HR: 0800h
AN: IN31A-04    [Abstracts]
TI: Acacia koa forest classification and productivity assessment across environmental gradients in Hawaii using fine resolution remotely sensed imagery
AU: * Martinez Morales, R
EM: rodolfom@hawaii.edu
AF: University of Hawaii at Manoa, 1910 East-West Rd, Honolulu, HI 96822, United States
AU: Idol, T
EM: idol@hawaii.edu
AF: University of Hawaii at Manoa, 1910 East-West Rd, Honolulu, HI 96822, United States
AU: Chen, Q
EM: qichen@hawaii.edu
AF: University of Hawaii at Manoa, 1910 East-West Rd, Honolulu, HI 96822, United States
AB: Koa (Acacia koa) is an important native tree species in Hawaii economically and ecologically. Different Acacia koa (koa) forest types are found across the elevation and rainfall gradients typical of the Hawaiian Islands. The purpose of this study was to develop methodologies to differentiate these forests and to assess indices and indicators of forest productivity across these gradients using fine resolution remotely sensed imagery. IKONOS satellite imagery was analyzed using advanced statistical modeling and compared to field measurements of productivity indices. The calculation of several vegetation indices that are commonly used in vegetation studies, allowed classification of various koa forest types into micro-regions in wet and dry locations across elevation gradients ranging from 300-850 m. Vegetation indices and image texture parameters strongly related to tree height, N, P and specific leaf area and less strongly with leaf area index and basal area across gradient sites. This allowed development of statistical models that can be used in the assessment of koa forest productivity indices at landscape and regional scales. This will also allow for the application of specific forest management strategies suitable to the environmental conditions and plant requirements for optimal tree growth in each micro-region.
DE: 0400 BIOGEOSCIENCES
DE: 0480 Remote sensing
SC: Earth and Space Science Informatics [IN]
MN: 2009 Joint Assembly