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

  • Authors: Crosby, Michael K.; Fan, Zhaofei; Spetich, Martin A.; Leininger, Theodor D.
  • Publication Year: 2012
  • Publication Series: Paper (invited, offered, keynote)
  • 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.

Abstract

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.

  • 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.
  • Keywords: Crown dieback, Ozarks, quantile regression
  • Posted Date: August 14, 2012
  • Modified Date: August 21, 2012
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