Comparative Assessment of Multiplatform and Multistage Remote Sensing for Rural Habitat Assessment Initiatives

The primary objective is to ascertain what the most efficient, effective, and accurate available remote sensing data are for wildlife habitat and land cover mapping in rural communities. Multiple resolution data are available at a broad scale of costs for users (Key et al., 2001). However, identifying cost versus effectiveness, labor versus accuracy, and processing time versus technical capabilities are still not determined for many common rural planning and management procedures.

Remotely sensed data are captured on multiple platforms at different spatial and temporal resolutions. The capability of the sensing device coupled with the altitude and temporal frequency at which data are acquired can impact results and accuracy of any investigation. The time and cost of fieldwork is usually a trade-off for the cost of high spatial and temporal resolution data. Finer resolution data provide an alternative to labor intensive field monitoring (Wulder et al. 2004). These data are usually purchased from private or international sources and require increased computer processing abilities and storage space. However, the use of finer spatial and spectral resolution data are often cost and resource prohibitive, especially by many rural users and agencies. The basis for this work will be to determine the cost, effectiveness, efficiency, and reproduction from four different remote sensing platforms, Landsat, ASTER, IKONOS, and aerial photography, for wildlife habitat mapping on the Yakama Nation Reservation, Washington.

We will draw upon readily available and free digital data sources and compare them with finer resolution data. Landsat, ASTER, air photos, and digital ortho-photoquadrangles (DOQQs) digital data are available as fairly inexpensive data. These will be compared using different variables to the finer resolution data purchased from IKONOS. Differences in data cost, methods, efficiency, and accuracy for wildlife habitat mapping will be assessed. The results from this data set may be used for restoration, planning, or improved management (Wulder et al., 2004; Tuxen 2007). A matrix with indicators determining the amount of time, ease, and spatial and temporal accuracy will be filled out during processing and then made available as part of the final results of this research.

The different types of digital data will be analyzed using ERDAS Imagine software and possibly integrated with ESRI ArcMap. All technicians involved in the project will be required to keep a detailed record for describing processing methods. The comparison of results from the different image sources will demonstrate and document the level of quality vs. cost for each data type. All information will be made available as a web-based educational and image database resource so rural users can better decide which data and resolutions may be the most effective. Results from this study will also be written up as a report and disseminated to the Yakama Nation Wildlife and Resource Management team.