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Full-Text Articles in Physical Sciences and Mathematics

Remotely Sensed Early Warning Of Algal Blooms In An Eastern Nebraska Reservoir: A Comparison Of Temporal And Spatial Indicators, Mercy Kipenda Aug 2024

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, …


A Comparative Analysis Of Openet For Evaluating Evapotranspiration In California Almond Orchards, Kyle Knipper, Martha Anderson, Nicholas Bambach, Forrest Melton, Zac Ellis, Yun Yang, John Volk, Andrew J. Mcelrone, William Kustas, Matthew Roby, Will Carrara, Sebastian Castro, Ayse Kilic, Joshua B. Fisher, Anderson Ruhoff, Gabriel B. Senay, Charles Morton, Sebastian Saa, Richard G. Allen Jul 2024

A Comparative Analysis Of Openet For Evaluating Evapotranspiration In California Almond Orchards, Kyle Knipper, Martha Anderson, Nicholas Bambach, Forrest Melton, Zac Ellis, Yun Yang, John Volk, Andrew J. Mcelrone, William Kustas, Matthew Roby, Will Carrara, Sebastian Castro, Ayse Kilic, Joshua B. Fisher, Anderson Ruhoff, Gabriel B. Senay, Charles Morton, Sebastian Saa, Richard G. Allen

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in …


The Core Of It All: From The Forest To The Concrete Jungle, Ayo Andra J. Deas Jun 2024

The Core Of It All: From The Forest To The Concrete Jungle, Ayo Andra J. Deas

Dissertations, Theses, and Capstone Projects

The Core of It All is a component of principle within Fasaha. The mission of Fasaha is to implement programming directed toward development of one’s Core through self-actualization. Self-Actualization is defined as bringing forth the total essential qualities of one’s own consciousness, character, and identity through positive behavior. Throughout this manuscript, principle is defined as the standard of natural essential qualities determining intrinsic consciousness, character and identity. Programming is defined as providing with intrinsic instructions for the automatic performance of a task.

Fasaha is a support service that enhances the existing organization’s service. Throughout this dissertation, it will be apparent …


From Pixels To Plants: Remote Sensing Of California Invasive Plants, Kenneth Rangel May 2024

From Pixels To Plants: Remote Sensing Of California Invasive Plants, Kenneth Rangel

Master's Projects and Capstones

Invasive plants cause significant impacts to ecosystems, the economy, and human health. California has experienced significant plant invasions and is well suited to future invasion because of its Mediterranean climate and human disturbance. Eradication or control of invasive plant species requires a detailed understanding of their spatial distribution, which typically involves on the ground surveys that can be expensive or inconsistent. Remote sensing offers a potential alternative or supplement to in-person invasive plant mapping. This study performed a comparative analysis of 41 remote sensing studies that mapped the distribution of California invasive plants. I found that while high spectral resolution …


Microwave Emission Model Parameter Tuning For Surface Soil Moisture Retrieval Using Uav-Mounted Dual Polarization L-Band Radiometer, Santiago Hoyos Echeverri May 2024

Microwave Emission Model Parameter Tuning For Surface Soil Moisture Retrieval Using Uav-Mounted Dual Polarization L-Band Radiometer, Santiago Hoyos Echeverri

Open Access Theses & Dissertations

Surface soil moisture retrieval from L-band brightness temperature has been developed for the past decades due to multiple beneficial characteristics of 1-2 GHz frequency bands for remote sensing of the environment. Numerous microwave emission models have been proposed for tower and satellite-based operations with successful retrieval of surface soil moisture and vegetation water content. As a result of the development of cost-effective and low-mass microwave L-band radiometers such as the Portable L-band Radiometer (PoLRa), surface soil moisture surveying traditionally developed by satellite missions SMOS and SMAP can now be developed at local scales, bringing these operations to commercial small unmanned …


Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos Apr 2024

Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos

Institute for ECHO Articles and Research

The recent decades have seen an increasing academic interest in leveraging machine learning approaches to nowcast, or forecast in a highly short-term manner, precipitation at a high resolution, given the limitations of the traditional numerical weather prediction models on this task. To capture the spatiotemporal associations of data on input variables, a deep learning (DL) architecture with the combination of a convolutional neural network and a recurrent neural network can be an ideal design for nowcasting rainfall. In this study, a long short-term memory (LSTM) modeling structure is proposed with convolutional operations on input variables. To resolve the issue of …


Relocating Lubra Village And Visualizing Himalayan Flood Damages With Remote Sensing, Ronan Wallace, Yungdrung Tsewang Gurung, Ryan Kastner Feb 2024

Relocating Lubra Village And Visualizing Himalayan Flood Damages With Remote Sensing, Ronan Wallace, Yungdrung Tsewang Gurung, Ryan Kastner

Journal of Critical Global Issues

As weather patterns change worldwide, isolated communities impacted by climate change go unnoticed and we need community-driven solutions. In Himalayan Mustang, Nepal, indigenous Lubra Village faces threats of increasing flash flooding. After every flood, residual muddy sediment hardens across the riverbed like concrete, causing the riverbed elevation to rise. As elevation increases, sediment encroaches on Lubra’s agricultural fields and homes, magnifying flood vulnerability. In the last monsoon season alone, the Lubra community witnessed floods swallowing several agricultural fields and damaging two homes. One solution considers relocating the village to a new location entirely. However, relocation poses a challenging task, as …


Road Extraction On Remote Sensing Imagery: Historical Mapping Of The Brazilian Amazon, Jonas Paiva Botelho Jr Jan 2024

Road Extraction On Remote Sensing Imagery: Historical Mapping Of The Brazilian Amazon, Jonas Paiva Botelho Jr

MSU Graduate Theses

This work proposes an artificial intelligence model based on U-Net architecture to map road networks in the Brazilian Amazon. Over the years, the Amazon region has been heavily exploited, leading to increased deforestation rates, contributing to CO2 emissions, amplifying global warming, and causing a disturbance in local fauna and flora. The expansion into the forest by illegal miners, loggers, and land grabbers can be tracked down by the construction of roads, which we can refer to as the arteries of deforestation. Previous works on the matter proposed algorithms that use high-resolution imagery to map roads precisely. However, this work approach …