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(1 - 16 of 16)
- Title
- Coastal Sediment Reflectance Analysis using Hyperspectral Remote Sensing.
- Creator
- Selch, Donna, Zhang, Caiyun, Graduate College, Oleinik, Anton E.
- Abstract/Description
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Quantitative assessment of substrate classification for sand properties is needed for land management and conservation. Establishing a sand spectral library is the first step in this process. Hyperspectal analysis allows for rapid, nondestructive data acquisition. This process uses an ASD spectrometer in a laboratory setting with an artificial light source to collect the spectra. Sand collected worldwide was also analyzed for grain size and composition. Development of spectral libraries of...
Show moreQuantitative assessment of substrate classification for sand properties is needed for land management and conservation. Establishing a sand spectral library is the first step in this process. Hyperspectal analysis allows for rapid, nondestructive data acquisition. This process uses an ASD spectrometer in a laboratory setting with an artificial light source to collect the spectra. Sand collected worldwide was also analyzed for grain size and composition. Development of spectral libraries of sand is an essential factor to facilitate analytical techniques to monitor coastal problems including erosion and beach nourishment. This in turn can affect various flora and fauna which requires specific substrate to grow, nest, or live. Preliminary results show that each sand sample has a unique signature that can be identified using hyperspectral data.
Show less - Date Issued
- 2014
- PURL
- http://purl.flvc.org/fau/fd/FA00005166
- Format
- Document (PDF)
- Title
- Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory.
- Creator
- Zhang, Caiyun, Zhou, Yuhong, Qiu, Fang
- Abstract/Description
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The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly...
Show moreThe objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques.
Show less - Date Issued
- 2015-06-16
- PURL
- http://purl.flvc.org/fau/fd/FAUIR000061
- Format
- Citation
- Title
- Sea-level rise vulnerability mapping using LiDAR DEMs.
- Creator
- Cooper, Hannah M., Fletcher, Chip, Chen, Qi, Graduate College, Barbee, Matthew, Zhang, Caiyun
- Abstract/Description
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Global sea-level rise SLR is projected to accelerate over the next century, with research indicating that global mean sea level may rise 18–48 cm by 2050, and 50–140 cm by 2100. Decision-makers, faced with the problem of adapting to SLR, utilize elevation data to identify assets that are vulnerable to inundation. This paper reviews techniques and challenges stemming from the use of Light Detection and Ranging LiDAR Digital Elevation Models DEMs in support of SLR decision-making. A significant...
Show moreGlobal sea-level rise SLR is projected to accelerate over the next century, with research indicating that global mean sea level may rise 18–48 cm by 2050, and 50–140 cm by 2100. Decision-makers, faced with the problem of adapting to SLR, utilize elevation data to identify assets that are vulnerable to inundation. This paper reviews techniques and challenges stemming from the use of Light Detection and Ranging LiDAR Digital Elevation Models DEMs in support of SLR decision-making. A significant shortcoming in the methodology is the lack of comprehensive standards for estimating LiDAR error, which causes inconsistent and sometimes misleading calculations of uncertainty. Workers typically aim to reduce uncertainty by analyzing the difference between LiDAR error and the target SLR chosen for decision-making. The practice of mapping vulnerability to SLR is based on the assumption that LiDAR errors follow a normal distribution with zero bias, which is intermittently violated. Approaches to correcting discrepancies between vertical reference systems for land and tidal datums may incorporate tidal benchmarks and a vertical datum transformation tool provided by the National Ocean Service VDatum. Mapping a minimum statistically significant SLR increment of 32 cm is difficult to achieve based on current LiDAR and VDatum errors. LiDAR DEMs derived from ‘ground’ returns are essential, yet LiDAR providers may fail to remove returns over vegetated areas successfully. LiDAR DEMs integrated into a GIS can be used to identify areas that are vulnerable to direct marine inundation and groundwater inundation reduced drainage coupled with higher water tables. Spatial analysis can identify potentially vulnerable ecosystems as well as developed assets. A standardized mapping uncertainty needs to be developed given that SLR vulnerability mapping requires absolute precision for use as a decision-making tool.
Show less - Date Issued
- 2014
- PURL
- http://purl.flvc.org/fau/fd/FA00005809
- Format
- Document (PDF)
- Title
- Mapping Habitats of Lionfish in Fort Lauderdale.
- Creator
- Hermit, Kathryn, Selch, Donna, Zhang, Caiyun, Charles E. Schmidt College of Science
- Abstract/Description
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The canal system of South Florida has become a new distributive focus for the invasive Lionfish (Pterois volitans). Lionfish are considered a pest here, having up to 18 venomous spines that can inflict pain if stepped on or handled. These fish also eat a variety of juvenile species affecting the commercial and recreational fishing industry. The canal system in south Florida is also a center for recreational activities. Water land cover information will aid in species removal by offering...
Show moreThe canal system of South Florida has become a new distributive focus for the invasive Lionfish (Pterois volitans). Lionfish are considered a pest here, having up to 18 venomous spines that can inflict pain if stepped on or handled. These fish also eat a variety of juvenile species affecting the commercial and recreational fishing industry. The canal system in south Florida is also a center for recreational activities. Water land cover information will aid in species removal by offering species information to areas with a high percent of water land cover and who are more likely to come into contact with Lionfish. This research, comparing classification techniques to map water land cover, is the first step to mitigate the stronghold the lionfish have in South Florida. Once mapped, species information can then be distributed to residents that have close proximity to danger zones.
Show less - Date Issued
- 2015
- PURL
- http://purl.flvc.org/fau/fd/FA00005196
- Subject Headings
- College students --Research --United States.
- Format
- Document (PDF)
- Title
- Data Fusion of LiDAR and Aerial Imagery to Map the Campus of Florida Atlantic University.
- Creator
- Gamboa, Nicole, Zhang, Caiyun, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
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Reliable geographic intelligence is essential for urban areas; land-cover classification creates the data for urban spatial decision making. This research tested a methodology to create a land-cover map for the main campus of Florida Atlantic University in Boca Raton, Florida. The accuracy of nine separate land-cover classification results were tested; the one with the highest accuracy was chosen for the final map. Object-based image segmentation was applied to fused and LiDAR point cloud ...
Show moreReliable geographic intelligence is essential for urban areas; land-cover classification creates the data for urban spatial decision making. This research tested a methodology to create a land-cover map for the main campus of Florida Atlantic University in Boca Raton, Florida. The accuracy of nine separate land-cover classification results were tested; the one with the highest accuracy was chosen for the final map. Object-based image segmentation was applied to fused and LiDAR point cloud (elevation and intensity) data and aerial imagery. These were classified by Random Forest, k-Nearest Neighbor and Support Vector Machines classifiers. Shadow features were reclassified hierarchically in order to create a complete map. The Random Forest classifier used with the fused data set gave the highest overall accuracy at 82.3%, and a Kappa value at 0.77. When combined with the results from the shadow reclassification, the overall accuracy increased to 86.3% and the Kappa value improved to 0.82.
Show less - Date Issued
- 2016
- PURL
- http://purl.flvc.org/fau/fd/FA00004595, http://purl.flvc.org/fau/fd/FA00004595
- Subject Headings
- Spatial analysis (Statistics), Geographic information systems., Cartography--Remote sensing., Thematic maps., Geospatial data--Mathematical models., Criminal justice, Administration of., African Americans, Violence against.
- Format
- Document (PDF)
- Title
- EVALUATING UNMANNED AIRCRAFT SYSTEM PHOTOGRAMMETRY FOR COASTAL FLORIDA EVERGLADES RESTORATION AND MANAGEMENT.
- Creator
- Durgan, Sara D., Zhang, Caiyun, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
- Abstract/Description
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The Florida Everglades ecosystem is experiencing increasing threats from anthropogenic modification of water flow, spread of invasive species, sea level rise (SLR), and more frequent and/or intense hurricanes. Restoration efforts aimed at rehabilitating these ongoing and future disturbances are currently underway through the implementation of the Comprehensive Everglades Restoration Plan (CERP). Efficacy of these restoration activities can be further improved with accurate and site-specific...
Show moreThe Florida Everglades ecosystem is experiencing increasing threats from anthropogenic modification of water flow, spread of invasive species, sea level rise (SLR), and more frequent and/or intense hurricanes. Restoration efforts aimed at rehabilitating these ongoing and future disturbances are currently underway through the implementation of the Comprehensive Everglades Restoration Plan (CERP). Efficacy of these restoration activities can be further improved with accurate and site-specific information on the current state of the coastal wetland habitats. In order to produce such assessments, digital datasets of the appropriate accuracy and scale are needed. These datasets include orthoimagery to delineate wetland areas and map vegetation cover as well as accurate 3-dimensional (3-D) models to characterize hydrology, physiochemistry, and habitat vulnerability.
Show less - Date Issued
- 2020
- PURL
- http://purl.flvc.org/fau/fd/FA00013501
- Subject Headings
- Everglades (Fla )--Environmental conditions--Remote sensing, Aerial photogrammetry, Wetland restoration--Florida--Everglades, Image analysis, Aerial photogrammetry--Data processing, Drone aircraft
- Format
- Document (PDF)
- Title
- Increasing Integrity in Sea-Level Rise Impact Assessment on Florida’s Coastal Everglades.
- Creator
- Cooper, Hannah M., Zhang, Caiyun, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
-
Over drainage due to water management practices, abundance of native and rare species, and low-lying topography makes the coastal Everglades especially vulnerable to Sea-Level Rise (SLR). Water depths have shown to have a significant relationship to vegetation community composition and organization while also playing a crucial role in vegetation health throughout the Everglades. Modeling potential habitat change and loss caused by increased water depths due to SLR requires better vertical...
Show moreOver drainage due to water management practices, abundance of native and rare species, and low-lying topography makes the coastal Everglades especially vulnerable to Sea-Level Rise (SLR). Water depths have shown to have a significant relationship to vegetation community composition and organization while also playing a crucial role in vegetation health throughout the Everglades. Modeling potential habitat change and loss caused by increased water depths due to SLR requires better vertical Root Mean Square Error (RMSE) and resolution Digital Elevation Models (DEMs) and Water Table Elevation Models (WTEMs). In this study, an object-based machine learning approach was developed to correct LiDAR elevation data by integrating LiDAR point data, aerial imagery, Real Time Kinematic (RTK)-Global Positioning Systems (GPS) and total station survey data. Four machine learning modeling techniques were compared with the commonly used bias-corrected technique, including Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), and Artificial Neural Network (ANN). The k-NN and RF models produced the best predictions for the Nine Mile and Flamingo study areas (RMSE = 0.08 m and 0.10 m, respectively). This study also examined four interpolation-based methods along with the RF, SVM and k-NN machine learning techniques for generating WTEMs. The RF models achieved the best results for the dry season (RMSE = 0.06 m) and the wet season (RMSE = 0.07 m) WTEMs. Previous research in Water Depth Model (WDM) generation in the Everglades focused on a conventional-based approach where a DEM is subtracted from a WTEM. This study extends the conventional-based WDM approach to a rigorous-based WDM technique where Monte Carlo simulation is used to propagate probability distributions through the proposed SLR depth model using uncertainties in the RF-based LiDAR DEM and WTEMs, vertical datums and transformations, regional SLR and soil accretion rates. It is concluded that a more rigorous-based WDM technique increases the integrity of derived products used to support and guide coastal restoration managers and planners concerned with habitat change under the challenge of SLR. Future research will be dedicated to the extension of this technique to model both increased water depths and saltwater intrusion due to SLR (saltwater inundation).
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00005991
- Subject Headings
- Everglades (Fla.), Sea level rise, Coastal ecology--Florida, Everglades (Fla)--Environmental conditions, Impact assessment
- Format
- Document (PDF)
- Title
- Salinity Assessment, Change, and Impact on Plant Stress / Canopy Water Content (CWC) in Florida Bay using Remote Sensing and GIS.
- Creator
- Selch, Donna, Zhang, Caiyun, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
-
Human activities in the past century have caused a variety of environmental problems in South Florida. In 2000, Congress authorized the Comprehensive Everglades Restoration Plan (CERP), a $10.5-billion mission to restore the South Florida ecosystem. Environmental projects in CERP require salinity monitoring in Florida Bay to provide measures of the effects of restoration on the Everglades ecosystem. However current salinity monitoring cannot cover large areas and is costly, time-consuming,...
Show moreHuman activities in the past century have caused a variety of environmental problems in South Florida. In 2000, Congress authorized the Comprehensive Everglades Restoration Plan (CERP), a $10.5-billion mission to restore the South Florida ecosystem. Environmental projects in CERP require salinity monitoring in Florida Bay to provide measures of the effects of restoration on the Everglades ecosystem. However current salinity monitoring cannot cover large areas and is costly, time-consuming, and laborintensive. The purpose of this dissertation is to model salinity, detect salinity changes, and evaluate the impact of salinity in Florida Bay using remote sensing and geospatial information sciences (GIS) techniques. The specific objectives are to: 1) examine the capability of Landsat multispectral imagery for salinity modeling and monitoring; 2) detect salinity changes by building a series of salinity maps using archived Landsat images; and 3) assess the capability of spectroscopy techniques in characterizing plant stress / canopy water content (CWC) with varying salinity, sea level rise (SLR), and nutrient levels. Geographic weighted regression (GWR) models created using the first three imagery components with atmospheric and sun glint corrections proved to be more correlated (R^2 = 0.458) to salinity data versus ordinary least squares (OLS) regression models (R^2 = 0.158) and therefore GWR was the ideal regression model for continued Florida Bay salinity assessment. J. roemerianus was also examined to assess the coastal Everglades where salinity modeling is important to the water-land interface. Multivariate greenhouse studies determined the impact of nutrients to be inconsequential but increases in salinity and sea level rise both negatively affected J. roemerianus. Field spectroscopic data was then used to ascertain correlations between CWC and reflectance spectra using spectral indices and derivative analysis. It was determined that established spectral indices (max R^2 = 0.195) and continuum removal (max R^2= 0.331) were not significantly correlated to CWC but derivative analysis showed a higher correlation (R^2 = 0.515 using the first derivative at 948.5 nm). These models can be input into future imagery to predict the salinity of the South Florida water ecosystem.
Show less - Date Issued
- 2016
- PURL
- http://purl.flvc.org/fau/fd/FA00004686, http://purl.flvc.org/fau/fd/FA00004686
- Subject Headings
- Environmental management, Florida Bay (Fla.), Geographic information systems, Geospatial data, Marine ecology, Plant water relationships, Remote sensing, Salinity -- Florida -- Florida Bay -- Measurement
- Format
- Document (PDF)
- Title
- MODELING GROUND ELEVATION OF LOUISIANA COASTAL WETLANDS AND ANALYZING RELATIVE SEA LEVEL RISE INUNDATION USING RSET-MH AND LIDAR MEASUREMENTS.
- Creator
- Liu, Jing, Zhang, Caiyun, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
- Abstract/Description
-
The Louisiana coastal ecosystem is experiencing increasing threats from human flood control construction, sea-level rise (SLR), and subsidence. Louisiana lost about 4,833 km2 of coastal wetlands from 1932 to 2016, and concern exists whether remaining wetlands will persist while facing the highest rate of relative sea-level rise (RSLR) in the world. Restoration aimed at rehabilitating the ongoing and future disturbances is currently underway through the implementation of the Coastal Wetlands...
Show moreThe Louisiana coastal ecosystem is experiencing increasing threats from human flood control construction, sea-level rise (SLR), and subsidence. Louisiana lost about 4,833 km2 of coastal wetlands from 1932 to 2016, and concern exists whether remaining wetlands will persist while facing the highest rate of relative sea-level rise (RSLR) in the world. Restoration aimed at rehabilitating the ongoing and future disturbances is currently underway through the implementation of the Coastal Wetlands Planning Protection and Restoration Act of 1990 (CWPPRA). To effectively monitor the progress of projects in CWPPRA, the Coastwide Reference Monitoring System (CRMS) was established in 2006. To date, more than a decade of valuable coastal, environmental, and ground elevation data have been collected and archived. This dataset offers a unique opportunity to evaluate the wetland ground elevation dynamics by linking the Rod Surface Elevation Table (RSET) measurements with environmental variables like water salinity and biophysical variables like canopy coverage. This dissertation research examined the effects of the environmental and biophysical variables on wetland terrain elevation by developing innovative machine learning based models to quantify the contribution of each factor using the CRMS collected dataset. Three modern machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were assessed and cross-compared with the commonly used Multiple Linear Regression (MLR). The results showed that RF had the best performance in modeling ground elevation with Root Mean Square Error (RMSE) of 10.8 cm and coefficient of coefficient (r) = 0.74. The top four factors contributing to ground elevation are the distance from monitoring station to closest water source, water salinity, water elevation, and dominant vegetation height.
Show less - Date Issued
- 2020
- PURL
- http://purl.flvc.org/fau/fd/FA00013568
- Subject Headings
- Coastal zone management--Louisiana, Sea level rise, Inundations, Wetland restoration--Louisiana, Machine learning, Computer simulation, Algorithms.
- Format
- Document (PDF)
- Title
- SALT MARSH SPECIES CLASSIFICATION AND SOIL PROPERTY MODELING USING MULTIPLE REMOTE SENSORS.
- Creator
- Nicholson, Heather M., Zhang, Caiyun, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
- Abstract/Description
-
Salt marshes are highly dynamic ecosystems that rely on multiple environmental and physical drivers that determine species distribution and soil property distribution. However, climate change and human interference are threatening the delicate ecosystem. One of the easiest ways to monitor marsh dynamics is through remote sensing. Traditional methods may not handle the large, non-parametric datasets well and often do not spatially determine areas of uncertainty. This dissertation research...
Show moreSalt marshes are highly dynamic ecosystems that rely on multiple environmental and physical drivers that determine species distribution and soil property distribution. However, climate change and human interference are threatening the delicate ecosystem. One of the easiest ways to monitor marsh dynamics is through remote sensing. Traditional methods may not handle the large, non-parametric datasets well and often do not spatially determine areas of uncertainty. This dissertation research developed a framework to map marsh species and predict ground soil properties using multiple remote sensing data sources by integrating modern Object-based Image Analysis (OBIA), machine learning, data fusion, and band indices techniques. It also sought to determine areas of uncertainty in the final outputs and differences between different spectral resolutions. Five machine learning classifiers were examined including Support Vector Machine (SVM) and Random Forest (RF) to map marsh species. Overall results illustrated that RF and SVM typically performed best, especially when using hyperspectral data combined with DEM information. Seven regressors were assessed to map three different soil properties. Again, RF and SVM performed the best no matter the dataset used, or soil property mapped. Soil salinity had r as high as 0.93, soil moisture had r as high as 0.91, and soil organic an r as high as 0.74 when using hyperspectral data.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014000
- Subject Headings
- Salt marshes, Salt marsh ecology, Species, Remote sensing
- Format
- Document (PDF)
- Title
- QUANTIFICATION OF PERMAFROST THAW DEPTH AND SNOW DEPTH IN INTERIOR ALASKA AT MULTIPLE SCALES USING FIELD, AIRBORNE, AND SPACEBORNE DATA.
- Creator
- Brodylo, David, Zhang, Caiyun, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
- Abstract/Description
-
Much of Interior Alaska contains permafrost, which is a permanently frozen layer found within or at the surface of the Earth. Historically, this permafrost has experienced relative stability, with limited thaw during warmer summer months and fire events. However, largely due to the impact of a warming climate, among other factors, permafrost that would typically experience limited thawing during the summer season has recently been thawing at an unprecedented rate. Trapped by this layer of...
Show moreMuch of Interior Alaska contains permafrost, which is a permanently frozen layer found within or at the surface of the Earth. Historically, this permafrost has experienced relative stability, with limited thaw during warmer summer months and fire events. However, largely due to the impact of a warming climate, among other factors, permafrost that would typically experience limited thawing during the summer season has recently been thawing at an unprecedented rate. Trapped by this layer of permafrost is a large quantity of carbon (C), which could be released into the atmosphere as greenhouse gases such as carbon dioxide (CO2) and methane (CH4). Due to the remoteness of the Arctic, there is a lack of yearly recorded permafrost thaw depth and snow depth values across much of the region. As such, the focus of this research was to establish a framework to identify how permafrost thaw depth and snow depth can be predicted across both a 1 km2 local scale and a 100 km2 regional scale in Interior Alaska by a combination of 1 m2 field data, airborne and spaceborne remote sensing products, and object-based machine learning techniques from 2014 – 2022. Machine learning techniques Random Forest, Support Vector Machine, k-Nearest Neighbor, Multiple Linear Regression, and Ensemble Analysis were applied to predict the permafrost thaw depth and snow depth. Results indicated that this methodology was able to successfully upscale both the 1 m2 field permafrost thaw depth and snow depth data to a 1 km2 local scale before successfully further upscaling the estimated results to a 100 km2 regional scale, while also linking the estimated values with ecotypes. The best results were produced by Ensemble Analysis, which tended to have the highest Pearson’s Correlation Coefficient, alongside the lowest Mean Absolute Error and Root Mean Square Error. Both Random Forest and k-Nearest Neighbor also provided encouraging results. The presence or absence of a thick canopy cover was strongly connected with thaw depth and snow depth estimates. Image resolution was an important factor when upscaling field data to the local scale, however it was overall less critical for further upscaling to the regional scale.
Show less - Date Issued
- 2023
- PURL
- http://purl.flvc.org/fau/fd/FA00014229
- Subject Headings
- Permafrost--Alaska, Remote sensing, Machine learning
- Format
- Document (PDF)
- Title
- IDENTIFYING AREAS AT RISK OF CLIMATE GENTRIFICATION IN TAMPA CITY.
- Creator
- Ramirez, David Alexander, Zhang, Caiyun, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
- Abstract/Description
-
Gentrification describes rapid infrastructure development and investment in areas with lower income classes. It may cause potential erasure of the original neighborhood's unique culture and the displacement of residents. Due to rising sea levels and the increase in the frequency and intensity of storms, the inundation of Florida will increase as time passes. This creates an ironic relationship where historical coastal areas inhabited by an affluent population will move inland to historically...
Show moreGentrification describes rapid infrastructure development and investment in areas with lower income classes. It may cause potential erasure of the original neighborhood's unique culture and the displacement of residents. Due to rising sea levels and the increase in the frequency and intensity of storms, the inundation of Florida will increase as time passes. This creates an ironic relationship where historical coastal areas inhabited by an affluent population will move inland to historically lower-income populations. This thesis developed a Climate Gentrification Index (CGI) to identify areas at risk of gentrification caused by inundation of storm scenarios in Tampa City, Florida. Socioeconomic data and inundation data produced from a hydrological model were combined to define CGI and areas with high risk were mapped and discussed.
Show less - Date Issued
- 2024
- PURL
- http://purl.flvc.org/fau/fd/FA00014397
- Subject Headings
- Gentrification, Tampa (Fla.), Climate change
- Format
- Document (PDF)
- Title
- COMBINING TRADITIONAL AND IMAGE ANALYSIS TECHNIQUES FOR UNCONSOLIDATED EXPOSED TERRIGENOUS BEACH SAND CHARACTERIZATION.
- Creator
- Smith, Molly Elizabeth, Zhang, Caiyun, Oleinik, Anton, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
- Abstract/Description
-
Traditional sand analysis is labor and cost-intensive, entailing specialized equipment and operators trained in geological analysis. Even a small step to automate part of the traditional geological methods could substantially improve the speed of such research while removing chances of human error. Digital image analysis techniques and computer vision have been well developed and applied in various fields but rarely explored for sand analysis. This research explores capabilities of remote...
Show moreTraditional sand analysis is labor and cost-intensive, entailing specialized equipment and operators trained in geological analysis. Even a small step to automate part of the traditional geological methods could substantially improve the speed of such research while removing chances of human error. Digital image analysis techniques and computer vision have been well developed and applied in various fields but rarely explored for sand analysis. This research explores capabilities of remote sensing digital image analysis techniques, such as object-based image analysis (OBIA), machine learning, digital image analysis, and photogrammetry to automate or semi-automate the traditional sand analysis procedure. Here presented is a framework combining OBIA and machine learning classification of microscope imagery for use with unconsolidated terrigenous beach sand samples. Five machine learning classifiers (RF, DT, SVM, k-NN, and ANN) are used to model mineral composition from images of ten terrigenous beach sand samples. Digital image analysis and photogrammetric techniques are applied and evaluated for use to characterize sand grain size and grain circularity (given as a digital proxy for traditional grain sphericity). A new segmentation process is also introduced, where pixel-level SLICO superpixel segmentation is followed by spectral difference segmentation and further levels of superpixel segmentation at the object-level. Previous methods of multi-resolution and superpixel segmentation at the object level do not provide the level of detail necessary to yield optimal sand grain-sized segments. In this proposed framework, the DT and RF classifiers provide the best estimations of mineral content of all classifiers tested compared to traditional compositional analysis. Average grain size approximated from photogrammetric procedures is comparable to traditional sieving methods, having an RMSE below 0.05%. The framework proposed here reduces the number of trained personnel needed to perform sand-related research. It requires minimal sand sample preparation and minimizes user-error that is typically introduced during traditional sand analysis.
Show less - Date Issued
- 2020
- PURL
- http://purl.flvc.org/fau/fd/FA00013517
- Subject Headings
- Sand, Image analysis, Remote sensing, Photogrammetry--Digital techniques, Machine learning
- Format
- Document (PDF)
- Title
- Sand Compositional Analysis Using a Combined Geological and Spectroscopic Approach.
- Creator
- Smith, Molly E., Oleinik, Anton E., Zhang, Caiyun, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
-
Many minerals, such as calcite and magnetite, show diagnostic overtone and combination bands in the 350-2500 nm window. Sand, though an important unconsolidated material with great abundance on the Earth’s surface, is largely overlooked in spectroscopic studies. Over 100 sand samples were analyzed through traditional microscopic methods and compared to spectral reflectance collected via an ASD Spectroradiometer. Multiple methods were chosen to compare spectroscopic data to sand composition...
Show moreMany minerals, such as calcite and magnetite, show diagnostic overtone and combination bands in the 350-2500 nm window. Sand, though an important unconsolidated material with great abundance on the Earth’s surface, is largely overlooked in spectroscopic studies. Over 100 sand samples were analyzed through traditional microscopic methods and compared to spectral reflectance collected via an ASD Spectroradiometer. Multiple methods were chosen to compare spectroscopic data to sand composition and grain size: 1) existing spectral indices, 2) continuum removal, 3) derivative analysis, and 4) correlation analysis. Particular focus was given to carbonate content. Results from derivative and correlation analysis showed strong correlations in the 2180-2240 nm and 2300-2360 nm windows to carbonate content. Proposed here is the Normalized Difference Carbonate Sand Index (NDCSI), which showed Pearson correlations of r=-0.78 for light-colored samples and r=-0.77 for all samples used. This index is viable for use with carbonate-rich sands.
Show less - Date Issued
- 2016
- PURL
- http://purl.flvc.org/fau/fd/FA00004634
- Subject Headings
- Physical geology., Environmental geology., Coast changes--Analysis., Beach erosion., Sand--Optical properties., Spectrophotometry.
- Format
- Document (PDF)
- Title
- Remote sensing of evapotranspiration using automated calibration: development and testing in the state of Florida.
- Creator
- Evans, Aaron H., Obeysekera, Jayantha, Zhang, Caiyun, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
-
Thermal remote sensing is a powerful tool for measuring the spatial variability of evapotranspiration due to the cooling effect of vaporization. The residual method is a popular technique which calculates evapotranspiration by subtracting sensible heat from available energy. Estimating sensible heat requires aerodynamic surface temperature which is difficult to retrieve accurately. Methods such as SEBAL/METRIC correct for this problem by calibrating the relationship between sensible heat and...
Show moreThermal remote sensing is a powerful tool for measuring the spatial variability of evapotranspiration due to the cooling effect of vaporization. The residual method is a popular technique which calculates evapotranspiration by subtracting sensible heat from available energy. Estimating sensible heat requires aerodynamic surface temperature which is difficult to retrieve accurately. Methods such as SEBAL/METRIC correct for this problem by calibrating the relationship between sensible heat and retrieved surface temperature. Disadvantage of these calibrations are 1) user must manually identify extremely dry and wet pixels in image 2) each calibration is only applicable over limited spatial extent. Producing larger maps is operationally limited due to time required to manually calibrate multiple spatial extents over multiple days. This dissertation develops techniques which automatically detect dry and wet pixels. LANDSAT imagery is used because it resolves dry pixels. Calibrations using 1) only dry pixels and 2) including wet pixels are developed. Snapshots of retrieved evaporative fraction and actual evapotranspiration are compared to eddy covariance measurements for five study areas in Florida: 1) Big Cypress 2) Disney Wilderness 3) Everglades 4) near Gainesville, FL. 5) Kennedy Space Center. The sensitivity of evaporative fraction to temperature, available energy, roughness length and wind speed is tested. A technique for temporally interpolating evapotranspiration by fusing LANDSAT and MODIS is developed and tested. The automated algorithm is successful at detecting wet and dry pixels (if they exist). Including wet pixels in calibration and assuming constant atmospheric conductance significantly improved results for all but Big Cypress and Gainesville. Evaporative fraction is not very sensitive to instantaneous available energy but it is sensitive to temperature when wet pixels are included because temperature is required for estimating wet pixel evapotranspiration. Data fusion techniques only slightly outperformed linear interpolation. Eddy covariance comparison and temporal interpolation produced acceptable bias error for most cases suggesting automated calibration and interpolation could be used to predict monthly or annual ET. Maps demonstrating spatial patterns of evapotranspiration at field scale were successfully produced, but only for limited spatial extents. A framework has been established for producing larger maps by creating a mosaic of smaller individual maps.
Show less - Date Issued
- 2014
- PURL
- http://purl.flvc.org/fau/fd/FA00004194, http://purl.flvc.org/fau/fd/FA00004194
- Subject Headings
- Climatic changes, Environmental sciences -- Remote sensing, Evapotranspiration -- Measurement, Geographic information systems, Remote sensing -- Data processing, Spatial analysis (Mathematics)
- Format
- Document (PDF)
- Title
- Evaluating the Impact of LiDAR DEM Uncertainties on Inundation Modeling in Coastal Sub-Watersheds: An Exploration Via Deterministic and Probabilistic Approaches.
- Creator
- Thapa, Madan Chhetri, Zhang, Caiyun, Su, Hongbo, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
- Abstract/Description
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This study examines the impact of uncertainty associated with Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) on flood risk mapping in the North Biscayne Bay sub-watershed. A comparison of flood extent and generation of the probability of flooding was carried out using the bathtub and probabilistic approaches respectively. The water level was computed separately for original and refined DEM using Cascade 2001 hydrological model. Using land cover based corrected...
Show moreThis study examines the impact of uncertainty associated with Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) on flood risk mapping in the North Biscayne Bay sub-watershed. A comparison of flood extent and generation of the probability of flooding was carried out using the bathtub and probabilistic approaches respectively. The water level was computed separately for original and refined DEM using Cascade 2001 hydrological model. Using land cover based corrected DEMs reveals a 12% reduction in flooded areas in contrast to original DEM, considering uncertainties associated with land cover. Probabilistic flood modeling via Gaussian Geostatistical Simulation accounts for DEM uncertainty, yielding nuanced probability flood risk maps (0-100%). Findings emphasize DEM refinement before conducting flood mapping to address uncertainties. Future research should explore other mediums of correction incorporating effects of point density of LiDAR, methods of DEM generation, use of diverse scenarios, and kriging techniques for flood modeling and mapping while using LiDAR derived DEM.
Show less - Date Issued
- 2024
- PURL
- http://purl.flvc.org/fau/fd/FA00014476
- Subject Headings
- Biscayne Bay (Fla.), Lidar, Digital elevation models
- Format
- Document (PDF)
