Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data
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,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-10-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/6/10/9802 |
| _version_ | 1818905327635005440 |
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| author | Mohammad Al-Hamdan James Cruise Douglas Rickman Dale Quattrochi |
| author_facet | Mohammad Al-Hamdan James Cruise Douglas Rickman Dale Quattrochi |
| author_sort | Mohammad Al-Hamdan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T21:21:34Z |
| format | Article |
| id | doaj.art-4ac6e57dce1448c29a8adce1c00d6e76 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-19T21:21:34Z |
| publishDate | 2014-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4ac6e57dce1448c29a8adce1c00d6e762022-12-21T20:05:13ZengMDPI AGRemote Sensing2072-42922014-10-016109802982810.3390/rs6109802rs6109802Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed DataMohammad Al-Hamdan0James Cruise1Douglas Rickman2Dale Quattrochi3Universities Space Research Association at NASA Marshall Space Flight Center, National Space Science and Technology Center, NASA Global Hydrology and Climate Center, Huntsville, AL 35805, USAEarth System Science Center, University of Alabama in Huntsville, National Space Science and Technology Center, Huntsville, AL 35805, USAEarth Science Office at NASA Marshall Space Flight Center, National Space Science and Technology Center, NASA Global Hydrology and Climate Center, Huntsville, AL 35805, USAEarth Science Office at NASA Marshall Space Flight Center, National Space Science and Technology Center, NASA Global Hydrology and Climate Center, Huntsville, AL 35805, USARegression 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.http://www.mdpi.com/2072-4292/6/10/9802remote sensingfractal dimensionsMoran’s Iforested landscapessize-species models |
| spellingShingle | Mohammad Al-Hamdan James Cruise Douglas Rickman Dale Quattrochi Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data Remote Sensing remote sensing fractal dimensions Moran’s I forested landscapes size-species models |
| title | Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data |
| title_full | Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data |
| title_fullStr | Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data |
| title_full_unstemmed | Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data |
| title_short | Forest Stand Size-Species Models Using Spatial Analyses of Remotely Sensed Data |
| title_sort | forest stand size species models using spatial analyses of remotely sensed data |
| topic | remote sensing fractal dimensions Moran’s I forested landscapes size-species models |
| url | http://www.mdpi.com/2072-4292/6/10/9802 |
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