Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data,random forest and support vector machines classifiers |
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| Institution: | 1. Agri-Science Queensland, Department of Agriculture, Fisheries and Forestry, Ecosciences Precinct, Dutton Park, Queensland, Australia;2. Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, Maroochydore DC, Queensland, Australia;3. Forest Industries Research Centre, University of the Sunshine Coast, Maroochydore DC, Queensland, Australia;1. Department of Land, Air and Water Resources, University of California, Davis, One Shields Ave, Davis, CA 95616, United States;2. Department of Geography, University of California, Santa Barbara, 1832 Ellison Hall, Santa Barbara, CA 93106, United States;3. Department of Geography and Center for Natural and Technological Hazards, University of Utah, 260 S Central Campus Dr., Room 270, Salt Lake City, UT 84112, United States |
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| Abstract: | The visual progression of sirex (Sirex noctilio) infestation symptoms has been categorized into three distinct infestation phases, namely the green, red and grey stages. The grey stage is the final stage which leads to almost complete defoliation resulting in dead standing trees or snags. Dead standing pine trees however, could also be due to the lightning damage. Hence, the objective of the present study was to distinguish amongst healthy, sirex grey-attacked and lightning-damaged pine trees using AISA Eagle hyperspectral data, random forest (RF) and support vector machines (SVM) classifiers. Our study also presents an opportunity to look at the possibility of separating amongst the previously mentioned pine trees damage classes and other landscape classes on the study area. The results of the present study revealed the robustness of the two machine learning classifiers with an overall accuracy of 74.50% (total disagreement = 26%) for RF and 73.50% (total disagreement = 27%) for SVM using all the remaining AISA Eagle spectral bands after removing the noisy ones. When the most useful spectral bands as measured by RF were exploited, the overall accuracy was considerably improved; 78% (total disagreement = 22%) for RF and 76.50% (total disagreement = 24%) for SVM. There was no significant difference between the performances of the two classifiers as demonstrated by the results of McNemar’s test (chi-squared; χ2 = 0.14, and 0.03 when all the remaining ASIA Eagle wavebands, after removing the noisy ones and the most important wavebands were used, respectively). This study concludes that AISA Eagle data classified using RF and SVM algorithms provide relatively accurate information that is important to the forest industry for making informed decision regarding pine plantations health protocols. |
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| Keywords: | Sirex grey stage Lighting damage Hyperspectral data Random forest Support vector machines |
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