Entropy Change as Influenced by Anthropogenic Impact on a Boreal Land Cover - A Case Study
Bibliographic record
Abstract
Boreal forests are important terrestrial carbon sinks and hence routine monitoring for its vegetation dynamics is imperative. Remote sensing has proved to be the best option for detecting the land use and land cover at larger spatial scales, but it is often subjected to misinterpretation due to a variety of reasons such as sensor characteristics and heterogeneity of land cover. Landscape heterogeneity can be quantified based on the spectral heterogeneity. We hypothesize that this could be explained with the help of an entropy parameter, based on vegetation related spectral characteristics. This study demonstrates the spatio-temporal dynamics of landuse in the boreal landscape as well as the change in entropy as a result of increased heterogeneity in the upper Ottawa River basin, a region which faced dramatic landscape dynamics due to hydroelectric projects and other human activities. NDVI based strategy of land cover classification and the derivation of vegetation vigourosity, quantified the decrease in vegetation in the landscape and clarified that the urban areas have actually increased and the eclipsing effect created by slight increase in vegetation in the urban areas caused errors in landcover classification. Borrowing the idea from quantitative ecology, entropy based quantification of the vegetation diversity through the spectral signatures, specific to vegetation was developed by computing the Shannon’s entropy using the NDVI for two periods. The entropy has increased by a factor of 2.2 over the decadal period whereas there has been a general decrease in the vegetation along with a parallel increase in waterbodies. We hence conclude that the heterogeneity of the landscape has drastically increased. This boils down to the fact that the probability of getting vegetated surfaces has decreased in the landscape owing to anthropogenic influence and hence the measure of entropy will be used to augment our understanding about the landscape dynamics when studied from satellite platforms.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".