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Articles 1 - 4 of 4
Full-Text Articles in Remote Sensing
Remotely Sensed Early Warning Of Algal Blooms In An Eastern Nebraska Reservoir: A Comparison Of Temporal And Spatial Indicators, Mercy Kipenda
Remotely Sensed Early Warning Of Algal Blooms In An Eastern Nebraska Reservoir: A Comparison Of Temporal And Spatial Indicators, Mercy Kipenda
School of Natural Resources: Dissertations, Theses, and Student Research
Cyanobacterial harmful algal blooms (CyanoHABs) detrimentally affect human, animal, and ecosystem health. Remotely sensed early warning systems for cyanoHABs in inland lakes could contribute to more proactive water quality monitoring and help mitigate negative impacts. Advances in freely available remote sensing imagery, with finer spatial, temporal, and spectral resolutions, present new opportunities for the development and comparative analysis of methods to detect sudden deterioration in lake water quality. In this thesis, I compared and tested for temporal and spatial early warning signals of cyanoHABs in field-based and remotely sensed datasets from 2019 to 2023 in Pawnee Lake in southeast Nebraska, …
Multi-Criteria Evaluation Model For Classifying Marginal Cropland In Nebraska Using Historical Crop Yield And Biophysical Characteristics, Andrew Laws
School of Natural Resources: Dissertations, Theses, and Student Research
Marginal cropland is suboptimal due to historically low and variable productivity and limiting biophysical characteristics. To support future agricultural management and policy decisions in Nebraska, U.S.A, it is important to understand where cropland is marginal for its two most economically important crops: corn (Zea mays) and soybean (Glycine max). As corn and soybean are frequently planted in a crop rotation, it is important to consider if there is a relationship with cropland marginality. Based on the current literature, there exists a need for a flexible yet robust methodology for identifying marginal land at different scales, which …
Remote Sensing Of Green Leaf Area Index In Maize And Soybean: From Close-Range To Satellite, Anthony L. Nguy-Robertson
Remote Sensing Of Green Leaf Area Index In Maize And Soybean: From Close-Range To Satellite, Anthony L. Nguy-Robertson
School of Natural Resources: Dissertations, Theses, and Student Research
This dissertation seeks to explore alternative methodologies for estimating green leaf area index (LAI) and crop developmental stages. Specifically this research [1] developed an approach for creating a Moderate Resolution Imaging Spectroradiometer (MODIS) high spatial resolution product for estimating green LAI on the base of data collected using two different close-range sensors. It was determined that the vegetation indices (VIs) Wide Dynamic Range Vegetation Index (WDRVI) and Enhanced Vegetation Index 2 (EVI2) were capable of accurate estimation of green LAI from MODIS 250 m data using models developed from hyperspectral (RMSE < 0.69 m2 m-2; CV < 33%) or multispectral sensors (RMSE < 0.69 m2 m-2; …
Using Landscape Pattern Metrics To Characterize Ecoregions, Martha Isabel Posada Posada
Using Landscape Pattern Metrics To Characterize Ecoregions, Martha Isabel Posada Posada
School of Natural Resources: Dissertations, Theses, and Student Research
Ecological regions, or ecoregions, are areas that exhibit “relative homogeneity in ecosystems”. The principal objective of this research was to determine if and how landscape structure (quantified by landscape pattern metrics) may be related to ecoregions defined using Omernik’s approach to ecoregionalization. Nine key landscape pattern metrics (number or LULC classes and the proportion of each class, number of patches, mean patch size and area-weighted fractal dimension, perimeter-area fractal dimension, contagion, mean Euclidean nearest neighbor distance and interspersion and juxtaposition index) where used to asses landscape structure in a sample of 26 Omernik Level III ecoregions located in the central …