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Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
From Pixels To Plants: Remote Sensing Of California Invasive Plants, Kenneth Rangel
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 …
Classification Of Remote Sensing Image Data Using Rsscn-7 Dataset, Satya Priya Challa
Classification Of Remote Sensing Image Data Using Rsscn-7 Dataset, Satya Priya Challa
Electronic Theses, Projects, and Dissertations
A novel technique for remote sensing image scene classification is employed using the Compact Vision Transformer (CVT) architecture. This model strengthens the power of deep learning and self-attention algorithms to significantly intensify the accuracy and efficiency of scene classification in remote sensing imagery. Through extensive training and evaluation of the RSSCNN7 dataset, our CVT-based model has achieved an impressive accuracy rate of 87.46% on the original dataset. This remarkable result underscores the prospect of CVT models in the domain of remote sensing and underscores their applicability in real-world scenarios. Our report furnishes an elaborate account of the model's architecture, training …
Microwave Emission Model Parameter Tuning For Surface Soil Moisture Retrieval Using Uav-Mounted Dual Polarization L-Band Radiometer, Santiago Hoyos Echeverri
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
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
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 …
Predicting Forage Provision Of Grasslands Across Climate Zones By Hyperspectral Measurements, F. A. Männer, J. Muro, J. Ferner, S. Schmidtlein, A. Linstädter
Predicting Forage Provision Of Grasslands Across Climate Zones By Hyperspectral Measurements, F. A. Männer, J. Muro, J. Ferner, S. Schmidtlein, A. Linstädter
IGC Proceedings (1997-2023)
The potential of grasslands’ fodder production is a crucial management measure, while its quantification is still laborious and costly. Remote sensing technologies, such as hyperspectral field measurements, enable fast and non-destructive estimation. However, such methods are still limited in transferability to other locations or climatic conditions. With this study, we aim to predict forage nutritive value, quantity, and energy yield from hyperspectral canopy reflections of grasslands across three climate zones. We took hyperspectral measurements with a field spectrometer from grassland canopies in temperate, tropical and semi-arid grasslands, and analyzed corresponding biomass samples for their quantity (BM), metabolizable energy content (ME) …
Drone And Digital Camera Imagery Estimate C3 And C4 Grass Ratios In Pastures, J. A. Bush, C. D. Teutsch, S. R. Smith, J. C. Henning
Drone And Digital Camera Imagery Estimate C3 And C4 Grass Ratios In Pastures, J. A. Bush, C. D. Teutsch, S. R. Smith, J. C. Henning
IGC Proceedings (1997-2023)
The following study investigates the accuracy and practicality of exploiting the color dichotomy present between C3 and C4 grass species to estimate their respective proportions from drone or camera captured imagery. Understanding the proportions of C3 and C4 grasses in pastures is vital to sound decision making for livestock production. The ability to monitor these proportions remotely will also allow for large scale monitoring as well as detection of changes in botanical composition over time and in response to weather events, management, or climate change. A free green canopy cover (GCC) analyzing software, Canopeo, was used to quantify green plants …
Prospects For Improving Alfalfa Yield Using Genomic- And Phenomic-Based Breeding, M. W. Francis, D. Pap, A. Krill-Brown, E. C. Brummer
Prospects For Improving Alfalfa Yield Using Genomic- And Phenomic-Based Breeding, M. W. Francis, D. Pap, A. Krill-Brown, E. C. Brummer
IGC Proceedings (1997-2023)
Alfalfa (Medicago sativa L.) is a perennial outcrossing legume that is cultivated as an important forage crop in many parts of the world. Yield is the most important trait for profitable alfalfa production, yet over the last 30 years yield improvement in California has stagnated. Current breeding methods focus on recurrent phenotypic selection; however, alternatives incorporating genomic- and phenomic-based information may enhance genetic gain and help to address the lack of yield improvement. Here we attempt to increase the yield potential of alfalfa using genomic selection (GS) in combination with high throughput phenotyping (HTP). A total of 193 families …
A Uav Based Cmos Ku-Band Metasurface Fmcw Radar System For Low-Altitude Snowpack Sensing, Adrian Tang, Nacer Chahat, Yangyho Kim, Arhison Bharathan, Gabriel Virbila, Hans-Peter Marshall, Thomas Van Der Weide, Gaurangi Gupta, Raunika Anand, Goutam Chattopadhyay, Mau-Chung Frank Chang
A Uav Based Cmos Ku-Band Metasurface Fmcw Radar System For Low-Altitude Snowpack Sensing, Adrian Tang, Nacer Chahat, Yangyho Kim, Arhison Bharathan, Gabriel Virbila, Hans-Peter Marshall, Thomas Van Der Weide, Gaurangi Gupta, Raunika Anand, Goutam Chattopadhyay, Mau-Chung Frank Chang
Geosciences Faculty Publications and Presentations
This article presents development of a UAV based frequency modulated continuous wave (FMCW) radar system for remotely sensing the water contained within snowpacks. To make the radar system compatible with the payload requirements of small UAV platforms, the radar electronics are implemented with CMOS technology, and the antenna is implemented as an extremely compact and lightweight metasurface (MTS) antenna. This article will discuss how the high absorption losses of snowpacks lead to dynamic range requirements much stricter than FMCW radars used for automotive and other sensing applications, and how these requirements are met through antenna isolation, leakage calibration and exploitation …
Integrating Remote Sensing With Ground-Based Observations To Quantify The Effects Of An Extreme Freeze Event On Black Mangroves (Avicennia Germinans) At The Landscape Scale, Melinda Martinez, Michael J. Osland, James B. Grace, Nicholas M. Enwright, Camille L. Stagg, Camille L. Stagg, Simen Kaalstad, Gordon H. Anderson, Elena A. Flores, Alejandro Fierro-Cabo
Integrating Remote Sensing With Ground-Based Observations To Quantify The Effects Of An Extreme Freeze Event On Black Mangroves (Avicennia Germinans) At The Landscape Scale, Melinda Martinez, Michael J. Osland, James B. Grace, Nicholas M. Enwright, Camille L. Stagg, Camille L. Stagg, Simen Kaalstad, Gordon H. Anderson, Elena A. Flores, Alejandro Fierro-Cabo
School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations
Climate change is altering the frequency and intensity of extreme weather events. Quantifying ecosystem responses to extreme events at the landscape scale is critical for understanding and responding to climate-driven change but is constrained by limited data availability. Here, we integrated remote sensing with ground-based observations to quantify landscape-scale vegetation damage from an extreme climatic event. We used ground- and satellite-based black mangrove (Avicennia germinans) leaf damage data from the northern Gulf of Mexico (USA and Mexico) to examine the effects of an extreme freeze in a region where black mangroves are expanding their range. The February 2021 …