Spatial distribution mapping of vegetation cover in urban environment using tdvi for quality of life monitoring
Bibliographic record
Abstract
In previous work, we have demonstrated that the semi-empirical model Transformed Difference Vegetation Index (TDVI) is less sensitive to soil optical properties variation, and more suitable for estimating the fraction of vegetation cover in forest and agricultural environment. This paper reports on a comparative study between TDVI, Normalized Difference Vegetation Index (NDVI), and the Soil Adjusted Vegetation Index (SAVI) for estimating fraction vegetation cover in urban environment using an image acquired with the linear Imaging Self Scanner-Ill (LISS-III) onboard of the Indian Remote Sensing Satellite-ID (IRS-1D). The data were acquired on august 13, 1998 over the Montreal Island, period of the year when the vegetation is in the maximum phonological stage. A combined correction of the atmospheric effects (scattering and absorption) and the radiometric drift of the sensor were applied to transform the raw data to the surface reflectance. The validation of the obtained results according to the ground truth shows that the TDVI is a very good tool for vegetation cover monitoring in urban environment. It does not saturate like NDVI or SAVI, it shows a good linearity as a function of vegetation cover rate.
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 | 0.000 | 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".