Biophysical and Socioeconomic Factors Associated to Deforestation and Forest Recovery in Brazilian Tropical Dry Forests
Bibliographic record
Abstract
The determination of land cover changes (LCCs) and their association to biophysical and socioeconomic factors is vital to support government policies toward the sustainable use of natural resources. The present study aimed to quantify deforestation, forest recovery and net cover change in tropical dry forests (TDFs) in Brazil from 2007 to 2016, and investigate how they are associated to biophysical and socioeconomic factors. We also assessed the effects of LCC variables in human welfare indicators. For this purpose, we used MODIS imagery to calculate TDF gross loss (deforestation), gross gain (forest recovery) and net cover change (the balance between deforestation and forest recovery) for 294 counties in three Brazilian states (Minas Gerais, Bahia, and Piauí). We obtained seven factors potentially associated to LCC at the county level: total county area, road density, humidity index, slope, elevation, and % change in human population and in cattle density. From 2007 to 2016, TDF cover increased from 76,693 to 80,964 km2 (+5.6%). This positive net change resulted from a remarkable forest recovery of 19,018 km2 (24.8%), offsetting a large deforested area (14,748 km2; 19.2%). Practically all these cover changes were a consequence of transitions from TDF to pastures and vice-versa, highlighting the importance of developing sustainable policies for cattle raising in TDF regions. Each LCC variable was associated to different set of factors, but two biophysical variables were significantly associated both to TDF area gained and lost per county: county area (positively) and slope (negatively), indicating that large and flat counties have very dynamic LCCs. The TDF net area change was only associated (negatively) to the humidity index, reflecting an increase in TDF cover in more arid counties. The net increase in Brazilian TDF area is likely a result from an interplay of biophysical and socioeconomic factors that reduced deforestation and caused pasture abandonment. Although the ecological integrity and permanence of secondary TDFs need further investigation, the recovery of this semi-arid ecosystem must be valued and accounted for in the national forest restoration programs, as it would significantly help achieving the goals established in the Bonn agreement and the Atlantic Rain Forest pact.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".