Evaluation of Soil Erosion Using 3S Techniques: A Case Study ofCangxi County in the Jialing River Basin, Yangtze River, China
Bibliographic record
Abstract
Soil erosion not only has a negative impact on the ecological environment, but also restricts the sustainable development.Therefore, it's significant to develop green ecology, evaluate soil erosion in large river valley, and propose feasible suggestions of soil and water conservation.This paper uses 3S technology to evaluate soil erosion of Cangxi County in the Jialing River basin, Yangtze River, China.The main steps include: (1)Image pre-processing: using ZY-3 and GF-1 satellite data, both geometric error and radiometric error are corrected so that image accuracy is improved; (2)Field investigation and indoor interpretation of remote sensing: field interpretation marks are established by GPS and RS; mainly image interpretation is carried out by machine learning or image classification, then results are checked by visual interpretation and confirmed by field review to improve interpretation accuracy;(3)Spatial analysis: soil erosion intensity is classified and soil erosion intensity map can be obtained through "standards for classification and gradation of soil erosion"(SL190-2007) based on three factors(land-use types, vegetation coverage and slope); (4)soil erosion evaluation: soil erosion is evaluated by soil erosion intensity map.The results show that: (1)The intensity of soil erosion in the study area is concentrated on slight and moderate soil erosion; the area with severe soil erosion is few; (2)The soil erosion of townships are mainly concentrated on the north and east of the county, while soil erosion is relatively slight in the northwest and southwest; (3)Under the influence of different factors, the soil erosion intensity is quite different and is often affected by many factors(exposed soil, human activities).Finally, this paper proposes some specific measures from the perspective of green ecology, which may provide a view to a certain reference for soil and water conservation in the study area and evaluation of soil erosion in other regions in Yangtze River, China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".