Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red &amp; SWIR (NDRS)

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

doi:10.1016/j.rse.2020.112240

Cited by 99 publications

(78 citation statements)

References 66 publications

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“…Our spectral-only models (UAV OA = 55%, G-LiHT OA = 62%) were less accurate when compared to existing multispectral [5,36] and hyperspectral research (~11% lower) [14,15], with comparable spectral and spatial resolution but lacking SWIR bands. The G-LIHT structure-only model result (OA = 77%), using calibrated and range-corrected lidar apparent reflectance (1550 nm) was comparable to previous hyperspectral research in the same SWIR region [6,14,37] and somewhat more accurate than previous terrestrial lidar work using lidar intensity [26].…”

Section: Classification Of Health Status Groupssupporting

confidence: 79%

“…The selection of lidar intensity metrics as important G-LiHT features suggest that SWIR-band lidar may be useful for assessing vertical variation in canopy water content and indicative of tree health status. The utility of SWIR information has been previously confirmed by [6,14,37], who achieved 83% (2080-2350 nm), 73% (1250 nm) and 86% (2202 nm) OA. While evidence has been provided regarding the utility of SWIR information in passive optical data [6] and we have shown the benefit of lidar apparent reflectance, other studies show limited use for SWIR optical data [14] and SWIR-band lidar [26].…”

Section: Contribution To Classification: Structural Metricsmentioning

confidence: 76%

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Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

Cessna

Alonzo

Foster

et al. 2021

Forests

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The frequency and severity of spruce bark beetle outbreaks are increasing in boreal forests leading to widespread tree mortality and fuel conditions promoting extreme wildfire. Detection of beetle infestation is a forest health monitoring (FHM) priority but is hampered by the challenges of detecting early stage (“green”) attack from the air. There is indication that green stage might be detected from vertical gradients of spectral data or from shortwave infrared information distributed within a single crown. To evaluate the efficacy of discriminating “non-infested”, “green”, and “dead” health statuses at the landscape scale in Alaska, USA, this study conducted spectral and structural fusion of data from: (1) Unoccupied aerial vehicle (UAV) multispectral (6 cm) + structure from motion point clouds (~700 pts m−2); and (2) Goddard Lidar Hyperspectral Thermal (G-LiHT) hyperspectral (400 to 1000 nm, 0.5 m) + SWIR-band lidar (~32 pts m−2). We achieved 78% accuracy for all three health statuses using spectral + structural fusion from either UAV or G-LiHT and 97% accuracy for non-infested/dead using G-LiHT. We confirm that UAV 3D spectral (e.g., greenness above versus below median height in crown) and lidar apparent reflectance metrics (e.g., mean reflectance at 99th percentile height in crown), are of high value, perhaps capturing the vertical gradient of needle degradation. In most classification exercises, UAV accuracy was lower than G-LiHT indicating that collecting ultra-high spatial resolution data might be less important than high spectral resolution information. While the value of passive optical spectral information was largely confined to the discrimination of non-infested versus dead crowns, G-LiHT hyperspectral band selection (~400, 675, 755, and 940 nm) could inform future FHM mission planning regarding optimal wavelengths for this task. Interestingly, the selected regions mostly did not align with the band designations for our UAV multispectral data but do correspond to, e.g., Sentinel-2 red edge bands, suggesting a path forward for moderate scale bark beetle detection when paired with suitable structural data.

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Section: Classification Of Health Status Groupssupporting

confidence: 79%

Section: Contribution To Classification: Structural Metricsmentioning

confidence: 76%

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

Cessna

Alonzo

Foster

et al. 2021

Forests

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“…Therefore, it is imperative to detect PWD at the early stage and take preventive measures as soon as possible. In recent years, "early monitoring" has been a hot topic in forest pest research [18,[48][49][50]. Nevertheless, the precise definition of "early stage" is difficult to determine, especially in the PWD research.…”

Section: Early Monitoring Of Pwdmentioning

confidence: 99%

Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images

Luo

et al. 2021

Remote Sensing

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As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.

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“…Recent approaches show that attacked trees have spectral characteristics corresponding to stress already before infestation and that those differences remain during the attack [37]. As bark beetle preferably infest already weakened trees, it cannot be excluded that the observable difference in spectral characteristics between healthy and attacked trees already exist before the infestation [6].…”

Section: State-of-the-artmentioning

confidence: 99%

Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements

et al. 2021

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The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks.

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Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS)

Cited by 99 publications

References 66 publications

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

Three-Dimensional Convolutional Neural Network Model for Early Detection of Pine Wilt Disease Using UAV-Based Hyperspectral Images

Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements

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