CitationMcCleary, Amy L.; Crews-Meyer, Kelley A.; & Young, K. R. (2008). Refining Forest Classifications in the Western Amazon Using an Intra-Annual Multitemporal Approach. International Journal of Remote Sensing, 29(4), 991-1006.
AbstractSeasonal dynamics in the north-eastern Peruvian Amazon were assessed within a multitemporal LULC framework informed by Landsat 7 ETM+ imagery of the study area for 2001 that coincided with seasonal flooding dynamics. Three images (12 March, 31 May, and 20 September 2001) were classified separately using a hybrid classification method that combined unsupervised and supervised techniques, and attributed with a classification scheme consisting of 18 LULC classes. A multitemporal classification that included 25 LULC classes was created from the three single-date classifications using a panel analysis technique. While some of the classes describe LULC 'changes' (land-use and land-cover change (LULCC)) that were stable over time (e.g. low sediment water during March, May, and September 2001), others were complex and included multiple trajectories of change. Panel analysis extracts pixel histories of change over three or more observations as a trend or trajectory, rather than segmenting those changes into piecemeal periods as normally done with from-to change detection. This technique was then assessed by testing hypothesized forest trajectories of LULCC. Traditional quantitative accuracy assessment techniques are less appropriate for panel analysis, and so a qualitative accuracy assessment was performed to evaluate the validity of the multitemporal classification. This study suggests that there may not be one typical year-round behaviour for seasonal environments, and that population-environment interaction studies would benefit from incorporating this knowledge into future research. This analysis further demonstrates the effectiveness of a multitemporal remote-sensing approach for gauging landscape fluctuations in seasonal environments.
Reference TypeJournal Article
Journal TitleInternational Journal of Remote Sensing
Author(s)McCleary, Amy L.
Crews-Meyer, Kelley A.
Young, K. R.