Bottomland hardwood forests are highly productive ecosystems which perform
many important ecological services. Unfortunately, many bottomland hardwood forests
have been degraded or lost. Accurate land cover mapping is crucial for management
decisions affecting these disappearing systems. SPOT-5 imagery from 2005 was
combined with Light Detection and Ranging (LiDAR) data from 2006 and several
ancillary datasets to map a portion of the bottomland hardwood system found in the
Sulphur River Basin of Northeast Texas. Pixel-based classification techniques, rulebased
classification techniques, and object-based classification techniques were used to
distinguish nine land cover types in the area. The rule-based classification (84.41%
overall accuracy) outperformed the other classification methods because it more
effectively incorporated the LiDAR and ancillary datasets when needed. This output
was compared to previous classifications from 1974, 1984, 1991, and 1997 to determine
abundance trends in the area’s bottomland hardwood forests. The classifications from
1974-1991 were conducted using identical class definitions and input imagery (Landsat
MSS 60m), and the direct comparison demonstrates an overall declining trend in
bottomland hardwood abundance. The trend levels off in 1997 when medium resolution imagery was first utilized (Landsat TM 30m) and the 2005 classification also shows an
increase in bottomland hardwood from 1997 to 2005, when SPOT-5 10m imagery was
used. However, when the classifications are re-sampled to the same resolution (60m),
the percent area of bottomland hardwood consistently decreases from 1974-2005.
Additional investigation of object-oriented classification proved useful. A major
shortcoming of object-based classification is limited justification regarding the selection
of segmentation parameters. Often, segmentation parameters are arbitrarily defined
using general guidelines or are determined through a large number of parameter
combinations. This research justifies the selection of segmentation parameters through a
process that utilizes landscape metrics and statistical techniques to determine ideal
segmentation parameters. The classification resulting from these parameters
outperforms the classification resulting from arbitrary parameters by approximately three
to six percent in terms of overall accuracy, demonstrating that landscape metrics can be
successfully linked to segmentation parameters in order to create image objects that
more closely resemble real-world objects and result in a more accurate final
classification.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2744 |
| Date | 15 May 2009 |
| Creators | Vernon, Zachary Isaac |
| Contributors | Srinivasan, Raghavan |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | electronic, application/pdf, born digital |
Page generated in 0.0017 seconds