Next Article in Journal
Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method
Next Article in Special Issue
Identification of Ecosystem Functional Types from Coarse Resolution Imagery Using a Self-Organizing Map Approach: A Case Study for Spain
Previous Article in Journal
Diurnal Dynamics of Wheat Evapotranspiration Derived from Ground-Based Thermal Imagery
Previous Article in Special Issue
Quantifying Forest Spatial Pattern Trends at Multiple Extents: An Approach to Detect Significant Changes at Different Scales
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(10), 9802-9828; doi:10.3390/rs6109802

Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data

1
Universities 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
2
Earth System Science Center, University of Alabama in Huntsville, National Space Science and Technology Center, Huntsville, AL 35805, USA
3
Earth Science Office at NASA Marshall Space Flight Center, National Space Science and Technology Center, NASA Global Hydrology and Climate Center, Huntsville, AL 35805, USA
*
Author to whom correspondence should be addressed.
Received: 19 May 2014 / Revised: 19 September 2014 / Accepted: 24 September 2014 / Published: 14 October 2014
(This article belongs to the Special Issue Earth Observation for Ecosystems Monitoring in Space and Time)
View Full-Text   |   Download PDF [5333 KB, uploaded 17 October 2014]   |  

Abstract

Regression models to predict stand size classes (sawtimber and saplings) and categories of species (hardwood and softwood) from fractal dimensions (FD) and Moran’s I derived from Landsat Thematic Mapper (TM) data were developed. Three study areas (Oakmulgee National Forest, Bankhead National Forest, and Talladega National Forest) were randomly selected and used to develop the prediction models, while one study area, Chattahoochee National Forest, was saved for validation. This study has shown that these spatial analytical indices (FD and Moran’s I) can distinguish between different forest trunk size classes and different categories of species (hardwood and softwood) using Landsat TM data. The results of this study also revealed that there is a linear relationship between each one of the spatial indices and the percentages of sawtimber–saplings size classes and hardwood–softwood categories of species. Given the high number of factors causing errors in the remotely sensed data as well as the Forest Inventory Analysis (FIA) data sets and compared to other studies in the research literature, the sawtimber–saplings models and hardwood–softwood models were reasonable in terms of significance and the levels of explained variance for both spatial indices FD and Moran’s I. The mean absolute percentage errors associated with the stand size classes prediction models and categories of species prediction models that take topographical elevation into consideration ranged from 4.4% to 19.8% and from 12.1% to 18.9%, respectively, while the root mean square errors ranged from 10% to 14% and from 11% to 13%, respectively. View Full-Text
Keywords: remote sensing; fractal dimensions; Moran’s I; forested landscapes; size-species models remote sensing; fractal dimensions; Moran’s I; forested landscapes; size-species models
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. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Al-Hamdan, M.; Cruise, J.; Rickman, D.; Quattrochi, D. Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data. Remote Sens. 2014, 6, 9802-9828.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top