Forest fragmentation is a pervasive consequence of human land use that creates novel forest boundaries in place of contiguous, intact forest. Boundary forests, or edges, experience environmental conditions distinct from the forest interior driven by lateral exposure to adjacent non-forest land cover. Forest edges tend to be hotter, drier and experience increased wind turbulence and atmospheric deposition with significant consequences for ecosystem processes and biogeochemical cycling. Much of what we know about forest edge structure and function derives from tropical forest research, despite prolific fragmentation in temperate forests. Building on recent field studies of temperate forest edges in the northeastern United States (US), I combine measurements from the US national forest inventory (NFI) with remotely-sensed maps of forest area to characterize broad patterns in the extent and impacts of fragmentation on temperate forest ecology. Using the US NFI to identify forest edges across a 20-state region, I report increased biomass and growth of edge forests compared their interior counterparts. I then compare the prevalence of forest edges in the US NFI and commonly-used forest maps to very-high-resolution land-cover maps, and I demonstrate that conventional methods of forest characterization systematically undercount and exclude forest edge area. Finally, I synthesize these findings to quantify aboveground carbon (C) cycling in New England using a novel approach that partitions forest C fluxes into forest edge and interior categories. I find that forest edges are disproportionately vulnerable to land-use conversion and are a critical component of both forest C uptake and emissions. Accounting for elevated growth rates in forest edges increases estimates of the net forest C sink in New England by 8.6% (4.36 Tg C). My dissertation research demonstrates the need to better understand the extent and effects of fragmentation in temperate forests, provides support for the treatment of forest edges as a distinct system, and highlights the need to include forest edges in current and future C accounting.

Abstract In Paper [I], I develop the FOR-DICE model to analyze optimal global forest carbon management. The FOR-DICE is a simple framework for assessing the role of the boreal, tropical, and temperate forests as both a source of renewable energy and a resource to sequester and store carbon. I find that forests play an important role in reducing global emissions, especially under ambitious climate targets. At the global level, efforts should focus on increasing the stock of forest biomass rather than increasing the use of the forest for bioenergy production. The results also highlight the important role of reducing tropical deforestation to reduce climate change. In Paper [II], I develop the FRICE to investigate the role of two key efforts to increase the stock of forest biomass, namely, afforestation and avoided deforestation. FRICE is a multi-regional integrated assessment model that captures the dynamics of forest carbon sequestration in a transparent way and allows me to investigate the allocation of these actions across space and time. I find that global climate policy can benefit considerably from afforestation and avoided deforestation in tropical regions, and in particular in Africa. Avoided deforestation is particularly effective in the short run while afforestation provides the largest emissions reductions in the medium run. This paper also highlights the importance of not solely relying on avoided deforestation as its capacity to reduce emissions is more limited than afforestation, especially under more stringent temperature targets. In Paper [III], we investigate how uncertainties linked to the forest affect the optimal climate policy. We incorporate parameter uncertainty on the intrinsic growth rate and climate effects on the forest by using the state-contingent approach. Our results show that forest uncertainty matters. We find that the importance of including forest in climate policy increases when the forest is subject to uncertainty. This occurs because optimal forest response allows us to reduce the costs associated with uncertainty. In Paper [IV], we explore the implications of asymmetries in climate policy arising from not recognizing forest carbon emissions and sequestration in the decision-making process. We show that not fully including carbon values associated with the forest will have large effects on different forest controls and lead to an increase in emissions, higher carbon prices, and lower welfare.  We further find, by investigating the relative importance of forest emissions compared to sequestration, that recognizing forest emissions from bioenergy and deforestation is especially important for climate policy.

Boreal old-growth forests are key determinants in the global carbon cycle. It is unknown how the role of persistent old-growth forests will be in the carbon cycle in the face of predicted climatic changes. Eddy-covariance measurements are commonly used to quantify carbon exchange between ecosystems, such as forests, and the atmosphere. Error due to gap-fill method is of particular interest in these datasets. Here we filled a 15-year eddy covariance dataset from the Northern Old-Growth Boreal Black Spruce (Picea mariana) site located near Thompson, in central Manitoba, Canada using four different gap-fill methods. Our objectives were to determine if choice of gap-fill method affected annual NEP and if these errors compounded to even greater differences over the 15-year study period. Most significant differences in NEP among methods occurred from September to December, but variations during the growing season were responsible for most of the annual differences. / October 2016

This thesis presents a summary of published data on forest fires and which during these fires are likely to arise and endanger human lives, especially professional firefighters. It also contains general information on fires and forests, but there are also listed the most common causes of this type of fire. It also deals with the knowledge of the public, both professional and lay, of substances generated during forest fires.

Over time, forests have played an important role in the ecosystems’ balance.   They store and retain a large amount of carbondioxide, preventing it to be released into the atmosphere. However, in recent years, deforestation and forest     degradation endanger their continuity in the provision of such service.In this article, the authors explain the program called “Reducing emissions from deforestation and forest degradation” (REDD+), which seeks to combat such problems. In that way, they introduce  which  are  the  challenges  for its implementation in our country. / A lo largo del tiempo, los bosques han desempeñado un rol importante en el equilibrio delos ecosistemas. Éstos almacenan y retienen una gran cantidad de dióxido de carbono, evitando así que este gas se libere a la atmosfera.A pesar de ello, en los últimos años, la deforestación y degradación forestal ponen en peligrosu continuidad en la provisión de ese servicio.En este artículo, los autores explican el programa denominado “Reducción de Emisiones por Deforestación y Degradación de bosques” (REDD+) que busca combatir los problemas descritos. Asimismo, plantean cuáles son losretos para su implementación en nuestro país.

Late-successional forests provide a unique opportunity to explore adaptive management approaches that mitigate atmospheric carbon dioxide levels through carbon storage while also enhancing ecological resilience to novel climate and disturbances. Typical benchmarks for adaptive forest management include species diversity and structural complexity, which are widely considered to increase ecosystem stability and productivity. However, the role of functional trait diversity (e.g., variation in leaf and stem traits) in driving forest productivity and ecosystem resilience remains underexplored. We leveraged existing continuous forest inventory (CFI) data and collected local functional trait observations from CFI plots within late-successional forests in western Massachusetts to explore links between aboveground carbon storage and different types of forest diversity. We then fit a linear model within a Bayesian hierarchical framework applying functional diversity, species diversity, and structural complexity as predictors of live aboveground biomass (AGB) within CFI plots. Our framework integrates local functional trait information with database species mean trait values using a multivariate structure to account for inherent trait syndromes and estimate functional diversity in each plot. Across 626 plot-timepoints, we found that integrating individual functional trait information from co-located plots yielded the best predictions of live AGB. Contrary to expectations, functional diversity had a negative relationship with live AGB. Whereas plots with low functional diversity and higher AGB were dominated by mid-to-late successional hardwood species, plots with high functional diversity had more shade-intolerant species and lower AGB mediated by recent small-scale disturbances. Our results reveal an ontogenetic shift in the effects of functional diversity on AGB productivity over the course of succession in northeastern temperate forests. Corroborating with classical models of biomass development in late-successional northern hardwood forests, our findings support the need for adaptive forest carbon management to facilitate a mosaic of different forest successional stages across the landscape to maximize live aboveground carbon benefits in northeastern mixed hardwood forests.

Made available in DSpace on 2016-06-02T20:00:52Z (GMT). No. of bitstreams: 1 5313.pdf: 9782065 bytes, checksum: f270ce2e273ed5f7437941eaf648f0cb (MD5) Previous issue date: 2013-06-27 / Financiadora de Estudos e Projetos / The intense greenhouse gas (GHG) emissions into the atmosphere, including Carbon Dioxide (CO2), are the main causes intensifying climate changes. According to Brazil s Forest Code, riparian forests are part of the Permanent Preservation Areas (PPAs) of water bodies, and their primary function regards their preservation, thereby contributing to biodiversity. Moreover, they contribute to minimize the greenhouse effect by promoting carbon sequestration (CO2). Thus, their preservation and/or recovery are the focal point of great concern. The objective of this study is to determine the potential for forest carbon sequestration in riparian forests of PPAs under the Unit for Water Resources Management of the Tietê-Jacaré (UGRHI 13) in the city of São Carlos SP, Brazil. The recuperation of the riparian areas was declared as high priority by the Committee of the Watershed UGRHI 13 in its Watershed Plan. Within this plan, forestry projects under the Clean Development Mechanism (CDM) represent an incentive for reforesting the PPAs of the water bodies of UGRHI 13 and other UGRHIs. The methodology used in this work consisted in the use of visual interpretation of satellite images, field samplings and the development of procedures to classify the degradation levels of the riparian forests into three categories: closed-canopy, open shrubland and predominantly herbaceous. The methodology also included applying a model to determine the Potential Biomass Index (PBI), estimating the carbon dioxide equivalent (CO2e) density, calculating the financial yield from the income of Certified Emission Reductions (CERs) for the forests in the study area, and determining the tCO2e value. For the closed canopy category, approximately 1.372 ha of PPAs with potential sequestration of 175.43tCO2e/ha were found, which would yield CDM of R$456.10 with the current price per tonne in April 2013; for the open shrubland category, approximately 1.573 ha of the PPAs, with a potential of 212.97tCO2e/ha and yield of R$553.70; and lastly, for the predominantly herbaceous category, 388 ha of PPAs, with a sequestration potential of 243.80tCO2e/ha and yield of R$633.90. This indicates that by restoring the still young and degraded forests of these PPAs, they will sequester significant amounts of carbon until they reach their maximum size. The financial revenues obtained by forest carbon sequestration would bear part of the costs of restoring the riparian forests in the PPAs. With the tCO2e values at R$26.00 for the closed canopy category, R$58.00 for the open shrubland category and R$84.00 for the predominantly herbaceous category, companies interested in offsetting their GHG emissions could buy the RECs at the values stipulated, adding their image to the carbon sequestration projects. / As intensas emissões de gases de efeito estufa (GEEs) para a atmosfera, incluindo o Dióxido de Carbono (CO2), passaram a ser o principal motivo da aceleração das mudanças climáticas. As matas ripárias fazem parte das Áreas de Preservação Permanente (APPs) de corpos d água conforme a legislação brasileira e têm como função primordial protegê-los e contribuir para a biodiversidade. Além disso, contribuem para minimizar o efeito estufa pelo sequestro de carbono (CO2) que promovem. Desse modo, tornaram-se alvo de grande preocupação no sentido da sua conservação ou recuperação. Este trabalho visou determinar o potencial de sequestro florestal de carbono em matas ripárias de APPs da Unidade de Gerenciamento de Recursos Hídricos do Tietê-Jacaré (UGRHI 13) no município de São Carlos SP. O restauro florestal das áreas de mata ripária foi indicado como prioridade maior para o Comitê de Bacia da UGRHI 13 em seu Plano de Bacia. Nesse sentido, projetos florestais no âmbito do Mecanismo de Desenvolvimento Limpo (MDL) podem se constituir em um incentivo para o restauro florestal das APPs dos corpos d água da UGRHI 13 e de outras UGRHIs. A metodologia empregada no trabalho consistiu no uso da interpretação visual de imagens de satélite, amostragens de campo e desenvolvimento de procedimentos para a classificação dos níveis de degradação das matas ripárias em três categorias: arbóreo fechado, arbóreo-arbustivo aberto e herbáceo predominante. A metodologia constituiu também da aplicação de um modelo para determinação do Índice de Biomassa Potencial (IBP), estimativa da densidade de dióxido de carbono equivalente (CO2e), cálculo do rendimento financeiro com a comercialização das Reduções de Emissões Certificadas (RECs) para as matas da área de estudo e determinação do valor da tCO2e. Para a categoria arbóreo fechado foram encontrados aproximadamente 1.372 ha de APPs com um potencial de sequestro de 175,43tCO2e/ha, que renderia pelo MDL R$456,10 com o preço da tonelada vigente em abril de 2013; para a categoria arbóreo-arbustivo aberto, aproximadamente 1.573 ha de APPs, com um potencial de 212,97tCO2e/ha e rendimento de R$553,70; e por último, para a categoria herbáceo predominante, 388 ha de APPs, com um potencial de sequestro de 243,80tCO2e/ha e rendimento de R$633,90. Isso permite concluir que com o restauro florestal dessas APPs as florestas, ainda jovens e degradadas, sequestrarão quantidades significativas de carbono até atingir o porte máximo. O rendimento financeiro obtido com o sequestro florestal de carbono arcaria parcialmente com os custos do restauro florestal das matas ripárias em APPs. Com o valor da tCO2e a R$26,00 para a categoria arbóreo fechado, R$58,00 para a categoria arbóreo-arbustivo aberto e R$84,00 para a categoria herbáceo predominante, empresas com o interesse de compensar suas emissões de GEEs poderiam comprar as RECs a estes valores estipulados, agregando sua imagem aos projetos de sequestro de carbono.

Research leading to this thesis aims to assess the policy relevant mitigation potential of Indian forests as well as aims to assess the impact of climate change on carbon stocks, vegetation boundary shifts, Net Primary Productivity (NPP) and the mitigation potential of Indian forests. To project the impact of climate change we chose a dynamic global vegetation model ‘Integrated Biosphere Simulator’ (IBIS V.2.6b4). We selected A2 and B2 scenarios for projecting the impacts. Mitigation potential was assessed using the ‘Generalized Comprehensive Mitigation Assessment Process’ (GCOMAP) model. We assess the mitigation potential of Indian forests in the light of India’s long-term policy objective of bringing 33% of its total geographical area under forest cover. We analyzed the mitigation potential of this policy objective under two scenarios: the first comprising of rapid afforestation scenario with the target to achieving the goal by 2020 and the second a moderate afforestation scenario in which this goal is achieved by 2030. We estimate that afforestation could offset about 9% of India’s average national emissions over the 2010-2030 period, while about 6.7% could be mitigated under the moderate afforestation scenario over the same period. We analyze the impact of climate change on the four key attributes of Indian forests, i.e. impact on vegetation distribution, impact on forest productivity (NPP), impact on soil carbon (SOC) and impact on biomass carbon. IBIS simulations suggest that approximately 39% and 34% of forest grids are projected to experience change in vegetation type under A2 and B2 climate scenarios, respectively over the period 2070¬2100. Simulations further indicate that NPP is projected to increase by an average of 66% under the A2 scenario and 49% under the B2 scenario. The increase is higher in the northeastern part of India. However, in the central and western Indian forests NPP remains stable or increases only moderately, and in some places even decreases. Our assessment of the impact of climate change on Soil Organic Carbon (SOC) suggests a trend similar to NPP distribution, which is to be expected as increased NPP is the primary driver of higher litter input to the soil. However, the quantum of increase in this case is lower, around 37% and 30%, for the A2 and B2 scenario respectively (averaged over India). The biomass carbon is also projected to increase all over India on the lines similar to NPP gains. However, projected gains in biomass, NPP and SOC should be viewed with caution as IBIS tends to simulate a fairly strong CO2 fertilization effect that may not necessarily be realized under conditions of nutrient and water limitations and under conditions of increased pest and fire outbreaks. Further we analyzed the impact of climate change on the mitigation potential of Indian forests by linking impact assessment models to mitigation potential assessment model GCOMAP. Two impact assessment models BIOME4 and IBIS are used for simulating the impact of climate change. IBIS is a dynamic vegetation model while BIOME4 is an equilibrium model. Our assessment suggests that with the BIOME4 simulations the cumulative mitigation potential increases by up to 21% under the A2 scenario over the period 2008 to 2108, whereas, under the B2 scenario the mitigation potential increases only by 14% over the same period. However cumulative mitigation potential estimates obtained from the IBIS simulations suggest much smaller gains, where mitigation potential increases by only 6% and 5% over the period 2008 to 2108, under A2 and B2 scenarios respectively. To enable effective policy analysis and to build a synergy between the mitigation and adaptation efforts in the Indian forest sector, a vulnerability index for the forested grids is constructed. The vulnerability index is based on the premise that forests in India are already subjected to multiple stresses including over extraction, insect outbreaks, live¬stock grazing, forest fires and other anthropogenic pressures -with climate change being an additional stress. The forest vulnerability index suggests that nearly 39% of the forest grids in India are projected to be vulnerable to the impacts of climate change under the A2 scenario, while 34% of the forest grids are projected to be vulnerable under the B2 scenario. The vulnerability index suggests that forests in the central part of India, a significant part of the western Himalayan forests and northern and central parts of the Western Ghats are particularly vulnerable to the impacts of climate change. Forests in the northeastern part of India are seemingly resilient to the impacts of climate change. It also suggests that given the high deforestation rate in northeast, this region be prioritized for reducing deforestation and forest degradation (REDD) projects under the United Nations Framework Convention on Climate Change (UNFCCC) mechanisms.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第21801号 / 農博第2314号 / 新制||農||1065(附属図書館) / 学位論文||H31||N5173(農学部図書室) / 京都大学大学院農学研究科森林科学専攻 / (主査)教授 神﨑 護, 教授 北島 薫, 教授 大澤 晃 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

Tropical forests store more than half of the world’s forest carbon and are particularly threatened by deforestation and degradation processes, which together represent the second largest source of anthropogenic CO2 emissions. Consequently, tropical forests are the focus of international climate policies (i.e. Reducing emissions from deforestation and forest degradation, REDD) aiming at reducing forest-related CO2 emissions. The REDD initiative lies on our ability to map forest carbon stocks (i.e. spatial dynamics) and to detect deforestation and degradations (i.e. temporal dynamics) at large spatial scales (e.g. national, forested basin), with accuracy and precision. Remote-sensing is as a key tool for this purpose, but numerous sources of error along the carbon mapping chain makes meeting REDD criteria an outstanding challenge. In the present thesis, we assessed carbon (quantified through aboveground biomass, AGB) estimation error at the tree- and plot-level using a widely used pantropical AGB model, and at the landscape-level using a remote sensing method based on canopy texture features from very high resolution (VHR) optical data. Our objective was to better understand and reduce AGB estimation error at each level using information on large canopy tree structure, distribution and spatial organization. Although large trees disproportionally contributed to forest carbon stock, they are under-represented in destructive datasets and subject to an under-estimation bias with the pantropical AGB model. We destructively sampled 77 very large tropical trees and assembled a large (pantropical) dataset to study how variation in tree form (through crown sizes and crown mass ratio) contributed to this error pattern. We showed that the source of bias in the pantropical model was a systematic increase in the proportion of tree mass allocated to the crown in canopy trees. An alternative AGB model accounting for this phenomenon was proposed. We also propagated the AGB model bias at the plot-level and showed that the interaction between forest structure and model bias, although often overlooked, might in fact be substantial. We further analyzed the structural properties of crown branching networks in light of the assumptions and predictions of the Metabolic Theory of Ecology, which supports the power-form of the pantropical AGB model. Important deviations were observed, notably from Leonardo’s rule (i.e. the principle of area conservation), which, all else being equal, could support the higher proportion of mass in large tree crowns. A second part of the thesis dealt with the extrapolation of field-plot AGB via canopy texture features of VHR optical data. A major barrier for the development of a broad-scale forest carbon monitoring method based on canopy texture is that relationships between canopy texture and stand structure parameters (including AGB) vary among forest types and regions of the world. We investigated this discrepancy using a simulation approach: virtual canopy scenes were generated for 279 1-ha plots distributed on contrasted forest types across the tropics. We showed that complementing FOTO texture with additional descriptors of forest structure, notably on canopy openness (from a lacunarity analysis) and tree slenderness (from a bioclimatic proxy) allows developing a stable inversion frame for forest AGB at large scale. Although the approach we proposed requires further empirical validation, a first case study on a forests mosaic in the Congo basin gave promising results. Overall, this work increased our understanding of mechanisms behind AGB estimation errors at the tree-, plot- and landscape-level. It stresses the need to better account for variation patterns in tree structure (e.g. ontogenetic pattern of carbon allocation) and forest structural organization (across forest types, under different environmental conditions) to improve general AGB models, and in fine our ability to accurately map forest AGB at large scale.