Modeling and predicting land use and land cover changes using remote sensing in tropical coastal ecosystems of southern Peru
Bibliographic record
Abstract
Understanding the spatial impacts of human activities on coastal marine ecosystems is fundamental to manage the dynamic changes in land use that affect these natural spaces. In this study, we assessed land-use and land-cover (LULC) changes from 1990 to 2020 and their projection to 2030 in the Ica region (Peru). Through the integration of geographic information systems (GIS) and remote sensing techniques, LULC changes were analyzed. The kappa index reported an accuracy of the LULC maps above 87% in the analysis period. In addition, the quantitative analysis revealed that in 1990, 2000, 2010 and 2020, cultivated areas increased by 48.9, 53.2, 60.11 and 75.72% in influence zones A1, A2, A3 and A4, respectively, while urban development increased by 2.84, 4.81, 4.82 and 7.82% ha in the same zones. Likewise, the loss and gain analysis of land cover by period revealed that, in 1990, 2000, 2010 and 2020, cultivated areas increased by 48.9, 53.2, 60.11 and 75.72% in the zones of influence A1, A2, A3 and A4, respectively, while urban development increased by 2.84, 4.81, 4.82 and 7.82% ha in the same zones. In addition, during the period 2010–2020, the rate of transformation reached 53.1 ha/year towards urban uses in the coastal zone (A3) and 981.2 ha/year towards crops in zone A4. By 2030, urban expansion along the coast and major roads is expected to significantly reduce natural cover. Importantly, these results underscore the greater relevance of our integrated approach, which is applicable to others like it.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".