Remote Sensing Commons^™

Using Lidar To Estimate Carbon Sequestration Of Evergreen Trees At Eastern Washington University (Ewu) Campus, Cheney, Washington, Kristy A. Snyder

2022 Symposium

EWU contains a variety of deciduous and evergreen trees across its campus, providing several benefits. However, no comprehensive record exists of the total number, location, species, or ages of these trees. This knowledge can inform facilities of proper care for individual trees and can be used to estimate carbon sequestration on campus. Traditional on-the-ground methods for assessing trees require tree cores or clinometers, making trees susceptible to pests or disease and leading to inaccurate results. Remote sensing using lidar data is a noninvasive, more precise method to measure tree height and subsequently assess tree age. This poster explores using point …

Lake Satellite Temperature Data Validation, Mamadou Balde, Pascal Kouogang

Publications and Research

In environmental remote sensing, satellite data isn't absolutely conclusive, for that reason, there is a natural need to verify the data acquired from the satellite. The most suitable tool to achieve such verification is on ground sensors that have the advantage of proximity. Addressing any possible discrepancies between the satellite data and the ground sensor data is sure to yield ways to come up with improvements of satellite band calibration and sensing capabilities. This research focused on correlating temperature data from the MODIS satellite with the data obtained from the In Situ sensor located in Lake Sunapee. Doing the latter …

Remote Sensing & Land Surface Temperature From Satellite Observations, Isatu Jollah

Publications and Research

The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument is designed and developed in 1995. • It is a critical instrument aboard Terra and Aqua satellites. Terra's orbit around the Earth is timed so that it crosses the equator from north to south in the morning, while Aqua crosses the equator from south to north in the afternoon. • Every 1 to 2 days, Terra MODIS and Aqua MODIS scan the entire Earth's surface, collecting data in groups of wavelengths. • In this research, a whole month of MODIS Land Surface Temperature data from both Aqua and Terra were explored and investigated.

Land And Water Resources For Irrigated Agriculture In The Pilbara, Paul Galloway, John A. Simons, Karen Holmes, Dennis Van Gool

Resource management technical reports

This report documents the procedures used to identify suitable locations for irrigation development in the Pilbara region. It is the first study to investigate the potential for irrigated agriculture across the Pilbara. We used a desktop analysis to ascertain water availability and spatial data modelling to determine the potential of the land and soil resource to support irrigated agriculture. This study was part of the Pilbara Hinterland Agricultural Development Initiative (PHADI).

We used existing rangeland land inventory information augmented with digital spatial environmental data, in a process known as map disaggregation, to create soil and landform maps that had a …

Multi-Decadal Analysis Of Remotely Sensed Vegetation Change In Berea College Forest - Seasonality Of Forest Patterns Using Remote Sensing., Jacob Foushee

College of Arts & Sciences Senior Theses

Satellite imagery is a practical and valuable tool for monitoring vegetation condition in forests. The longevity of the USGS/NASA Landsat program along with its medium spatial resolution (30m) gives researchers the ability to make informed statements on land cover generally, and specifically on aspects such as forest conditions. The Landsat program’s nearly 50-year archive of imagery show how Earth’s surface has changed through modern development and how these developments have influenced forests. Google Earth Engine (GEE) is a cloud-based repository of satellite imagery dating as far back as the 1970s. This study utilizes Landsat 5-8 imagery from GEE to calculate …

Multi-Trophic Biodiversity Increases With Increasing Structural Complexity Of Forest Canopy, Ayanna St. Rose

Graduate Theses and Dissertations

Understanding the effects of forest canopy structural complexity on multi-trophic diversity is critical for conserving biodiversity and managing land sustainably. But multi-trophic diversity is often ignored when making decisions about land management due to lack of cost- and time-effective methods to evaluate it. Here, we explored a new method based on widely available remote sensing data to quantify canopy structural complexity and its relationships with multi-trophic biodiversity at landscape scale using 32 forested sites of the National Ecological Observatory Network. We investigated the influence of vertical and horizontal structural complexity of forest canopy on multi-trophic (primary producers, herbivores (beetles), omnivores …

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 …

Toward Global Localization Of Unmanned Aircraft Systems Using Overhead Image Registration With Deep Learning Convolutional Neural Networks, Rachel Linck

Graduate Theses and Dissertations

Global localization, in which an unmanned aircraft system (UAS) estimates its unknown current location without access to its take-off location or other locational data from its flight path, is a challenging problem. This research brings together aspects from the remote sensing, geoinformatics, and machine learning disciplines by framing the global localization problem as a geospatial image registration problem in which overhead aerial and satellite imagery serve as a proxy for UAS imagery. A literature review is conducted covering the use of deep learning convolutional neural networks (DLCNN) with global localization and other related geospatial imagery applications. Differences between geospatial imagery …

Composite Style Pixel And Point Convolution-Based Deep Fusion Neural Network Architecture For The Semantic Segmentation Of Hyperspectral And Lidar Data, Kevin T. Decker, Brett J. Borghetti

Faculty Publications

Multimodal hyperspectral and lidar data sets provide complementary spectral and structural data. Joint processing and exploitation to produce semantically labeled pixel maps through semantic segmentation has proven useful for a variety of decision tasks. In this work, we identify two areas of improvement over previous approaches and present a proof of concept network implementing these improvements. First, rather than using a late fusion style architecture as in prior work, our approach implements a composite style fusion architecture to allow for the simultaneous generation of multimodal features and the learning of fused features during encoding. Second, our approach processes the higher …

Possible Location For Us Embassy In Copenhagen, Denmark, Danny Jang

Symposium of Student Scholars

The purpose of this research is for students to experience using GIS in a professional setting for a public organization. Copenhagen, Denmark hosts numerous embassies for other countries including the USA which was established nearly a century ago! After going through a couple of public opinions left by the locals (Google review), it would be nice if another location was established to assist both USA and Denmark’s bureaucratic needs. The population has surged compared to the numbers from when the building was established, which also changed the geospatial environment that was established when the building was opened. This research will …

A Tectonic Geomorphic Study Of Neotectonics And Seismic Hazard Of The Fold And Thrust Belt Complex Of Bengal Basin, Eastern Himalayan Range, Niroj Shrestha

Masters Theses & Specialist Projects

Tectonic activity continues to shape and create challenges for human populations in tectonically active areas. The Bengal Basin is one of the most important sedimentary basins of the Eastern Himalayan Range, aiding in one of the densely populated regions of the world. The basin is part of the tectonically active Himalayan Orogen that is undergoing oblique subduction between the Eurasian Plate and the Indian Plate. As a result of this subduction-related convergence, the eastern part of the Basin, particularly the Chittagong-Tripura Fold Belt (CTFB) and western part of the Indo-Burman Range (IBR) has structurally deformed into a series of mostly …

Assessment Of Natural Hazards Impact On Heritage Sites In The United Arab Emirates (Uae) Using Geographic Information System (Gis), Abdulla Salem Ahmed Saeed Alyammahi

Theses

The United Arab Emirates (UAE) pays significant attention to preserving its heritage sites and archaeological finds and treats them as a vital part of its culture, history, and economy. In recent years, the field of archaeology in the UAE has witnessed tangible and significant developments, with many government initiatives being devoted to establishing archaeological departments in all emirates. The firmly stated policy of these institutions is to preserve the UAE heritage sites and educate the public about their importance. However, these sites are vulnerable to natural hazards, considered one of the most critical threats to the UAE heritage sites. In …

A Remote Sensing And Machine Learning-Based Approach To Forecast The Onset Of Harmful Algal Bloom (Red Tides), Moein Izadi

Dissertations

In the last few decades, harmful algal blooms (HABs, also known as “red tides”) have become one of the most detrimental natural phenomena all around the world especially in Florida’s coastal areas due to local environmental factors and global warming in a larger scale. Karenia brevis produces toxins that have harmful effects on humans, fisheries, and ecosystems. In this study, I developed and compared the efficiency of state-of-the-art machine learning models (e.g., XGBoost, Random Forest, and Support Vector Machine) in predicting the occurrence of HABs. In the proposed models, the K. brevis abundance is used as the target, and 10 …

Short-Term 2d And 3d Geomorphic Change Detection At A Public Park On Lake Michigan Using Uas Remote Sensing Techniques, Scott Patrick Fitzgerald

Masters Theses

The high-water level of Lake Michigan (LM) in the past few years has led to significant periods of erosion and increased the risk to private property owners on the coast. To cope with this, many property owners on the coast of LM have constructed coastal protections, some opting for seawalls. Previous studies have assessed the effects of seawalls but disagreed on their impacts, and only laboratory studies were able to establish their range of influence. Using a different method to study their effects will be pertinent to understanding them.

This research aims to use a higher temporal and spatial resolution …

Using Deep Learning And Uav Imagery To Detect Elkhorn Coral In St. Croix’S East End Marine Park, Samuel Wyatt

Master's Theses

Elkhorn coral, or Acropora palmata, is an important reef building species that promotes species abundance and other ecological services to the communities in the US Virgin Islands. We captured high resolution imagery of a reef in St. Croix’s East End Marine Park using a Wingtra One UAV. We then used deep learning techniques to detect individual coral colonies. We compared two deep learning models, FasterRCNN and MaskRCNN, and found that the models achieved accuracy shores up to 0.78. These scores improved when examining only larger corals in shallow waters. The model was able to both detect Elkhorn coral and …

Extreme Development Of Dragon Fruit Agriculture With Nighttime Lighting In Southern Vietnam, Shenyue Jia, Son V. Nghiem, Seung-Hee Kim, Laura Krauser, Andrea E. Gaughan, Forest R. Stevens, Menas Kafatos, Khanh D. Ngo

Institute for ECHO Faculty Books and Book Chapters

Dragon fruit is widely grown in Southeast Asia and other tropical or subtropical regions. As a high-value cash crop ideal for exportation, dragon fruit cultivation has boomed during the past decade in southern Vietnam. Light supplementing during the winter months using artificial lighting sources is a widely adopted cultivation technique to boost productivity in the major dragon fruit planting regions of Vietnam. The application of electric lighting at night leads to a significant increase of nighttime light (NTL) observable by satellite sensors. The strong seasonality signal of NTL in dragon fruit cultivation enables identifying dragon fruit plantations using NTL images. …

Global Gnss-Ro Electron Density In The Lower Ionosphere, Dong L. Wu, Daniel J. Emmons Ii, Nimalan Swarnalingam

Faculty Publications

Lack of instrument sensitivity to low electron density (N_e) concentration makes it difficult to measure sharp N_e vertical gradients (four orders of magnitude over 30 km) in the D/E-region. A robust algorithm is developed to retrieve global D/E-region N_e from the high-rate GNSS radio occultation (RO) data, to improve spatiotemporal coverage using recent SmallSat/CubeSat constellations. The new algorithm removes F-region contributions in the RO excess phase profile by fitting a linear function to the data below the D-region. The new GNSS-RO observations reveal many interesting features in the diurnal, seasonal, solar-cycle, and magnetic-field-dependent variations in the …

Accuracy Assessment, Comparative Performance, And Enhancement Of Public Domain Digital Elevation Models (Aster 30 M, Srtm 30 M, Cartosat 30 M, Srtm 90 M, Merit 90 M, And Tandem-X 90 M) Using Dgps, Kumari Preety, Anup K. Prasad, Atul K. Varma, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Publicly available Digital Elevation Models (DEM) derived from various space-based platforms (Satellite/Space Shuttle Endeavour) have had a tremendous impact on the quantification of landscape characteristics, and the related processes and products. The accuracy of elevation data from six major public domain satellite-derived Digital Elevation Models (a 30 m grid size—ASTER GDEM version 3 (Ast30), SRTM version 3 (Srt30), CartoDEM version V3R1 (Crt30)—and 90 m grid size—SRTM version 4.1 (Srt90), MERIT (MRT90), and TanDEM-X (TDX90)), as well as the improvement in accuracy achieved by applying a correction (linear fit) using Differential Global Positioning System (DGPS) estimates at Ground Control Points (GCPs) …

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems, Adam Plowcha, Justin Bradley, Jacob Hoberg, Thomas Ammon, Mark Nail, Brittany Duncan, Carrick Detweiler

School of Computing: Faculty Publications

Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. …

Machine Learning For Modeling Wildfire Susceptibility At The State Level: An Example From Arkansas, Usa, Abdullah Al Saim, Mohamed H. Aly

Geosciences Faculty Publications and Presentations

Fire susceptibility modeling is crucial for sustaining and managing forests among many other valuable land resources. With 56% of its area covered by forests, Arkansas is known as the "natural state". About 1000 wildfires occurred and burned more than 10,000 acres each year during 1981-2018. In this paper, we use remote-sensing-based machine learning methods to address the natural and anthropogenic factors influencing wildfires and model fire susceptibility in Arkansas. Among the 15 explored variables, potential evapotranspiration, soil moisture, Palmer drought severity index, and dry season precipitation were recognized as the most significant factors contributing to the fire density. The obtained …