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Deforestation and Reforestation of Latin America and the Caribbean (2001–2010)

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TLDR
In this paper, the authors presented a wall-to-wall, annual maps of change in woody vegetation and other land-cover classes between 2001 and 2010 for each of the 16,050 municipalities in Latin American and the Caribbean region (LAC).
Abstract
Forest cover change directly affects biodiversity, the global carbon budget, and ecosystem function. Within Latin American and the Caribbean region (LAC), many studies have documented extensive deforestation, but there are also many local studies reporting forest recovery. These contrasting dynamics have been largely attributed to demographic and socio-economic change. For example, local population change due to migration can stimulate forest recovery, while the increasing global demand for food can drive agriculture expansion. However, as no analysis has simultaneously evaluated deforestation and reforestation from the municipal to continental scale, we lack a comprehensive assessment of the spatial distribution of these processes. We overcame this limitation by producing wall-to-wall, annual maps of change in woody vegetation and other land-cover classes between 2001 and 2010 for each of the 16,050 municipalities in LAC, and we used nonparametric Random Forest regression analyses to determine which environmental or population variables best explained the variation in woody vegetation change. Woody vegetation change was dominated by deforestation (541,835 km 2 ), particularly in the moist forest, dry forest, and savannas/shrublands biomes in South America. Extensive areas also recovered woody vegetation (+362,430 km 2 ), particularly in regions too dry or too steep for modern agriculture. Deforestation in moist forests tended to occur in lowland areas with low population density, but woody cover change was not related to municipality-scale population change. These results emphasize the importance of quantitating deforestation and reforestation at multiple spatial scales and linking these changes with global drivers such as the global demand for food.

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Citations
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Spatial autocorrelation reduces model precision and predictive power in deforestation analyses

TL;DR: In this article, both spatially explicit and non-spatial models of land-use data were tested for Colombia's deforestation for Colombia Parameter estimates, analyses of residual spatial autocorrelation, and Bayesian posterior predictive checks were used to compare model performance.
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Neighborhood effects in the Brazilian Amazônia: Protected areas and deforestation

TL;DR: In this paper, a dynamic spatial Durbin model is used to assess the impact of different types of protected areas (integral protected areas, sustainable protected areas and indigenous lands) on deforestation.
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A Comparison of Governance Challenges in Forest Restoration in Paraguay’s Privately-Owned Forests and Madagascar’s Co-managed State Forests

TL;DR: This paper examines how different drivers and pressures affect the restoration of forests under these two different property regimes in Paraguay and Madagascar.
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Arthropods on plants in a fragmented Neotropical dry forest: A functional analysis of area loss and edge effects

TL;DR: The results support the key role of forest area for conservation of arthropods taxonomic and functional diversity in a highly threatened region, and emphasize the need to understand the interactions between area and edge effects on such diversity.
References
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Journal ArticleDOI

Random Forests

TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Overview of the radiometric and biophysical performance of the MODIS vegetation indices

TL;DR: In this paper, the authors evaluated the performance and validity of the MODIS vegetation indices (VI), the normalized difference vegetation index (NDVI) and enhanced vegetation index(EVI), produced at 1-km and 500-m resolutions and 16-day compositing periods.
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Random forest: a classification and regression tool for compound classification and QSAR modeling.

TL;DR: It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.
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Dynamics of Land-Use and Land-Cover Change in Tropical Regions

TL;DR: In this article, the authors highlight the complexity of land-use/cover change and propose a framework for a more general understanding of the issue, with emphasis on tropical regions, and argue that a systematic analysis of local-scale land use change studies, conducted over a range of timescales, helps to uncover general principles that provide an explanation and prediction of new land use changes.
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