Spatial distribution of forest aboveground biomass estimated from remote sensing and forest inventory data in New England, USA.

Abstract We combined satellite (Landsat 7 and Moderate Resolution Imaging Spectrometer) and U.S. Department of Agriculture forest inventory and analysis (FIA) data to estimate forest aboveground biomass (AGB) across New England, USA. This is practical for large-scale carbon studies and may reduce uncertainty of AGB estimates. We estimate that total regional forest AGB was 1,867 teragram (1012, dry weight) in 2001, with a mean AGB density of 120 Mg/ha (Standard deviation = 54 Mg/ha) ranging from 15 to 240 Mg/ha within a 95% percentile. The majority of regional AGB density was in the range of 80 to 160 Mg/ha (58.2%). High AGB densities were observed along the Appalachian Mountains from northwestern Connecticut to the Green Mountains in Vermont and White Mountains in New Hampshire, while low AGB densities were concentrated in the Downeast area of Maine (ME) and the Cape Cod area of Massachusetts (MA). At the state level, the averaged difference in mean AGB densities between simulated and FIA (as reference) was -2.0% ranging from 0% to -4.2% with a standard error of 3.2%. Within the 95% confidence interval the differences between FIA and simulated AGB densities ranged from 0 to 6% (absolute value). Our study may provide useful information for regional fuel-loading estimates

The neotropical reforestation hotspots : a biophysical and socioeconomic typology of contemporary forest expansion

Tropical reforestation is a significant component of global environmental change that is far less understood than tropical deforestation, despite having apparently increased widely in scale during recent decades. The regional contexts defining such reforestation have not been well described. They are likely to differ significantly from the geographical profiles outlined by site-specific observations that predominate in the literature. In response, this article determines the distribution, extent, and defining contexts of apparently spontaneous reforestation. It delineates regional ‘hotspots’ of significant net reforestation across Latin America and the Caribbean and defines a typology of these hotspots with reference to the biophysical and socioeconomic characteristics that unite and distinguish amongst them. Fifteen regional hotspots were identified on the basis of spatial criteria pertaining to the area, distribution, and rate of reforestation 2001–2014, observed using a custom continental MODIS satellite land-cover classification. Collectively, these hotspots cover 11% of Latin America and the Caribbean and they include 167,667.7 km2 of new forests. Comparisons with other remotely sensed estimates of reforestation indicate that these hotspots contain a significant amount of tropical reforestation, continentally and pantropically. The extent of reforestation as a proportion of its hotspot was relatively invariable (3–14%) given large disparities in hotspot areas and contexts. An ordination analysis defined a typology of five clusters, distinguished largely by their topographical roughness and related aspects of agro-ecological marginality, climate, population trends, and degree of urbanization: ‘Urban lowlands’, ‘Mountainous populated areas’, ‘Rural highlands’, ‘Rural humid lands’ and ‘Rural dry lands’. The typology highlights that a range of distinct, even oppositional regional biophysical, demographic, and agricultural contexts have equally given rise to significant, regional net reforestation, urging a concomitant diversification of forest transition science

On the use of MODIS EVI to assess gross primary productivity of North American ecosystems

[1] Carbon flux models based on light use efficiency (LUE), such as the MOD17 algorithm, have proved difficult to parameterize because of uncertainties in the LUE term, which is usually estimated from meteorological variables available only at large spatial scales. In search of simpler models based entirely on remote‐sensing data, we examined direct relationships between the enhanced vegetation index (EVI) and gross primary productivity (GPP) measured at nine eddy covariance flux tower sites across North America. When data from the winter period of inactive photosynthesis were excluded, the overall relationship between EVI and tower GPP was better than that between MOD17 GPP and tower GPP. However, the EVI/GPP relationships vary between sites. Correlations between EVI and GPP were generally greater for deciduous than for evergreen sites. However, this correlation declined substantially only for sites with the smallest seasonal variation in EVI, suggesting that this relationship can be used for all but the most evergreen sites. Within sites dominated by either evergreen or deciduous species, seasonal variation in EVI was best explained by the severity of summer drought. Our results demonstrate that EVI alone can provide estimates of GPP that are as good as, if not better than, current versions of the MOD17 algorithm for many sites during the active period of photosynthesis. Preliminary data suggest that inclusion of other remote‐sensing products in addition to EVI, such as the MODIS land surface temperature (LST), may result in more robust models of carbon balance based entirely on remote‐sensing data

Responses and adaptation strategies of terrestrial ecosystems to climate change

Terrestrial ecosystems are likely to be affected by climate change, as climate change-induced shift of water and heat stresses patterns will have significant impacts on species composition, habitat distribution, and ecosystem functions, and thereby weaken the terrestrial carbon (C) sink and threaten global food security and biofuel production. This thesis investigates the responses of terrestrial ecosystems to climate change and is structured in four main chapters.;The first chapter of the thesis is directed towards the impacts of snow variation on ecosystem phenology. Variations in seasonal snowfall regulate regional and global climatic systems and vegetation growth by changing energy budgets of the lower atmosphere and land surface. We investigated the effects of snow on the start of growing season (SGS) of temperate vegetation in China. Across the entire temperate region in China, the winter snow depth increased at a rate of 0.15 cm•yr-1 (p=0.07) during the period 1982-1998, and decreased at a rate of 0.36 cm•yr-1 (p=0.09) during the period 1998-2005. Correspondingly, the SGS advanced at a rate of 0.68 d•yr-1 (p\u3c0.01) during 1982 to 1998, and delayed at a rate of 2.13 d•yr-1 (p=0.07) during 1998 to 2005, against a warming trend throughout the entire study period of 1982-2005. Spring air temperature strongly regulated the SGS of both deciduous broad-leaf and coniferous forests; whilst the winter snow had a greater impact on the SGS of grassland and shrubs. Snow depth variation combined with air temperature contributed to the variability in the SGS of grassland and shrubs, as snow acted as an insulator and modulated the underground thermal conditions. Additionally, differences were seen between the impacts of winter snow depth and spring snow depth on the SGS; as snow depths increased, the effect associated went from delaying SGS to advancing SGS. The observed thresholds for these effects were snow depths of 6.8 cm (winter) and 4.0 cm (spring). The results of this study suggest that the response of the vegetation\u27s SGS to seasonal snow change may be attributed to the coupling effects of air temperature and snow depth associated with the soil thermal conditions.;The second chapter further addresses snow impacts on terrestrial ecosystem with focus on regional carbon exchange between atmosphere and biosphere. Winter snow has been suggested to regulate terrestrial carbon (C) cycling by modifying micro-climate, but the impacts of snow cover change on the annual C budget at the large scale are poorly understood. Our aim is to quantify the C balance under changing snow depth. Here, we used site-based eddy covariance flux data to investigate the relationship between snow cover depth and ecosystem respiration (Reco) during winter. We then used the Biome-BGC model to estimate the effect of reductions in winter snow cover on C balance of Northern forests in non-permafrost region. According to site observations, winter net ecosystem C exchange (NEE) ranged from 0.028-1.53 gC•m-2•day-1, accounting for 44 +/- 123% of the annual C budget. Model simulation showed that over the past 30 years, snow driven change in winter C fluxes reduced non-growing season CO2 emissions, enhancing the annual C sink of northern forests. Over the entire study area, simulated winter ecosystem respiration (Reco) significantly decreased by 0.33 gC•m-2•day -1•yr-1 in response to decreasing snow cover depth, which accounts for approximately 25% of the simulated annual C sink trend from 1982 to 2009. Soil temperature was primarily controlled by snow cover rather than by air temperature as snow served as an insulator to prevent chilling impacts. A shallow snow cover has less insulation potential, causing colder soil temperatures and potentially lower respiration rates. Both eddy covariance analysis and model-simulated results showed that both Reco and NEE were significantly and positively correlated with variation in soil temperature controlled by variation in snow depth. Overall, our results highlight that a decrease in winter snow cover restrains global warming through emitting less C to the atmosphere.;The third chapter focused on assessing drought\u27s impact on global terrestrial ecosystems. Drought can affect the structure, composition and function of terrestrial ecosystems, yet the drought impacts and post-drought recovery potential of different land cover types have not been extensively studied at a global scale. Here, we evaluated drought impacts on gross primary productivity (GPP), evapotranspiration (ET), and water use efficiency (WUE) of different global terrestrial ecosystems, as well as the drought-resilience of each ecosystem type during the period of 2000 to 2011. We found the rainfall and soil moisture during drought period were dramatically lower than these in non-drought period, while air temperatures were higher than normal during drought period with amplitudes varied by land cover types. The length of recovery days (LRD) presented an evident gradient of high (\u3e 60 days) in mid- latitude region and low (\u3c 60 days) in low (tropical area) and high (boreal area) latitude regions. As average GPP increased, the LRD showed a significantly decreasing trend among different land covers (R 2=0.53, p\u3c0.0001). Moreover, the most dramatic reduction of the drought-induced GPP was found in the mid-latitude region of north Hemisphere (48% reduction), followed by the low-latitude region of south Hemisphere (13% reduction). In contrast, a slightly enhanced GPP (10%) was showed in the tropical region under drought impact. Additionally, the highest drought-induced reduction of ET was found in the Mediterranean area, followed by Africa. The water use efficiency, however, showed a pattern of decreasing in the north Hemisphere and increasing in the south Hemisphere.;The last chapter compared the differences of performance in trading water for carbon in planted forest and natural forest, with specific focus on China. Planted forests have been widely established in China as an essential approach to improving the ecological environment and mitigating climate change. Large-scale forest planting programs, however, are rarely examined in the context of tradeoffs between carbon sequestration and water yield between planted and natural forests. We reconstructed evapotranspiration (ET) and gross primary production (GPP) data based on remote-sensing and ground observational data, and investigated the differences between natural and planted forests, in order to evaluate the suitability of tree-planting activity in different climate regions where the afforestation and reforestation programs have been extensively implemented during the past three decades in China. While the differences changed with latitude (and region), we found that, on average, planted forests consumed 5.79% (29.13mm) more water but sequestered 1.05% (-12.02 gC m-2 yr -1) less carbon than naturally generated forests, while the amplitudes of discrepancies varied with latitude. It is suggested that the most suitable lands in China for afforestation should be located in the moist south subtropical region (SSTP), followed by the mid-subtropical region (MSTP), to attain a high carbon sequestration potential while maintain a relatively low impact on regional water balance. The high hydrological impact zone, including the north subtropical region (NSTP), warm temperate region (WTEM), and temperate region (TEM) should be cautiously evaluated for future afforestation due to water yield reductions associated with plantations

Combining tower mixing ratio and community model data to estimate regional-scale net ecosystem carbon exchange by boundary layer inversion over 4 flux towers in the U.S.A.

We evaluated an idealized boundary layer (BL) model with simple parameterizations using vertical transport information from community model outputs (NCAR/NCEP Reanalysis and ECMWF Interim Analysis) to estimate regional-scale net CO2 fluxes from 2002 to 2007 at three forest and one grassland flux sites in the United States. The BL modeling approach builds on a mixed-layer model to infer monthly average net CO2 fluxes using high-precision mixing ratio measurements taken on flux towers. We compared BL model net ecosystem exchange (NEE) with estimates from two independent approaches. First, we compared modeled NEE with tower eddy covariance measurements. The second approach (EC-MOD) was a data-driven method that upscaled EC fluxes from towers to regions using MODIS data streams. Comparisons between modeled CO2 and tower NEE fluxes showed that modeled regional CO2 fluxes displayed interannual and intra-annual variations similar to the tower NEE fluxes at the Rannells Prairie and Wind River Forest sites, but model predictions were frequently different from NEE observations at the Harvard Forest and Howland Forest sites. At the Howland Forest site, modeled CO2 fluxes showed a lag in the onset of growing season uptake by 2 months behind that of tower measurements. At the Harvard Forest site, modeled CO2 fluxes agreed with the timing of growing season uptake but underestimated the magnitude of observed NEE seasonal fluctuation. This modeling inconsistency among sites can be partially attributed to the likely misrepresentation of atmospheric transport and/or CO2gradients between ABL and the free troposphere in the idealized BL model. EC-MOD fluxes showed that spatial heterogeneity in land use and cover very likely explained the majority of the data-model inconsistency. We show a site-dependent atmospheric rectifier effect that appears to have had the largest impact on ABL CO2 inversion in the North American Great Plains. We conclude that a systematic BL modeling approach provided new insights when employed in multiyear, cross-site synthesis studies. These results can be used to develop diagnostic upscaling tools, improving our understanding of the seasonal and interannual variability of surface CO2 fluxes

Climate change mitigation: annual carbon balance accounting and mapping in the national forest ecosystems (continental Portugal)

We present in this article the carbon balance accounting and mapping in the Portugal continental forests (Mediterranean forest), which occupies 36% of the national territory, mostly private (93%). These forests are characterized by their economic, social and environmental importance values, but during these last years, they are undergoing natural and anthropogenic disturbances and also a strong wood demand for supplying the industry sector. The first goal of this study was to quantify the different components of the carbon (C) cycle, gain and losses, using atmospheric flow approach (gain-loss approach) developed by Intergovernmental Panel on Climate Change (IPCC). The carbon gain reflects the yearly photosynthetic sequestration. The carbon losses reflect the different yearly disturbances like fires, forest logging, pests and diseases attacks. This method allows us to assess the carbon balance evolution from 1995 until 2014 and to identify the most important species in climate change mitigation regarding the air purification or the greenhouse gases emissions contribution. Our second purpose is mapping the carbon-density areas with two different approaches, firstly the direct Remote Sensing (DRS) approach using MODIS images, secondly the indirect approach named Combine and Assign (CA) Approach. MODIS images allow the accounting of Net Primary Productivity (NPP) which presents the quantity of carbon absorbed by vegetation cover during a period of time as a key indicator of ecosystem performance. The CA Approach combines remote sensing and field data in GIS environment to assess the yearly carbon sequestration for each ecozone and the carbon losses by fires in 2010, using the atmospheric flow proposed by IPCC. Our third objective is to link the NPP in 2017 derived from MOD17A3 (MODIS product) with abiotic factors (precipitation, temperature, elevation), to find the best conditions for carbon sequestration. Several geostatistical technics were tested to interpolate climatic factors for all the country. Towards the end, mitigation measures will be proposed.Apresentamos nesta tese a quantificação e o mapeamento do balanço de carbono nas florestas de Portugal continental, que ocupa 36% do território nacional, maioritariamente privado (93%). A floresta portuguesa possui elevado valor económico, social e ambiental, mas durante os últimos anos tem sofrido distúrbios naturais e antropogénicos. Tem também crescido a procura de madeira para suprir o sector industrial. O primeiro objectivo deste estudo foi quantificar os diferentes componentes do ciclo de carbono (C), ganhos e perdas, utilizando a abordagem de fluxo atmosférico (abordagem ganho-perda) desenvolvida pelo Painel Intergovernamental sobre Mudanças Climáticas (IPCC). O ganho de carbono reflecte o sequestro fotossintético anual. As perdas de carbono reflectem as diferentes perturbações anuais, como incêndios, extracção de madeira, insectos e ataques de doenças. Este método permite-nos avaliar a evolução do balanço de carbono de 1995 até 2014 e identificar as espécies mais importantes na mitigação da mudança climática em relação à purificação do ar ou a contribuição das emissões de gases de efeito estufa. Nosso segundo objectivo foi mapear a densidade de carbono por áreas homogéneas com duas abordagens diferentes, em primeiro lugar a abordagem directa de Detecção Remota (DR) usando imagens MODIS, em segundo lugar a abordagem indirecta denominada “Combine and Assign” (CA). As imagens MODIS permitem a quantificação da Produtividade Primária Líquida (NPP) que apresenta a quantidade de carbono absorvida pela cobertura vegetal durante um período de tempo como um indicador chave do desempenho do ecossistema. A abordagem CA combina dados de DR e dados de campo em ambiente SIG para avaliar o sequestro anual de carbono para cada zona ecológica homogénea considerada e as perdas de carbono por incêndios em 2010, usando o fluxo atmosférico proposto pelo IPCC. O terceiro objectivo foi vincular a NPP de 2017 obtida a partir do MOD17A3 (produto MODIS) com factores abióticos (precipitação, temperatura, elevação), para pesquisar as condições mais favoráveis para o sequestro de carbono. Diversas técnicas geoestatísticas foram testadas para interpolar factores climáticos para todo o país. No final, algumas medidas de mitigação foram propostas

Remote Sensing Technologies for Assessing Climate-Smart Criteria in Mountain Forests

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Toward a better understanding of changes in Northern vegetation using long-term remote sensing data

Cascading consequences of recent changes in the physical environment of northern lands associated with rapid warming have affected a broad range of ecosystem processes, particularly, changes in structure, composition, and functioning of vegetation. Incomplete understanding of underlying processes driving such changes is the primary motivation for this research. We report here the results of three studies that use long-term remote sensing data to advance our knowledge of spatiotemporal changes in growing season, greenness and productivity of northern vegetation. First, we improve the remote sensing-based detection of growing season by fusing vegetation greenness, snow and soil freeze/thaw condition. The satellite record reveals extensive lengthening trends of growing season and enhanced annual total greenness during the last three decades. Regionally varying seasonal responses are linked to local climate constraints and their relaxation. Second, we incorporate available land surface histories including disturbances and human land management practices to understand changes in remotely sensed vegetation greenness. This investigation indicates that multiple drivers including natural (wildfire) and anthropogenic (harvesting) disturbances, changing climate and agricultural activities govern the large-scale greening trends in northern lands. The timing and type of disturbances are important to fully comprehend spatially uneven vegetation changes in the boreal and temperate regions. In the final part, we question how photosynthetic seasonality evolved into its current state, and what role climatic constraints and their variability played in this process and ultimately in the carbon cycle. We take the ‘laws of minimum’ as a basis and introduce a new framework where the timing of peak photosynthetic activity (DOYPmax) acts as a proxy for plants adaptive state to climatic constraints on their growth. The result shows a widespread warming-induced advance in DOYPmax with an increase of total gross primary productivity across northern lands, which leads to an earlier phase shift in land-atmosphere carbon fluxes and an increase in their amplitude. The research presented in this dissertation suggests that understanding past, present and likely future changes in northern vegetation requires a multitude of approaches that consider linked climatic, social and ecological drivers and processes

Hybrid modeling of aboveground biomass carbon using disturbance history over large areas of boreal forest in eastern Canada

Le feu joue un rôle important dans la succession de la forêt boréale du nord-est de l’Amérique et le temps depuis le dernier feu (TDF) devrait être utile pour prédire la distribution spatiale du carbone. Les deux premiers objectifs de cette thèse sont: (1) la spatialisation du TDF pour une vaste région de forêt boréale de l'est du Canada (217,000 km2) et (2) la prédiction du carbone de la biomasse aérienne (CBA) à l’aide du TDF à une échelle liée aux perturbations par le feu. Un modèle non paramétrique a d’abord été développé pour prédire le TDF à partir d’historiques de feu, des données d'inventaire et climatiques à une échelle de 2 km2. Cette échelle correspond à la superficie minimale d’un feu pour être inclus dans la base de données canadienne des grands feux. Nous avons trouvé un ajustement substantiel à l’échelle de la région d’étude et à celle de paysages régionaux, mais la précision est restée faible à l’échelle de cellules individuelles de 2 km2. Une modélisation hiérarchique a ensuite été développée pour spatialiser le CBA des placettes d’inventaire à la même échelle de 2 km2. Les proportions des classes de densité du couvert étaient les variables les plus importantes pour prédire le CBA. Le CBA co-variait également avec la vitesse de récupération du couvert au travers de laquelle le TDF intervient indirectement. Finalement, nous avons comparé des estimations de CBA obtenues par télédétection satellitaire avec celles obtenues précédemment. Les résultats indiquent que les proportions des classes de densité du couvert et des types de dépôts ainsi que le TDF pourraient servir comme variables auxiliaires pour augmenter substantiellement la précision des estimés de CBA par télédétection. Les résultats de cette étude ont montré: 1) l'importance d’allonger la profondeur temporelle des historiques de feu pour donner une meilleure perspective des changements actuels du régime de feu; 2) l'importance d'intégrer l’information sur la reprise du couvert après feu aux courbes de rendement de CBA dans les modèles de bilan de carbone; et 3) l'importance de l'historique des feux et de la récupération de la végétation pour améliorer la précision de la cartographie de la biomasse à partir de la télédétection.Fire is as a main succession driver in northeastern American boreal forests and time since last fire (TSLF) is seen as a useful covariate to infer the spatial variation of carbon. The first two objectives of this thesis are: (1) to elaborate a TSLF map over an extensive region in boreal forests of eastern Canada (217,000 km2) and (2) to predict aboveground carbon biomass (ABC) as a function of TSLF at a scale related to fire disturbances. A non-parametric model was first developed to predict TSLF using historical records of fire, forest inventory data and climate data at a 2-km2 scale. Two kilometer square is the minimum size for fires to be considered important enough and included in the Canadian large fire database. Overall, we found a substantial agreement at the scale of both the study area and landscape units, but the accuracy remained fairly low at the scale of individual 2-km2 cells. A hierarchical modeling approach is then presented for scaling-up ABC from inventory plots to the same 2 km2 scale. The proportions of cover density classes were the most important variables to predict ABC. ABC was also related to the speed of post-fire canopy recovery through which TSLF acts indirectly upon ABC. Finally, we compared remote sensing based aboveground biomass estimates with our inventory based estimates to provide insights on improving their accuracy. The results indicated again that abundances of canopy cover density classes of surficial deposits, and TSLF may serve as ancillary variables for improving substantially the accuracy of remotely sensed biomass estimates. The study results have shown: 1) the importance of lengthening the historical records of fire records to provide a better perspective of the actual changes of fire regime; 2) the importance of incorporating post-fire canopy recovery information together with ABC yield curves in carbon budget models at a spatial scale related to fire disturbances; 3) the importance of adding disturbance history and vegetation recovery trends with remote sensing reflectance data to improve accuracy for biomass mapping

Limitations and Challenges of MODIS-Derived Phenological Metrics Across Different Landscapes in Pan-Arctic Regions

Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R-2 values were generally low (0.01-0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates.Peer reviewe