The land ecosystems in northern high latitudes (>45° N) occupy 22% of the global surface and store more than 1600 Pg soil organic carbon. Warming in this region has been documented during the past decades. Warming-induced changes in regional carbon dynamics are expected to loom large in the global carbon cycle and exert large feedbacks to the global climate system. Numerous Earth System Models have been widely used to quantify the response of terrestrial ecosystem carbon dynamics to climatic changes. However, predictions of terrestrial ecosystem carbon responses to increasing levels of atmospheric carbon dioxide (CO2) and climate change is still uncertain due to model limitations. The limitations include relatively low levels of representation of ecosystem processes that determine the exchanges of water, energy, and carbon between land ecosystems and the atmosphere and omitting some key biogeochemical mechanisms. To improve model realism and provide a better projection of the Arctic carbon dynamics, this dissertation developed three new versions of a process-based biogeochemistry models that involve more fundamental processes of terrestrial ecosystems. First, microbial dynamics and enzyme kinetics that catalyze soil carbon decomposition have been incorporated into the extant terrestrial ecosystem model TEM to remedy the inadequate representation of soil decomposition process. Furthermore, a vital microbial life-history trait, microbial dormancy, has been implemented into previous microbial-based model to consider the impacts of microbial dormancy in modeling. Additionally, the role of moss in the Arctic terrestrial ecosystem carbon quantification was also demonstrated by incorporating moss and higher plant interactions in modelling.

<p>All of the work described in this dissertation involves the use of Indigenous research frameworks to design research projects, to facilitate communication with Indigenous communities that I have collaborated with, and also to teach and mentor undergraduate and graduate students. Indigenous research frameworks emphasize the importance of place in relation to the integrity of cultural values espoused by many Indigenous communities. This entails a respect for the spirituality component of Indigenous people because this is often directly tied to relationships between the land, animals, and plants of their local environments.</p> <p>While some research has been conducted to help understand Indigenous people’s understandings of geoscience, less emphasis has been placed on recognizing and leveraging common connections Indigenous students make between their Traditional cultures and Western science. Thus, the research presented in this dissertation identifies connections Indigenous learners make between geology concepts and their everyday lives and cultural traditions in both formal and informal settings. Some of these connections have been integrated into place-based geoscience education modules that were implemented within an introductory environmental science course. </p> <p>Qualitative analysis, using a socioTransformative constructivism theoretical lens, of semi-structured interviews after implementation of a Sharing/Learning program for an Acoma pilot project, implemented informally, and for a series of geoscience education modules at a private university provides evidence that elements reflective of the use of sociotransformative constructivism (e.g. connections between global and localized environmental issues) were acknowledged by the participants as particularly impactful to their experience during implementation of the geoscience-focused activities. In addition to the socioTransformative theoretical perspective, Indigenous research frameworks (i.e. Tribal Critical Race Theory) were used to contextualize the educational interventions for two different Indigenous communities, Acoma Pueblo and the Confederated Tribes and Bands of the Yakama Nation. Tribal Critical Race Theory was not used to analyze the semi-structured interviews. Instead the Indigenous research frameworks were used to ensure that the research practices undertaken within these Indigenous communities were respectful of the Indigenous community’s cultural values, that Indigenous data sovereignty was paramount, and so that the research objectives were transparent. In addition, permission to publish the results of this research was sought from the governing entities of both Tribal Councils of Acoma Pueblo and the Yakama Nation.</p> <p>The research presented in this dissertation provides evidence that academic research can be undertaken in respectful ways that benefit Indigenous communities. The connections that participants in the Acoma Sharing/Learning program could potentially be used to create more culturally relevant educational materials for the Acoma Pueblo community, if that is what the governing entities of the Acoma Pueblo community desire. The modules implemented more formally at a private university could potentially, with permission from the governing entities of the Yakama Nation, be integrated into geoscience programs at a broader level creating opportunities for contemporary Indigenous perspectives to be valued alongside Western modern science. Moving forward, this could potentially increase interest among Indigenous community members in pursuing academic pathways within geoscience disciplines.</p> <p>The research pursued in this dissertation is only a beginning. Approaches to research that promote the agency of local communities in the types of research questions asked and how that research is conducted should be a priority for Western scientists to maintain a respectful relationship with the many communities, Indigenous and non-Indigenous, in which they work. It is my intention to be part of this revolution in how academic researchers interact with contemporary Indigenous communities as well as the next generation of scientists. In the future, my research will continue to serve and benefit Indigenous communities, but I will also begin asking research questions that will help increase the use of diverse and equitable practices within academia. In this way, I hope to bridge the two worlds of Indigenous Knowledge systems and Western science with the primary purpose of maintaining respect among these two communities. In the future, my research will focus on how these respectful practices can move beyond academic research and pedagogy into the realms of professional development, mentoring, and community revitalization.</p>

The potential of hyperspectral proximal sensing to quantify sward characteristics important in making critical decisions on the management of sheep and dairy pastures in New Zealand has been investigated. Hyperspectral data were acquired using an ASD FieldSpec® Pro FR spectroradiometer attached to the Canopy Pasture Probe (CAPP). The CAPP was developed to enable the collection of in situ reflectance data from New Zealand pasture canopies independent of ambient light conditions. A matt white ceramic tile was selected as a reflectance standard to be used with the CAPP, after testing a variety of materials. Pasture reflectance factor spectra between 350-2500 nm (with spectral resolutions of 3 nm between 350-1000 nm and 10 nm between 1000-2500 nm) and pasture samples were collected from six hill country and lowland areas, across all seasons (August 2006 to September 2007) in a number of regions in the North Island of New Zealand. After pre-processing (e.g. spectral averaging, de-stepping, elimination of noisy wavelengths, smoothing) the spectral data collected from sites were correlated against pasture botanical composition (expressed as proportions of grass, legume and weed) and pasture nutrients (nitrogen, phosphorus, potassium, calcium, magnesium, sodium and sulphur) expressed in percentage of dry matter (%) and amount (kg ha-1) using partial least squares regressions (PLSR). The accuracy and precision of the calibrations were tested using either the full cross-validation leave-one-out method or testing datasets. Regressions were carried out using the reflectance factor data per se and after mathematical transformation, including first derivative, absorbance and continuum-removed spectra. Overall best results were obtained using the first derivative data. The quality of predictions varied greatly with the pasture attribute, site and season. Some reasonable results were achieved for the prediction of pasture grass and legume proportions when analysing samples collected during autumn (grass: R2 > 0.81 and SD/RMSEP 2.3 and legume: R2 > 0.80 and SD/RMSEP 2.2), but predicting pasture weed content was poor for all sites and seasons (R2 = 0.44 and SD/RMSEP = 1.2). The inaccurate predictions might be explained by the fact that the diversity found in the field and observed in the pasture spectral data was not taken into account in the pasture botanical separation. The potential for using proximal sensing techniques to predict pasture nutrients in situ was confirmed, with the sensing of pasture N, P and K increased by the procedure of separating the data according to the season of the year. The full potential of the technology will only be realised if a substantial dataset representing all the variability found in the field is gathered. The importance of obtaining representative datasets that embrace all the biophysical factors (e.g. pasture type, canopy structure) likely to affect the relat ionship, when building prediction calibrations, was highlighted in this research by the variance in the predictions for the same nutrient using different datasets, and by the inconsistency in the number of common wavelengths when examining the wavelengths contributing to the relationship. The ability to use a single model to predict multiple nutrients, or indeed individual nutrients, will only come through a good understanding of the factors likely to influence any calibration function. It has been demonstrated in this research that reasonably accurate and precise pasture nutrient predictions (R2 > 0.74 and SD/RMSEP 2.0) can be made from fresh in situ canopy measurements. This still falls short of the quality of the predictions reported for near infrared reflectance spectroscopy (NIRS) for dried, ground samples analysed under controlled laboratory conditions

<p>Tropical ecosystems play a key role in regulating the global climate and the carbon cycle thanks to the large amounts of water and carbon exchanged with the atmosphere. These biogeochemical fluxes are largely the result of high photosynthetic rates. Photosynthetic activity is highly dependent on climate and vegetation, and therefore can be easily modified along with changes in those two factors. A better understanding of what drives or alters photosynthetic activity in the tropics will lead to more accurate predictions of climate and subsequent effects on ecosystems. The seasonal pattern of photosynthetic activity is one of the main uncertainties that we still have about tropical ecosystems. However, this seasonality of tropical vegetation and its relationship to climate change and land cover is key to understanding how these ecosystems could be affected and have an effect on climate.</p><p>In this dissertation, I present three projects to improve our understanding about tropical ecosystems and how their photosynthetic activity is affected by climate and land cover change. The lack of field-based data has been one of the main limiting factors in our study of tropical ecosystems. Therefore, in these projects I extensively use remote sensing-derived data to analyze large scale and long term patterns. In the first study, I looked at the seasonal relationship between photosynthetic activity and climate, and how model simulations represent it. Vegetation in most of the tropics is either positively correlated with both water and light, or positively correlated with one of them and negatively with the other. Ecosystem models largely underestimate positive correlations with light and overestimate positive correlations with water. In the second study, I focus on the effect of land cover change in photosynthetic activity and transpiration in a highly deforested region in the Amazon. I find that land cover change decreases tropical forests photosynthetic activity and transpiration during the dry season. Also, land cover change increases the range of photosynthetic activity and transpiration in forests and shrublands. These effects are intensified with increasing land cover change. In the last project, I quantify the amount of change in evapotranspiration due to land cover change in the entire Amazon basin. Our remote sensing-derived estimates are well aligned with model predictions published in the past three decades. These results increase our confidence in climate models representation of evapotranspiration in the Amazon.</p><p>Findings from this dissertation highlight (1) the importance of the close relationship between climate and photosynthetic activity and (2) how land cover change is altering that relationship. We hope our results can build on our knowledge about tropical ecosystems and how they could change in the future. We also expect our analysis to be used for model benchmarking and tropical ecosystem monitoring.</p>

<p>Methane (CH₄) is the second most powerful greenhouse gas (GHG) behind carbon dioxide (CO₂), and is able to trap a large amount of long-wave radiation, leading to surface warming. Carbon monoxide (CO) plays an important role in controlling the oxidizing capacity of the atmosphere by reacting with OH radicals that affect atmospheric CH₄ dynamics. Terrestrial ecosystems play an important role in determining the amount of these gases into the atmosphere. However, global quantifications of CH₄ emissions from wetlands and its sinks from uplands, and CO exchanges between land and the atmosphere are still fraught with large uncertainties, presenting a big challenge to interpret complex atmospheric CH₄ dynamics in recent decades. In this dissertation, I apply modeling approaches to estimate the global CH₄ and CO exchanges between land ecosystems and the atmosphere and analyze how they respond to contemporary and future climate change.</p> <p>Firstly, I develop a process-based biogeochemistry model embedded in Terrestrial Ecosystem Model (TEM) to quantify the CO exchange between soils and the atmosphere at the global scale (Chapter 2). Parameterizations were conducted by using the CO <i>in situ</i> data for eleven representative ecosystem types. The model is then extrapolated to global terrestrial ecosystems. Globally soils act as a sink of atmospheric CO. Areas near the equator, Eastern US, Europe and eastern Asia will be the largest sink regions due to their optimum soil moisture and high temperature. The annual global soil net flux of atmospheric CO is primarily controlled by air temperature, soil temperature, SOC and atmospheric CO concentrations, while its monthly variation is mainly determined by air temperature, precipitation, soil temperature and soil moisture. </p> <p>Secondly, to better quantify the global CH₄ emissions from wetlands and their uncertainties, I revise, parameterize and verify a process-based biogeochemical model for methane for various wetland ecosystems (Chapter 3). The model is then extrapolated to the global scale to quantify the uncertainty induced from four different types of uncertainty sources including parameterization, wetland type distribution, wetland area distribution and meteorological input. Spatially, the northeast US and Amazon are two hotspots of CH₄ emissions, while consumption hotspots are in the eastern US and eastern China. The relationships between both wetland emissions and upland consumption and El Niño and La Niña events are analyzed. This study highlights the need for more in situ methane flux data, more accurate wetland type and area distribution information to better constrain the model uncertainty.</p> <p>Thirdly, to further constrain the global wetland CH₄ emissions, I develop a predictive model of CH₄ emissions using an artificial neural network (ANN) approach and available field observations of CH₄ fluxes (Chapter 4). Eleven explanatory variables including three transient climate variables (precipitation, air temperature and solar radiation) and eight static soil property variables are considered in developing the ANN models. The models are then extrapolated to the global scale to estimate monthly CH₄ emissions from 1979 to 2099. Significant interannual and seasonal variations of wetland CH₄ emissions exist in the past four decades, and the emissions in this period are most sensitive to variations in solar radiation and air temperature. This study reduced the uncertainty in global CH₄ emissions from wetlands and called for better characterizing variations of wetland areas and water table position and more long-term observations of CH₄ fluxes in tropical regions.</p> <p>Finally, in order to study a new pathway of CH₄ emissions from palm tree stem, I develop a two-dimensional diffusion model. The model is optimized using field data of methane emissions from palm tree stems (Chapter 5). The model is then extrapolated to Pastaza-Marañón foreland basin (PMFB) in Peru by using a process-based biogeochemical model. To our knowledge, this is among the first efforts to quantify regional CH₄ emissions through this pathway. The estimates can be improved by considering the effects of changes in temperature, precipitation and radiation and using long-period continuous flux observations. Regional and global estimates of CH₄ emissions through this pathway can be further constrained using more accurate palm swamp classification and spatial distribution data of palm trees at the global scale.</p>

<p>The Geologic <i>TimeScale Creator </i>(TSCreator) project has compiled a range of paleo-environmental and bio-diversity data which provides the opportunity to explore origination, speciation and extinction events. My PhD research has four major interconnected themes which include the visualization methods of evolutionary tree and the impacts of climate change on the evolution of life in longer and shorter timeframes: <b>(1) </b>Evolutionary range data of planktonic foraminifera and nannofossils over the Cenozoic era have been updated with our latest geological timescale. These evolutionary ranges can be visualized in the form of interactive, extensible evolutionary trees and can be compared with other geologic data columns. <b>(2) </b>A novel approach of integrating morphospecies and lineage trees is proposed to expand the scope of exploration of the evolutionary history of microfossils. It is now possible to visualize morphological changes and ancestor-descendant lineage relationships on TSCreator charts which helps mutual learning of these species based on genetic and bio-stratigraphic studies. <b>(3) </b>These evolutionary datasets have been used to analyze semi-periodic cycles in the past bio-diversity and characteristic rates of turnover. Well-known Milankovitch cycles have been found as the drivers of fluctuations in the speciation and extinction processes. <b>(4) </b>Within a shorter 2000-year time period, global cooling events might have been a factor of human civilization turnover. Using our regional and global cultural turnover time series data, the effect of climate change on human culture has been proposed. The enhancement of the evolutionary visualization system accomplished by this research will hopefully allow academic and non-academic users across the world to research and easily explore Earth history data through publicly available TSCreator program and websites. </p>

<p>Understanding climate-driven changes in global land-based ice volume is a critical component in our capability to predict how global sea level will rise as a consequence of the current human-driven climate change. At the last glacial maximum (LGM, which peaked around 20 ka), ephemeral ice sheets covered vast regions of the northern hemisphere while both the Greenland and Antarctic ice sheets were more extensive than at present. As global temperatures rose at the transition into the Holocene, driving the LGM deglaciation, eustatic sea level rose by approximately 125 m. The east Antarctic ice sheet (EAIS) is the largest ice sheet on Earth today, holding an ice volume equivalent to ca. 53 m rise in global sea level. Considering current trends in global climate, specifically rapidly increasing atmospheric CO₂ levels and global temperature, it is important to improve our understanding of how the EAIS will respond to global warming so that we can make better predictions of future sea level changes to guide community adaptation and planning efforts. Numerical ice sheet models which inform projections of future ice volume changes, and can, therefore, yield projections of sea level rise, rely on empirical data to test their ability to accurately represent former and present ice configurations. However, there is a general lack of data on the paleoglaciology of the EAIS along the western Dronning Maud Land (DML) margin. In order to address this situation, the paleoglaciology of western DML forms the focus of the work presented in this thesis.</p><p><b> </b></p><p>Together with collaborators within the MAGIC-DML consortium (Mapping, Measuring and Modelling Antarctic Geomorphology and Ice Change in Dronning Maud Land) that provides the funding for this MS project, the author has performed geomorphological mapping across western DML; an area of approximately 200,000 km². The results of the mapping presented in this thesis will provide the basis for a detailed glacial reconstruction of the region. The geomorphological mapping was completed almost entirely by remote sensing using very high-resolution (sub-meter in the panchromatic) WordView-2 and WorldView-3 (WV) satellite imagery, combined with ground validation studies during field work. Compared to Landsat products, the improved spatial resolution provided by WV imagery has fundamentally changed the scale and detail at which remote sensing based geomorphological mapping can be completed. The mapping presented here is focused on the glacial geomorphology of mountain summits and flanks that protrude through the ice sheet’s surface (nunataks). In our study area of western DML these nunatak surfaces make up <0.2 % of the total surface area, and the landforms mapped here are generally smaller than can be identified from Landsat products (30 m spatial resolution). The detail achieved in our mapping, across such a vast, remote area that presents numerous obstacles to accessibility highlights the benefits of utilizing the new VHR WV data. As such an evaluation of the WV data, as applied to geomorphological mapping is presented here together with our mapping of the glacial geomorphology of western DML. The results of which provides evidence of ice having overridden sites at all elevations across the entire study area; from the highest elevation inland nunataks that form the coast-parallel escarpment, to low-elevation emerging nunataks close to the coast. Hence from our studies of the glacial geomorphology of this region we can ascertain that, at some point in the glacial history of western DML, ice covered all of the mountain summits that are exposed today, indicating an ice sheet surface lowering of up to 700 m in some places.</p>

<p>Three prescribed burn sites and seven selective timber harvest sites were surveyed using a UAS equipped with a PPK-triggered RGB sensor to determine optimal image collection parameters surrounding each type of disturbance and land cover. The image coordinates were corrected with a third-party base station network (CORS) after the flight, and photogrammetrically processed to produce high-resolution georeferenced orthomosaics. This addressed the first objective of this study, which was to <i>establish effective data procurement methods from both before and after planned </i>disturbances. <br></p><p>Orthomosaic datasets surrounding both a prescribed burn and a selective timber harvest, were used to classify land covers through geographic image-based analysis (GEOBIA). The orthomosaic datasets were segmented into image objects, before classification with a machine-learning algorithm. Land covers for the prescribed prairie burn were 1) bare ground, 2) litter, 3) green vegetation, and 4) burned vegetation. Land covers for the selective timber harvest were 1) mature canopy, 2) understory vegetation, and 3) bare ground. 65 samples per class were collected for prairie burn datasets, and 80 samples per class were collected for timber harvest datasets to train the classifier. A supported vector machines (SVM) algorithm was used to produce four land cover classifications for each site surrounding their respective planned disturbance. Pixel counts for each class were multiplied by the ground sampled distance (GSD) to obtain area calculations for land covers. Accuracy assessments were conducted by projecting 250 equalized stratified random (ESR) reference points onto the georeferenced orthomosaic datasets to compare the classification to the imagery through visual interpretation. This addressed the second objective of this study, which was to <i>establish effective data classification methods from both before and after planned </i>disturbances.<br></p><p>Finally, a two-tailed t-Test was conducted with the overall accuracies for each disturbance type and land cover. Results showed no significant difference in the overall accuracy between land covers. This was done to address the third objective of this study which was to <i>determine if a significant difference exists between the classification accuracies between planned disturbance types</i>. Overall, effective data procurement and classification parameters were established for both <i>before </i>and <i>after </i>two common types of <i>planned </i>disturbances within the CHF region, with slightly better results for prescribed burns than for selective timber harvests.<br></p>