Mapping Forest Types 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 type 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 NC OneMap, http://www.nconemap.com/). NC OneMap is a public resource for North Carolina geospatial data. Aerial photographic data was used to distinguish the different features indicated in the Landsat ETM+ image. Both remote sensing data sets were calibrated using a variety of field verification (ground truth) measurements acquired during summer 2006 session. This paper presents the final product, a land cover map of the campus, produced using unsupervised classification methods provided by MultiSpec. MultiSpec is data analysis and image processing software and was used to evaluate the ETM+ data. The ETM+ data provided multispectral data at 30 m spatial resolution, while the aerial photography provided hyperspatial data. The combination of the two sensors provided complimentary data allowing 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 sensor 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 remo- - te sensing methods facilitated the production of accurate and up-to-date mapping of the ECSU campus not possible using other cartographic methods.