Quantification and reduction of bidirectional effects in aerial cir imagery of deciduous forest using two reference land surface types
Bibliographic record
Abstract
Bidirectional effects in airborne remote sensing data are governed by land surface reflectance characteristics, atmospheric scattering and the sun‐object‐sensor angular relationship. These factors introduce brightness variations in the data that render quantitative analysis more difficult. On the other hand, bidirectional reflectance distribution functions (BRDF) of land surfaces provide information about the physical characteristics of the surface. This study has two objectives. The first is to reduce brightness variations in aerial false colour imagery using empirical methods. The second is to test if bidirectional effects must be modelled using sample data only for the land surface type under analysis or can another surrogate land cover type, which may be easier to sample in the imagery, be used. The method is based on multiple view angle imaging of two reference land surfaces using highly overlapping stereo frame format imagery. Reduction of BRDF effects includes derivation of a quantification factor used in a formula based on the Rayleigh scattering function. The performance of the method is determined by evaluating the residual variations in mean digital number (DN) of several test plots located in two overlapping images. The study area is a temperate deciduous forest damaged to varying degrees by a severe ice storm in 1998. For sixteen test plots throughout the forest, it was found that the mean DN values of the plots in the two images became more similar when the correction model was derived from samples of the same deciduous forests. However, a BRDF model derived from samples of vegetated fields nearby reduced the variations between images for some of the sixteen forest plots more than the forest BRDF model. These plots were less damaged, had high canopy cover, and were located on steep slopes oriented towards the sun. They had scattering characteristics more similar to the fields than to the deciduous forest as a whole. Two principal conclusions were derived. First, it is essential that the model used to derive the BRDF correction be from the same land cover as the land cover type under study. Second, the suitability of the quantification factors for the BRDF correction provided additional information about the forest structure and damage level.
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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 | 0.001 | 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) | 0.000 | 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".