Improved remote sensing methods to detect northern wild rice (Zizania palustris L.)

Declining populations of Zizania palustris L. (northern wildrice, or wildrice) during the last century drives the demand for new and innovative techniques to support monitoring of this culturally and ecologically significant crop wild relative. We trained three wildrice detection models in R and Google Earth Engine using data from annual aquatic vegetation surveys in northern Minnesota. Three di erent training datasets, varying in the definition of wildrice presence, were combined with Landsat 8 Operational Land Imager (OLI) and Sentinel-1 C-band synthetic aperture radar (SAR) imagery to map wildrice in 2015 using random forests. Spectral predictors were derived from phenologically important time periods of emergence (June–July) and peak harvest (August–September). The range of the Vertical Vertical (VV) polarization between the two time periods was consistently the top predictor. Model outputs were evaluated using both point and area-based validation (polygon). While all models performed well in the point validation with percent correctly classified ranging from 83.8% to 91.1%, we found polygon validation necessary to comprehensively assess wildrice detection accuracy. Our practical approach highlights a variety of applications that can be applied to guide field excursions and estimate the extent of occurrence at landscape scales. Further testing and validation of the methods we present may support multiyear monitoring which is foundational for the preservation of wildrice for future generations

Characterizing distributions and drivers of emergent aquatic vegetation in Minnesota

2020 Summer.Includes bibliographical references.The emergent aquatic vegetation (EAV) communities across the lakes of Minnesota serve critical functions within ecosystems by providing habitat and forage for native waterfowl and fish species, moderating water chemistry, and serving as a cultural and economic resource. Communities of EAV are changing dramatically in response to alterations in hydrologic flow regimes, nutrient availability, biological homogenization, and near-shore development. To address the conservation of these communities at a spatial scale relevant for landscape management, the changes need to be evaluated at local and regional scales. Previous efforts to map and monitor EAV have utilized field surveys, aerial imagery, multispectral imagery, and synthetic aperture radar (SAR). However, it is difficult to apply the findings of previous studies to broader spatial scales because they lack field surveys, clear or repeatable methodologies, rigorous validation, and/or applying methods to broad spatial extents, all of which are all necessary for providing direct implications for landscape level management. The first chapter of this thesis aimed to overcome these challenges and create statewide maps of EAV in Minnesota at a spatial resolution relevant to landscape management at both broad and local scales. We paired detailed field surveys of EAV communities with Sentinel-1 SAR and Sentinel-2 Multispectral Imager to create annual maps of EAV across the lakes of Minnesota at a 10 m spatial resolution in 2017 and 2018. We created two random forest models, a species model predicting general classes of EAV and a water model identifying open water regions across hydrologic features in Minnesota. We validated both classification models using withheld field sample locations to measure overall accuracy as well as individual class user's and producer's accuracies. The species and water map predictions were combined into a final map representing water and EAV classes each year. We also evaluated each map by the area-based percentage of overlap between model predictions and field surveys which ranged from 54.5 to 90.1% agreement. The 2017 map was further evaluated using an area-based weighted probability with an overall accuracy of 89.9% (±0.7%). The methods and promising results highlighted by this study set the stage for subsequent analyses at broader spatial scales to quantify temporal shifts or trends in EAV communities. The combination of these diverse and detailed datasets provides methods for generating annual maps of EAV distribution across Minnesota, and ultimately provide a tool to support landscape-scale conservation efforts of EAV communities in Minnesota. The second chapter investigated the influence of systemic drivers related to the decline of northern wild rice (Zizania palustris L.) over the last century. Wild rice is an environmental indicator species that is sensitive to hydrologic changes and disturbances and serves an essential role in ecological, cultural, and economic systems in Minnesota. Due to the previous lack of comprehensive information regarding its extent and distribution, previous efforts to study its decline have been limited to small regions or small samples of lakes across the state. We utilized 2018 presence maps of wild rice from the first chapter and summarized wild rice cover across 366 lakes. Then, we employed a suite of spatial, hydrological, ecological, and environmental variables summarized at a variety of spatial scales within a three-step modeling framework to select the most significant drivers of wild rice cover, explore interactions between drivers, and account for inherent spatial autocorrelation in the datasets. A final spatial lag model revealed that dispersal and population connectivity had the strongest relationships with wild rice cover on each lake. While further exploration may better quantify this relationship, land managers should consider the degree of connectivity between wild rice lakes and their spatial configuration on the landscape during conservation planning to maximize population resilience. Our results suggest that it may be more suitable to approach populations as connected habitat regions, in contrast to the more widely accepted notion that wild rice lakes are self-contained or independent populations

Nightlight Intensity Change Surrounding Nature Reserves: A Case Study in Orbroicher Bruch Nature Reserve, Germany

Persistent global urbanization has a direct relationship to measurable artificial light at night (ALAN), and the Defense Meteorological Satellite Program has served an important role in monitoring this relationship over time. Recent studies have observed significant declines in insect abundance and populations, and ALAN has been recognized as a contributing factor. We investigated changes in nightlight intensity at various spatial scales surrounding insect traps located in Orbroicher Bruch Nature Reserve, Germany. Using a time series of global nighttime light imagery (1992–2010), we evaluated pixel-level trends through linear regressions and the Mann–Kendall test. Paired with urban land cover delineation, we compared nightlight trends across rural and urban areas. We utilized high-resolution satellite imagery to identify landscape features potentially related to pixel-level trends within areas containing notable change. Approximately 96% of the pixel-level trends had a positive slope, and 22% of pixels experienced statistically significant increases in nightlight intensity. We observed that 80% of the region experienced nightlight intensity increases >1%, concurrent with the observed decline in insect biomass. While it is unclear if these trends extend to other geographic regions, our results highlight the need for future studies to concurrently investigate long-term trends in ALAN and insect population decline across multiple scales, and consider the spatial and temporal overlaps between these patterns