In the Oueme Basin, primarily in the Republic of Benin, riparian forest cover borders the many waterways, providing ecosystem services at local, regional, and basin-wide scales. However with agricultural expansion, market growth, and increasing access to roads, riparian forests may be under new anthropogenic threats. Identifying and monitoring riparian forest cover in the context of current land-use change is important to maintaining the integrity of these forests and their hydrologic networks in central Benin. Remote sensing has the potential to play an important role in this effort. This project compares the viability of the land-use change sensing techniques of normalized difference vegetation index (NDVI) overlay, unsupervised classification, and supervised classification for detecting changes in riparian forest cover in the central Oueme Basin in the Republic of Benin using Landsat TM and ETM+ images from 1986 and 2000. Qualitative observation of NDVI change was found to be the most powerful tool at this scale and stage.
Future work will focus on identifying relevant classes within the riparian buffer, ignoring all others, at least for the initial classification. I hope to work on a smaller scale or at a better resolution in the future, particularly to identify the initial classes. Finally, thorough ground truth data would increase the validity of the classifications greatly. Instead of doing an identification of Riparian forests and a change analysis simultaneously, future work will begin by developing an algorithm for isolating riparian cover, and then will use it on images of different years to assess change, recognizing that decreases in riparian forests could be natural (drought) or anthropogenic (land clearing). Once the riparian buffer has been isolated and characterized, the influences of adjacent vegetation could be considered. Field work from June to August 2007 will provide some data for future assessments.