisture Index (NDMI) were derived from Landsat TM imagery, annual temperature range was calculated from mean temperature data, and percent slope was calculated from a Digital Elevation Model. Quantile regression analysis was then utilized to determine the relationship between the predictor variables and red oak dieback at various quantiles of dieback. Red oak crown dieback has increased throughout the period since a low in 2004. The quantile regression analysis also indicated a difference in the relationship between the variables from linear regression estimates at higher quantiles (e.g. 90th-95th). This indicates that data at the upper tail of the distribution may point to causal relationships between variables. NDMI has the most significant relationship with red oak crown dieback although additional research is needed to determine if there is any interaction between this and other variables.

Keywords: Crown dieback, Ozarks, quantile regression

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.
  • You may send email to pubrequest@fs.fed.us to request a hard copy of this publication. (Please specify exactly which publication you are requesting and your mailing address.)

XML: View XML

Citation:


Crosby, Michael K.; Fan, Zhaofei; Spetich, Martin A.; Leininger, Theodor D. 2012. Remote Sensing of Forest Health Indicators for Assessing Change in Forest Health. In: Merry, K.; Bettinger, P.; Lowe, T.; Nibbelink, N.; Siry, J., eds. Proceedings of the 8th Southern Forestry and Natural Resources GIS Conference (2012). Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA.

 


 [ 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.