227 research outputs found

    Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize

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    This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornitz (1991) and enhanced using Machine learning (ML) clustering. ML has been employed to divide the coastline based on the geotechnical conditions observed to establish relative vulnerability. This has been demonstrated to alleviate bias and enhanced the scalability of the approach – especially in areas with poor data coverage – a known hinderance to the CVI approach (Koroglu et al., 2019).Belize provides a demonstrator for this novel methodology due to limited existing data coverage and the recent removal of the Mesoamerican Reef from the International Union for Conservation of Nature (IUCN) List of World Heritage In Danger. A strong characterization of the coastal zone and associated pressures is paramount to support effective management and enhance resilience to ensure this status is retained.Areas of consistent vulnerability have been identified using the KMeans classifier; predominantly Caye Caulker and San Pedro. The ability to automatically scale to conditions in Belize has demonstrated disparities to vulnerability along the coastline and has provided more realistic estimates than the traditional CVI groups. Resulting vulnerability assessments have indicated that 19% of the coastline at the highest risk with a seaward distribution to high risk observed. Using data derived using Sentinel-2, this study has also increased the accuracy of existing habitat maps and enhanced survey coverage of uncharted areas.Results from this investigation have been situated within the ability to enhance community resilience through supporting regional policies. Further research should be completed to test the robust nature of this model through an application in regions with different geographic conditions and with higher resolution input datasets

    University of Maine Undergraduate Catalog, 2022-2023

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    The University of Maine undergraduate catalog for the 2022-2023 academic year includes an introduction, the academic calendars, general information about the university, and sections on attending, facilities and centers, and colleges and academic programs including the Colleges of Business, Public Policy and Health, Education and Development, Engineering, Liberal Arts and Sciences, and Natural Sciences, Forestry and Agriculture

    A review of carbon monitoring in wet carbon systems using remote sensing

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    Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry

    Examining Ecosystem Drought Responses Using Remote Sensing and Flux Tower Observations

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    Indiana University-Purdue University Indianapolis (IUPUI)Water is fundamental for plant growth, and vegetation response to water availability influences water, carbon, and energy exchanges between land and atmosphere. Vegetation plays the most active role in water and carbon cycle of various ecosystems. Therefore, comprehensive evaluation of drought impact on vegetation productivity will play a critical role for better understanding the global water cycle under future climate conditions. In-situ meteorological measurements and the eddy covariance flux tower network, which provide meteorological data, and estimates of ecosystem productivity and respiration are remarkable tools to assess the impacts of drought on ecosystem carbon and water cycles. In regions with limited in-situ observations, remote sensing can be a very useful tool to monitor ecosystem drought status since it provides continuous observations of relevant variables linked to ecosystem function and the hydrologic cycle. However, the detailed understanding of ecosystem responses to drought is still lacking and it is challenging to quantify the impacts of drought on ecosystem carbon balance and several factors hinder our explicit understanding of the complex drought impacts. This dissertation addressed drought monitoring, ecosystem drought responses, trends of vegetation water constraint based on in-situ metrological observations, flux tower and multi-sensor remote sensing observations. This dissertation first developed a new integrated drought index applicable across diverse climate regions based on in-situ meteorological observations and multi-sensor remote sensing data, and another integrated drought index applicable across diverse climate regions only based on multi-sensor remote sensing data. The dissertation also evaluated the applicability of new satellite dataset (e.g., solar induced fluorescence, SIF) for responding to meteorological drought. Results show that satellite SIF data could have the potential to reflect meteorological drought, but the application should be limited to dry regions. The work in this dissertation also accessed changes in water constraint on global vegetation productivity, and quantified different drought dimensions on ecosystem productivity and respiration. Results indicate that a significant increase in vegetation water constraint over the last 30 years. The results highlighted the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate

    Mid-Term Report 2019–2022

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    With the Midterm Report of the Cluster of Excellence “ROOTS - Social, Environmental, and Cultural Connectivity in Past Societies”, we inform you about the first years of the cluster established in 2019. The Midterm Report was prepared under the special conditions of the last pandemic and thus under extraordinary circumstances, which also applies to the first years of our research. Above all, the report gives you a broad impression of the new and interesting research activities of the cluster, which have developed in many ways in our research space. The joint research on past societies is determined by excavations, laboratory work, archival studies and source interpretations. The diversity of the archives – from soil sediments to human skeletons and from architecture to written evidence – is targeted in our six subclusters. Reconstructing the ROOTS of hazards, diet, knowledge, urbanity, inequality, and conflict and conciliation took us to different areas of the world and very different laboratory depths. The joint research on connectivity started from the basic hypothesis that the degree of connectivity within and between societies, but also between societies and the environment, is crucial for the possibilities to develop resilient and sustainable structures. This is where past societies and environments provide us with a mirror for current and future developments

    Wildfire Hazard and Risk Assessment

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    Wildfire risk can be perceived as the combination of wildfire hazards (often described by likelihood and intensity) with the susceptibility of people, property, or other valued resources to that hazard. Reflecting the seriousness of wildfire risk to communities around the world, substantial resources are devoted to assessing wildfire hazards and risks. Wildfire hazard and risk assessments are conducted at a wide range of scales, from localized to nationwide, and are often intended to communicate and support decision making about risks, including the prioritization of scarce resources. Improvements in the underlying science of wildfire hazard and risk assessment and in the development, communication, and application of these assessments support effective decisions made on all aspects of societal adaptations to wildfire, including decisions about the prevention, mitigation, and suppression of wildfire risks. To support such efforts, this Special Issue of the journal Fire compiles articles on the understanding, modeling, and addressing of wildfire risks to homes, water resources, firefighters, and landscapes

    Locality of Topographic Ground Truth Data for Salt Marsh Lidar DEM Elevation Bias Mitigation Using Machine Learning

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    Light detection and ranging (lidar) digital elevation models (DEMs) are crucial for modeling coastal salt marsh systems, simulating the coastal dynamics of sea level rise (SLR), and predicting storm surge inundation depth and duration. Improvements in lidar acquisition technology and data processing over the last decade have led to increased accuracy. However, the lidar-derived DEMs for coastal salt marshes that are densely vegetated are generally unreliable without adjustment based on local ground truth elevations. In this study, Random Forest (RF) DEM adjustment models are trained for two similar Northern Gulf of Mexico salt marshes. The need for local topographic ground truth data to train the models is also investigated. Two Real-Time Kinematic (RTK) GNSS field surveys were conducted by others to acquire ground truth elevations near St. Marks, Florida (n=377) and Pascagoula, Mississippi (n=610). These elevations, along with lidar elevations and Sentinel-2A multispectral satellite imagery (MSI) reflectance values were used to train the RF salt marsh DEM adjustment models and apply them under two scenarios: local and non-local. A local adjustment relies on data collected within the adjustment domain to train the model whereas a non-local adjustment uses data collected outside the adjustment domain. The RF-local models achieved the lowest mean absolute error (MAE) values for St. Marks and Pascagoula. The predictions using non-local RF models were unsatisfactory. The evidence suggests that local ground truth data are necessary for mitigating bias in salt marsh lidar DEMs, although future work should investigate if increasing the data set size could narrow the accuracy gap. This mitigation adjustment technique can be replicated in other coastal regions with similar vegetation profiles. As the world becomes increasingly vulnerable to the effects of climate change and SLR, it is important to accurately characterize the current state of the system to model marsh restoration and migration, natural and nature based protective infrastructure, and land use planning policies, for example

    UAVs for the Environmental Sciences

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    This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

    High Resolution Remote Sensing Observations of Summer Sea Ice

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    During the Arctic summer melt season, the sea ice transitions from a consolidated ice pack with a highly reflective snow-covered surface to a disintegrating unconsolidated pack with melt ponds spotting the ice surface. The albedo of the Arctic decreases by up to 50%, resulting in increased absorption of solar radiation, triggering the positive sea ice albedo feedback that further enhances melting. Summer melt processes occur at a small scale and are required for melt pond parameterization in models and quantifying albedo change. Arctic-wide observations of melt features were however not available until recently. In this work we develop original techniques for the analysis of high-resolution remote sensing observations of summer sea ice. By applying novel algorithms to data acquired from airborne and satellite sensors onboard IceBridge, Sentinel-2, WorldView and ICESat-2, we derive a set of parameters that describe melt conditions on Arctic sea ice in summer. We present a new, pixel-based classification scheme to identify melt features in high-resolution summer imagery. We apply the classification algorithm to IceBridge Digital Mapping System data and find a greater melt pond fraction (25%) on sea ice in the Beaufort and Chukchi Seas, a region consisting of predominantly first year ice, compared to the Central Arctic, where the melt pond fraction is 14% on predominantly multiyear ice. Expanding the study to observations acquired by the Sentinel-2 Multispectral Instrument, we track the variability in melt pond fraction and sea ice concentration with time, focusing on the anomalously warm summer of 2020. So as to obtain a three-dimensional view of the evolution of summer melt we also exploit ICESat-2 surface elevation measurements. We develop and apply the Melt Pond Algorithm to track ponds in ICESat-2 photon cloud data and derive their depth. Pond depth measurements in conjunction with melt pond fraction and sea ice concentration provide insights into the regional patterns and temporal evolution of melt on summer sea ice. We found mean melt pond fraction increased rapidly in the beginning of the melt season, peaking at 16% on 24 June 2020, while median pond depths increased steadily from 0.4 m at the beginning of the melt season, to peaking at 0.97 m on 16 July, even as melt pond fraction had begun to decrease. Our findings may be used to improve parameterization of melt processes in models, quantify freshwater storage, and study the partitioning of under ice light

    Remote Sensing Applications in Coastal Environment

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    Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments
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