Using Color-infrared Photography and GIS to Quantify Cattail Coverage in Wetlands
Bibliographic record
Abstract
Analyzing changes in habitat features at very large scales with GIS requires digital images with both high spatial and spectral resolutions. As part of an experiment to reduce blackbird (Icterinae) damage to sunflower in North Dakota, we used large-scale infrared photography to monitor regrowth of herbicide-treated cattail (Typha spp.) in wetlands used by roosting blackbirds. We aerially photographed the wetlands at 460-610 m above ground level. All photographs were taken vertically through a 38-cm diameter port in the floor of the plane's fuselage. We used a SLR 35-mm camera loaded with Kodak Ektachrome® Professional Infrared EIR film. The photographs were shot through a 24-mm lens; a Wratten #12 filter and haze filter were attached to the lens to counteract blue light effects and improve clarity. The ground cell resolution was ~ 1 m for the photographic images. To reduce distortion and shadowing of the ground features, photographs were taken nearly perpendicular to the wetlands on cloudless days from 1100 to 1400 h CT. Film speed was set manually at EI 100, the recommended speed for the AR-5 developing process used for infrared accuracy. Shutter speed and aperture settings were 1/500 sec and F-5.6, respectively. The developed images were scanned at 2,100 pixels/inch with a Polaroid® Sprint Scan 35 Plus. The scans were converted to Tagged Image Format files. File size was ~16MB. We used ArcView® 3.2a software with the Image Analysis extension to categorize pixels into four habitat features through a supervised classification. Annual changes in proportions of living cattail, dead cattail, open water, and floating vegetation were tracked from 1999- 2002. A pixel-based coordinate system was used to coregister raster images of wetlands across years. Changes in the proportions of categorized features were tracked through time by summarizing pixel counts between coregistered rasters. We suggest this approach of data acquisition and analysis when monitoring habitat changes at very large spatial scales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".