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ArticleAssessing land use and cover change, forest degradation and secondary forest databases for better understand of airborne CO2 measurements over the Brazilian Amazon
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Tropical forests are essential for ecosystem services provision and for climate change mitigation. Amazon forest, the largest continue tropical forests in the world, have been decreasing due to deforestation and forest degradation. Brazil, a country containing most of the Amazon forests, also presents the highest deforestation rates within the Pan-Amazonian countries. The CARBAM project has been collecting bimonthly CO2 atmospheric measurements from an airplane since 2010 in the Brazilian Amazon, showing that there is a reduction on the forest capacity to absorb carbon for deforestation and climate change patterns. To understand these CO2 fluxes, we need to analyze the land use and cover change processes including forest degradation and secondary forest growth. Our goal is to assess different databases to better understand deforestation, degradation and secondary forest dynamics in the Amazon. For this, we merged different databases for the period 2010-2018: MapBiomas for land use and cover change; PRODES for deforestation; Bullock et al. (2020) for degradation; and Silva et al. (2020) for secondary forest. We found that, from the total accumulated deforested area in 2018 (17% of the Brazilian Amazon), pasture represent 69% fallowed by secondary vegetation 21% and agriculture 8%. The annual deforested area, smaller than secondary vegetation area, is increasing since 2012. Degradation has a different area each year. The carbon uptake by secondary forest and degradation dynamics is underestimated in the national communications of greenhouse gases, and its mapping is extremely relevant to policy makers to accomplish the National Determined Contribution. The large pasture areas deserve attention because it may permit the secondary forest to increase and provide agriculture expansion areas, decreasing in this way the pressure for deforestation and degradation of primary forest and contributing to preserve biodiversity and ecosystem services of the Amazon forests. Keywords: Amazon forests, deforestation, degradation, secondary forests, CO2 emissions ID: 3623188 -
ArticleRecovery of tree communities on degraded tropical forests after restoration planting
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Increasing rates of deforestation in tropical rain forests have been linked to agricultural encroachment. How well trees recover into a more species rich ecosystem after restoration planting remains unclear. The aim of the study was to evaluate the recovery of communities of tree, assess the influence of understory vegetation like Acanthus pubescens, and Pennisetum urpureum, on the recovery in Kibale National Park (KNP), Uganda. We studied six restored forests fragments ranging in age from 3 to 16 years and three primary forests. Our results showed that although recovery with natural regeneration was more effective than restoration planting the latter enhanced recruitment of native tree seedling. Tree recovery was generally correlated with age so that species density and diversity increased although at different rates. A reverse pattern was found for dominance but no clear pattern was found for tree density. Understory vegetation like Acanthus pubescens and Pennisetum purpureum negatively correlated with species density, tree density and diversity but a positive correlation was found for dominance. Although restoration planting can enhance recovery, understory vegetation significantly affects recovery of degraded forests. This can affect the cost of restoration of degraded areas. Keywords: Acanthus pubescens, P. purpureum, Recruitment, Restoration planting, Tree recovery ID: 3613266 -
ArticleImplications of forest definition for quantifying disturbance regime characteristics in Mediterranean forests
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.In the context of climate change and the increasing incidence of extreme events, global-scale assessments of forest disturbance regimes are needed to monitor changes, inform decision-making and adjust management and planning. But estimations of disturbance regime parameters can be strongly influenced by the criteria used for defining what constitutes a forest. Forests in Mediterranean regions are subject to increasingly higher pressure from climate change and anthropogenic factors. These pressures take the form of slow and diffuse degradation processes, such as drought-induced mortality and competition with shrubs, but also sudden stand- replacing disturbances, such as fire. Many Mediterranean forests, composed of widely spaced trees, are at the lower limit of the 10% canopy cover threshold set by the FAO for defining forests, while others are highly fragmented and mixed with a bushy matrix, qualifying them as other wooded lands. We thus expect assessments of forest disturbance regimes in the Mediterranean region to be particularly sensitive to the thresholds chosen. We assess the uncertainty linked to using different forest definitions for calculating disturbance rotation periods for the 40 ecoregions composing the Mediterranean biome. Using Google Earth Engine and remotely sensed data between 2000-2015, we apply different criteria for calculating the extent of forests and the areas disturbed within them, accounting for forest type, minimum size, canopy density and land use change. We identify the ecoregions with the disturbance rotation periods that are the most sensitive to these different definitions and compare the overall uncertainty with other major forest biomes. We conclude by discussing the importance of identifying harmonized definitions that can best support the sustainable management of Mediterranean forests, as well as opportunities for making consistent and accurate global baselines that can help improve projections of climate change impacts. Keywords: Forests, Disturbances, Remote Sensing, Planning, Climate Change ID: 3622233
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