Mapping prosopis juliflora invasion within rainwater harvesting structures in India using Google Earth Engine
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 km2/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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".