Next Article in Journal
Next Article in Special Issue
Previous Article in Journal
Previous Article in Special Issue
Sensors 2011, 11(6), 5677-5694; doi:10.3390/s110605677
Article

Assessment of Acacia Koa Forest Health across Environmental Gradients in Hawai‘i Using Fine Resolution Remote Sensing and GIS

1,* , 2,*  and 2,*
Received: 25 April 2011; in revised form: 16 May 2011 / Accepted: 16 May 2011 / Published: 27 May 2011
(This article belongs to the Special Issue 10 Years Sensors - A Decade of Publishing)
View Full-Text   |   Download PDF [809 KB, uploaded 21 June 2014]   |   Browse Figures
Abstract: Koa (Acacia koa) forests are found across broad environmental gradients in the Hawai‘ian Islands. Previous studies have identified koa forest health problems and dieback at the plot level, but landscape level patterns remain unstudied. The availability of high-resolution satellite images from the new GeoEye1 satellite offers the opportunity to conduct landscape-level assessments of forest health. The goal of this study was to develop integrated remote sensing and geographic information systems (GIS) methodologies to characterize the health of koa forests and model the spatial distribution and variability of koa forest dieback patterns across an elevation range of 600–1,000 m asl in the island of Kaua‘i, which correspond to gradients of temperature and rainfall ranging from 17–20 °C mean annual temperature and 750–1,500 mm mean annual precipitation. GeoEye1 satellite imagery of koa stands was analyzed using supervised classification techniques based on the analysis of 0.5-m pixel multispectral bands. There was clear differentiation of native koa forest from areas dominated by introduced tree species and differentiation of healthy koa stands from those exhibiting dieback symptoms. The area ratio of healthy koa to koa dieback corresponded linearly to changes in temperature across the environmental gradient, with koa dieback at higher relative abundance in warmer areas. A landscape-scale map of healthy koa forest and dieback distribution demonstrated both the general trend with elevation and the small-scale heterogeneity that exists within particular elevations. The application of these classification techniques with fine spatial resolution imagery can improve the accuracy of koa forest inventory and mapping across the islands of Hawai‘i. Such findings should also improve ecological restoration, conservation and silviculture of this important native tree species.
Keywords: forest health; dieback; Acacia koa; Hawaii; GeoEye; remote sensing; GIS forest health; dieback; Acacia koa; Hawaii; GeoEye; remote sensing; GIS
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Morales, R.M.; Idol, T.; Friday, J.B. Assessment of Acacia Koa Forest Health across Environmental Gradients in Hawai‘i Using Fine Resolution Remote Sensing and GIS. Sensors 2011, 11, 5677-5694.

AMA Style

Morales RM, Idol T, Friday JB. Assessment of Acacia Koa Forest Health across Environmental Gradients in Hawai‘i Using Fine Resolution Remote Sensing and GIS. Sensors. 2011; 11(6):5677-5694.

Chicago/Turabian Style

Morales, Rodolfo Martinez; Idol, Travis; Friday, James B. 2011. "Assessment of Acacia Koa Forest Health across Environmental Gradients in Hawai‘i Using Fine Resolution Remote Sensing and GIS." Sensors 11, no. 6: 5677-5694.



Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert