Using Multitemporal Hyper- And Multispectral Uav Imaging for Detecting Bark Beetle Infestation on Norway Spruce

Abstract

Abstract. Various biotic and abiotic stresses are threatening forests. Modern remote sensing technologies provide powerful means for monitoring forest health, and provide a sustainable basis for forest management and protection. The objective of this study was to develop unmanned aerial vehicle (UAV) based spectral remote sensing technologies for tree health assessment, particularly, for detecting the European spruce bark beetle (Ips typographus L.) attacks. Our focus was to study the early detection of bark beetle attack, i.e. the “green attack” phase. This is a difficult remote sensing task as there does not exist distinct symptoms that can be observed by the human eye. A test site in a Norway spruce (Picea abies (L.) Karst.) dominated forest was established in Southern-Finland in summer 2019. It had an emergent bark beetle outbreak and it was also suffering from other stress factors, especially the root and butt rot (Heterobasidion annosum (Fr.) Bref. s. lato). Altogether seven multitemporal hyper- and multispectral UAV remote sensing datasets were captured from the area in August to October 2019. Firstly, we explored deterioration of tree health and development of spectral symptoms using a time series of UAV hyperspectral imagery. Secondly, we trained assessed a machine learning model for classification of spruce health into classes of “bark beetle green attack”, “root-rot”, and “healthy”. Finally, we demonstrated the use of the model in tree health mapping in a test area. Our preliminary results were promising and indicated that the green attack phase could be detected using the accurately calibrated spectral image data.

Multispectral Imagery Provides Benefits for Mapping Spruce Tree Decline Due to Bark Beetle Infestation When Acquired Late in the Season

“…The results of this study are also in line with the study of Kloucek et al [27] where the best results were obtained in the end of the infestation period (overall accuracy: 96%) 78%-96% with a Maximum Likelihood classifier with less than 55 reference trees. In the study of Honkavaara et al [42], there was not such a high difference in classification accuracies between different dates. The comparison to this study is not as straightforward since the time span was shorter (August-October), when all trees stayed in the green-attack phase during the data collection period, indicating a low colonization rate.…”

High Spatial and Temporal Resolution Energy Flux Mapping of Different Land Covers Using an Off-the-Shelf Unmanned Aerial System

“…These systems are advantageous because in one single flight a multitude of datasets can be collected (e.g., RGB orthomosaics, vegetation and soil indices, 3D point clouds, as well as radiometric temperature). An example of this system is the Micasense Altum, which has been used for a variety of purposes, including drainage mapping [37], high throughput plant phenotyping [38], bark beetle infestation mapping [39], and vineyard crop health [40]. Many of the existing studies have used thermal infrared cameras mounted on multicopter UAVs, while these platforms can be very versatile, their range can be very limited even without accounting for high image overlap (typically < 20 hectares).…”

Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data

“…Nevertheless, it must be noted that the spatial resolutions of multispectral Worldview-2 (1.84 m at nadir) and RapidEye (6.5 m at nadir) allow the detection of smaller groups or even individual trees when compared with Sentinel-2. Although bark beetle damage mapping has been carried out using UAV very high resolution imagery with promising results [24,[27][28][29], they rely on planned flights, which usually require high costs and cannot be repeated frequently. The value of using Sentinel-2 data is the free cost of imagery at relatively high spatial resolution (10 m), but mainly the revisit time [15] of 2-3 days at mid-latitudes.…”