Predicting Tree Mortality Using Spectral Indices Derived from Multispectral UAV Imagery
Bibliographic record
Abstract
Past research has shown that remotely sensed spectral information can be used to predict tree health and vitality. Recent developments in unmanned aerial vehicles (UAVs) have now made it possible to derive such information at the tree and stand scale from high-resolution imagery. We used visible and multispectral bands from UAV imagery to calculate a set of spectral indices for 52,845 individual tree crowns within 38 forest stands in western Canada. We then used those indices to predict the mortality of these canopy trees over the following year. We evaluated whether including multispectral indices leads to more accurate predictions than indices derived from visible wavelengths alone and how the performance varies among three different tree species (Picea glauca, Pinus contorta, Populus tremuloides). Our results show that spectral information can be effectively used to predict tree mortality, with a random forest model producing a mean area under the receiver operating characteristic curve (AUC) of 89.8% and a balanced accuracy of 83.3%. The exclusion of multispectral indices worsened the model performance, but only slightly (AUC = 87.9%, balanced accuracy = 81.8%). We found variation in model performance among species, with higher accuracy for the broadleaf species (balanced accuracy = 85.2%) than the two conifer species (balanced accuracy = 73.3% and 77.8%). However, all models overpredicted tree mortality by a major degree, which limits the use for tree mortality predictions on an individual level. Further improvements such as long-term monitoring, the use of hyperspectral data and cost-sensitive learning algorithms, and training the model with a larger and more balanced data set are necessary. Nevertheless, our results demonstrate that imagery from UAVs has strong potential for predicting annual mortality for individual canopy trees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".