Current Search: Department of Geosciences (x) » Remote sensing (x)
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- 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
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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
- Comparing salinity models in Whitewater Bay using remote sensing.
- Creator
- Selch, Donna, Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
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This study compared models that used remote sensing to assess salinity in Whitewater Bay. The quantitative techniques in this research allow for a less costly and quicker assessment of salinity values. Field observations and Landsat 5 TM imagery from 2003-2006 were separated into wet and dry seasons and temporally matched. Interpolation models of Inverse Distance Weighting and Kriging were compared to empirical regression models (Ordinary Least Squares and Geographically Weighted Regression -...
Show moreThis study compared models that used remote sensing to assess salinity in Whitewater Bay. The quantitative techniques in this research allow for a less costly and quicker assessment of salinity values. Field observations and Landsat 5 TM imagery from 2003-2006 were separated into wet and dry seasons and temporally matched. Interpolation models of Inverse Distance Weighting and Kriging were compared to empirical regression models (Ordinary Least Squares and Geographically Weighted Regression - GWR) via their Root Mean Square Error. The results showed that salinity analysis is more accurate in the dry season compared with the wet season. Univariate and multivariate analysis of the Landsat bands revealed the best band combination for salinity analysis in this local area. GWR is the most conducive model for estimating salinity because field observations are not required for future predictions once the local formula is established with available satellite imagery.
Show less - Date Issued
- 2012
- PURL
- http://purl.flvc.org/FAU/3356015
- Subject Headings
- Water quality, Measurement, Marine ecology, Remote sensing, Electromagnetic interactions, Water-supply
- Format
- Document (PDF)
- Title
- Development of a remote sensing technique for woody vegetation in Rotenberger Wildlife Management Area.
- Creator
- Franklin, Sarah., Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
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The Florida Fish and Wildlife Conservation Commission lacks a viable method for monitoring woody vegetation in expansive wetland communities, such as the Florida Everglades. This study used aerial photographs of Rotenberger Wildlife Management Area in southeastern Palm Beach County, Florida to develop techniques for remotely monitoring changes in woody vegetation. Imagery from 2006, 2008, and 2010 were classified into woody and non-woody categories using Adobe Photoshop's Magic Wand Tool....
Show moreThe Florida Fish and Wildlife Conservation Commission lacks a viable method for monitoring woody vegetation in expansive wetland communities, such as the Florida Everglades. This study used aerial photographs of Rotenberger Wildlife Management Area in southeastern Palm Beach County, Florida to develop techniques for remotely monitoring changes in woody vegetation. Imagery from 2006, 2008, and 2010 were classified into woody and non-woody categories using Adobe Photoshop's Magic Wand Tool. Selection was performed with a bias toward over classification, as project objectives required identifying as many trees as possible. Classified pixels in Time 1 within 4 feet (2 pixels) of classified pixels from Time 2 were considered the same canopy. Overall accuracy for the study was 98%.
Show less - Date Issued
- 2011
- PURL
- http://purl.flvc.org/FAU/3175018
- Subject Headings
- Environmental monitoring, Remote sensing, Vegetation dynamics, Ecosystem management
- Format
- Document (PDF)
- Title
- Interpretation of seafloor topologies based on IKONOS satellite imagery of a shallow-marine carbonate platform: Florida Bay to the Florida Reef Tract.
- Creator
- Steinle, Jacob Thomas., Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
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A benthic environments classification system is devised from digital interpretations of multi-spectral IKONOS satellite imagery for 1,360 km2 of the carbonate platform and presented in a comprehensive digitized map. The classification scheme is designed as a 7th order hierarchical structure that integrates 5 Physiographic Realms, 24 Morphodynamic Zones, 11 Geoforms, 39 Landforms, 6 dominant surface sediment types, 9 dominant biological covers and 3 densities of biological covers for the...
Show moreA benthic environments classification system is devised from digital interpretations of multi-spectral IKONOS satellite imagery for 1,360 km2 of the carbonate platform and presented in a comprehensive digitized map. The classification scheme is designed as a 7th order hierarchical structure that integrates 5 Physiographic Realms, 24 Morphodynamic Zones, 11 Geoforms, 39 Landforms, 6 dominant surface sediment types, 9 dominant biological covers and 3 densities of biological covers for the description of benthic environments. Digital analysis of the high-resolution (4 m) IKONOS imagery employed ESRI's ArcMap to manually digitize 412 mapping units at a scale of 1:6,000 differentiated by spectral reflectance, color tones, and textures of seafloor topologies. The context of each morphodynamic zone is characterized by the content and areal distribution (in km2) of geomorphic forms and biological covers. Over 58% of the mapping area is occupied by sediment flats, and seagrasses are colonized in almost 80% of the topologies.
Show less - Date Issued
- 2011
- PURL
- http://purl.flvc.org/FAU/3174075
- Subject Headings
- Submarine topography, Marine sediments, Remote sensing, Marine ecosystem management, Ocean bottom, Sampling, Ocean bottom, Sampling, Coral reef ecology
- 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
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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
- Mapping urban land cover using multi-scale and spatial autocorrelation information in high resolution imagery.
- Creator
- Johnson, Brian A., Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
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Fine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of...
Show moreFine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of within-class spectral variability and between-class spectral similarity of many types of land cover leads to low classification accuracy when pixel-based, purely spectral classification techniques are used. Object-based classification methods, which involve segmenting an image into relatively homogeneous regions (i.e. image segments) prior to classification, have been shown to increase classification accuracy by incorporating the spectral (e.g. mean, standard deviation) and non-spectral (e.g. te xture, size, shape) information of image segments for classification. One difficulty with the object-based method, however, is that a segmentation parameter (or set of parameters), which determines the average size of segments (i.e. the segmentation scale), is difficult to choose. Some studies use one segmentation scale to segment and classify all types of land cover, while others use multiple scales due to the fact that different types of land cover typically vary in size. In this dissertation, two multi-scale object-based classification methods were developed and tested for classifying high resolution images of Deerfield Beach, FL and Houston, TX. These multi-scale methods achieved higher overall classification accuracies and Kappa coefficients than single-scale object-based classification methods., Since the two dissertation methods used an automated algorithm (Random Forest) for image classification, they are also less subjective and easier to apply to other study areas than most existing multi-scale object-based methods that rely on expert knowledge (i.e. decision rules developed based on detailed visual inspection of image segments) for classifying each type of land cover.
Show less - Date Issued
- 2012
- PURL
- http://purl.flvc.org/FAU/3342110
- Subject Headings
- Image processing, Digital techniques, Remote sensing, Mathematics, Remote-sensing images, Computational intelligence, Cities and towns, Remote sensing, Environmental sciences, Remote sensing, Spatial analysis (Statistics)
- Format
- Document (PDF)
- Title
- Commercialization of high-resolution earth observation satellite remote sensing.
- Creator
- Jarica, Cornelia Christa, Florida Atlantic University, Tata, Robert J., Charles E. Schmidt College of Science, Department of Geosciences
- Abstract/Description
-
The imminent availability of high resolution satellite imagery is causing a paradigm shift in remote sensing. Detente brought about new policy directives in the U.S. and abroad, which opened up for civilian use former Earth observation spy technology down to one meter resolution, previously considered classified and strictly used by the intelligence communities for national security. This study describes a number of new ventures in the private sector which have been formed to launch...
Show moreThe imminent availability of high resolution satellite imagery is causing a paradigm shift in remote sensing. Detente brought about new policy directives in the U.S. and abroad, which opened up for civilian use former Earth observation spy technology down to one meter resolution, previously considered classified and strictly used by the intelligence communities for national security. This study describes a number of new ventures in the private sector which have been formed to launch commercial high resolution systems. The satellites' technical capabilities are analyzed, and application development options for the new imagery are discussed in detail. This new remote sensing data source is also seen within the framework of the larger GeoTechnology Industry to which it belongs, and the author proposes appropriate business strategies for successful commercialization.
Show less - Date Issued
- 1996
- PURL
- http://purl.flvc.org/fcla/dt/15321
- Subject Headings
- Earth resources technology satellites, Remote sensing, Remote-sensing images, Geographic information systems
- Format
- Document (PDF)
- Title
- IDENTIFICATION OF SURFACE DEPRESSIONAL FEATURES POTENTIALLY RELATED TO SINKHOLES IN MARTIN COUNTY, FLORIDA, USING REMOTE SENSING TECHNIQUES.
- Creator
- Sanju, Khatri, Comas, Xavier, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
- Abstract/Description
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Sinkholes are common karst features in Florida, having the highest rate of sinkhole occurrence in the US, which results in hundreds of millions estimated costs in damage per year and occasional life losses. While most sinkhole incidents reported in Florida relate to surface subsidence and collapse processes, other sinkhole formation mechanisms (like sagging) have received little attention as a relevant subsidence process. This is important since extensive areas of karst bedrock are overlain...
Show moreSinkholes are common karst features in Florida, having the highest rate of sinkhole occurrence in the US, which results in hundreds of millions estimated costs in damage per year and occasional life losses. While most sinkhole incidents reported in Florida relate to surface subsidence and collapse processes, other sinkhole formation mechanisms (like sagging) have received little attention as a relevant subsidence process. This is important since extensive areas of karst bedrock are overlain by variable thicknesses of non-soluble formations that may affect both the kinematics and damaging potential of these sinkholes in Florida. This research presents an automated GIS-based method to easily delineate surface depressional features in Martin County that result in surface depressional features and are related to cover sagging sinkholes. A total of 3,091 depressional features in Martin County were mapped using GIS methods and constrained with already existing direct drill cores. Results show a consistent statistically significant negative correlation between several morphometric features (i.e., area, perimeter, or depth) from these depressional features and depth to the limestone, suggesting that depressions are linked to sinkholes developed in deep-seated karst. While further subsurface imaging is needed to confirm this correlation, previous studies confirm these results and suggest that cover sagging, or cover suffusion sinkholes may represent a very large group of sinkholes traditionally unaccounted for in current sinkhole assessment maps in Florida. The methodology presented in this study can be easily extrapolated to other areas to further expand current sinkhole hazard and distribution maps.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013872
- Subject Headings
- Sinkholes--Florida, Martin County (Fla.), Karst, Remote sensing
- Format
- Document (PDF)
- Title
- MONITORING AND MODELING URBAN GROWTH PROCESS AND MEASURING COMMUNITY RESILIENCE TO DISASTERS IN THE COASTAL UNITED STATES.
- Creator
- Rifat, Shaikh Abdullah Al, Liu, Weibo, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
- Abstract/Description
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Global population is increasing at an alarming rate with rapid urbanization of the earth’s land surface. Currently, more than half of the world’s population lives in urban areas and this number is projected to increase to 66% by 2050. Urban expansion in coastal zones is more complex due to the rapid urbanization and higher population growth. In the United States (US), more than 39% of the total population now lives in coastal counties. Although urbanization offers some advantages such as...
Show moreGlobal population is increasing at an alarming rate with rapid urbanization of the earth’s land surface. Currently, more than half of the world’s population lives in urban areas and this number is projected to increase to 66% by 2050. Urban expansion in coastal zones is more complex due to the rapid urbanization and higher population growth. In the United States (US), more than 39% of the total population now lives in coastal counties. Although urbanization offers some advantages such as economic development, unplanned urbanization can adversely affect our environment. Additionally, coastal communities in the US are frequently impacted by disasters. Climate change such as sea level rise could intensify these coastal disasters and impact more lives and properties. Therefore, using Geographic Information Systems (GIS) and remote sensing, this study examines these pressing environmental challenges with the coastal US as the Study area. We first quantified the historical spatiotemporal patterns and major explanatory factors of urban expansion in the Miami Metropolitan Area during 1992 - 2016 at different spatial scales. Additionally, different urban expansion dynamics such as expansion rate, pattern, types, intensity, and landscape metrics were analyzed. Multi-level spatiotemporal analyses suggest that urban growth varied both spatially and temporally across the study area. We then measured the community resilience to coastal disasters by constructing a composite index. Additionally, spatial relationships between resilience components and disaster impacts were investigated. Results suggest that northeastern coastal communities in the US are more resilient to disasters compared to the southeastern communities. Furthermore, community resilience varies across the space and resilience components used in this study can explain disaster damages. Finally, this research also simulates and predicts three future urban growth scenarios including business as usual, planned growth, and sustainable growth in the study area. Then current and future exposures to flooding were estimated by considering different sea level rise scenarios. Results suggest that future urban areas will be developed significantly in the flood risk areas if development is not restricted in the high-risk flooding zone. Findings from this study could be useful for area-specific disaster management policy guidelines and formation of land use policy and planning.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013855
- Subject Headings
- Coastal Zones, Urban growth, Disasters, Remote sensing
- 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
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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
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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
- THE MANTEÑO OF BOLA DE ORO: PAST HUMAN RESILIENCY TO CLIMATE CHANGE THROUGH REMOTE SENSING, EXCAVATION, AND CHRONOLOGICAL RECONSTRUCTION OF LANDSCAPE MODIFICATIONS.
- Creator
- Garzón-Oechsle, Andrés E., Johanson, Erik, Florida Atlantic University, Department of Geosciences, Charles E. Schmidt College of Science
- Abstract/Description
-
The term "collapse" has become a widely used term that oversimplifies the intricate histories of human-environment interactions. It has contributed to the belief that civilizations in the Americas and the tropics could not endure over time. However, the Manteño civilization of the Ecuadorian coast challenges this notion. Flourishing for a thousand years (ca. 650–1700 CE), the Manteños inhabited the neotropics at the gates of one of the world's most influential climatic forces, the El Niño...
Show moreThe term "collapse" has become a widely used term that oversimplifies the intricate histories of human-environment interactions. It has contributed to the belief that civilizations in the Americas and the tropics could not endure over time. However, the Manteño civilization of the Ecuadorian coast challenges this notion. Flourishing for a thousand years (ca. 650–1700 CE), the Manteños inhabited the neotropics at the gates of one of the world's most influential climatic forces, the El Niño-Southern Oscillation (ENSO). To thrive, the Manteños needed to navigate the extremes of ENSO during the Medieval Climate Anomaly (MCA, ca. 950–1250 CE) and the Little Ice Age (LIA, ca. 1400–1700 CE) while capitalizing on ENSO's milder phases. This research uses change detection analysis of Normalized Difference Vegetation Index (NDVI) on Landsat satellite imagery under various ENSO conditions from 1986 to 2020 in southern Manabí, where the 16th-century Manteño territory of Salangome was situated. The findings indicate that the cloud forests found in the highest elevations of the Chongón-Colonche Mountains provide the most resilient environment in the region to adapt to a changing climate. Further investigations of the cloud forest of the Bola de Oro Mountain using Uncrewed Aerial Vehicles (UAV) equipped with LiDAR, ground-truthing, and excavation uncovered a landscape shaped by the Manteños.
Show less - Date Issued
- 2023
- PURL
- http://purl.flvc.org/fau/fd/FA00014225
- Subject Headings
- Climate change, Remote sensing, Archaeology
- Format
- Document (PDF)