Combining GF-2 and Sentinel-2 Images to Detect Tree Mortality Caused by Red Turpentine Beetle during the Early Outbreak Stage in North China

Abstract

In recent years, the red turpentine beetle (RTB) (Dendroctonus valens LeConte) has invaded the northern regions of China. Due to the short invasion time, the outbreak of tree mortality corresponded to a low level of damage. Important information about tree mortality, provided by remote sensing at both single-tree and forest stand scale, is needed in forest management at the early stages of outbreak. In order to detect RTB-induced tree mortality at a single-tree scale, we evaluated the classification accuracies of Gaofen-2 (GF2) imagery at different spatial resolutions (1 and 4 m) using a pixel-based method. We also simultaneously applied an object-based method to 1 m pan-sharpened images. We used Sentinel-2 (S2) imagery with different resolutions (10 and 20 m) to detect RTB-induced tree mortality and compared their classification accuracies at a larger scale—the stand scale. Three kinds of machine learning algorithms—the classification and regression tree (CART), the random forest (RF), and the support vector machine (SVM)—were applied and compared in this study. The results showed that 1 m resolution GF2 images had the highest classification accuracy using the pixel-based method and SVM algorithm (overall accuracy = 77.7%). We found that the classification of three degrees of damage percentage within the S2 pixel (0%, <15%, and 15% < x < 50%) was not successful at a forest stand scale. However, 10 m resolution S2 images could acquire effective binary classification (<15%: overall accuracy = 74.9%; 15% < x < 50%: overall accuracy = 81.0%). Our results indicated that identifying tree mortality caused by RTB at a single-tree and forest stand scale was accomplished with the combination of GF2 and S2 images. Our results are very useful for the future exploration of the patterns of spatial and temporal changes in insect pest transmission at different spatial scales.

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

“…The damage periods of conifer trees are recognized by changes in the canopy color from green to yellow to red (Zhan et al 2020); however, there is no clear indicator for the damage period of broad-leaved trees. Previous studies on the damage level of broad-leaved trees at the single wood scale were mostly based on a specific index, such as leaf loss rate, emergence hole number, or dead branch rate (Waser et al 2014).…”

Landscape-Level And Stand-Level Factors Play Diverse Roles During Different Periods of Dendroctonus Valens Invasion In North China

“…Forestry management of RTB outbreak should depend on knowledge combined from multiple spatial scales, from the stand scale to the landscape scale. In addition, our research also demonstrated that the UAV-based remote sensing technique has some advantages in the epidemiological study of RTB, which can replace the traditional eld survey that is time-consuming and labor-intensive (Zhan et al, 2020).…”

Effects of Forest Structure On Dendroctonus Valens Damage Under Natural and Human Disturbances

“…May and August are the ight periods of RTB, during which the temperature is suitable for their feeding and spread (Zhan et al, 2020). A total of 109 sample plots, in which the trees showed a continuum of degree of RTB damage, were randomly selected and investigated in August of 2019 and 2020, including 79 plots affected by neither re (natural disturbance) nor stolen felling (human disturbance), 15 plots affected by re, and 16 plots affected by stolen felling (Fig.…”