Classifying the Degree of Bark Beetle-Induced Damage on Fir (Abies mariesii) Forests, from UAV-Acquired RGB Images

Abstract

Bark beetle outbreaks are responsible for the loss of large areas of forests and in recent years they appear to be increasing in frequency and magnitude as a result of climate change. The aim of this study is to develop a new standardized methodology for the automatic detection of the degree of damage on single fir trees caused by bark beetle attacks using a simple GIS-based model. The classification approach is based on the degree of tree canopy defoliation observed (white pixels) in the UAV-acquired very high resolution RGB orthophotos. We defined six degrees (categories) of damage (healthy, four infested levels and dead) based on the ratio of white pixel to the total number of pixels of a given tree canopy. Category 1: <2.5% (no defoliation); Category 2: 2.5–10% (very low defoliation); Category 3: 10–25% (low defoliation); Category 4: 25–50% (medium defoliation); Category 5: 50–75% (high defoliation), and finally Category 6: >75% (dead). The definition of “white pixel” is crucial, since light conditions during image acquisition drastically affect pixel values. Thus, whiteness was defined as the ratio of red pixel value to the blue pixel value of every single pixel in relation to the ratio of the mean red and mean blue value of the whole orthomosaic. The results show that in an area of 4 ha, out of the 1376 trees, 277 were healthy, 948 were infested (Cat 2, 628; Cat 3, 244; Cat 4, 64; Cat 5, 12), and 151 were dead (Cat 6). The validation led to an average precision of 62%, with Cat 1 and Cat 6 reaching a precision of 73% and 94%, respectively.

Evaluating a Novel Approach to Detect the Vertical Structure of Insect Damage in Trees Using Multispectral and Three-Dimensional Data from Drone Imagery in the Northern Rocky Mountains, USA

“…Remote sensing studies mapping healthy and damaged trees using fine-resolution drone imagery have reported similar high overall accuracies [43,45,70]. Furthermore, similar moderate overall accuracies have been reported when mapping different damage severities, including the classification of the tree-level health status [71,72] and estimation of the damage severity [33,73]. Some confusion occurred between the "minor damage" and "moderate damage" classes.…”

Variables Affecting the Pine Processionary Moth Flight: A Survey in the North-Western Italian Alps

“…In addition to conventional time-consuming field surveys, satellite-based images may be a useful tool for detecting defoliations in forest systems. In recent years, there have been several studies using unmanned aerial vehicles (UAV)acquired images, proving to be effective for forestry applications [52,53]. In the case of PPM infestations, this technology may be a useful tool to estimate the infestation severity, even if the number of nests captured by UAV images may be potentially underestimated if some nests on lower branches or in dense stands are not counted.…”

Treetop Detection in Mountainous Forests Using UAV Terrain Awareness Function

“…Nevertheless, the higher density of the DPC is important not only for treetop detection, but also for accurate detection of tree canopy characteristics that can be used for the precise evaluation of, for example, forest health [7,9], forest fire disturbance [29], or the estimation of dendrometric parameters [30]. This is especially relevant for fir forests in the Zao Mountains in Japan because, as shown in [22], the rate of single fir tree defoliation can be used as a proxy, and the results of using the TAF will contribute to a higher precision of tree canopy evaluation of the forest stand along the slope.…”

“…Fir is a highly valuable tree in Japan because of its obvious ecological function, but mainly as a tourist attraction, as they form the famous "Snow Monsters" in winter when they are covered completely by snow. In recent years, bark beetle attacks have seriously affected fir trees' health [22].…”