Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level

By R. Näsi, Eija Honkavaara, P. Lyytikäinen‐Saarenmaa, M. Blomqvist, Paula Litkey, Teemu Hakala, Niko Viljanen, T. Kantola, Topi Tanhuanpää, Markus Holopainen. Remote Sensing, 2015

367 citations recorded by the source corpus.

Low-cost, miniaturized hyperspectral imaging technology is becoming available for small unmanned aerial vehicle (UAV) platforms. This technology can be efficient in carrying out small-area inspections of anomalous reflectance characteristics of trees at a very high level of detail. Increased frequency and intensity of insect induced forest disturbance has established a new demand for effective methods suitable in mapping and monitoring tasks. In this investigation, a novel miniaturized hyperspectral frame imaging sensor operating in the wavelength range of 500–900 nm was used to identify mature Norway spruce (Picea abies L. Karst.) trees suffering from infestation, representing a different outbreak phase, by the European spruce bark beetle (Ips typographus L.). We developed a new processing method for analyzing spectral characteristic for high spatial resolution photogrammetric and hyperspectral images in forested environments, as well as for identifying individual anomalous trees. The dense point clouds, measured using image matching, enabled detection of single trees with an accuracy of 74.7%. We classified the trees into classes of healthy, infested and dead, and the results were promising. The best results for the overall accuracy were 76% (Cohen’s kappa 0.60), when using three color classes (healthy, infested, dead). For two color classes (healthy, dead), the best overall accuracy was 90% (kappa 0.80). The survey methodology based on high-resolution hyperspectral imaging will be of a high practical value for forest health management, indicating a status of bark beetle outbreak in time.

Subfields: Ecology, Global and Planetary Change, Insect Science

View Paper: https://www.semanticscholar.org/paper/4c5ec9d621b9c71ca76b5e7fa4a12b98928cce69

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Citations in the corpus

Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, methodology): “[143] developed a new processing approach to analyze spectral characteristics for high spatial resolution photogrammetric and hyperspectral image data in a forested environment.”
Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging (2017, methodology): “[50] presented the first study with 3D hyperspectral UAV imaging in individual tree-level analysis of bark beetle damage in spruce forests.”
Forestry applications of UAVs in Europe: a review (2017, methodology): “In only one case (Näsi et al. 2015), a hyperspectral sensor was used for forest health monitoring and diseases mapping.”
Unmanned Aerial Vehicles (UAVs) in environmental biology: a review (2019, background): “This sensor operated in a range of 500–900 nm and was sufficient to detect three classes of spruce individuals, healthy, infected and dead, with an accuracy of 75% (Näsi et al., 2015).”
Forest Structure Estimation from a UAV-Based Photogrammetric Point Cloud in Managed Temperate Coniferous Forests (2017, methodology): “Then, the three band values of each grid were normalized radiometrically by dividing each band value in the grid by the sum of the values of all bands in the corresponding grid, as described in previous studies [17,33,34].”