Evaluating the Potential of WorldView-3 Data to Classify Different Shoot Damage Ratios of Pinus yunnanensis

Abstract

Tomicus yunnanensis Kirkendall and Faccoli and Tomicus minor Hartig have caused serious shoot damage in Yunnan pine (Pinus yunnanensis Faranch) forests in the Yunnan province of China. However, very few remote sensing studies have been conducted to detect the different shoot damage ratios of individual trees. The aim of the study was to evaluate the suitability of eight-band WorldView-3 satellite image for detecting different shoot damage ratios (e.g., “healthy”, “slightly”, “moderately”, and “severely”). An object-based supervised classification method was used in this study. The tree crowns were delineated on a 0.3 m pan-sharpened worldview-3 image as reference data. Besides the original eight bands, normalized two-band indices were derived as spectral variables. For classifying individual trees, three classifiers—multinomial logistic regression (MLR), a stepwise linear discriminant analysis (SDA), and random forest (RF)—were evaluated and compared in this study. Results showed that SDA classifier based on all spectral variables had the highest classification accuracy (78.33%, Kappa = 0.712). Compared to original eight bands of Worldview-3, normalized two-band indices could improve the overall accuracy. Furthermore, the shoot damage ratio was a good indicator for detecting different levels of individual damaged trees. We concluded that the Worldview-3 satellite data were suitable to classify different levels of damaged trees; therefore, the best mapping results of damaged trees was predicted based on the best classification model which is very useful for forest managers to take the appropriate measures to decrease shoot beetle damage in Yunnan pine forests.

Combining WV-2 images and tree physiological factors to detect damage stages of Populus gansuensis by Asian longhorned beetle (Anoplophora glabripennis) at the tree level

“…Considering the error of automatic segmentation by the software, the WV-2-sharpened multispectral canopy image automatically drawn from a 0.5-m resolution is not always consistent with the actual object. We, therefore, chose to use the eCognition's multiresolutional segmentation method for single wood segmentation (Yu et al 2020). Based on UAV images, we adjusted the parameters several times to obtain an accurate individual tree segmentation map.…”