Remote Sensing Commons^™

Comparing Unmanned Aerial Systems (Uas) Structure-From-Motion Digital Surface Modeling To Laser Imaging Detection And Ranging (Lidar), Joshua D. Schane

Geography Masters Research Papers

UAS (unmanned-aerial systems) mapping is an emerging technology in spatial science. UAS, more informally referred to as “drones,” are small, radio-controlled or autonomous aerial platforms outfitted with camera sensor systems that can be deployed to fly missions for mapping terrain and structures. With the use of a photogrammetry technique called structure-from-motion, overlapping aerial images can be developed into high-resolution spatial data (< 5-cm). These data are georeferenced by GPS location information assigned to aerial targets which act as ground-control points (GCPs). This research explores the accuracy of the UAS-derived digital surface model (DSM) as it relates to a LIDAR DSM of the same study area in St. Helens, Oregon.

The site that was chosen to be mapped was a large wastewater treatment plant, with surrounding natural areas, residential, and industrial properties. The site was mapped using a small quadcopter-style UAS which was programmed autonomously; the overlapping aerial …

Using Landsat Tm Imagery To Monitor Vegetation Change Following Flow Restoration To The Lower Owens River, California, Lesley Crandell Bross

Dissertations and Theses

Rehabilitating river corridors to restore valuable riparian habitat consumes significant resources from both governments and private companies. Given these considerable expenditures, it is important to monitor the progress of such projects. This study evaluated the utility of using Landsat Thematic Mapper remotely-sensed data from 2002 and 2009 to monitor vegetation change induced by instream flow restoration to the Lower Owens River in central California. This study compared the results of an unsupervised classification with an NDVI threshold classification to appraise the resources required and effectiveness of each analysis method. The results were inspected by creating standard remote sensing accuracy error …

Advances In Autonomous-Underwater-Vehicle Based Passive Bottom-Loss Estimation By Processing Of Marine Ambient Noise, Lanfranco Muzi

Dissertations and Theses

Accurate modeling of acoustic propagation in the ocean waveguide is important to SONAR-performance prediction, and requires, particularly in shallow water environments, characterizing the bottom reflection loss with a precision that databank-based modeling cannot achieve. Recent advances in the technology of autonomous underwater vehicles (AUV) make it possible to envision a survey system for seabed characterization composed of a short array mounted on a small AUV. The bottom power reflection coefficient (and the related reflection loss) can be estimated passively by beamforming the naturally occurring marine ambient-noise acoustic field recorded by a vertical line array of hydrophones. However, the reduced array …

Reconstruction Of Daily 30 M Data From Hj Ccd, Gf-1 Wfv, Landsat, And Modis Data For Crop Monitoring, Mingquan Wu, Xiaoyang Zhang, Wenjiang Huang, Zheng Niu, Changyao Wang, Wang Li, Pengyu Hao

GSCE Faculty Publications

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, …

Estimating The Water Quality Condition Of River And Lake Water In The Midwestern United States From Its Spectral Characteristics, Jing Tan

Open Access Dissertations

This study focuses on developing/calibrating remote sensing algorithms for water quality retrieval in Midwestern rivers and lakes. In the first part of this study, the spectral measurements collected using a hand-held spectrometer as well as water quality observations for the Wabash River and its tributary the Tippecanoe River in Indiana were used to develop empirical models for the retrieval of chlorophyll (chl) and total suspended solids (TSS). A method for removing sky and sun glint from field spectra for turbid inland waters was developed and tested. Empirical models were then developed using a subset of the field measurements with the …

Spatial Analysis Of Distributions And Habitat Conditions Of Fallopia Japonica (Japanese Knotweed) Invasive Species Applying Unmanned Helicopter Remote Sensing, Jiazhen Zhang

Great Lakes Center Masters Theses

Abstract:

Fallopia japonica (Japanese knotweed) is a perennial herbaceous plant that is native to East Asia. It is considered as one of the worst invasive species worldwide because of its serious impact on biological diversity and human activities (Lowe et al., 2001). Once established, Japanese knotweed forms dense stands that shade and crowd out native plant species. The objectives of this research were to verify and confirm the distribution of Japanese knotweed as published online by the New York Department of Environmental Conservation (DEC) – iMap and to identify the geographic areas of spreading and the local habitat conditions. In …

Predictive Modeling Of Terrestrial Radiation Exposure From Geologic Materials, Daniel A. Haber

UNLV Theses, Dissertations, Professional Papers, and Capstones

Aerial gamma ray surveys are an important tool for national security, scientific, and industrial interests in determining locations of both anthropogenic and natural sources of radioactivity. There is a relationship between radioactivity and geology and in the past this relationship has been used to predict geology from an aerial survey. The purpose of this project

is to develop a method to predict the radiologic exposure rate of the geologic materials in an area by creating a model using geologic data, images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data, and pre-existing low spatial resolution aerial surveys …

Predicting Toucan Locations In Panama Using Arcgis, Daniel J. Herrera

Geography: Student Scholarship & Creative Works

Toucans are omnivorous birds native to southern Latin America and South America. They are non-migratory, and their range is disputed among experts. In an attempt to develop a better understanding of the range and behavior of toucans, correlations between toucan presence and geographic features of the area were analyzed to create a location probability map.

Geography: A Career Without Borders

DePaul Magazine

What do mosquito abatement, education policy, retail site location and public transportation have in common? Although it might not seem obvious, these disparate pursuits all involve the work of geographers. This article looks at the wide variety of occupations open to geographers, who manipulate and map big data, use geographic information systems for municipal functions and pest removal, analyze transportation and job siting, pursue human and environmental sustainability practices, and teach. The article also includes a brief summary of the 2015 Annual Meeting of the Association of American Geographers.

Locating A Wildlife Corridor For The Wild Tiger In India, Carmen George

Geography and the Environment: Graduate Student Capstones

The tiger (Panthera Tigris) has been on the ICUN red list of endangered species since 1972. In the early 20^th century, 100,000 wild tigers roamed Asia and today approximately 3,600 remain. India is home to over half of the remaining wild tigers and continues to struggle in creating effective conservation plans. Poaching, habitat destruction and prey depletion are several primary causes of tiger population degradation and remain major barriers to rejuvenation of healthy populations in the wild. Wildlife corridors are essential to the process of repairing fragmented habitats. Through the use of GIS and remote sensing this …

An Approach To Optimal Hyperspectral And Multispectral Signature And Image Fusion For Detecting Hidden Targets On Shorelines, Charles R. Bostater

Ocean Engineering and Marine Sciences Faculty Publications

Hyperspectral and multispectral imagery of shorelines collected from airborne and shipborne platforms are used following pushbroom imagery corrections using inertial motion motions units and augmented global positioning data and Kalman filtering. Corrected radiance or reflectance images are then used to optimize synthetic high spatial resolution spectral signatures resulting from an optimized data fusion process. The process demonstrated utilizes littoral zone features from imagery acquired in the Gulf of Mexico region. Shoreline imagery along the Banana River, Florida, is presented that utilizes a technique that makes use of numerically embedded targets in both higher spatial resolution multispectral images and lower spatial …

Aerosol And Urban Heat Island Effects On Warm Cloud-Top Shortwave Infrared Reflectance And Visible Albedo, Heather Glickman-Eliezer

Dissertations, Theses, and Capstone Projects

This study uses ten years of Geostationary Operational Environmental Satellite (GOES) data (1999-2009) to assess urban effects of anthropogenic aerosols and Urban Heat Island (UHI) on cloud-top radiative properties of optically thick clouds. GOES images at noon local time of channels 1, 2, and 4 are used to calculate visible albedo, and shortwave infrared reflectance at 3.9-µm. Cloud-top particle radius is inversely related with SWIRR. Albedo and cloud fraction are measured for cold and warm clouds, and SWIRR is used as a particle-radius proxy for warm clouds only. AERONET fine-mode particle retrievals (fAOD) from the CCNY station are used for …

Moving Fluid Mud Sondes, Optical And Acoustic Sensing Methods In Support Of Coastal Waterway Dredging, Charles R. Bostater, Tyler A. Rotkiske

Ocean Engineering and Marine Sciences Faculty Publications

Airborne, Satellite and In-Situ optical and acoustical imaging provides a means to characterize surface and subsurface water conditions in shallow marine systems. An important research topic to be studied during dredging operations in harbors and navigable waterways is the movement of fluidized muds before, during and after dredging operations. The fluid movement of the surficial sediments in the form of flocs, muck and mud is important to estimate in order to model the transport of solids material during dredging operations. Movement of highly turbid bottom material creates a lutocline or near bottom nephelometric layers, reduces the penetration of light reaching …

A Multiscale Mapping Assessment Of Lake Champlain Cyanobacterial Harmful Algal Blooms, Nathan Torbick, Megan Corbiere, Yu-Pin Lin

Dartmouth Scholarship

Lake Champlain has bays undergoing chronic cyanobacterial harmful algal blooms that pose a public health threat. Monitoring and assessment tools need to be developed to support risk decision making and to gain a thorough understanding of bloom scales and intensities. In this research application, Landsat 8 Operational Land Imager (OLI), Rapid Eye, and Proba Compact High Resolution Imaging Spectrometer (CHRIS) images were obtained while a corresponding field campaign collected in situ measurements of water quality. Models including empirical band ratio regressions were applied to map chlorophylla and phycocyanin concentrations; all sensors performed well with R² and root-mean-square error (RMSE) ranging …

Building A Better Model: Variable Selection To Predict Poverty In Pakistan And Sri Lanka, Marium Afzal, Jonathan Hersh, David Newhouse

Economics Faculty Articles and Research

Numerous studies have developed models to predict poverty, but surprisingly few have rigorously examined different approaches to developing prediction models. This paper applies out of sample validation techniques to household data from Pakistan and Sri Lanka, to compare the accuracy of regional poverty predictions from models derived using manual selection, stepwise regression, and Lasso-based procedures. It also examines how much incorporating publically available satellite data into the model improves its accuracy. The five main findings are that: 1) Lasso tends to outperform both discretionary and stepwise models in Pakistan, where the set of potential predictors is large. 2) Lasso and …

Forest Baseline And Deforestation Map Of The Dominican Republic Through The Analysis Of Time Series Of Modis Data, Florencia Sangermano, Leslie Bol, Pedro Galvis, Raymond E. Gullison, Jared Hardner, Gail S. Ross

Geography

Deforestation is one of the major threats to habitats in the Dominican Republic. In this work we present a forest baseline for the year 2000 and a deforestation map for the year 2011. Maps were derived from Moderate Resolution Imaging Radiometer (MODIS) products at 250. m resolution. The vegetation continuous fields product (MOD44B) for the year 2000 was used to produce the forest baseline, while the vegetation indices product (MOD13Q1) was used to detect change between 2000 and 2011. Major findings based on the data presented here are reported in the manuscript "Habitat suitability and protection status of four species …

A Geospatial Tool For Wetland Prioritization At The Watershed Scale, Debra Sue Blackmore, Heejun Chang

Institute for Natural Resources Publications

There is an increasing demand for assessing ecosystem functions for freshwater wetlands, especially when comparing or prioritizing among wetlands at the watershed scale. We estimated the relative potential of selected ecosystem functions for freshwater wetlands within a watershed using widely available geospatial data. We developed four functions to estimate 1) flood storage, 2) late season flow, 3) sediment retention and 4) temperature control in four pilot watersheds in Oregon (Tualatin, Coquille, Upper Grande Ronde and Sprague). These watersheds are geographically separated from each other representing diverse ecoregion environments. Spatial analysis and geographic information system (GIS) were designed for maximum re-use, …

Image Interpretation-Guided Supervised Classification Using Nested Segmentation, A. V. Egorov, M. C. Hanson, David P. Roy, A. Kommareddy, P. V. Patapov

GSCE Faculty Publications

We present a new binary (two-class) supervised non-parametric classification approach that is based on iterative partitioning of multidimensional feature space into variably-sized and nested hyper-cubes (partitions). The proposed method contains elements of active learning and includes classifier to analyst queries. The spectral transition zone between two thematic classes (i.e., where training labels of different classes overlap in feature space) is targeted through iterative training derivation. Three partition categories are defined: pure, indivisible and unlabeled. Pure partitions contain training labels from only one class, indivisible partitions contain training data from different classes, and unlabeled partitions do not contain training …

Solubility And Detectability Of Hydrocarbons On The Surface Of Titan, Sandeep Singh

Graduate Theses and Dissertations

Titan’s thick atmosphere is enriched with organic compounds and its surface holds reservoirs of hydrocarbons. This makes Titan the only other candidate in our solar system (apart from Earth) to have stable liquid at the surface. The stability and characteristics of liquid and ices on the surface of Titan are of high importance in understanding its surface-atmosphere interactions. Titan’s hydrological cycle is similar to what we see on Earth, with the exception of methane (CH4) and ethane (C2H6) taking the place of water. The smoggy atmosphere veils the surface of Titan from the view of Cassini spacecraft, except at seven …

Lidar And Machine Learning Estimation Of Hardwood Forest Biomass In Mountainous And Bottomland Environments, Bowei Xue

Graduate Theses and Dissertations

Light detection and ranging (lidar) has been applied in various forest applications, such as to retrieve forest structural information, to build statistical models for identification of tree species, and to monitor forest growth. However, despite significant progress in these areas, the choice of regression approach and parameter tuning remains an ongoing critical question. This study focused on choosing the right spatial generalization level to transform lidar point clouds to 2D images which can be further processed by mature image processing and pattern recognition approaches. It also compared the prediction ability of popular machine learning algorithms applied to aboveground forest biomass …