DETECTING FORESTS DAMAGED BY PINE WILT DISEASE AT THE INDIVIDUAL TREE LEVEL USING AIRBORNE LASER DATA AND WORLDVIEW-2/3 IMAGES OVER TWO SEASONS

Abstract. Pine wilt disease is caused by the pine wood nematode (Bursaphelenchus xylophilus) and Japanese pine sawyer (Monochamus alternatus). This study attempted to detect damaged pine trees at different levels using a combination of airborne laser scanning (ALS) data and high-resolution space-borne images. A canopy height model with a resolution of 50 cm derived from the ALS data was used for the delineation of tree crowns using the Individual Tree Detection method. Two pan-sharpened images were established using the ortho-rectified images. Next, we analyzed two kinds of intensity-hue-saturation (IHS) images and 18 remote sensing indices (RSI) derived from the pan-sharpened images. The mean and standard deviation of the 2 IHS images, 18 RSI, and 8 bands of the WV-2 and WV-3 images were extracted for each tree crown and were used to classify tree crowns using a support vector machine classifier. Individual tree crowns were assigned to one of nine classes: bare ground, Larix kaempferi, Cryptomeria japonica, Chamaecyparis obtusa, broadleaved trees, healthy pines, and damaged pines at slight, moderate, and heavy levels. The accuracy of the classifications using the WV-2 images ranged from 76.5 to 99.6 %, with an overall accuracy of 98.5 %. However, the accuracy of the classifications using the WV-3 images ranged from 40.4 to 95.4 %, with an overall accuracy of 72 %, which suggests poorer accuracy compared to those classes derived from the WV-2 images. This is because the WV-3 images were acquired in October 2016 from an area with low sun, at a low altitude.

摘要。松材线虫病是由松材线虫（Bursaphelenchus xylophilus）和松墨天牛（Monochamus alternatus）引起的。本研究试图结合机载激光扫描（ALS）数据和高分辨率星载图像来检测不同受损程度的松树。利用从ALS数据中获取的分辨率为50厘米的冠层高度模型，通过单木检测方法来描绘树冠。利用正射校正图像建立了两幅全色锐化图像。接下来，我们分析了两种强度 - 色调 - 饱和度（IHS）图像以及从全色锐化图像中得出的18种遥感指数（RSI）。针对每个树冠提取了2种IHS图像、18种RSI以及WV - 2和WV - 3图像的8个波段的均值和标准差，并使用支持向量机分类器对树冠进行分类。将单木树冠分为九类：裸地、日本落叶松、日本柳杉、日本扁柏、阔叶树、健康松树以及轻度、中度和重度受损松树。使用WV - 2图像分类的准确率在76.5%到99.6%之间，总体准确率为98.5%。然而，使用WV - 3图像分类的准确率在40.4%到95.4%之间，总体准确率为72%，这表明与从WV - 2图像得出的分类相比准确率较低。这是因为WV - 3图像是2016年10月从一个太阳高度角较低且海拔较低的区域获取的。