Remote Sensing for Detecting and Mapping Flowering Rush: A Case Study in the Ottawa National Wildlife Refuge (ONWR), Ohio
Bibliographic record
Abstract
Predicting and mapping invasive wetland plant species is an important process for future management decisions and strategies.Controlling and mapping such plant species requires robust methods that are applicable at different ecological scales to map and monitor their spread.In particular, this study tested the feasibility of classification tree analysis (CTA) by using a high resolution Applanix 439 Digital Sensor System (DSS) aerial imagery (< 20 cm) and linear spectral unmixing (LSU) analysis by using Landsat Thematic Mapper (TM) data to produce different distribution maps of invasive flowering rush (Butomus umbellatus L.) potential in the Ottawa National Wildlife Refuge (ONWR) wetlands, in Northwest Ohio.The classification accuracy from CTA maps derived from different splitting rules wasevaluated by kappa statistics.The overall accuracy within the different runs varied between 35 to 56 % while the "Gini" splitting rule had the best performance.The endmembers from the best CTA performing map were utilized by the LSU method for estimating sub-pixel endmember fractions at a broader geographical scale.The results derived from the aerial imagery were slightly better than those from the Landsat imagery, as the goodness of fit between the flowering rush fraction map and the data measured in the field was lower.This study was intended to demonstrate the potential for flowering rush mapping over larger area using knowledge developed from smaller geographical scale using high resolution imagery.Results indicate that both methods show promising results for the prediction of flowering rush, but additional research iv that encompass different field data collection techniques, datasets of imagery and modeling methods need to be explored.v I dedicate this to my mother MarieAnne, my father Bob, and to my brothers Bob Jr, Cilvani and Dilano.Thank you for all of your love, support, and encouragement.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".