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Articles 1 - 30 of 190
Full-Text Articles in Remote Sensing
Cropland Change And Prime Farmland In Calloway County, Kentucky, 2015–2025: A Gis Analysis Of Corn And Soybean Fields, Shiba Hari Dhakal Shiba
Cropland Change And Prime Farmland In Calloway County, Kentucky, 2015–2025: A Gis Analysis Of Corn And Soybean Fields, Shiba Hari Dhakal Shiba
Scholars Week
Understanding where cropland change occurs relative to soil quality helps indicate whether farmland is being used on the most productive land. This study used GIS-based change detection analysis to evaluate changes in corn and soybean fields in Calloway County, Kentucky, between 2015 and 2025 and to test whether new corn and soybean fields were located on prime farmland. USDA Cropland Data Layer maps for 2015 and 2025 (30 m resolution) were reclassified into seven classes and compared in ArcGIS Pro, and the results were overlaid on USDA NRCS gSSURGO farmland classification. Combined corn and soybean area declined about 19% from …
Nasa’S Ecostress Satellite Reveals Widespread Midday Depression In Ecosystem Evapotranspiration, Jingyi Bu, Jingfeng Xiao, Joshua B. Fisher, Yiqi Luo
Nasa’S Ecostress Satellite Reveals Widespread Midday Depression In Ecosystem Evapotranspiration, Jingyi Bu, Jingfeng Xiao, Joshua B. Fisher, Yiqi Luo
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
Plants often exhibit a midday depression in water use (i.e., transpiration), reflecting a constraint on their ability to sustain maximum water transport, which may occur at the cost of reduced photosynthesis. Eddy covariance observations and geostationary satellites cannot quantify this widespread phenomenon globally while resolving fine-scale spatial variability. Using evapotranspiration measurements from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and machine learning, we quantify the global distribution of midday depression in evapotranspiration. Midday depression primarily occurs during peak-growing seasons in temperate zones and dry periods in the tropics, with a morning shift of the peak evapotranspiration time …
Passive Microwave Remote Sensing Of Flash Drought Impacts On Vegetation, Quinton R. Deppert
Passive Microwave Remote Sensing Of Flash Drought Impacts On Vegetation, Quinton R. Deppert
School of Natural Resources: Dissertations, Theses, and Student Research
In 2002, Dr. Mark Svoboda characterized a new form of drought known as flash drought. Flash drought was defined as a rapid decline in vegetation health caused by severe heat and drought. In recent years, attempts to quantify the impacts of flash drought via precipitation, soil moisture, evapotranspiration, and temperature indicators have proliferated. What has rarely been quantified is what the rapid decline in vegetation health amid flash drought looks like through remote sensing. This is because vegetation health indices like the Normalized Difference Vegetation Index (NDVI) are derived from the visible and infrared regions of the electromagnetic spectrum and …
A Distributed Model For Undergraduate Education In Environmental Remote Sensing: Increased Student Interest In Science And Sense Of Science Identity And Belonging, Gregory R. Goldsmith, Monae Verbeke, Jeremy Forsythe, Joshua B. Fisher
A Distributed Model For Undergraduate Education In Environmental Remote Sensing: Increased Student Interest In Science And Sense Of Science Identity And Belonging, Gregory R. Goldsmith, Monae Verbeke, Jeremy Forsythe, Joshua B. Fisher
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
"We present results from a new open-access course in satellite remote sensing of the environment that uses evidence-based, active learning pedagogy to train the next generation of interdisciplinary scientists The course, called Observing Earth from Above, teaches students how to access, visualize, and communicate satellite remote sensing data from NASA’s ECOSTRESS instrument to address a variety of environmental challenges. The resources focus on follow-along tutorials for students and also include recorded lectures and interviews with remote sensing scientists."
Long-Term Variability Of Air Quality And Greenhouse Gas Emissions From Rice Crop Burning In Punjab During 2012–2020, Harsimranjit Kaur Romana, Dericks Praise Shukla, Ramesh P. Singh
Long-Term Variability Of Air Quality And Greenhouse Gas Emissions From Rice Crop Burning In Punjab During 2012–2020, Harsimranjit Kaur Romana, Dericks Praise Shukla, Ramesh P. Singh
Mathematics, Physics, and Computer Science Faculty Articles and Research
Punjab, India's primary rice and wheat production hub, has witnessed rapid expansion of paddy cultivation over the past two decades, driven by minimum support price incentives, changes in government policies, alignment of sowing with the monsoon season and the adoption of high-yielding varieties. This transition has intensified groundwater extraction and shortened the fallow period between rabi and kharif crop seasons, reducing the window between rice harvesting and wheat sowing, leading to widespread open-field burning of rice residue and recurrent post-monsoon air-quality deterioration across the Indo-Gangetic Plain. Despite numerous short-term or single-pollutant assessments, a spatially resolved, multi-pollutant and multi-decadal evaluation linking …
High Spatiotemporal Resolution Monitoring Of Crop Water Stress Across The Contiguous United States Using Harmonized Landsat And Sentinel-2 Data, Na Chen, Yanlei Feng, Na Wang, Jevan Yu, Mohammad Reza Alizadeh, Yifeng Cui, Ning Ye, Wenzhe Jiao, Joshua B. Fisher, César Terrer
High Spatiotemporal Resolution Monitoring Of Crop Water Stress Across The Contiguous United States Using Harmonized Landsat And Sentinel-2 Data, Na Chen, Yanlei Feng, Na Wang, Jevan Yu, Mohammad Reza Alizadeh, Yifeng Cui, Ning Ye, Wenzhe Jiao, Joshua B. Fisher, César Terrer
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
Accurate and timely monitoring of crop water stress is essential for efficient agricultural water management, ultimately maintaining and improving crop productivity. While Landsat has been used for this purpose, its temporal resolution hampers timely detection of crop water stress. The recently released Harmonized Landsat and Sentinel-2 Version 2.0 dataset, which enables a higher-frequency time series of satellite observations (2–3 days, 30 m), offers a promising solution to this challenge. However, its potential for crop stress monitoring remained unexplored. In this study, we utilized 923 HLS satellite tiles to assess crop water stress across the contiguous United States (CONUS). Crop water …
Regional Oceanographic Controls On Water Column Nitrogen Fixation In Northern Australian Waters, Douglas G. Capone, Ajit Subramaniam, Yubin Raut, Joseph P. Montoya, Margaret R. Mulholland, Rachel Ann Foster, Miles Furnas, Edward J. Carpenter
Regional Oceanographic Controls On Water Column Nitrogen Fixation In Northern Australian Waters, Douglas G. Capone, Ajit Subramaniam, Yubin Raut, Joseph P. Montoya, Margaret R. Mulholland, Rachel Ann Foster, Miles Furnas, Edward J. Carpenter
OES Faculty Publications
Large blooms of the diazotrophic cyanobacteria, Trichodesmium, have been re-ported along the north coast of Australia and are readily evident in remote sensing images. During a research cruise in November 1999, we sampled from Townsville to Broome, examined Trichodesmium population densities and their rates of carbon and N₂ fixation alongside microscopy-based cell counts of them and other cyanobacte-rial diazotrophs. Additionally, we also enumerated the picophytoplankton community using flow-cytometry, measured bulk chlorophyll concentrations, carbon and N₂ fixation rates, and water column hydrography, enabling comparison of diazotrophic and picophytoplankton functional groups across the system. Agglomerative hierarchical clustering analysis of physicochemical oceanographic …
Mapping Robusta Coffee (Coffea Canephora) Cropping Systems In Uganda: A Two-Step Pixel And Sub-Pixel Based Approach With Sentinel-2 Data, Getachew Kebede, Bester Tawona Mudereri, Onisimo Mutanga, Tobias Landmann, John Odindi, Natacha Motisi, Fabrice Pinard, Henri E.Z. Tonnang, Elfatih M. Abdel-Rahman
Mapping Robusta Coffee (Coffea Canephora) Cropping Systems In Uganda: A Two-Step Pixel And Sub-Pixel Based Approach With Sentinel-2 Data, Getachew Kebede, Bester Tawona Mudereri, Onisimo Mutanga, Tobias Landmann, John Odindi, Natacha Motisi, Fabrice Pinard, Henri E.Z. Tonnang, Elfatih M. Abdel-Rahman
All Peer-Reviewed Publications
Coffee is a highly valued commodity and a widely consumed beverage, playing an important role in global trade. However, coffee farming landscapes are increasing transitioning into smaller-scale agricultural setups. This transformation highlights the critical need for accurate classification and mapping of coffee cropping systems (CS), especially in countries like Uganda, where dense vegetation and complex terrain present substantial challenges to traditional land survey methods. Moreover, understanding the spatial distribution of Robusta coffee (Coffea canephora) CS is essential for developing site-specific management strategies, guiding extension services, and informing evidence-based policy decisions. To address this gap, the present study aimed to enhance …
Detecting Prescribed Fire, Haying And Grazing Events Via Remote Sensing To Create Grassland Disturbance Landcovers For The Ring-Necked Pheasant (Phasianus Colchicus), Megan Amy Baldissara
Detecting Prescribed Fire, Haying And Grazing Events Via Remote Sensing To Create Grassland Disturbance Landcovers For The Ring-Necked Pheasant (Phasianus Colchicus), Megan Amy Baldissara
Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–
This dissertation developed disturbance detection models to fulfill the need for remote sensing landcover products describing grassland structure. The use of landcover products derived from remote sensing is increasing over time in pheasant (Phasianus colchicus) research. Such landcover, however, does not provide relevant pheasant structural habitat information (Chapter 1). Pheasants require tall, high-density grassland for nesting, tall grassland with medium density for brood rearing, and tall grassland for wintering. Time since disturbance can serve as a proxy for structure, as it shapes vegetation by removing biomass and resetting succession. Disturbance is easier to detect than structure with current …
Harnessing Hyperspectral Imaging And Deep Learning For Terrestrial Habitat Mapping In Arid Landscapes: A Case Study In Saudi Arabia, Ali Elgendy, Hesham Morgan, Brandon Tran, Rejoice Thomas, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary
Harnessing Hyperspectral Imaging And Deep Learning For Terrestrial Habitat Mapping In Arid Landscapes: A Case Study In Saudi Arabia, Ali Elgendy, Hesham Morgan, Brandon Tran, Rejoice Thomas, Tamer Ismail, Yehya Kh. Shehadeh, Ahmed Elgharib, Ahmed Abdullah Al-Dughairi, Ali El Muburak, Khaled Allam Harhash, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Arid ecosystems remain under-mapped at actionable scales despite their ecological importance. Decision makers lack reliable, high-resolution habitat maps in drylands to prioritize protection and target restoration. This research integrates spaceborne hyperspectral imaging from the Environmental Mapping and Analysis Program (EnMAP) with deep learning semantic segmentation models to produce an updated level of habitat classification based on the International Union for Conservation of Nature (IUCN) for part of the Imam Turki bin Abdullah Royal Reserve, Saudi Arabia. Using ground control points and the full EnMAP spectral cube without band selection, U-Net and DeepLabV3+ architectures were each implemented with VGG19 and ResNet-101 …
Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary
Unraveling Crop Nitrogen-Water Dynamics With Hyperspectral-Thermal Sensing In Northern Central Valley, California, Shahryar Fazli, Surendra Maharjan, Wenzhao Li, Joshua B. Fisher, Rejoice Thomas, Fernando Romero Galvan, Gabriela Shirkey, Hesham El-Askary
Mathematics, Physics, and Computer Science Faculty Articles and Research
Ensuring global food security in the face of climate change requires optimizing crop water use and nutrient management. This study investigates the relationship between canopy nitrogen (N) and evapotranspiration (ET) across sunflower, rice, walnut, alfalfa, and plum crops using advanced remote sensing technologies. High-resolution hyperspectral data from NASAs Earth Surface Mineral Dust Source Investigation (EMIT) and thermal multispectral data from the Landsat-based OpenET system were analyzed over 1,135 km2 in California. Regression analysis revealed strong spatial association between canopy N and ET for sunflower (R2 = 0.82), rice (R2 = 0.71), and walnut (R2 = 0.68), …
First Report Of Fibropapillomatosis And Critical Habitat Use In Green Sea Turtles In Curaçao, M. Tripepi, I. J. R. Van Veghel, A. D. Vreugdenhil, E. Brunelli
First Report Of Fibropapillomatosis And Critical Habitat Use In Green Sea Turtles In Curaçao, M. Tripepi, I. J. R. Van Veghel, A. D. Vreugdenhil, E. Brunelli
College of Life Sciences Faculty Papers
Fibropapillomatosis, a disease affecting green sea turtles (Chelonia mydas), has been documented in many regions of the world, including the Caribbean, but has not been scientifically reported in Curaçao until now. The present study utilized VHF telemetry in Caracas Bay, Curaçao, to track green sea turtles, both healthy and affected by fibropapillomatosis, with the objective of ascertaining their foraging locations. VHF telemetry proved to be a pivotal method for assessing habitat use in areas characterized by limited visibility and high boat traffic, conditions that present significant challenges for divers and snorkellers attempting to access the area. The results …
High Spatial Resolution Crop Type And Land Use Land Cover Classification Without Labels: A Framework Using Multi-Temporal Planetscope Images And Variational Bayesian Gaussian Mixture Model, Minh Tri Le
Mathematics, Physics, and Computer Science Faculty Articles and Research
Previous studies often combined high spatial resolution data (e.g., PlanetScope) with wider spectral range data (e.g., Sentinel-2) and relied on supervised classification methods to produce land use and land cover (LULC) maps. This study proposed a new unsupervised framework to generate crop type and LULC maps at high spatial resolution (< 5 m) using available PlanetScope data solely without requiring ground truths. We used PlanetScope surface reflectance images and their derived spectral indices during growing seasons to create multi-temporal input features, which were fed into an unsupervised Variational Bayesian Gaussian Mixture Model (VBGMM). The VBGMM, unlike the traditional unsupervised classification methods, (1) first estimated optimal parameters that are most suitable based on the input features and then (2) assigned pixels to the cluster with maximum posteriori probability of a mixture of several Gaussian distributions. The crop type and LULC maps were then generated by labeling the derived clusters using the best possible assignment method, referring to the existing crop type or LULC products. We evaluated the produced PlanetScope-based crop type and LULC maps using true labels, corresponding reference maps, and other unsupervised classification methods. The results demonstrated the robustness and effectiveness of the proposed framework in mapping crop types and LULC at 3–5 m pixels across various ecosystems, climate zones, and human-managed landscapes. The spatial patterns of PlanetScope-based maps were (1) highly comparable with all the reference datasets at 10–30 m spatial resolution and (2) better than the traditional GMM and K-means clustering methods. The VBGMM produced classification maps with high confidence, yielding class probabilities above 0.9 for over 90 % of all study areas. The area percentage for all crop type and LULC classes agreed well with their reference maps, with R2 of 0.95 and RMSE of 1.04 %. The confusion matrices using true labels indicated that PlanetScope-based maps achieved a higher overall accuracy of 84 % than the supervised referenced maps of 81 %. Besides, the entropy comparison showed that our framework-based maps were better at capturing fine-scale features such as developed areas within cities that commonly mix with open space and vegetation, deforestation and cropland conversion in South America, smallholder croplands in Africa and Asia, and generating homogeneous crop fields in North America. This study further highlighted the potential for future research to implement our proposed framework to generate timely and extensive annotated datasets, which can be used for operationally training machine learning models to map crop types and LULC, track deforestation, detect wildfires, and delineate flooded areas at larger scales using medium/coarse Earth observations.
Du Undergraduate Showcase Abstracts: Research, Scholarship, And Creative Works, Kabe Aberle, Nadia Kako, Kateri Mcrae, Brooke Agulnek, Sky Palmon, Yasmine Ramirez, Francisca Aguirre Beltran, Ashley Juarez, Bridget Kim, Tessa Appel, Sterling Kerr, Spencer Ingley, Gabe Meyer, Robin Tinghitella, Dale Broder, Lily Baeza, Chloe Beers, Julia Coakley, Whitney Kelsey, Sydney Gainforth, Gabi Wing, Audrey Martin, Aaliyah Amore Berry, Brooke Watley, Kiruthika Venkatesan, Rachel Bienstock, Annabella Brotherston, Madison Bryant, Mia Burgener, Emma P. Lieb, Rachel A. Johnson, Jennifer L. Hoffman, Kania Campbell, Kiena Campbell, Courtney Cassidy, Sage Krzyzkowski, Maddox Jones, Skylar Abookire, Luke Hawkins, Sunnah Yoon, Andrea Chu, Yan Qin, Nyah Cubbison, Brian Gearity, Daniel Mcintosh, Mariely Cruz, Edward Garrido, Grady Dionne, Nicole Doris, Lyndsie Salvagio, Ann-Charlotte Granholm-Bentley, Anna Dymov, Hannah Eckert, Gabrielle Welsh, Erica Larson, Charlie Ernst, Anna Zhou, Sarah Watamura, Larissa Fedorovich-Klein, Georgie Fields, Kimberly A. Guevara, Aven Mccall, Ben Peltier, Feruz Yahia, Patrick Flores, Jadyn Floyd, Sophia Forcier, J. Von R. Monteza, Peter Sokol-Hessner, Gwendolyn Geiger, Scott Nichols, Camryn Gunter, Kendal Hengst, Charlie Bednarz, Issy Garside, Addison Baker, Rachel Mina, Brooke Hermanson, Amanda Klingler, William Highfill, Sydney Jaques, Kerstin Lewey, Allison Grossery, Daniel Linseman, Ethan Lim, Jagger Livengood, Owen Mantelli, Gabby Pappas, Abby Mcdonald, Madeleine Dierking, Eve Miller, Emma Loeber, Anna Marlow, Michael Kerwin, Ella Mathews, Hillary Hamann, Khadija Mohamed, Vivian Nguyen, Gabri Notov, Ifunayachi Ogbonna-Ukuku, Sunil Kumar, Charles Baysah, Sarah Olson, Don Sullivan, Anna Paradiso, Jay Parrish, Mira Pronobis, Alisha Pravasi, Kerstin Haring, Diego Ramirez, Christopher Reardon, Juliana Ramirez, Casey Doherty, Ella Kestner, Teagan Weindel, Cate Billings, Pablo Torre-Walter, Lucy Rand, Samantha Reynolds, Mark Siemens, Khadeeja Rashid, Laine Satterlee, Piper Heilbronner, Lily Pound, Ben Whitehurst, Anna Respet, Lizzie Lesoing, Sydney Hertel, Aya Saad-Masri, Brooke Ballenger, Max Proske, Hannah Rosenberg, Ellia Nakahara, Sophia Espinoza, Ivan Woolhouse, Simon Ruland, Gorkem Er, Timothy Sweeny, Melaku Saketa, Michela Schenk, Maren Lynch, Madi Hamm, Grace Schroeder, Michelle Rozenman, Rana Seif, Jackson Hall, Marisela Simental, Daniel Paredes, Aaron Mena, Preston Spaan, Evelyn Stovin, David Andrew Swartz, Anh Tran, Daniel Pittman, Luke Farchione, Emily Boyer, Ukari Verner, Lacey Conrad, Jonathan Velotta, James Weiner, Jagger Gossett, Noah Sherry, Sam Proud, Ben Block, Avi Narayana, Zoey Weiss, Alyssa Wilson, Gabrielle Walsh, David Zonana, Keely Wright, Kena Riveria, Lillybelle Deer, Jena Doom, Elysia Davis, Isabelle Yaremenko, Caitlyn Young
Du Undergraduate Showcase Abstracts: Research, Scholarship, And Creative Works, Kabe Aberle, Nadia Kako, Kateri Mcrae, Brooke Agulnek, Sky Palmon, Yasmine Ramirez, Francisca Aguirre Beltran, Ashley Juarez, Bridget Kim, Tessa Appel, Sterling Kerr, Spencer Ingley, Gabe Meyer, Robin Tinghitella, Dale Broder, Lily Baeza, Chloe Beers, Julia Coakley, Whitney Kelsey, Sydney Gainforth, Gabi Wing, Audrey Martin, Aaliyah Amore Berry, Brooke Watley, Kiruthika Venkatesan, Rachel Bienstock, Annabella Brotherston, Madison Bryant, Mia Burgener, Emma P. Lieb, Rachel A. Johnson, Jennifer L. Hoffman, Kania Campbell, Kiena Campbell, Courtney Cassidy, Sage Krzyzkowski, Maddox Jones, Skylar Abookire, Luke Hawkins, Sunnah Yoon, Andrea Chu, Yan Qin, Nyah Cubbison, Brian Gearity, Daniel Mcintosh, Mariely Cruz, Edward Garrido, Grady Dionne, Nicole Doris, Lyndsie Salvagio, Ann-Charlotte Granholm-Bentley, Anna Dymov, Hannah Eckert, Gabrielle Welsh, Erica Larson, Charlie Ernst, Anna Zhou, Sarah Watamura, Larissa Fedorovich-Klein, Georgie Fields, Kimberly A. Guevara, Aven Mccall, Ben Peltier, Feruz Yahia, Patrick Flores, Jadyn Floyd, Sophia Forcier, J. Von R. Monteza, Peter Sokol-Hessner, Gwendolyn Geiger, Scott Nichols, Camryn Gunter, Kendal Hengst, Charlie Bednarz, Issy Garside, Addison Baker, Rachel Mina, Brooke Hermanson, Amanda Klingler, William Highfill, Sydney Jaques, Kerstin Lewey, Allison Grossery, Daniel Linseman, Ethan Lim, Jagger Livengood, Owen Mantelli, Gabby Pappas, Abby Mcdonald, Madeleine Dierking, Eve Miller, Emma Loeber, Anna Marlow, Michael Kerwin, Ella Mathews, Hillary Hamann, Khadija Mohamed, Vivian Nguyen, Gabri Notov, Ifunayachi Ogbonna-Ukuku, Sunil Kumar, Charles Baysah, Sarah Olson, Don Sullivan, Anna Paradiso, Jay Parrish, Mira Pronobis, Alisha Pravasi, Kerstin Haring, Diego Ramirez, Christopher Reardon, Juliana Ramirez, Casey Doherty, Ella Kestner, Teagan Weindel, Cate Billings, Pablo Torre-Walter, Lucy Rand, Samantha Reynolds, Mark Siemens, Khadeeja Rashid, Laine Satterlee, Piper Heilbronner, Lily Pound, Ben Whitehurst, Anna Respet, Lizzie Lesoing, Sydney Hertel, Aya Saad-Masri, Brooke Ballenger, Max Proske, Hannah Rosenberg, Ellia Nakahara, Sophia Espinoza, Ivan Woolhouse, Simon Ruland, Gorkem Er, Timothy Sweeny, Melaku Saketa, Michela Schenk, Maren Lynch, Madi Hamm, Grace Schroeder, Michelle Rozenman, Rana Seif, Jackson Hall, Marisela Simental, Daniel Paredes, Aaron Mena, Preston Spaan, Evelyn Stovin, David Andrew Swartz, Anh Tran, Daniel Pittman, Luke Farchione, Emily Boyer, Ukari Verner, Lacey Conrad, Jonathan Velotta, James Weiner, Jagger Gossett, Noah Sherry, Sam Proud, Ben Block, Avi Narayana, Zoey Weiss, Alyssa Wilson, Gabrielle Walsh, David Zonana, Keely Wright, Kena Riveria, Lillybelle Deer, Jena Doom, Elysia Davis, Isabelle Yaremenko, Caitlyn Young
DU Undergraduate Research Journal Archive
Abstracts from the DU Undergraduate Research Showcase.
Use Of High-Throughput Phenomics With And Without Fungicide As A Wheat Breeding Tool, Gerardo Ivan Rivera Collazo
Use Of High-Throughput Phenomics With And Without Fungicide As A Wheat Breeding Tool, Gerardo Ivan Rivera Collazo
Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–
Wheat (Triticum aestivum) is an important food staple for many countries around the world and current consumption and production data demonstrate a production deficit. Breeders are challenged to help close this gap in production by selecting better cultivars with improved yields. Several methods aim to accelerate the breeding process to reduce the time required for releasing an elite variety. One of the main breeding bottlenecks is the lack of fast and reliable phenotypic data acquisition that could dissect physiological and morphological plant data. High-throughput remote sensing could have the potential to reduce this bottleneck by streamlining data acquisition, …
Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros
Assessment Of Spatial Autocorrelation And Scalability In Fine-Scale Wildfire Random Forest Prediction Models, Madeleine Pascolini-Campbell, Joshua B. Fisher, Kerry Cawse-Nicholson, Christine M. Lee, Natasha Stavros
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
Wildfire prediction models that can be applied across diverse regions at fine scales (< 100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3–5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km2 from 2021 and 2022 in New Mexico, USA. These fires ranged from low to high burn intensity, and covered a diverse range of ecoregions (deserts, grasslands, forests), plant species, and topographies. We used a single model to predict the burn severity of all wildfires one week before occurrence. The prediction accuracy was greatest when using all predictors (ECOSTRESS, weather, topography) (R2 = 0.77). We assessed the role of spatial autocorrelation in driving model performance by: (1) increasing the sample spacing of our dataset, (2) …
Identifying The Drivers Behind The Decline Of Douglas-Fir And True Fir Species In Sw Oregon, Alexander Bryant Bentley
Identifying The Drivers Behind The Decline Of Douglas-Fir And True Fir Species In Sw Oregon, Alexander Bryant Bentley
Dissertations and Theses
Increased levels of Pseudotsuga menziesii (DF) and Abies species (TF) decline throughout dry forests in the Klamath Mountains and Western Cascades ecoregions have been recorded in recent years, with preliminary evidence suggesting that factors influenced by changing climatic conditions and encroachment from fire suppression policies are primarily to blame. Yet, the physiological and environmental mechanisms of drought-driven tree decline are poorly understood across biophysical and spatial gradients. Given the complex relations that drive decline, determining the degree of influence of specific drivers is difficult, creating unique challenges for modern forest management. To elucidate the most important factors driving DF and …
Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher
Remotely Sensed High-Resolution Soil Moisture And Evapotranspiration: Bridging The Gap Between Science And Society, Jingyi Huang, Vinit Sehgal, Laura V. Alvarez, Luca Brocca, Shuohao Cai, Rui Cheng, Xinghua Cheng, Jinyang Du, Bassil El Masri, K. Arthur Endsley, Yilin Fang, Jie Hu, Mahesh Jampani, Md Golam Kibria, Gerbrand Koren, Lingcheng Li, Laibao Liu, Jiafu Mao, Hernan A. Moreno, Angela Rigden, Mingjie Shi, Xiaoying Shi, Yaoping Wang, Xi Zhang, Joshua B. Fisher
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
This paper reviews the current state of high-resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high-resolution remote sensing using visible, near-infrared, thermal-infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio-geo-physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). …
Assessing Spatial And Temporal Variation In Photoprotective Responses Of Deciduous And Evergreen Tree Canopies With Leaf Spectroscopy, Alexander Piper
Assessing Spatial And Temporal Variation In Photoprotective Responses Of Deciduous And Evergreen Tree Canopies With Leaf Spectroscopy, Alexander Piper
School of Natural Resources: Dissertations, Theses, and Student Research
Environmental conditions frequently prevent carbon fixation by plants, leading to the absorption of excess light that can damage photosynthetic machinery if not dissipated. To do so, plants utilize several photoprotective mechanisms, some detectable remotely using Photochemical Reflectance Index (PRI). Two components of PRI correspond to the facultative engagement of the xanthophyll cycle (ΔPRI) and constitutive changes in xanthophyll pigment pool sizes (PRI0), representing distinct mechanisms regulating shorter and longer-term photoprotection, respectively. Our understanding of interspecific and intraspecific variation in these mechanisms is limited, primarily because PRI components are often not clearly distinguished. This study aimed to assess the variation in …
Considerations And Techniques For Producing Urban Tree Canopy Maps Using Freely Available And Accessible Methods, Hugh Reed Ellerman
Considerations And Techniques For Producing Urban Tree Canopy Maps Using Freely Available And Accessible Methods, Hugh Reed Ellerman
Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–
Urban forests are valued as green infrastructure for the variety of benefits they provide across ecological, social, public health, and economic domains. To understand the distribution of trees, maps are required. Many methods of mapping tree cover use expensive and data-intensive datasets such as hyperspectral and LiDAR imagery, making these methods inaccessible to municipal forest managers. Variations in the modelling approaches of existing, freely available methods are not addressed in sufficient enough detail to understand the trade-offs implicit in these variations. Further, characteristics of urban tree canopy mapping (high spatial resolution imagery, heterogeneous urban environments, the importance of the tree …
Enhancing Remote Sensing Imagery Temporal Resolution Using Starfm Data Fusion Approach For Improved Land Surface Monitoring, Ahmadreza Pourghodrat
Enhancing Remote Sensing Imagery Temporal Resolution Using Starfm Data Fusion Approach For Improved Land Surface Monitoring, Ahmadreza Pourghodrat
School of Computing: Dissertations, Theses, and Student Research
High-resolution remote sensing imagery plays a critical role in various domains, such as farm-level agricultural operations, environmental monitoring, and natural resource management. However, data with high spatial resolution typically have low temporal resolution, and those with high temporal resolution often lack spatial detail. For example, Landsat 8 and 9 satellites deliver high spatial resolution images with a 30-meter pixel size but suffer from low temporal resolution, with a 16-day revisit cycle. In contrast, satellites like MODIS and VIIRS provide daily images but with a much coarser spatial resolution (375 meters or more), reducing spatial details. Additionally, there is a lack …
Transfer Learning In Junction With A Light Use Efficiency Model For Estimating Grassland Gross Primary Production, Ruiyang Yu, Yunjun Yao, Qingxin Tang, Xueyi Zhang, Changliang Shao, Joshua B. Fisher, Jiquan Chen, Xiaotong Zhang, Yufu Li, Jia Xu, Lu Liu, Zijing Xie, Jing Ning, Jiahui Fan, Luna Zhang
Transfer Learning In Junction With A Light Use Efficiency Model For Estimating Grassland Gross Primary Production, Ruiyang Yu, Yunjun Yao, Qingxin Tang, Xueyi Zhang, Changliang Shao, Joshua B. Fisher, Jiquan Chen, Xiaotong Zhang, Yufu Li, Jia Xu, Lu Liu, Zijing Xie, Jing Ning, Jiahui Fan, Luna Zhang
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
It is significant to simulate grassland gross primary production (GPP) to understand the terrestrial carbon budget over Inner Mongolia (IMG), China. Nevertheless, there is not sufficient in situ GPP data over this region. In this study, we proposed a novel model-based transfer learning (MTL) approach with generative adversarial networks-long short-term memory (GAN-LSTM) and light use efficiency (LUE) models to derive grassland GPP over IMG, China. We first used 25 grassland eddy covariance sites over the conterminous United States to establish the GAN-LSTM model and then fine-tuned it with six sites over IMG to estimate water constraints that were embedded into …
Spatiotemporal Trends In Anopheles Funestus Breeding Habitats, Grace R. Aduvukha, Elfatih M. Abdel-Rahman, Bester Tawona Mudereri, Onisimo Mutanga, John Odindi, Henri E.Z. Tonnang
Spatiotemporal Trends In Anopheles Funestus Breeding Habitats, Grace R. Aduvukha, Elfatih M. Abdel-Rahman, Bester Tawona Mudereri, Onisimo Mutanga, John Odindi, Henri E.Z. Tonnang
All Peer-Reviewed Publications
Effective identification and control of malaria vector larval breeding habitats are crucial for the management and eradication of malaria. Despite its importance, the last decade has seen a decline in data availability and intervention efforts due to reduced attention and prioritization. This study addresses the geographic data scarcity concerning Anopheles funestus larval breeding habitats in a malaria-prone region of western Kenya. Employing a two-step methodological approach, we integrated multi-criteria decision analysis (MCDA) and rule-based fuzzy logic analysis to evaluate the spatiotemporal similarity or divergence of these habitats. The analysis spanned a five-year interval, 2008, 2013, and 2018 with 2013 serving …
Scaling Arctic Landscape And Permafrost Features Improves Active Layer Depth Modeling, Wouter Hantson, Daryl Yang, Shawn P. Serbin, Joshua B. Fisher, Daniel J. Hayes
Scaling Arctic Landscape And Permafrost Features Improves Active Layer Depth Modeling, Wouter Hantson, Daryl Yang, Shawn P. Serbin, Joshua B. Fisher, Daniel J. Hayes
Biology, Chemistry, and Environmental Sciences Faculty Articles and Research
Tundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, vegetation, and disturbance. This complexity results in high spatiotemporal variability in permafrost distribution and active layer depth (ALD). Moreover, these key tundra processes interact at different scales, and an observational mismatch can limit our understanding of intrinsic connections and dynamics between above and below-ground processes. Consequently, this could limit our ability to model and anticipate how ALD …
Forest Above-Ground Biomass Estimation Using Nasa Gedi Lidar Waveforms And Global Tree Allometry, Ian Grant
Forest Above-Ground Biomass Estimation Using Nasa Gedi Lidar Waveforms And Global Tree Allometry, Ian Grant
Theses and Dissertations
Above-ground forest biomass plays a crucial role in global carbon cycles, yet accurately estimating biomass at global scales remains challenging. This thesis addresses two key challenges in processing NASA’s Global Ecosystem Dynamics Investigation (GEDI) space-borne lidar data to estimate above-ground biomass density (AGBD): accounting for global variation in forest structure and developing robust physical interpretations of lidar returns. The first component of the thesis analyzes global patterns of tree allometry using the Tallo tree allometry dataset, examining relationships between tree dimensions across biomes, continents, and plant functional types. This analysis reveals consistent allometry across continents for some biomes (e.g., tropical …
Impacts Of Southern Pine Beetle (Dendroctonus Frontalis Zimmerman) On Loblolly Pine (Pinus Taeda L.) Canopy And Water Use In The Homochitto National Forest, Mississippi, Usa, Sasha Goodnow, Yun Yang, Hui Liu, Ashley Schulz
Impacts Of Southern Pine Beetle (Dendroctonus Frontalis Zimmerman) On Loblolly Pine (Pinus Taeda L.) Canopy And Water Use In The Homochitto National Forest, Mississippi, Usa, Sasha Goodnow, Yun Yang, Hui Liu, Ashley Schulz
Endeavors: Mississippi State Undergraduate Research Journal
Abiotic and biotic forest disturbances can have many impacts to forest ecosystem services, including to forest water use. Studies on impacts to forest evapotranspiration have been conducted on the mountain pine beetle (Dendroctonus ponderosae) in western North America, but not on the southern pine beetle (Dendroctonus frontalis), which is a native pest of loblolly pine (Pinus taeda) and shortleaf pine (Pinus echinata), in the southeastern United States. Stressed pine trees produce pheromones that attract southern pine beetles and, with enough stressed trees, beetle populations can quickly grow to epidemic levels and attack healthy trees, which results in widespread tree mortality. …
Remote Sensing - Based Mapping And Analysis Of Winter Cover Crop Adoption For Sustainable Agriculture, Minnehaha County, Belinda Buechler
Remote Sensing - Based Mapping And Analysis Of Winter Cover Crop Adoption For Sustainable Agriculture, Minnehaha County, Belinda Buechler
Electronic Theses and Dissertations
Winter cover crops planted by farmers such as cereal rye, crimson clover, radishes, hairy vetch, and winter wheat are used to conserve and protect the soil during winter. These crops offer numerous benefits such as improving soil health, reducing erosion, fixing nitrogen, increasing carbon sequestration, and weed suppression. Over time, winter cover cropping has been recognized as a sustainable agricultural practice and has gained attention from Federal and State conservation programs, farmers, and non-governmental organizations. Due to their vital benefits, agencies such as the United States Department of Agriculture (USDA), Natural Resources Conversation Services (NRCS) have partnered with cost-share programs …
Restored Wetlands Show Rapid Vegetation Recovery And Substantial Surface-Water Expansion, Thilina D. Surasinghe, Yin-Hsuen Chen, Kunwar K. Singh
Restored Wetlands Show Rapid Vegetation Recovery And Substantial Surface-Water Expansion, Thilina D. Surasinghe, Yin-Hsuen Chen, Kunwar K. Singh
Center for Geospatial Science, Education & Analytics Faculty Publications
Ecological restoration is essential for improving the ecological integrity of degraded ecosystems to enhance ecosystem services and biodiversity. In this study, we assessed the effectiveness of wetland restoration on retired cranberry farms by analyzing vegetation recovery and surface-water dynamics using the enhanced vegetation index (EVI) and normalized difference water index (NDWI) derived from Sentinel-2 satellite imagery. To quantify temporal dynamics of both vegetation recovery and surface-water cover, we identified the spectral distinctions among restored wetland plant communities. Our results indicated the emergence of distinct plant communities upon restoration. Restored wetlands in general showed significant and progressive vegetation recovery and expanding …
Annual Diversity Of Honey Bee Pollen Sources In Two Pumpkin Growing Landscapes, Machakos County, Kenya, Marystella W. Nang’Oni, Muo Kasina, Rebecca Karanja, Mary M. Guantai, Rahab N. Kinyanjui, Evanson R. Omuse, Michael H.G. Lattorff, Elfatih M. Abdel-Rahman, Marian Adan, Samira A. Mohamed, Thomas Dubois
Annual Diversity Of Honey Bee Pollen Sources In Two Pumpkin Growing Landscapes, Machakos County, Kenya, Marystella W. Nang’Oni, Muo Kasina, Rebecca Karanja, Mary M. Guantai, Rahab N. Kinyanjui, Evanson R. Omuse, Michael H.G. Lattorff, Elfatih M. Abdel-Rahman, Marian Adan, Samira A. Mohamed, Thomas Dubois
All Peer-Reviewed Publications
Multi-floral foraging sources for honey bee (Apis mellifera L.) have been threatened by landscape changes and unsustainable farming practices. In East Africa, the biodiversity of forage resources that could support honey bees, especially in agricultural lands, remains least explored. This study investigated pollen diversity for honey bees in Yatta and Masinga Sub-counties in Machakos County, Kenya. Honey bee hives were installed on eight pumpkin (Cucurbita maxima Duchesne ex Lam) farms (one hive per farm) in two varying landscape vegetation classes (low and medium) based on normalized difference vegetation index (NDVI). Pollen traps were installed at the hive entrance and pollen …
Earth Observations Reveal Impacts Of Climate Variability On Maize Cropping Systems In Sub-Saharan Africa, Adomas Liepa, Michael Thiel, Hannes Taubenböck, Doris Klein, Ingolf Steffan-Dewenter, Marcell K. Peters, Sarah Schönbrodt-Stitt, Insa Otte, Tobias Landmann, Zeyaur R. Khan, Michael Ochieng Obondo, Frank Chidawanyika, Emily A. Martin, Tobias Ullmann
Earth Observations Reveal Impacts Of Climate Variability On Maize Cropping Systems In Sub-Saharan Africa, Adomas Liepa, Michael Thiel, Hannes Taubenböck, Doris Klein, Ingolf Steffan-Dewenter, Marcell K. Peters, Sarah Schönbrodt-Stitt, Insa Otte, Tobias Landmann, Zeyaur R. Khan, Michael Ochieng Obondo, Frank Chidawanyika, Emily A. Martin, Tobias Ullmann
All Peer-Reviewed Publications
In Kenya, climate variability and change threaten smallholder, rainfed farms, with crop failures, yield reductions, and pest infestations. Efficient agroecological strategies, such as Push-Pull intercropping, offer documented benefits including pest control, improved soil fertility, and water conservation compared to traditional maize monocropping. To date, no studies exist comparing traditional maize monocropping and Push-Pull intercropping using earth observation tools over several growing seasons in East Africa. Our research addresses this by harmonizing Landsat 7, 8, 9 with Sentinel-2 remote sensing time series from 2016 to 2023. Phenological metrics of 15 growing seasons are extracted based on a threshold method using the …