Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions

Huo, Langning; Lindberg, Eva; Bohlin, Jonas; Persson, Henrik J.

doi:10.1016/j.rse.2023.113484

Cited by 25 publications

(26 citation statements)

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“…Vegetation indices and mathematical transformations can be employed to assess the severity of disease under various growth conditions [30]. For instance, developing new spectral combinations based on the specificity of pests and diseases can be employed for monitoring Ips typographus using vegetation indices [31]. However, passive canopy spectral reflectance is susceptible to various factors such as solar angle, lighting conditions, canopy background, and shadows, which can impact the quality of the data [32].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation

Sun,

Zhou,

Shu

et al. 2024

Agriculture

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Southern blight significantly impacts peanut yield, and its severity is exacerbated by high-temperature and high-humidity conditions. The mycelium attached to the plant’s interior quickly proliferates, contributing to the challenges of early detection and data acquisition. In recent years, the integration of machine learning and remote sensing data has become a common approach for disease monitoring. However, the poor quality and imbalance of data samples can significantly impact the performance of machine learning algorithms. This study employed the Synthetic Minority Oversampling Technique (SMOTE) algorithm to generate samples with varying severity levels. Additionally, it utilized Fractional-Order Differentiation (FOD) to enhance spectral information. The validation and testing of the 1D-CNN, SVM, and KNN models were conducted using experimental data from two different locations. In conclusion, our results indicate that the SMOTE-FOD-1D-CNN model enhances the ability to monitor the severity of peanut white mold disease (validation OA = 88.81%, Kappa = 0.85; testing OA = 82.76%, Kappa = 0.75).

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Section: Introductionmentioning

confidence: 99%

Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation

Sun,

Zhou,

Shu

et al. 2024

Agriculture

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“…In the first and second stages of development, cutting down trees promptly to mechanically remove the beetles from the forest to the timber processing areas is recommended. In contrast, in the third stage, the tree itself no longer poses a sanitary risk because the bark beetles are no longer in it [8]. At the same time, from April to September, maintenance felling and sanitary random felling should not be carried out in spruce stands older than fifty years because the smell of felling residues attracts bark beetles [9].…”

Section: Introductionmentioning

confidence: 99%

“…To effectively reduce the impact of the bark beetle and prevent an outbreak, it is essential to recognise infested trees during the green attack period and remove the tree whilst the larvae are still contained [11]. However, due to the current high outbreak levels, monitoring large areas effectively and promptly is difficult [8]. In the last three years, the population of bark beetles has increased rapidly in Latvia; consequently, the damage has increased tenfold in three years [12].…”

Section: Introductionmentioning

confidence: 99%

“…Understanding the physiological and biochemical characteristics of infested trees with respect to specific spectral information is critical to successfully applying VI-based ML and DL algorithms [28]. Detailed comparisons of the accuracy and effectiveness of current methods for detecting spruce bark beetle have been undertaken [18,29], and the MS efficiency of UAV/UAS imagery [8,30,31] and the MS efficiency of Sentinel-2 imagery [32,33], including a review of the factors influencing the accuracy of the detection of infestations [28] and current challenges of RS in forestry [20].…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach

Goldbergs,

Upenieks

2024

Forests

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This study aimed to examine the efficiency of the vegetation index (VI) thresholding approach for mapping deadwood caused by spruce bark beetle outbreak. For this, the study used upscaling from individual dead spruce detection by unmanned aerial (UAS) imagery as reference data for continuous spruce deadwood mapping at a stand/landscape level by VI thresholding binary masks calculated from satellite Sentinel-2 imagery. The study found that the Normalized Difference Vegetation Index (NDVI) was most effective for distinguishing dead spruce from healthy trees, with an accuracy of 97% using UAS imagery. The study results showed that the NDVI minimises cloud and dominant tree shadows and illumination differences during UAS imagery acquisition, keeping the NDVI relatively stable over sunny and cloudy weather conditions. Like the UAS case, the NDVI calculated from Sentinel-2 (S2) imagery was the most reliable index for spruce deadwood cover mapping using a binary threshold mask at a landscape scale. Based on accuracy assessment, the summer leaf-on period (June–July) was found to be the most appropriate for spruce deadwood mapping by S2 imagery with an accuracy of 85% and a deadwood detection rate of 83% in dense, close-canopy mixed conifer forests. The study found that the spruce deadwood was successfully classified by S2 imagery when the spatial extent of the isolated dead tree cluster allocated at least 5–7 Sentinel-2 pixels.

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“…For fine-scale (local) analysis, methods using Unmanned Aerial Vehicle (UAV) based technology are potentially valuable, especially in forests with high conservation or economic value. In such areas, very detailed and repeated field surveys are needed, which well fits the advantages of UAVs (Näsi et al, 2015;Minařík and Langhammer, 2016;Dash et al, 2017;Brovkina et al, 2018;Stoyanova et al, 2018;Klouček et al, 2019;Minařík et al, 2020Minařík et al, , 2021Slavík et al, 2020;Huo et al, 2023). The regional scale analysis, a "compromise" between the above-mentioned RS approaches, uses airborne RS with, in particular, hyperspectral sensors for bark beetle detection (Lausch et al, 2013;Fassnacht et al, 2014;Näsi et al, 2018;Bárta et al, 2022).…”

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confidence: 99%

Early detection of bark beetle infestation using UAV-borne multispectral imagery: a case study on the spruce forest in the Czech Republic

Klouček,

Modlinger,

Zikmundová

et al. 2024

Front. For. Glob. Change

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Over the last decade, biotic disturbances caused by bark beetles have represented a serious environmental and economic issue in Central Europe. Great efforts are expended on the early detection and management of bark beetle infestation. Our study analyses a time series of UAV-borne multispectral imagery of a 250-ha forest in the Vysočina region in the Czech Republic. The study site represents a typical European spruce forest with routine silvicultural management. UAV-borne data was acquired three times during the vegetation period, specifically (a) before swarming, (b) at the early stage of infestation, and (c) in the post-abandon phase, i.e., after most bark beetle offspring left the trees. The spectral reflectance values and vegetation indices calculated from orthorectified and radiometrically calibrated imageries were statistically analyzed by quadratic discriminant analysis (QDA). The study shows that healthy and infested trees could be distinguished at the early stage of infestation, especially using NIR-related vegetation indices (NDVI and BNDVI in our case). Detecting infested trees is more significant by vegetation indices than spectral bands and increases with the increasing time after infestation. The study verified the usability of UAV-borne multispectral imageries for early detection of bark beetle infestation at the level of individual trees. Thus, these methods can contribute to precise and effective forest management on a local level.

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Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions

Cited by 25 publications

References 50 publications

Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation

Estimation of Peanut Southern Blight Severity in Hyperspectral Data Using the Synthetic Minority Oversampling Technique and Fractional-Order Differentiation

Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach

Early detection of bark beetle infestation using UAV-borne multispectral imagery: a case study on the spruce forest in the Czech Republic

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