UAV‐derived greenness and within‐crown spatial patterning can detect ash dieback in individual trees

• Published in: Ecological solutions and evidence 2024-04, Vol.5 (2), p.n/a
• Main Authors: Flynn, W. R. M., Grieve, S. W. D., Henshaw, A. J., Owen, H. J. F., Buggs, R. J. A., Metheringham, C. L., Plumb, W. J., Stocks, J. J., Lines, E. R.
• Format: Article
• Language: English
• Subjects: Assessments; Data acquisition; Dieback; disease detection and monitoring; Extremities; Foliage; Growing season; Leaves; Motion detection; Pattern formation; Photogrammetry; Remote sensing; RGB; Sensors; SfM; spatial patterning; Three dimensional models; Trees; UAV; Unmanned aerial vehicles; Visual effects; Woodlands
• ISSN: 2688-8319; 2688-8319
• DOI: 10.1002/2688-8319.12343

Ash Dieback (ADB) has been present in the UK since 2012 and is expected to kill up to 80% of UK ash trees. Detecting and quantifying the extent of ADB in individual tree crowns (ITCs), which is crucial to understanding resilience and resistance, currently relies on visual assessments which are impractical over large scales or at high frequency. The improved imaging capabilities and declining cost of consumer UAVs, together with new remote sensing methods such as structure from motion photogrammetry (SfM) offers potential to quantify the fine‐scale structural and spectral metrics of ITCs that are indicative of ADB, rapidly, and at low‐cost. We extract high‐resolution 3D RGB point clouds derived from SfM of canopy ash trees taken monthly throughout the growing season at Marden Park, Surrey, UK, a woodland impacted by ADB. We segment ITCs, extract green chromatic coordinate (gcc), and test the relationship with visual assessments of crown health. Next, we quantify spatial patterning of dieback within ITCs by testing the relationship between internal variation of gcc and path length, a measure of the distance from foliage to trunk, for small clusters of foliage. We find gcc correlates with visual assessments of crown health throughout the growing season, but the strongest relationships are in measurements taken after peak greenness, when the effects of ADB on foliage are likely to be most prevalent. We also find a negative relationship between gcc and path length in infected trees, indicating foliage loss is more severe at crown extremities. We demonstrate a new method for identifying ADB at scale using a consumer‐grade 3D RGB UAV system and suggest this approach could be adopted for widespread rapid monitoring. We recommend the optimum time of year for data acquisition, which we find to be an important factor for detecting ADB. Although here applied to ADB, this framework is applicable to a multitude of drivers of crown dieback, presenting a method for identifying spectral‐structural relationships which may be characteristic of disturbance type. Ash Dieback (ADB) has been present in the UK since 2012 and is expected to kill up to 80% of UK ash trees. Using high‐resolution 3D UAV data, we show correlation with visual assessments of crown health. In infected trees, we find characteristic spatial patterning of leaves, with dieback most evident at the extremities of crowns.