Accuracy of landsat-TM and GIS rule-based methods for forest wetland classification in Maine
Sader SA, Ahl D, Liou W. 1995. Accuracy of landsat-TM and GIS rule-based methods for forest wetland classification in Maine. Remote Sensing of Environment. 53:133-144
'An investigation was undertaken to compare satellite image classification techniques to delineate forest wetlands in Maine. Four classification techniques were compared, including a GIS rule-based model. Accuracy assessments of the four methods on two study sites, Orono and Acadia, revealed very similar results. Overall accuracy for four super groups (forest wetland, other wetland, forest upland, other upland) ranged from 72% to 81% at Orono and 74% to 82% at Acadia. Pairwise significance tests indicated that the GIS model was significantly better than unsupervised classification at both study sites, and significantly better than tassled cap (Acadia) in classifying the four super groups. Although Kappa coefficients were slightly higher for the GIS model compared to hybrid classification, there was no significant difference between the two methods at eigher study site. Forest wetland user's and producer's accuracy was in the 80% range for the highestaccuracy achievedeither by the GIS model or hybrid classification. Hydric soils, National Wetland Inventory data, and slope percentage were the most important variables in the GIS model. From this study, it appears that a combination of hybrid and GIS rule-based classification methods are the most promising for further investigations of forest wetland delineation.' /STUDY LOCATIONS:/ /ACADIA NATIONAL PARK
- Type
- Journal Article
- Authors
- Sader, Steven; Ahl, Douglas; Liou, Wen-Shu
- Units
- ACAD
- Keywords
- Acadia National Park, Geographic Information System (GIS), Remote Sensing, Satellite Imagery, Wetlands