Greenhouse area detection in Guanzhong Plain, Shaanxi, China: spatio-temporal change and suitability classification
Bibliographic record
Abstract
The extensive use of greenhouses has brought soared economic benefits for farming practitioners in China and an overview of the spatio-temporal distribution of greenhouses is of great interest to agricultural practitioners and decision-makers. In this study, Landsat image based greenhouse maps in Guanzhong Plain, Shaanxi, China were made using random forest classification algorithm through visual interpretation on the Google Earth Engine. The 7-year's changes in greenhouse areas were investigated (i.e. 2000, 2003, 2006, 2010, 2013, 2015 and 2019) with yearly overall accuracy more than 90%. The results showed that the total area of greenhouses in Guanzhong Plain demonstrated an increasing trend, from 5.92 km2 in 2000 to 194.42 km2 in 2019 with a considerable growth between 2010 and 2015. The dominant drivers for the increase are largely attributed to the government policy as well as economic profitability. The distribution of greenhouse shifts to central and eastern regions of Guanzhong Plain. Greenhouses preferentially expand to the area near to rural roads, main rivers, and high elevation, with more than 45% greenhouses distributed within 1 km of the county rural road. The principal component analysis based suitability evaluation showed that a total of 38.44% of the area was suitable for greenhouse.
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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 | 0.000 | 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 | 0.001 |
| 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 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".