Remote Sensing Commons^™

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …

Atmospheric Chemistry Experiment (Ace) V.5.3 Winds: Validation And Model Comparisons, Matthew Wyatt, Peter F. Bernath, Chris Boone, Leo Lavy, Ryan Johnson

Physics Faculty Publications

The Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) uses limb geometry to measure transmittance spectra of Earth's atmosphere by solar occultation. Line-of-sight wind speeds can be derived via Doppler shifts of molecular lines in infrared spectra. The wind look direction angles relative to geodetic north are derived from geometry. We validate the new ACE version 5.3 (v.5.3) line-of-sight winds with MIGHTI and meteor radar vector wind observations and find a -15 m s^-¹ (+15 m s^-¹) sunrise (sunset) shift above 80 km. We also compare line-of-sight winds from ACE-FTS v.5.2 and v.5.3 with vector winds from …

Performance Of Acoustic Telemetry And Space Use Of Pallid Sturgeon In The Lower Platte River, Nebraska, Christopher F. Pullano

School of Natural Resources: Dissertations, Theses, and Student Research

Pallid Sturgeon (Scaphirhynchus albus) are centenarian, potamodromous, rheophiles that historically occupied the Missouri River and Mississippi River basins. Listed on the U.S. Endangered Species Act in 1990, population declines are attributed to habitat fragmentation and degradation, as well as overharvest, and hybridization. A knowledge gap exists regarding the extent to which tributaries facilitate key life stages for Pallid Sturgeon. This study evaluated the capacity of acoustic telemetry to monitor the movements of Pallid Sturgeon in a shallow, braided tributary to the Missouri River. The specific objectives were to (1) evaluate the environmental variables influencing the performance of acoustic …

Time Series Decomposition Of Land Surface Temperature For Long-Term Trend Forecasting And Impact On Nesting Sea Turtle Habitats In The Arabian Gulf, Sachi Perera, Rommel H. Maneja, Mohamed Allali, Cyril Rakovski, Erik Linstead, Daniele Struppa, Ali Qasem, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Improving land surface temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats’ long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, …

Modeling Snow Surface Properties From Lidar And Imaging Spectroscopy, Brenton A. Wilder

Boise State University Theses and Dissertations

Seasonal snow surface plays an important role in altering terrestrial hydrology and global climate patterns. Snow reflects a majority of incoming shortwave radiation thereby reducing the net shortwave radiation received into snowpack throughout the season. This property is commonly referred to as snow albedo and impacts water cycles and air temperatures by modulating the timing and magnitude of melt. This reflectivity of snow is difficult to measure accurately in mountain environments and at a large enough scale to be meaningful for water resource managers and climate scientists. The work presented herein aims to improve methodologies to measure snow reflectivity from …

Redefining Approaches For Measuring Landscape Subsidence And Permafrost Degradation In Arctic Tundra Environments, Tabatha Lynn Fuson

Open Access Theses & Dissertations

As climate change accelerates in the Arctic, the degradation of permafrost is leading to significant landscape transformation in tundra landscapes. This dissertation investigates the multifaceted responses of permafrost systems to warming, focusing on the dynamics of surface elevation changes and active layer thickness (ALT) across the North Slope of Alaska. In this study, I explore the capacity of repeat Terrestrial Laser Scanning (TLS) technology for modeling tundra features and detecting surface subsidence, specifically how different climate and landscape conditions during scanning impact TLS model precision. We also compare TLS model precision estimates to TLS model accuracy by comparing elevation values …

Impact Of Weather Systems On Uav Parameters Using Computational Fluid Dynamics, Saif Aljuhaishi, Yaseen K. Al-Timimi, Basim I. Wahab

Karbala International Journal of Modern Science

Since drones cannot fly in any kind of weather, they are not safe for time-sensitive activities. The study examines how the passage of weather systems in Iraq leads to the ban on drone flights, and how these weather conditions impact the aerodynamic forces of the drone. Hourly climate data for the study area were obtained from ECMWF ERA5 and CAMS in NetCDF format for four climate stations (Erbil, Baghdad, Rutbah, and Basrah). A ScanEagle drone was chosen for this study. The Python programming language was used to perform mathematical operations to calculate the ban on drone flights. ArcGIS 10.8 was …

Multi-Temporal Analysis Of Urbanization-Driven Slope And Ecological Impact Using Machine-Learning And Remote Sensing Techniques, Zhang Hao, Muhammad Haseeb, Zheng Xiangtian, Zainab Tahir, Syed Amer Mahmood, Aqil Tariq, Rana Waqar Aslam, M. Abdullah-Al-Wadud, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Rapid urbanization in Lahore, Pakistan, has led to significant ecological and thermal challenges, particularly the intensification of Urban Heat Island (UHI) effects and increased thermal stress as measured by the Urban Thermal Field Variance Index (UTFVI). This study employs a multi-temporal evaluation of Landsat satellite imagery and GIS-based analysis to investigate the Spatio-temporal trends in land-use and land-cover (LULC) changes from 1994 to 2024. We detected substantial changes in urban growth, vegetation cover, and barren areas using supervised classification (Random Forest) methods and remote sensing indices such as NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index), NDBI (Normalized …

Spatial Gap-Filling Of Himawari-8 Hourly Aod Products Using Machine Learning With Model-Based Aod And Meteorological Data: A Focus On The Korean Peninsula, Youjeong Youn, Seoyeon Kim, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, …

Climatological Trends And Effects Of Aerosols And Clouds On Large Solar Parks: Application Examples In Benban (Egypt) And Al Dhafrah (Uae), Harshal Dhake, Panagiotis Kosmopoulos, Antonis Mantakas, Yashwant Kashyap, Hesham El-Askary, Omar Elbadawy

Mathematics, Physics, and Computer Science Faculty Articles and Research

Solar energy production is vastly affected by climatological factors. This study examines the impact of two primary climatological factors, aerosols and clouds, on solar energy production at two of the world’s largest solar parks, Benban and Al Dhafrah Solar Parks, by using Earth observation data. Cloud microphysics were obtained from EUMETSAT, and aerosol data were obtained from the CAMS and assimilated with MODIS data for higher accuracy. The impact of both factors was analysed by computing their trends over the past 20 years. These climatological trends indicated the variations in the change in each of the factors and their resulting …

Coupling Between Evapotranspiration, Water Use Efficiency, And Evaporative Stress Index Strengthens After Wildfires In New Mexico, Usa, Ryan C. Joshi, Annalise Jensen, Madeleine Pascolini-Campbell, Joshua B. Fisher

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

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Challenges And Future Directions In Quantifying Terrestrial Evapotranspiration, Koong Yi, Gabriel B. Senay, Joshua B. Fisher, Lixin Wang, Kosana Suvočarev, Housen Chu, Georgianne W. Moore, Kimberly A. Novick, Mallory L. Barnes, Trevor F. Keenan, Kanishka Mallick, Xiangzhong Luo, Justine E. C. Missik, Kyle B. Delwiche, Jacob A. Nelson, Stephen P. Good, Xiangming Xiao, Steven A. Kannenberg, Arman Ahmadi, Tianxin Wang, Gil Bohrer, Marcy E. Litvak, David E. Reed, A. Christopher Oishi, Margaret S. Torn, Dennis Baldocchi

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Terrestrial evapotranspiration is the second-largest component of the land water cycle, linking the water, energy, and carbon cycles and influencing the productivity and health of ecosystems. The dynamics of ET across a spectrum of spatiotemporal scales and their controls remain an active focus of research across different science disciplines. Here, we provide an overview of the current state of ET science across in situ measurements, partitioning of ET, and remote sensing, and discuss how different approaches complement one another based on their advantages and shortcomings. We aim to facilitate collaboration among a cross-disciplinary group of ET scientists to overcome the …

Challenges Of Using More Precise Temporal And Spatial Resolution Of Remote Sensing Data For Surface Water Quality Monitoring, Ivan Rykin

Dartmouth College Master’s Theses

Monitoring river suspended sediment concentration (SSC) is critical for environmental challenges such as understanding the fate of thawed permafrost sediment and its impact on global carbon cycling. However, traditional SSC monitoring using Landsat imagery is limited by spatial and temporal constraints, particularly for narrow rivers in cloudy and/or snowy regions.

This study investigates the use of higher spatial (3 m) and temporal (daily) resolution satellite imagery from the PlanetScope constellation to estimate SSC in remote rivers such as those in the Arctic. I compare the performance of PlanetScope’s spectral resolution (4 and 8 bands) with Landsat 7. Merging data from …

Conducting Structure-From-Motion (Sfm) Modeling Of Freshwater Environments Using Unpiloted Aerial Systems (Uas): Challenges And Lessons Learned, Benjamin T. Fraser, Christine L. Bunyon, Russell G. Congalton

Faculty Publications

The pairing of Unpiloted Aerial Systems (UAS) and Structure from Motion (SfM) has provided new capabilities for modeling freshwater environments. Applications of UAS-SfM range from water quality monitoring to the mapping of aquatic vegetation. The models produced provide users with the ability to analyze features at ultra-high-resolutions and across scales not easily achieved through in situ sampling. Despite the demonstrated benefits of UAS-SfM in freshwater and other natural resource disciplines, there remain fundamental technical challenges in the modeling of environments with homogenous surfaces (e.g., water). In this research, the effectiveness of several image collection and processing approaches for the modelling …

Seasonal Dynamics In Land Surface Temperature In Response To Land Use Land Cover Changes Using Google Earth Engine, Lei Feng, Sajjad Hussain, Narcisa G. Pricope, Sana Arshad, Aqil Tariq, Li Feng, Muhammad Mubeen, Rana Waqar Aslam, Mohammed S. Fnais, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Changes in land use and land cover (LULC) are critical for evaluating global spatiotemporal trends, especially regarding climate change and urbanization. This study investigates the dynamics of Landsat surface temperature (LST) in response to LULC changes and their effects on the seasonal microclimate in Kasur District, Pakistan. Using the Google Earth Engine platform, we employed a random forest algorithm to detect LULC changes (cropland, forest, built-up, fallow, barren, and water) and analyze seasonal spectral indices from Landsat imagery for 1988, 2002, and 2022. Significant LULC changes were observed, including a 9.8% increase in built-up areas, a 4.2% decrease in cropland, …

An Automated Machine Learning Approach To The Retrieval Of Daily Soil Moisture In South Korea Using Satellite Images, Meteorological Data, And Digital Elevation Model, Nari Kim, Soo-Jin Lee, Eunha Sohn, Mija Kim, Seonkyeong Seong, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Soil moisture is a critical parameter that significantly impacts the global energy balance, including the hydrologic cycle, land–atmosphere interactions, soil evaporation, and plant growth. Currently, soil moisture is typically measured by installing sensors in the ground or through satellite remote sensing, with data retrieval facilitated by reanalysis models such as the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) and the Global Land Data Assimilation System (GLDAS). However, the suitability of these methods for capturing local-scale variabilities is insufficiently validated, particularly in regions like South Korea, where land surfaces are highly complex and heterogeneous. In contrast, artificial intelligence …

Key Largo Mangrove Population Monitoring: A Remote Sensing Analysis And Classification Methodology Review, David Lackajs

Geography and the Environment: Graduate Student Capstones

Mangrove forests are some of the world's most bio-diverse habitats, providing essential services to the surrounding coasts. Removal of these habitats has a devastating impact on the ecosystems within them. The Florida Keys are some of the last areas in the United States with extensive mangrove populations. One specific area, John Pennekamp Coral Reef State Park in Key Largo, has been under state protection since 1959. For that reason, mangrove forest habitats there are less fragmented. This study uses remotely sensed imagery to quantify and analyze mangrove populations in this area using two methods: sub-pixel analysis and supervised classification. The …

Aerialformer: Multi-Resolution Transformer For Aerial Image Segmentation, Taisei Hanyu, Kashu Yamazaki, Minh Tran, Roy A. Mccann, Haitao Liao, Chase Rainwater, Meredith Adkins, Jackson Cothren, Hoang Le

Industrial Engineering Faculty Publications and Presentations

When performing remote sensing image segmentation, practitioners often encounter various challenges, such as a strong imbalance in the foreground–background, the presence of tiny objects, high object density, intra-class heterogeneity, and inter-class homogeneity. To overcome these challenges, this paper introduces AerialFormer, a hybrid model that strategically combines the strengths of Transformers and Convolutional Neural Networks (CNNs). AerialFormer features a CNN Stem module integrated to preserve low-level and high-resolution features, enhancing the model’s capability to process details of aerial imagery. The proposed AerialFormer is designed with a hierarchical structure, in which a Transformer encoder generates multi-scale features and a multi-dilated CNN (MDC) …

A Study Of The Impact And Mitigation Strategies For Community-Defined Historic Places At Immediate (Within Three Decades) Risk Of Sea Level Rise, Isaac Quaye

All Theses

The rising sea level casts formidable threats on community-based historic places within coastal communities. These community-defined historic places derive their meaning from local values and are more recognized within the local community. Unlike recognized iconic historic places, these resources are confronted with inadequate resources to address the impacts of punctuated climate events. This study investigates the imminent threats posed by sea level rise (SLR) to community-defined historic places within the next three decades (2025) and explores mitigation strategies proposed by stakeholders. The study used a GIS-based (enhanced bathtub model) to model the impact of coastal inundation on three historic places …

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, …