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Research |
Mapping Elizabeth CIty State University Campus Using Multi-sensor Remote Sensing Methods |
| As climate change becomes more critical in the future, having access to accurate maps of forest types and conditions will allow climate modelers to more accurately predict the carbon sequestration capacity of forested landscapes. Remote Sensing tools make mapping of forest types and conditions possible. The Remote Sensing Team members of the Elizabeth City State University (ECSU) Undergraduate Research Experience (URE) program mapped the ECSU campus using both Landsat Enhanced Thematic Mapper (ETM+) data (acquired 6/12/99) and aerial photographic data (acquired from ncOneMap). Both remote sensing data sets were calibrated using a variety of field verification (ground truth) measurements acquired during summer 2006 session. The final product, a land cover map of the campus, has been produced using unsupervised classification methods provided by MultiSpec data analysis and image processing software to evaluate the ETM+ data. The ETM+ data provided multispectral data at 30m spatial resolution, while the aerial photography provided data at 2 meter resolution. The combination of the infrared and panchromatic data allowed identification and mapping of dominant land cover types, including forest types, non-forest vegetation, and categories of development (parking lots, roadways, buildings, campus landmarks, etc.), not possible using either data type separately. Mapping of the distribution of forest species assemblages (hardwoods, softwoods, and mixtures of the two) was also possible. Field methods included the identification of: dominant forest species, forest canopy height, tree age, and relative state -of-health of selected tree species. Tree cores provided insight into changing growth patterns over the past century. The use of these remote sensing methods facilitated the production of accurate and up-to-date mapping of the ECSU campus not possible using other cartographic methods. |
| For the full description of this research project, please visit: Remote Sensing Team Website |