About this Journal Submit a Manuscript Table of Contents
Advances in Civil Engineering
Volume 2012 (2012), Article ID 945613, 14 pages
http://dx.doi.org/10.1155/2012/945613
Research Article

Characterization of Forested Landscapes from Remotely Sensed Data Using Fractals and Spatial Autocorrelation

1Universities Space Research Association at NASA Marshall Space Flight Center, National Space Science and Technology Center, NASA Global Hydrology and Climate Center, Huntsville, AL 35805, USA
2Civil and Environmental Engineering Department, University of Alabama in Huntsville, Huntsville, AL 35899, USA
3Earth Science Office at NASA Marshall Space Flight Center, National Space Science and Technology Center, NASA Global Hydrology and Climate Center, Huntsville, AL 35805, USA

Received 5 August 2011; Accepted 27 December 2011

Academic Editor: Kirk Hatfield

Copyright © 2012 Mohammad Z. Al-Hamdan et al. 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.

Abstract

The characterization of forested landscapes is frequently required in civil engineering practice. In this study, some spatial analysis techniques are presented that might be employed with Landsat TM data to analyze forest structure characteristics. A case study is presented wherein fractal dimensions (FDs), along with a simple spatial autocorrelation technique (Moran’s I), were related to stand density parameters of the Oakmulgee National Forest located in the southeastern United States (Alabama). The results indicate that when smaller trees do not dominate the landscape (<50%), forested areas can be differentiated according to breast sizes and thus important flood plain characteristics such as ratio of obstructed area to total area can be estimated from remotely sensed data using the studied indices. This would facilitate the estimation of hydraulic roughness coefficients for computation of flood profiles needed for bridge design. FD and Moran’s I remained fairly constant around the values of 2.7 and 0.9 (resp.) for samples with either greater than 50% saplings or less than 50% sawtimber and with ranges of 2.7–2.9 and 0.6–0.9 as the saplings decreased or the sawtimber increased. Those indices can also distinguish hardwood and softwood species facilitating forested landscapes mapping for preliminary environmental impact analysis.