Monitoring post disturbance forest regeneration with hierarchical object-based image analysis
Moskal LM and Jakubauskas ME. 2013. Monitoring post disturbance forest regeneration with hierarchical object-based image analysis. Forests. 4(4):808-829
The main goal of this exploratory project was to quantify seedling density in post fire regeneration sites, with the following objectives: to evaluate the application of second order image texture (SOIT) in image segmentation, and to apply the object-based image analysis (OBIA) approach to develop a hierarchical classification. With the utilization of image texture we successfully developed a methodology to classify hyperspatial (high-spatial) imagery to fine detail level of tree crowns, shadows and understory, while still allowing discrimination between density classes and mature forest versus burn classes. At the most detailed hierarchical Level I classification accuracies reached 78.8%, a Level II stand density classification produced accuracies of 89.1% and the same accuracy was achieved by the coarse general classification at Level III. Our interpretation of these results suggests hyperspatial imagery can be applied to post-fire forest density and regeneration mapping.
- Type
- Journal Article
- Authors
- Moskal, L.; Jakubauskas, Mark
- Units
- YELL
- Keywords
- hierarchical classification, object based image analysis, seedling regeneration
- Subjects
- Ecological Framework: Landscapes | Fire and Fuel Dynamics | Fire and Fuel Dynamics , Ecological Framework: Landscapes | Extreme Disturbance Events | Extreme Disturbance Events