Classifying and Mapping Aquatic Vegetation in Heterogeneous Stream Ecosystems Using Visible and Multispectral UAV Imagery

The need for assessment and management of aquatic vegetation in stream ecosystems is recognized given the importance in impacting water quality, hydrodynamics, and aquatic biota. However, existing approaches to monitor are laborious and its currently not feasible to track spatial and temporal differences at broad scales. The objective of this study was therefore to map and classify aquatic vegetation of a shallow stream with heterogenous mixtures of emergent and submerged aquatic vegetation. Data was collected in the Camden Creek watershed within the Inner Bluegrass Region of central Kentucky. The use of unmanned aerial vehicles (UAVs) was employed and both visible (RGB) and multispectral imagery were collected. Machine learning techniques were applied in an off-the-shelf software (QGIS environment) to develop visible and multispectral classification land-cover maps following an effective object-based image analysis workflow. Visible images were additionally coupled with high frequency water quality data to examine the spatial and temporal behavior of the aquatic vegetation. Results showed high overall classification accuracies (OA=83.5% for the training dataset and OA=83.73% for the validation dataset) for the visible imagery, with excellent user’s and producer’s accuracies for duckweed, both for training and validation. Surprisingly, multispectral overall accuracies were substantial (OA=77.8% for the training dataset and OA=70.2% for the validation dataset) but were inferior to the visible classification results. User’s and producer’s accuracies were lower for almost all classes. However, this approach was unsuccessful in detecting, segmenting and classifying submerged aquatic vegetation (algae) for both datasets. Finally, a change detection algorithm was applied to the visible classified maps and the changes in duckweed areal coverage were successfully estimated

Numerical modeling of thermal bar and stratification pattern in Lake Ontario using the EFDC model

Thermal bar is an important phenomenon in large, temperate lakes like Lake Ontario. Spring thermal bar formation reduces horizontal mixing, which in turn, inhibits the exchange of nutrients. Evolution of the spring thermal bar through Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers, and lake bathymetry. The simulation is performed from April to July, 2011; on a 2-km grid. The numerical model has been calibrated by specifying: appropriate initial temperature and solar radiation attenuation coefficients. The existing evaporation algorithm in EFDC is updated to modified mass transfer approach to ensure correct simulation of evaporation rate and latent heatflux. Reasonable values for mixing coefficients are specified based on sensitivity analyses. The model simulates overall surface temperature profiles well (RMSEs between 1-2°C). The vertical temperature profiles during the lake mixed phase are captured well (RMSEs < 0.5°C), indicating that the model sufficiently replicates the thermal bar evolution process. An update of vertical mixing coefficients is under investigation to improve the summer thermal stratification pattern. Keywords: Hydrodynamics, Thermal BAR, Lake Ontario, GIS

The Stability of Temperate Lakes Under the Changing Climate

There is a collective prediction among ecologists that climate change will enhance phytoplankton biomass in temperate lakes. Yet there is noteworthy variation in the structure and regulating functions of lakes to make this statement challengeable and, perhaps, inaccurate. To generate a common understanding on the trophic transition of lakes, I examined the interactive effects of climate change and landscape properties on phytoplankton biomass in 12,644 lakes located in relatively intact forested landscapes. Chlorophyll-a (Chl-a) concentration was used as a proxy for phytoplankton biomass. Chl-a concentration was obtained via analyzing Landsat satellite imagery data over a 28-year period (1984-2011) and using regression modelling. The most common lake trophic state was oligotrophic (median Chl-a \u3c 2.6 μg L-1), while the least common was hyper-eutrophic (median Chl-a \u3e 56 μg L-1). Lake volume was the most important factor in determining the present trophic state of the lakes. The majority of the lakes (91.6%) did not show a change in trophic state over an almost 3-decade long sampling period; only 4.0% of the lakes became more eutrophic, and 4.4% of the lakes became more oligotrophic. Lakes with smaller volumes were further responsive to temperature (warmer lakes were more eutrophic), while lakes with larger volumes were more responsive to precipitation (wetter lakes were more oligotrophic). Early warning indicators of change in trophic state were examined in the patterns of the residuals of the time series of Chl-a once non-stationary and stationary trends were removed. Remarkably, the majority (56.5%) of the lakes showed patterns in the residuals that were not defined by a single trophic metric but fluctuated among different trophic states. There was an unexpected instability among some lakes as they switched between oligotrophic and eutrophic states (12.5%) or were transitioning from eutrophic towards oligotrophic states (23.4%), or from oligotrophic towards eutrophic states (20.6%). The complex responses of phytoplankton biomass to climate change suggests that our ability to predict the future trophic state of lakes will be limited but enhanced if we recognize that lakes and their catchments will be both impacted by climate change

Investigating Drivers of Algal Bloom Succession in Lake Erie

Harmful Algal Blooms (HABs) are algae undergoing prolific, unregulated growth. A well-documented HAB taxa is the cyanobacterium Microcystis spp., which induces anthropogenic, ecological, and economic consequences due to the production of toxins and biomass which results in lake hypoxia. Microcystis spp. blooms are globally distributed in freshwater systems, with climate change and the aquatic continuum serving to further exacerbate bloom distribution, duration, and frequency. Thus, there is a need to elucidate the factors driving the ecological success of Microcystis spp., and the ecological “failures” of their competitors, such as diatoms. In Lake Erie, a seasonal pattern of algal bloom succession occurs: Microcystis spp. blooms dominate summer-fall, and diatom blooms dominate winter-spring. My dissertation assessed the drivers of these respective algal blooms and the factors contributing to their ecological success and succession across temporal, spatial and climatic scales

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

Influence of Morphology, Climate Change and Landuse Change on Water Partitioning in Olifants River Basin

Das Einzugsgebiet des Olifants River befindet sich derzeit in einer umfassenden Entwicklung der landwirtschaftlichen Landnutzungsaktivität. Trotz verschiedener Schutzpraktiken und Schutzmaßnahmen führt die Veränderung der Landnutzung immer noch zu einer Verschiebung im hydrologischen Regime. Als Treiber dieser rasanten Entwicklung in der Landnutzungsänderung durch landwirtschaftliche Nutzung sind der stetig steigende Nahrungsmittelbedarf und günstige klimatische Bedingung für die Landwirtschaft zu nennen. Ein stetiges Bevölkerungswachstum in Südafrika von etwa 1,4% pro Jahr weist auf eine kontinuierliche Nachfrage nach Nahrungsmitteln hin, die zu weiteren landwirtschaftlichen Expansionen und anschließend zu weiteren Veränderungen in der Hydrologie führen werden. Diese Situation könnte durch den Klimawandel und dadurch bedingte zunehmende Schwere extremer Phänomene wie Dürren und Überschwemmungen weiter verschärft werden. Diese Studie quantifiziert die Veränderungen des Klimas und der Landnutzung in den Teileinzugsgebieten Blyde River und Steelpoort River des Olifants Rivers, analysiert deren Einfluss auf die Hydrologie und schlägt eine Methode für die Landnutzungsplanung vor, mit der Änderungen im hydrologischen Regime abgemindert werden können. Historische Abflüsse, Temperatur und Niederschläge wurden mit statistischen Methoden ausgewertet, um das Vorhandensein von Veränderungen in den Zeitreihen für 37 Jahre ab dem Jahr 1980 festzustellen. 1996 und 2012 wurden zwei abrupte Veränderungen im Abflussgeschehen festgestellt. Diese Veränderungen wurden auf die hohe Häufigkeit extremer Niederschläge (> 40 mm / Tag) zwischen 1996 und 2012 zurückgeführt. Es wurde auch ein allmählicher Anstieg des Abflusses nachgewiesen, der jedoch nicht auf klimatische Faktoren zurückzuführen war. Darüber hinaus wurde ein allmählicher Temperaturanstieg festgestellt, der jedoch keinen nachweisbaren Einfluss auf die Evapotranspiration und andere hydrologische Faktoren hatte. Fernerkundliche Daten wurden zur Erkennung von Landnutzungsänderungen verwendet; vier Karten für 1992, 1998, 2002 und 2014 aus LANDSAT-Bildern. Die festgestellten signifikanten Veränderungen waren hauptsächlich auf die Urbanisierung und die landwirtschaftliche Entwicklung von etwa 169 km2 und 514 km2 zurückzuführen. Das SWAT-Modell wurde basierend auf dem LULC von 1992 kalibriert und zur Bewertung der Auswirkungen von Landnutzungsänderungen auf die Hydrologie verwendet. Basierend auf den LULC-Szenarien von 1992 und 2002 zeigten die Modellergebnisse eine Verringerung der Evapotranspiration um 6 mm, insbesondere in Gebieten, in denen Wälder durch Landwirtschaft ersetzt wurden, und eine allgemeine Erhöhung des Oberflächenabflusses um 3 mm, was auf die Verringerung der Oberflächenbedeckung zurückzuführen ist. Die weitere Ausdehnung des urbanen Bereichs und der Landwirtschaft zwischen 2002 und 2014 führte zu einer weiteren Erhöhung des Oberflächenabflusses um ca. 3 mm. Diese Studie schlägt einen Ansatz für die landwirtschaftliche Landnutzungsplanung vor, bei dem die Wechselwirkungen von Morphologie und Klima genutzt werden, um Gebiete zu identifizieren, die zu minimalen Auswirkungen auf die Landwirtschaft führen werden. Grünland wurde als Landnutzung identifiziert, die engere hydrologische Eigenschaften als die Landwirtschaft aufwies. Das Grünland wurde als LULC-Klasse ausgewählt, die durch Landwirtschaft ersetzt werden kann. Morphologische Analysen zeigten, dass eine geringe Hangneigung, eine höhere Bodenschüttdichte und eine geringe Robustheit des Geländes die besten physikalischen Bedingungen für die landwirtschaftliche Praxis sind. Dies würde jedoch zu einem Verlust der Vegetationsvielfalt bei anhaltender landwirtschaftlicher Expansion führen. Daher sollte das Ausmaß der Umwidmung von Grünland auf Landwirtschaft begrenzt werden und es sollten zusätzliche Studien zu den Auswirkungen dieser Methode auf die biologische Vielfalt durchgeführt werden

Investigating summer thermal stratification in Lake Ontario

Summer thermal stratification in Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). Summer temperature differences establish strong vertical density gradients (thermocline) between the epilimnion and hypolimnion. Capturing the stratification and thermocline formation has been a challenge in modeling Great Lakes. Deviating from EFDC's original Mellor-Yamada (1982) vertical mixing scheme, we have implemented an unidimensional vertical model that uses different eddy diffusivity formulations above and below the thermocline (Vincon-Leite, 1991; Vincon-Leite et al., 2014). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers; and lake bathymetry is interpolated on a 2-km grid. The model has 20 vertical layers following sigma vertical coordinates. Sensitivity of the model to vertical layers' spacing is thoroughly investigated. The model has been calibrated for appropriate solar radiation coefficients and horizontal mixing coefficients. Overall the new implemented diffusivity algorithm shows some successes in capturing the thermal stratification with RMSE values between 2-3°C. Calibration of vertical mixing coefficients is under investigation to capture the improved thermal stratification