Evaluation of topographic effects on four commonly used vegetation indices
Bibliographic record
Abstract
Vegetation Indices(VIs) derived from remotely sensed data have been developed to monitor the Earth's vegetation cover.However,the topographic influence on VIs is an inevitable issue and is usually neglected in their large scale applications.In this study,the topographic effects on four commonly used vegetation indices,including Simple Ratio(SR),Normalized Difference Vegetation Index(NDVI),Reduced Simple Ratio(RSR),and Modified Normalized Difference Vegetation Index(MNDVI),derived from Landsat TM data over a mountainous forest area are evaluated.Two simple methods,the cosine correction and C-correction models,with different treatments of the influence of the diffused irradiance on reflectance,are used to remove the topographic effects on selected VIs.The results indicate that the reflectance in the Near Infrared(NIR) and Short Wave Infrared(SWIR) bands are more sensitive to topographical variations than that in the red band.Diffused radiance from the sky in the red band can moderate the variations of red band reflectance with topography,while this moderation is weak in the NIR and SWIR bands.The topography affects strongly vegetation indices which are not expressed as band ratios,such as RSR and MNDVI,resulting in negative biases on Sun-facing slopes and positive biases on Sun-backing slopes.As the slope increases,these biases increase rapidly.Therefore,the topographic effects should be carefully removed before using these non-band-ratio vegetation indices for vegetation parameter retrieval.Vegetation indices which are expressed as band ratios,such as SR,NDVI,can greatly reduce the noise caused by topographical variations.However,these indices still include significant topographic effects on steep slopes.SR is more sensitive to topographical variations on steep slopes than NDVI.The C-correction model is much better than the cosine correction model in removing topographic effects on VIs,especially on steep slopes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".