Use of Multi-Date and Multi-Spectral UAS Imagery to Classify Dominant Tree Species in the Wet Miombo Woodlands of Zambia
Bibliographic record
Abstract
Accurate maps of tree species distributions are necessary for the sustainable management of forests with desired ecological functions. However, image classification methods to produce species distribution maps for supporting sustainable forest management are still lacking in the Miombo woodland ecoregion. This study used multi-date multispectral Unmanned Aerial Systems (UAS) imagery collected at key phenological stages (leaf maturity, transition to senescence, and leaf flushing) to classify five dominant canopy species of the wet Miombo woodlands in the Copperbelt Province of Zambia. Object-based image analysis (OBIA) with a random forest algorithm was used on single date, multi-date, and multi-feature UAS imagery for classifying the dominant canopy tree species of the wet Miombo woodlands. It was found that classification accuracy varies both with dates and features used. For example, the August image yielded the best single date overall accuracy (OA, 80.12%, 0.68 kappa), compared to October (73.25% OA, 0.59 kappa) and May (76.64% OA, 0.63 kappa). The use of a three-date image combination improved the classification accuracy to 84.25% OA and 0.72 kappa. After adding spectral indices to multi-date image combination, the accuracy was further improved to 87.07% and 0.83 kappa. The results highlight the potential of using multispectral UAS imagery and phenology in mapping individual tree species in the Miombo ecoregion. It also provides guidance for future studies using multispectral UAS for sustainable management of Miombo tree species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 source (direct Gemma or distilled Codex), 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".