Effect of Deforestation on Land Surface Temperature in the Chiquitania Region, Bolivia
Bibliographic record
Abstract
Neotropical forests offer alternatives to surface cooling and their conservation is an effective solution for mitigating the effects of climate change. Little is known about the importance of tropical dry forests for temperature regulation in Chiquitania, a region with increasing deforestation rates. The impact that deforestation processes are having on the surface temperature in Chiquitania remains an open question. This study evaluated trends in forest cover loss based on land surface temperatures (°C) in forested and deforested areas in Chiquitania. We hypothesized a positive relationship between higher deforestation and a temperature increase, which would decrease the resilience of highly disturbed Chiquitano forests. We evaluated ten sampling sites (10 × 10 km), including five in forested areas with some type of protection and the other five in areas with populated centers and accelerated forest loss. We developed scripts on the Google Earth Engine (GEE) platform using information from the Normalized Difference Vegetation Index (NDVI, MOD13A2) and the daytime and nighttime Land Surface Temperature (LST, MYD11A1) from MODIS products for the period 2001–2021. The statistical significance of the trends of the time series averages of the MODIS products was analyzed using a nonparametric Mann–Kendall test and the degree of the relationship between the variables was determined using the Pearson statistic. Our results based on NDVI analysis showed consistent vegetation growth in forested areas across the study period, while the opposite occurred in deforested lands. Regarding surface temperature trends, the results for daytime LST showed a positive increase in the four deforested areas. Comparatively, daytime LST averages in deforested areas were warmer than those in forested areas, with a difference of 3.1 °C. Additionally, correlation analyses showed a significant relationship between low NDVI values due to deforestation in three sites and an increase in daytime LST, while for nighttime LST this phenomenon was registered in two deforested areas. Our results suggest a significant relationship between the loss of forest cover and the increase in land surface temperature in Chiquitania. This study could be the first step in designing and implementing an early climate–forest monitoring system in this region.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".