| 1. |
- Huo, Langning, et al.
(författare)
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Influence of Crown Pixel Selection on the Early Detection of Bark Beetle Infestations Using Multispectral Drone Images
- 2024
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Konferensbidrag (refereegranskat)abstract
- In recent years, the European spruce bark beetle (Ips typographus, L.) has damaged large amounts of forests in Europe, and detecting infested trees is crucial for damage control and informative decision-making regarding management. This study explores efficient methods of detecting infestations using multispectral drone images, focusing on how using different pixels from the crown segments influences the detection rates. Tree crowns were first segmented using marker-controlled watershed segmentation, and then two pixel-selection strategies were tested, including selecting the pixels closer to the tree tops, and selecting the bright pixels with values higher than certain percentiles of the entire crown segments. Two datasets were used from the same area, including 2021 with an epidemic outbreak and 2023 with an endemic outbreak, to present the potential differences caused by attack intensity. The results showed that, in the early stages (1 – 9 weeks of infestation), using the centermost pixels or the brightest pixels in the tree crowns had higher detectability than using all pixels. Red-edge-based VIs were more sensitive than red-green-based VIs. In the middle stage (10 – 16 weeks of infestation), using pixels from the entire tree crown, including tree tops and the low branches, showed higher detectability than using fewer pixels. For the late stages (after 19 weeks of infestation), using only the center pixel was sufficient, and there were minor differences between different VIs. The results were supported by observations from two datasets from different years, although variations in the detectability between different years and stands were also observed.
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| 2. |
- Huo, Langning, et al.
(författare)
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Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions
- 2023
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Ingår i: Remote Sensing of Environment. - : Elsevier BV. - 0034-4257 .- 1879-0704. ; 287
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Tidskriftsartikel (refereegranskat)abstract
- Detecting disease- or insect-infested forests as early as possible is a classic application of remote sensing. Under conditions of climate change and global warming, outbreaks of the European spruce bark beetle (Ips typographus, L.) are threatening spruce forests and the related timber industry across Europe, and early detection of infestations is important for damage control. Infested trees without visible discoloration (green attack) have been identified using multispectral images, but how early green attacks can be detected is still unknown. This study aimed to determine when infested trees start to show an abnormal spectral response compared with healthy trees, and to quantify the detectability of infested trees during the infestation process. Pheromone bags were used to attract bark beetles in a controlled experiment, and subsequent infestations were assessed in the field on a weekly basis. In total, 977 trees were monitored, including 208 attacked trees. Multispectral drone images were obtained before and during the insect attacks, representing different periods of infestation. Individual tree crowns (ITC) were delineated by marker-controlled watershed segmentation, and the average reflectance of ITCs was analyzed based on the duration of infestation. The detectability of green attacks and driving factors were examined. We propose new Multiple Ratio Disease-Water Stress Indices (MR-DSWIs) as vegetation indices (VI) for detecting infestations. We defined a VI range of 5-95% as a healthy tree, and a VI value outside that range as an infested tree. Detection rates using multispectral images were always higher than discoloration rates observed in the field, and the newly proposed MR-DSWIs detected more infested trees than the established VIs. Infestations were detectable at 5 and 10 weeks after an attack at a rate of 15% and 90%, respectively, from the multispectral drone images. Weeks 5-10 of infestation therefore represent a suitable period for using the proposed methodology to map infestation at an early stage.
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| 3. |
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| 4. |
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| 5. |
- Lindberg, Eva, et al.
(författare)
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Potential of mapping forest damage from remotely sensed data
- 2022
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Remote sensing is an efficient tool for mapping, monitoring, and assessing forest damage and the risk of damage. This report presents ongoing research on those topics with preliminary results as well as research planned by the Department of Forest Resource Management, SLU in Umeå, in the near future. The damage types include spruce bark beetle attacks, storm damage, and forest fire. The report also outlines proposed continued research in the area and possible collaborations within and outside SLU.
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| 6. |
- Thulin, Carl-Gustaf, et al.
(författare)
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Black Stork Back: Species distribution model predictions of potential habitats for Black Stork Ciconia nigra in Sweden
- 2022
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Ingår i: Ornis Svecica. - : Ornis Svecica. - 1102-6812 .- 2003-2633. ; 32, s. 14-25
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Tidskriftsartikel (refereegranskat)abstract
- Increased understanding of the need to save endangered and locally extinct species has led to restoration or preservation of populations through reintroductions. Reintroduction of a species is worthwhile if the prerequisites for existence at the historical location have improved. Thus, background information about the habitat requirements of a target species is important for introduction programmes to be successful. The Black Stork Ciconia nigra was lost as a breeding species in Sweden during the 20th century, but recent observations and reports of potential breeding indicate that habitat conditions for Black Stork in Sweden may have improved. In this study, we used species characteristics and references to identify habitats in Sweden suitable for potential reintroduction of Black Stork. We identified several suitable areas in the former distribution range of this species in southern Sweden. Seven Swedish counties contained more than 18 % suitable habitat within their total area, with highest proportions in Jönköping County (25.8 %), Blekinge County (23.9 %), Västra Götaland County (22.1 %) and Kronoberg County (20.7 %). We suggest these areas to be made the primary targets for Black Stork reintroduction in Sweden.
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| 7. |
- Thulin, Carl-Gustaf, et al.
(författare)
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Revansch för svart stork?
- 2022
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Ingår i: Vår Fågelvärld. - 0042-2649. ; , s. 18-23
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
- Svart stork häckade i Sverige till mitten av 1900-talet. Sedan dess har den setts endast tillfälligt. En ny studie gjord av Sveriges lantbruksuniversitet visar att förutsättningarna för en återkomst är relativt goda i södra och mellersta Sverige. Ett återintroduktionsprojekt skulle dessutom kunna gynna många andra arter. Kanske kan Odins svala åter bli en del av landets häckfågelfauna.
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| 8. |
- Thulin, Carl-Gustaf, et al.
(författare)
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Svarta storkens återkomst
- 2022
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Ingår i: Serinus. ; , s. 14-19
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Tidskriftsartikel (populärvet., debatt m.m.)
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| 9. |
- Bohlin, Jonas, et al.
(författare)
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Extraction of Spectral Information from Airborne 3D Data for Assessment of Tree Species Proportions
- 2021
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud and the importance of different metrics were compared for tree species-specific estimates at a coniferous hemi-boreal test site in southern Sweden. A total of three different data sets of aerial image-based products and one multi-spectral lidar data set were used to estimate tree species-specific proportion and stem volume using an area-based approach. Metrics were calculated for 156 field plots (10 m radius) from point cloud data and used in a Random Forest analysis. Plot level accuracy was evaluated using leave-one-out cross-validation. The results showed small differences in estimation accuracy of species-specific variables between the colouring methods. Simple averages of the spectral metrics had the highest importance and using spectral data from two seasons improved species prediction, especially deciduous proportion. Best tree species-specific proportion was estimated using multi-spectral lidar with 0.22 root mean square error (RMSE) for pine, 0.22 for spruce and 0.16 for deciduous. Corresponding RMSE for aerial images was 0.24, 0.23 and 0.20 for pine, spruce and deciduous, respectively. For the species-specific stem volume at plot level using image data, the RMSE in percent of surveyed mean was 129% for pine, 60% for spruce and 118% for deciduous.
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| 10. |
- Bohlin, Jonas, et al.
(författare)
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Remote measuring of the depth of wheel ruts in forest terrain using a drone
- 2021
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Ingår i: International Journal of Forest Engineering. - : Informa UK Limited. - 1494-2119 .- 1913-2220. ; 32, s. 224-234
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Tidskriftsartikel (refereegranskat)abstract
- Even at a well-managed harvesting site, vehicle trafficking occurs on at least 12% of the area and might cause ruts and compaction. The use of drones for inventory and mapping in forestry is still a new method. The purpose of this study was to develop a method for measuring the size and depth of wheel ruts caused by forest machines in harvested areas, using drones and Structure from Motion photogrammetry. In order to investigate the accuracy of drone photogrammetry, measurements from flight altitudes of 60 m and 120 m above ground level were compared with manual measurements. The same methods were used at a control site on farm land, taking into account the rut depth and the location of the sample surface (close to trees or in a fully open area). No statistically significant differences were found between manual measurements and remote measurements from 60 m or 120 m altitude at the harvesting site (R-2 0.77-0.83). At the control site, an underestimation of 2.2 cm of the rut depth was found for remote measurements made from 120 m altitude. The data derived from drone images were able to reproduce the 3D model of surface features, such as bulges and ruts; these measurements were considered to be equivalent to manual measurements. For practical applications, a post-harvest survey using drones could contribute to verifying compliance with international forest certification standards or by private contractors to evaluate rut formation on their harvest sites.
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