Mapping prosopis juliflora invasion within rainwater harvesting structures in India using Google Earth Engine
Why this work is in the frame
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Bibliographic record
Abstract
Prosopis juliflora, a drought-tolerant fast-growing tree species, has invaded thousands of storage tanks in India: systems installed decades ago for capturing rainfall during the monsoon period. In this study, we applied Google Earth Engine (GEE) to detect and map P. juliflora invasion for a region of Tamil Nadu, India to determine the change in P. juliflora over two and a half decades. Both the Landsat legacy data and the new Sentinel-2 (S2) data were used with different setups with three classifiers - classification and regression tree (CART), random forest (RF), and support vector machine (SVM). The SVM classifier using Landsat-8 (L8) data outperformed the RF and CART classifiers, reaching overall accuracies of 90 %. When comparing S2 and L8 data for P. juliflora mapping, the use of S2 resulted in higher classification accuracies and the ability to identify dense patches of the species instead of only P. juliflora presence or absence. Over the full Gundar river basin, P. juliflora was found to invade new areas at an average rate of 27 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /annum over the period 1993-2015. P. juliflora was detected mainly along rivers and water bodies, as well as in urban areas. Expansion occurred heavily in the tank systems throughout the basin while abandoned farmland was primarily invaded in the lower basin.
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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) | 0.001 | 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 it