Evaluating the Utility of NDVI as a Damage Indicator for Crops in the Enhanced Fujita Scale
Bibliographic record
Abstract
Abstract Tornadoes in Canada frequently impact rural regions, particularly croplands, where traditional enhanced Fujita scale (EF scale) assessments are limited due to the absence of standard damage indicators. Not knowing the true intensities of these tornadoes results in an inaccurate tornado climatology for these regions. This study evaluates the potential of the normalized difference vegetation index (NDVI), derived from high-resolution multispectral satellite imagery, as a proxy for crop damage assessment in the EF-scale framework. According to the results, NDVI percent change analysis reliably detects EF2+ damage but is largely ineffective for EF0 and EF1 tornadoes. The results also indicate that the decrease in plant health is correlated with the EF-scale rating of a tornado. Crop type and seasonal timing significantly influence NDVI detectability, with pasture and forage crops yielding weak signals and peak growing season offering the clearest damage signatures. This study also concludes that it is possible to revisit EF0-Default/EF-Unknown tornadoes that occur in agricultural areas to give them a more definitive rating on the EF scale. While NDVI analysis cannot delineate full tornado paths or replace ground surveys, it offers a scalable, remote sensing–based method to supplement tornado intensity assessments in agricultural regions. This approach may help reclassify underreported tornadoes and improve climatological accuracy in rural areas. Significance Statement Many tornadoes in rural areas go unclassified because they pass through croplands and current rating systems do not account for crop damage. This study explores a method to assess tornado strength in croplands by using satellite imagery to measure changes in plant health, through a remote sensing tool called the normalized difference vegetation index (NDVI). Looking at tornadoes in Canada from 2017 to 2023, the method was able to detect damage from stronger tornadoes but was unable to identify weaker ones. Seasonality and crop type also play a critical role in the overall results. While the results indicate that this approach cannot track the entire tornado path, it offers a promising step toward improving how we assess strong tornadoes in agricultural areas where traditional damage surveys fall short.
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 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".