Combining Sentinel-2 Data and Risk Maps to Detect Trees Predisposed to and Attacked by European Spruce Bark Beetle DOI Creative Commons
Per‐Ola Olsson, Pengxiang Zhao, Mitro Müller

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(22), P. 4166 - 4166

Published: Nov. 8, 2024

The European spruce bark beetle is a major disturbance agent in Norway forests Europe, and with changing climate it predicted that damage will increase. To prevent the population buildup, to limit further spread during outbreaks, crucial detect attacked trees early. In this study, we utilize Sentinel-2 data combination risk map, created from geodata forestry data, predisposed by beetle. Random forest models were trained over two tiles (90 × 90 km) southern Sweden for all dates sufficient number of cloud-free pixels period May–September 2017 2018. classified into healthy study how detection accuracy changed time after swarming find which bands are more important detecting trees. (1) single-date (2) temporal features (1-year difference), (3) combined, (4) map combined. We also included spatial variability metric. results show was high already before May 2018, indicating early signs attack low at being attacked. For models, ranged 63 79% 84 94% tiles. features, 65 81% 81 92%. When 70 84% 96% tiles, included, 83 91% 92 97%, showing remote sensing can be combined increase accuracy. differences between indicate local influence accuracy, suggesting geographically weighted methods should applied. SWIR, red-edge, blue generally important, SWIR attack, they most suitable attack. metric, green band important.

Language: Английский

Multispectral drone images for the early detection of bark beetle infestations: assessment over large forest areas in the Italian South-Eastern Alps DOI Creative Commons
A. Bozzini, Langning Huo,

Stefano Brugnaro

et al.

Frontiers in Forests and Global Change, Journal Year: 2025, Volume and Issue: 8

Published: Feb. 27, 2025

Introduction European forests face increasing threats from climate change-induced stressors, which create favorable conditions for bark beetle outbreaks. The most critical spruce forest pest in Europe is the Spruce Bark Beetle ( Ips typographus L.). Effective management of this beetles’ outbreaks necessitates timely detection recently attacked trees, challenging given difficulty identifying symptoms on infested tree crowns, especially over large areas. This study assessed detectability trees dominated areas (20–60 ha) using high-resolution drone multispectral imagery. Methods A sensor mounted an Unmanned Aerial Vehicle (UAV) was used to capture images investigated stands weekly during June 2023. These were compute reflectance all single derive vegetation indices, and then compare these between healthy ones. Results results showed that it possible separate spectral features final developmental stage first generation, despite limitations due difficulties image processing best performing indices included NDRE (Normalized Difference Red Edge index) GNDVI (Green Normalized Vegetation Index), allowed earlier separation trees. Discussion shows use UAV imagery can present some when early larger integration sensors focused narrower windows around Red-Edge Green bands other remote sensing methods (e.g., satellite imagery) could help overcome improve early-detection proposed approach will increase understanding factors consider with techniques. In particular, add insights upscaling spatial scales, providing useful guidance suffering

Language: Английский

Citations

1
Sensitivity analysis of the Green Shoulder indices in pre-emergence detection of single trees attacked by European spruce bark beetle DOI Creative Commons
Langning Huo, Niko Koivumäki, Roope Näsi

et al.

International Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: March 28, 2025

Language: Английский

Citations

0
Spectral signatures discrimination of Norway spruce trees under experimentally induced drought and acute thermal stress using hyperspectral imaging DOI Creative Commons

Matúš Pivovar,

Roope Näsi, Eija Honkavaara

et al.

Forest Ecology and Management, Journal Year: 2025, Volume and Issue: 586, P. 122692 - 122692

Published: April 1, 2025

Language: Английский

Citations

0
Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone Images DOI Creative Commons
Langning Huo, Iryna Matsiakh, Jonas Bohlin

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 271 - 271

Published: Jan. 14, 2025

Multispectral imagery from unmanned aerial vehicles (UAVs) can provide high-resolution data to map tree mortality caused by pests or diseases. Although many studies have investigated UAV-imagery-based methods detect trees under acute stress followed mortality, few tested the feasibility and accuracy of detecting chronic stress. This study aims develop test how well UAV-based multispectral pine needle disease long before mortality. images were acquired four times through growing season in an area with infected pathogens. Vegetation indices (VIs) used quantify decline vitality, which was verified retention (%) estimated ground. Results showed that several VIs had strong correlations level identify severely defoliated (<75% retention) 0.71 overall classification accuracy, while slightly (>75% very low. The results one also implied more defoliation observed UAV (top view) than ground (bottom view). We conclude using efficiently needle-cast pathogens, thus assisting forest health monitoring.

Language: Английский

Citations

0
Combining Sentinel-2 Data and Risk Maps to Detect Trees Predisposed to and Attacked by European Spruce Bark Beetle DOI Creative Commons
Per‐Ola Olsson, Pengxiang Zhao, Mitro Müller

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(22), P. 4166 - 4166

Published: Nov. 8, 2024

The European spruce bark beetle is a major disturbance agent in Norway forests Europe, and with changing climate it predicted that damage will increase. To prevent the population buildup, to limit further spread during outbreaks, crucial detect attacked trees early. In this study, we utilize Sentinel-2 data combination risk map, created from geodata forestry data, predisposed by beetle. Random forest models were trained over two tiles (90 × 90 km) southern Sweden for all dates sufficient number of cloud-free pixels period May–September 2017 2018. classified into healthy study how detection accuracy changed time after swarming find which bands are more important detecting trees. (1) single-date (2) temporal features (1-year difference), (3) combined, (4) map combined. We also included spatial variability metric. results show was high already before May 2018, indicating early signs attack low at being attacked. For models, ranged 63 79% 84 94% tiles. features, 65 81% 81 92%. When 70 84% 96% tiles, included, 83 91% 92 97%, showing remote sensing can be combined increase accuracy. differences between indicate local influence accuracy, suggesting geographically weighted methods should applied. SWIR, red-edge, blue generally important, SWIR attack, they most suitable attack. metric, green band important.

Language: Английский

Citations

1