Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis
| Autor: | L. Monika Moskal, Mark E. Jakubauskas |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
seedling regeneration
Forest regeneration Computer science business.industry object based image analysis Object based Forestry Pattern recognition lcsh:QK900-989 Image segmentation Understory hierarchical classification Image (mathematics) Tree (data structure) Image texture lcsh:Plant ecology Level iii Artificial intelligence business |
| Zdroj: | Forests, Vol 4, Iss 4, Pp 808-829 (2013) |
| ISSN: | 1999-4907 |
| DOI: | 10.3390/f4040808 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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