Detection of bark beetle infestation in drone imagery via thresholding cellular automata

Schaeffer, Satu Elisa; Jimenez-Lizarraga, Manuel; Rodríguez-Sánchez, Sara V.; Cuéllar-Rodríguez, Gerardo; Calderón, Óscar Alberto Aguirre; Reyna-González, Angel M.; Escobar, Alan

doi:10.1117/1.jrs.15.016518

Cited by 7 publications

(3 citation statements)

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“…A very different approach was described by Schaeffer et al [110]. They manually sampled bark-beetle-damaged trees in UAV-based orthomosaics to determine threshold rules for training classifiers based on cellular automata.…”

Section: Biotic Stressors (A) Pestsmentioning

confidence: 99%

“…Repetitive UAV surveys of the same area were designed to collect time series as a database for change analysis. Hence, the scientists analyzed spectral and structural changes over time to assess mechanical crown damage [97,98], fire damage based on pre-and postfire data [99][100][101], phenological differences [89], and different stages of stress-induced symptoms evident in the tree canopy [92,94,[102][103][104][105][106][107][108][109][110][111][112]. The primary period of FHM typically lay within the growing season.…”

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confidence: 99%

“…The researchers mainly took reference images of single or multiple reflectance targets in the field, with known or directly measured reflectance values across the spectrum of light, typically using the empirical line method (ELM) for calibration during image postprocessing. Occasionally, the target information was enhanced with irradiance values taken from light sensors attached to the UAV [22,94,109,120,123,[129][130][131][132], which were sometimes also used as the only calibration source [87,99,110,133]. In another study, the total incoming irradiance was simulated using data from a sun photometer [85].…”

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confidence: 99%

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UAV-Based Forest Health Monitoring: A Systematic Review

et al. 2022

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In recent years, technological advances have led to the increasing use of unmanned aerial vehicles (UAVs) for forestry applications. One emerging field for drone application is forest health monitoring (FHM). Common approaches for FHM involve small-scale resource-extensive fieldwork combined with traditional remote sensing platforms. However, the highly dynamic nature of forests requires timely and repetitive data acquisition, often at very high spatial resolution, where conventional remote sensing techniques reach the limits of feasibility. UAVs have shown that they can meet the demands of flexible operation and high spatial resolution. This is also reflected in a rapidly growing number of publications using drones to study forest health. Only a few reviews exist which do not cover the whole research history of UAV-based FHM. Since a comprehensive review is becoming critical to identify research gaps, trends, and drawbacks, we offer a systematic analysis of 99 papers covering the last ten years of research related to UAV-based monitoring of forests threatened by biotic and abiotic stressors. Advances in drone technology are being rapidly adopted and put into practice, further improving the economical use of UAVs. Despite the many advantages of UAVs, such as their flexibility, relatively low costs, and the possibility to fly below cloud cover, we also identified some shortcomings: (1) multitemporal and long-term monitoring of forests is clearly underrepresented; (2) the rare use of hyperspectral and LiDAR sensors must drastically increase; (3) complementary data from other RS sources are not sufficiently being exploited; (4) a lack of standardized workflows poses a problem to ensure data uniformity; (5) complex machine learning algorithms and workflows obscure interpretability and hinders widespread adoption; (6) the data pipeline from acquisition to final analysis often relies on commercial software at the expense of open-source tools.

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Section: Biotic Stressors (A) Pestsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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UAV-Based Forest Health Monitoring: A Systematic Review

et al. 2022

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Uncovering Early Traces of Bark Beetle Induced Forest Stress via Semantically Enriched Sentinel-2 Data and Spectral Indices

Mandl

Lang

2023

PFG

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Forest ecosystems are shaped by both abiotic and biotic disturbances. Unlike sudden disturbance agents, such as wind, avalanches and fire, bark beetle infestation progresses gradually. By the time infestation is observable by the human eye, trees are already in the final stages of infestation—the red- and grey-attack. In the relevant phase—the green-attack—biochemical and biophysical processes take place, which, however, are not or hardly visible. In this study, we applied a time series analysis based on semantically enriched Sentinel-2 data and spectral vegetation indices (SVIs) to detect early traces of bark beetle infestation in the Berchtesgaden National Park, Germany. Our approach used a stratified and hierarchical hybrid remote sensing image understanding system for pre-selecting candidate pixels, followed by the use of SVIs to confirm or refute the initial selection, heading towards a 'convergence of evidence approach’. Our results revealed that the near-infrared (NIR) and short-wave-infrared (SWIR) parts of the electromagnetic spectrum provided the best separability between pixels classified as healthy and early infested. Referring to vegetation indices, we found that those related to water stress have proven to be most sensitive. Compared to a SVI-only model that did not incorporate the concept of candidate pixels, our approach achieved distinctively higher producer’s accuracy (76% vs. 63%) and user’s accuracy (61% vs. 42%). The temporal accuracy of our method depends on the availability of satellite data and varies up to 3 weeks before or after the first ground-based detection in the field. Nonetheless, our method offers valuable early detection capabilities that can aid in implementing timely interventions to address bark beetle infestations in the early stage.

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