Impact of Land Cover Change on a Typical Mining Region and Its Ecological Environment Quality Evaluation Using Remote Sensing Based Ecological Index (RSEI)
Bibliographic record
Abstract
Ecological environment in mining cities has become an important part of ecological construction. This paper takes Tongling, a mining city, as the research area, and uses Landsat series remote sensing images from 2000 to 2020 as data sources. Using the principal component analysis method and the Remote Sensing Ecological Index (RSEI) integrated with four indexes of greenness, humidity, dryness, and heat, the ecological disturbance of the mining area was evaluated and studied. Meanwhile, the land cover spatiotemporal classification of Tongling city was extracted by the maximum likelihood method. Furthermore, landscape metrics were used, based on the information on open-pit mining areas, to quantitatively analyze the ecological environment quality and its change characteristics in the study area. The results show that (1) RSEI can better characterize the ecological quality of Tongling city, greenness and humidity are positively correlated with it, dryness and heat are negatively correlated with it, and dryness and RSEI have the highest correlation coefficient, indicating that urban expansion will cause ecological environment deterioration to a certain extent. (2) The ecological environment quality of the research area showed a “decline-rising” trend, and the mean value of RSEI decreased from 0.706 to 0.644. Spatially, the areas with poor RSEI are mainly distributed in the central urban area and the open-pit mining area in the south. (3) Land cover change leads to changes in landscape metrics, and most landscape-level metrics are positively or negatively correlated with RSEI. The more concentrated the land cover type distribution is, the smaller the change is, and the more regional RSEI can be improved. (4) The mean value of RESI of the ten open-pit mining areas in Tongling city decreased significantly, with a maximum decrease of 52.73%. Among them, the RESI decline rate in the area around the no.1 open pit mine is 0.034/year. The ecological degradation in Tongling city is attributed to the rapid expansion of built-up areas and the development of the mining industry. The research results can provide a scientific basis for protecting the ecological environment of mining cities.
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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 | 0.000 |
| 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) | 0.001 | 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 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".