Translating Fire Impacts in Southwestern Amazonia into Economic Costs

Optimal Rotation Age in Fast Growing Plantations: A Dynamical Optimization Problem

Forest plantations are economically and environmentally relevant, as they play a key role in timber production and carbon capture. It is expected that the future climate change scenario affects forest growth and modify the rotation age for timber production. However, mathematical models on the effect of climate change on the rotation age for timber production remain still limited. We aim to determine the optimal rotation age that maximizes the net economic benefit of timber volume in a negative scenario from the climatic point of view. For this purpose, a bioeconomic optimal control problem was formulated from a system of Ordinary Differential Equations (ODEs) governed by the state variables live biomass volume, intrinsic growth rate, and area affected by fire. Then, four control variables were associated to the system, representing forest management activities, which are felling, thinning, reforestation, and fire prevention. The existence of optimal control solutions was demonstrated, and the solutions of the optimal control problem were also characterized using Pontryagin's Maximum Principle. The solutions of the model were approximated numerically by the Forward-Backward Sweep method. To validate the model, two scenarios were considered: a realistic scenario that represents current forestry activities for the exotic species Pinus radiata D. Don, and a pessimistic scenario, which considers environmental conditions conducive to a higher occurrence of forest fires. The optimal solution that maximizes the net benefit of timber volume consists of a strategy that considers all four control variables simultaneously. For felling and thinning, regardless of the scenario considered, the optimal strategy is to spend on both activities depending on the amount of biomass in the field. Similarly, for reforestation, the optimal strategy is to spend as the forest is harvested. In the case of fire prevention, in the realistic scenario, the optimal strategy consists of reducing the expenses in fire prevention because the incidence of fires is lower, whereas in the pessimistic scenario, the opposite is true. It is concluded that the optimal rotation age that maximizes the net economic benefit of timber volume in P. radiata plantations is 24 and 19 years for the realistic and pessimistic scenarios, respectively. This corroborates that the presence of fires influences the determination of the optimal rotation age, and as a consequence, the net economic benefit.

Economic impacts associated with the health effects of climate change in South America: a scoping review

This scoping review assesses the current evidence on the health impacts of climate change and associated economic costs in South America. In total, 3281 studies were identified using a systematic search strategy, but only 23 articles met the inclusion criteria and were analysed. The results from these articles indicate that the health effects of climate change will likely be costly for South America; however, evidence is limited to a handful of countries or regional analyses that ignore heterogeneity across and within countries. Most of the analysed studies looking at extreme weather events related to climate change focus on the effects and costs of droughts and fire events. A broader understanding of the topic could be achieved by estimating other extreme weather events' health effects and costs, using appropriate research methods to identify causal impacts, and including a more comprehensive and representative regional population sample. Beyond identifying effects, it is important to investigate demand responses for healthcare services, associated costs, availability and expansion of infrastructure, and cost-effectiveness of policies aimed at coping with and adapting to the health dimension of climate change.

The drivers and impacts of Amazon forest degradation

Approximately 2.5 × 10⁶ square kilometers of the Amazon forest are currently degraded by fire, edge effects, timber extraction, and/or extreme drought, representing 38% of all remaining forests in the region. Carbon emissions from this degradation total up to 0.2 petagrams of carbon per year (Pg C year^-1), which is equivalent to, if not greater than, the emissions from Amazon deforestation (0.06 to 0.21 Pg C year^-1). Amazon forest degradation can reduce dry-season evapotranspiration by up to 34% and cause as much biodiversity loss as deforestation in human-modified landscapes, generating uneven socioeconomic burdens, mainly to forest dwellers. Projections indicate that degradation will remain a dominant source of carbon emissions independent of deforestation rates. Policies to tackle degradation should be integrated with efforts to curb deforestation and complemented with innovative measures addressing the disturbances that degrade the Amazon forest.

Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil.

Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data

Fire is a major forest degradation component in the Amazon forests. Therefore, it is important to improve our understanding of how the post-fire canopy structure changes cascade through the spectral signals registered by medium-resolution satellite sensors over time. We contrasted accumulated yearly temporal changes in forest aboveground biomass (AGB), measured in permanent plots, and in traditional spectral indices derived from Landsat-8 images. We tested if the spectral indices can improve Random Forest (RF) models of post-fire AGB losses based on pre-fire AGB, proxied by AGB data from immediately after a fire. The delta normalized burned ratio, non-photosynthetic vegetation, and green vegetation (ΔNBR, ΔNPV, and ΔGV, respectively), relative to pre-fire data, were good proxies of canopy damage through tree mortality, even though small and medium trees were the most affected tree size. Among all tested predictors, pre-fire AGB had the highest RF model importance to predicting AGB within one year after fire. However, spectral indices significantly improved AGB loss estimates by 24% and model accuracy by 16% within two years after a fire, with ΔGV as the most important predictor, followed by ΔNBR and ΔNPV. Up to two years after a fire, this study indicates the potential of structural and spectral-based spatial data for integrating complex post-fire ecological processes and improving carbon emission estimates by forest fires in the Amazon.

Effects of widespread non-native trees on regulating ecosystem services

Tree taxa are often planted beyond their native range to increase the provision of some ecosystem services. Yet, they can disrupt ecosystem processes in their new ranges, causing changes in the provision of other services. Here we review the effects of five widespread tree taxa (Acacia, Ailanthus, Eucalyptus, Pinus and Robinia) on six regulating ecosystem services in areas where they are non-native. We conducted a literature search for pair-wise comparisons between sites dominated by any of the selected taxa and sites with native vegetation. An array of variables were used as indicators for each ecosystem service. Data were analysed using multi-level meta-analyses to compare effects of taxa on each ecosystem service, and effects of the same taxa across contexts. We compiled 857 case studies from 107 source papers. Several taxa tended to increase climate regulation, mostly Eucalyptus. Acacia decreased fire risk prevention. Robinia, Acacia and Ailanthus increased soil fertility, while Eucalyptus and Pinus, tended to decrease it. Soil formation was enhanced by Robinia and Ailanthus. Acacia promoted the increase of water in land pools, while Eucalyptus tended to decrease them. All effects show a large heterogeneity across case studies. Part of this heterogeneity could be attributed to gross climatic differences (i.e. biome), to species differences within each genus, to the structure of the recipient ecosystem, and/or to human management. Managers and policy-makers should consider the context-dependency and the potential effects of non-native trees on a wide range of services to ground their decisions. Our analyses also revealed important gaps of knowledge (e.g. on fire risk prevention, erosion control or water cycle regulation) and some potential publication bias. The methodology used here easily allows for future updates as new information will become available.

Burning in southwestern Brazilian Amazonia, 2016-2019

Fire is one of the most powerful modifiers of the Amazonian landscape and knowledge about its drivers is needed for planning control and suppression. A plethora of factors may play a role in the annual dynamics of fire frequency, spanning the biophysical, climatic, socioeconomic and institutional dimensions. To uncover the main forces currently at play, we investigated the area burned in both forested and deforested areas in the outstanding case of Brazil's state of Acre, in southwestern Amazonia. We mapped burn scars in already-deforested areas and intact forest based on satellite images from the Landsat series analyzed between 2016 and 2019. The mapped burnings in already-deforested areas totalled 550,251 ha. In addition, we mapped three forest fires totaling 34,084 ha. Fire and deforestation were highly correlated, and the latter occurred mainly in federal government lands, with protected areas showing unprecedented forest fire levels in 2019. These results indicate that Acre state is under increased fire risk even during average rainfall years. The record fires of 2019 may continue if Brazil's ongoing softening of environmental regulations and enforcement is maintained. Acre and other Amazonian states must act quickly to avoid an upsurge of social and economic losses in the coming years.

Drivers of Fire Anomalies in the Brazilian Amazon: Lessons Learned from the 2019 Fire Crisis

The 2019 fire crisis in Amazonia dominated global news and triggered fundamental questions about the possible causes behind it. Here we performed an in-depth investigation of the drivers of active fire anomalies in the Brazilian Amazon biome. We assessed a 2003–2019 time-series of active fires, deforestation, and water deficit and evaluated potential drivers of active fire occurrence in 2019, at the biome-scale, state level, and local level. Our results revealed abnormally high monthly fire counts in 2019 for the states of Acre, Amazonas, and Roraima. These states also differed from others by exhibiting in this year extreme levels of deforestation. Areas in 2019 with active fire occurrence significantly greater than the average across the biome had, on average, three times more active fires in the three previous years, six times more deforestation in 2019, and five times more deforestation in the five previous years. Approximately one-third of yearly active fires from 2003 to 2019 occurred up to 1 km from deforested areas in the same year, and one-third of deforested areas in a given year were located up to 500 m from deforested areas in the previous year. These findings provide critical information to support strategic decisions for fire prevention policies and fire combat actions.

Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon

Carbon (C) emissions from forest fires in the Amazon during extreme droughts may correspond to more than half of the global emissions resulting from land cover changes. Despite their relevant contribution, forest fire-related C emissions are not directly accounted for within national-level inventories or carbon budgets. A fundamental condition for quantifying these emissions is to have a reliable estimation of the extent and location of land cover types affected by fires. Here, we evaluated the relative performance of four burned area products (TREES, MCD64A1 c6, GABAM, and Fire_cci v5.0), contrasting their estimates of total burned area, and their influence on the fire-related C emissions in the Amazon biome for the year 2015. In addition, we distinguished the burned areas occurring in forests from non-forest areas. The four products presented great divergence in the total burned area and, consequently, total related C emissions. Globally, the TREES product detected the largest amount of burned area (35,559 km2), and consequently it presented the largest estimate of committed carbon emission (45 Tg), followed by MCD64A1, with only 3% less burned area detected, GABAM (28,193 km2) and Fire_cci (14,924 km2). The use of Fire_cci may result in an underestimation of 29.54 ± 3.36 Tg of C emissions in relation to the TREES product. The same pattern was found for non-forest areas. Considering only forest burned areas, GABAM was the product that detected the largest area (8994 km2), followed by TREES (7985 km2), MCD64A1 (7181 km2) and Fire_cci (1745 km2). Regionally, Fire_cci detected 98% less burned area in Acre state in southwest Amazonia than TREES, and approximately 160 times less burned area in forests than GABAM. Thus, we show that global products used interchangeably on a regional scale could significantly underestimate the impacts caused by fire and, consequently, their related carbon emissions.