Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images
| Autor: | Kevin De Mille, Amy B. Mui, Jian Yang, Yuhong He, Justin Murfitt |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Agrilus
Watershed 010504 meteorology & atmospheric sciences Science Natural forest 0211 other engineering and technologies Early detection 02 engineering and technology medicine.disease_cause 01 natural sciences Emerald ash borer Infestation High spatial resolution medicine forest health 021101 geological & geomatics engineering 0105 earth and related environmental sciences biology Ecology segmentation Forestry biology.organism_classification Random forest emerald ash borer General Earth and Planetary Sciences Environmental science random forest |
| Zdroj: | Remote Sensing, Vol 8, Iss 3, p 256 (2016) Remote Sensing Volume 8 Issue 3 Pages: 256 |
| ISSN: | 2072-4292 |
| Popis: | The invasive emerald ash borer (EAB, Agrilus planipennis Fairmaire) infects and eventually kills endemic ash trees and is currently spreading across the Great Lakes region of North America. The need for early detection of EAB infestation is critical to managing the spread of this pest. Using WorldView-2 (WV2) imagery, the goal of this study was to establish a remote sensing-based method for mapping ash trees undergoing various infestation stages. Based on field data collected in Southeastern Ontario, Canada, an ash health score with an interval scale ranging from 0 to 10 was established and further related to multiple spectral indices. The WV2 image was segmented using multi-band watershed and multiresolution algorithms to identify individual tree crowns, with watershed achieving higher segmentation accuracy. Ash trees were classified using the random forest classifier, resulting in a user’s accuracy of 67.6% and a producer’s accuracy of 71.4% when watershed segmentation was utilized. The best ash health score-spectral index model was then applied to the ash tree crowns to map the ash health for the entire area. The ash health prediction map, with an overall accuracy of 70%, suggests that remote sensing has potential to provide a semi-automated and large-scale monitoring of EAB infestation. |
| Databáze: | OpenAIRE |
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