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Florida depends on the oceans, yet its waters have not been extensively mapped to the highest standards. While there is a need for marine spatial data for a wide range of applications and issues, there is also a need to develop data acquisition, processing , and analytical workflows and to integrate different surveying instruments that can capture the complex and extensive coastal environment-both above and below the waterline. This note provides an overview of the research performed by scientists at the School of Forest, Fisheries, and Geomatics Sciences, University of Florida, in the field of hydrography and marine geomatics.
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Marine Geomatics at the University of Florida
By Vincent Lecours, Amr Abd-Elrahman, Benjamin E. Wilkinson
School of Forest, Fisheries, and Geomatics Sciences, University of Florida
Florida depends on the oceans, yet its waters have not been extensively mapped to
the highest standards. While there is a need for marine spatial data for a wide range
of applications and issues, there is also a need to develop data acquisition, pro-
cessing, and analytical workflows and to integrate different surveying instruments
that can capture the complex and extensive coastal environment – both above and
below the waterline. This note provides an overview of the research performed by
scientists at the School of Forest, Fisheries, and Geomatics Sciences, University of
Florida, in the field of hydrography and marine geomatics.
The State of Florida’s coastal waters are the most valuable in the United States, and
many have highlighted the need to secure and expand Florida’s Blue Economy.
However, the human and natural infrastructures that sustain it are at risk (FOA,
2020). Water quality is declining (e.g., Brand & Compton, 2007), natural habitats are
being damaged or lost (Parkinson & Wdowinski, 2021), and coastal infrastructure
and communities are increasingly vulnerable to the growing occurrences of bigger
and more frequent extreme weather events (Gao et al. 2012). Within this context,
there is a critical need for geospatial and hydrospatial (Hains et al., 2021) data that
span the land-water interface in Florida to produce maps to understand natural re-
source distribution, examine sea-level rise indicators, and inform decision-making in
contexts such as coastal zone management, navigation, and coastal resilience plan-
Researchers in the School of Forest, Fisheries, and Geomatics Sciences (SFFGS,
Figure 1) at the University of Florida and their partners are at the forefront of
the efforts to address the challenges faced by Florida’s residents and natural sys-
tems. Our three programmatic areas, which focus on forestry, fisheries and aquatic
sciences, and geomatics, allow our scientists and extension agents who transfer
scientific knowledge and expertise to the public to adopt a multidisciplinary and
sometimes interdisciplinary perspective to address these challenges.
Figure 1: The Marine Geomatics Lab of the School of
Forest, Fisheries, and Geomatics Sciences at the University
of Florida and its partners use geospatial technologies and
methods to study the marine and coastal environments,
often within an ecological context.
In this note and through our work, we adopt the ISO/TC 211 definition of geomatics, meaning that
we consider it as the “discipline concerned with the collection, distribution, storage, analysis, pro-
cessing, and presentation of geographic data or geographic information.” Our projects most often
deal with one or many of the components of geomatics, and when mapping the seafloor, our work
is often grounded in principles of hydrography. In fact, the “Marine Geomatics” course that we
teach at the undergraduate and graduate levels is anchored in hydrography as we introduce
students to the technologies, concepts, and methods required to acquire, analyze, and manage
spatial data used in seafloor mapping and imaging. Course content includes instruments and plat-
forms for ocean mapping, multisensor integration, positioning, hydrographic survey design and
standards like S-44 and S-100, and acoustic data processing, interpretation, and management.
Other courses within our curriculum introduce students to more applied contexts such as marine
habitat mapping that make use of marine spatial data, including hydrographic data. This note
provides an overview of how SFFGS works to address Florida’s marine spatial data needs and
related applications (Figure 2).
Figure 2: The location of some of the projects described in this note. Our program, based in Gainesville and
other areas of Florida, has been mapping numerous coastal habitats and estuaries between Apalachicola Bay and Ce-
dar Key. We also produced satellite-derived bathymetry for an area ranging from Tampa Bay to Marco Island, contribut-
ed solutions to map coral reefs in the Florida Keys – all the way to the Dry Tortugas National Park, and we have studied
seafloor environments off Cape Canaveral.
The Florida coast has the highest recreational use (e.g., tourism, fisheries) and has one of the
highest concentrations of coastal communities in the United States. The Intergovernmental Panel
on Climate Change (IPCC) estimates that coastal communities will be particularly affected by cli-
mate change, in particular due to sea-level rise, more frequent heavy precipitation events, fishery
declines, water pollution, and habitat loss (Cheung et al., 2009; Bender et al., 2010; Tobey et al.,
2010; IPCC, 2012). Our School has been working towards documenting the effects of climate
change on Florida and predicting its impacts. We are also teaching our students, tomorrow’s sur-
veyors, how to combine traditional surveying with hydrographic surveying to inform sustainable
coastal development in the context of predicted sea-level rise.
Through sea-level rise and increased storm activity, climate change increases coastal erosion.
The Florida Department of Environmental Protection estimates that more than half of the state’s
sandy shorelines are critically eroded, threatening development, recreational, cultural, and envi-
ronmental interests (FDEP, 2020). A commonly-used method to restore eroded beaches is to per-
form beach nourishment through offshore dredging. However, the impacts of dredging on seafloor
geo- and ecosystems are relatively unknown. The Bureau of Ocean Energy Management
(BOEM) awarded a significant grant to SFFGS’s Dr. Debra Murie and colleagues to quantify the
potential impacts of dredging on seafloor environments and biological communities off Cape
Canaveral. This involved collecting multibeam bathymetric and backscatter data over dredged
and control areas, before and after dredge events, over multiple years. The hydrographic data
were combined with oceanographic data, sediment samples, biological observations, and acous-
tic telemetry data, among other data types, to provide a comprehensive understanding of the sea-
floor habitats and how they changed over time and dredging events. BOEM is still reviewing the
outcomes of this long-term study. However, it demonstrated that hydrography and its concepts,
like spatially-explicit vertical uncertainty, play a critical role in assessing changes in seafloor con-
ditions through time and highlighted how seafloor morphology is directly linked to other compo-
nents of the environment like hydrodynamics and biological communities.
Hydrography and marine geomatics do not only help study the effects of current climate change,
but can also provide insights into changes that have occurred in the past. Towards the end of the
last ice age, the Gulf of Mexico coastline of Florida was roughly 200 km seaward from where it is
today, and by the beginning of the most recent interval of marine transgression, about 5,000
years ago, the shoreline would have been located about 25 km away from the current day coast.
Archaeological sites found along the modern-day coast of Florida suggest that people may have
lived along the former shores of Florida, which are now submerged. Funded by the National Oce-
anic and Atmospheric Administration (NOAA) Office of Ocean Exploration and Research, we
have been working with a team of anthropologists and underwater archaeologists to find evidence
of cultural heritage sites along the Paleo-Suwannee River channel and tidal flats, which were first
located through satellite imagery (Figure 3). This past fall, we used a small autonomous surface
vehicle (Figure 4) equipped with an EdgeTech 2205 sonar to collect bathymetric, backscatter, and
sidescan sonar data in the area. The data are currently being processed in collaboration with
Dr. Anand Hiroji’s team at The University of Southern Mississippi and will be used to identify tar-
gets for a team of underwater archaeologists to investigate. In line with the “collect once and use
many times” spirit that should guide most seafloor mapping efforts, the data will be used to pro-
duce geomorphological maps of the area. Combining them with georeferenced underwater video
data from which we can identify species and substrate types will also facilitate the production of
species distribution models. Since water was a constraining factor during the Pleistocene
(Thulman, 2009), surveying waterways that served as conduits for travel, provided navigational
waypoints, and provided habitats for large and small game resources is critical to identifying cul-
tural heritage sites (Dunbar, 2016). With these waterways now submerged, hydrography is neces-
sary to support research on how peoples responded to social and environmental factors and miti-
gated the effects of past sea-level rise, which could contribute to current resilience efforts in the
face of modern-day sea-level rise (Cooper & Peros, 2010).
Figure 3: Satellite image analysis showing the location of the Paleo-Suwannee River channel, delta, and
tidal flats in relation to the modern-day Suwannee River delta. Analysis by graduate student Matthew Newton.
Figure 4: The SR-Surveyor M1.8 (
class), developed and owned by SeaRobotics, a local Florida company, and used for a couple of our projects. Photo
credit: Brianne Lehan.
In recent years, satellite-derived bathymetry (SDB) has gained significant traction as a relatively
low-cost, broad-coverage, and high temporal resolution means to produce bathymetric data in
coastal areas. Our program is involved with the Satellite-Derived Bathymetry Best Practice
Project Team (SDB-PT) of the International Hydrographic Organization Hydrographic Surveys
Working Group (IHO HSWG), an international effort to identify best practices in satellite-derived
bathymetry in line with the S-44 Standards for Hydrographic Surveys. In parallel to these efforts,
we also contribute to advances in satellite-derived bathymetry methods and applications. For
example, our team developed a framework for SDB analysis applicable to large diversified areas
using multi-date Sentinel-2 satellite imagery and regression-based random forest analysis. We
tested our approach in the nearshore along more than 200 km of coast in southwest Florida. Mod-
calibration and validation were done using airborne lidar bathymetric data. Combining the spectral
bands of multiple historical satellite images yielded the highest accuracy, with root mean square
error values of 8% in waters shallower than 13.5 m, which corresponds to the depth penetration
limit of the lidar surveys used. Since the quality of the satellite imagery is a significant factor influ-
encing the accuracy of the bathymetry estimation, one of our graduate students, Sanduni Mudi-
yanselage, developed a workflow to optimize the selection of Sentinel-2 images to incorporate in
the analysis. In terms of applications, we often integrate SDB within our other projects. For exam-
ple, we are exploring using the acoustic data collected for the underwater archaeology project de-
scribed above as training and validation data for producing a broader-scale bathymetric model of
the area from satellite imagery. Akin to what has been done in land-based archaeology (e.g.,
Chase et al., 2012; Jones et al., 2019), the potential of the produced satellite-derived bathymetry
to serve as proxies of sites of cultural heritage will then be tested.
When studying coastal regions, it is often necessary to combine hydrographic data with terrestrial
elevation data to get a more complete understanding of complex and dynamic environmental
patterns and processes. We have collected a huge amount of unoccupied aerial systems (UAS)
geospatial data in almost 50 field campaigns since 2019 in support of geomatics research and
ecological monitoring efforts along the Florida coast, partnering with universities and local, state,
and federal entities. The UAS imagery and laser scanning data collected represent fine-scale
snapshots of a wide variety of coastal ecosystems that can be used to document and model pro-
cesses occurring due to climate change, sea-level rise, and storm events. In addition to collabora-
tive efforts, our group has independently investigated and developed best practices for UAS data
collection and calibration, uncertainty estimation, and machine learning approaches to extract
highly accurate critical information from the raw data. In particular, we have investigated methods
for precise UAS lidar adjustment, accuracy assessment, range bias mitigation, and the challeng-
ing task of bare earth filtering for vegetated coastal areas (e.g., Pinton et al., 2020, 2021), all cru-
cial for rigorous modeling of fine-scale coastal changes.
Coastal habitats in Florida provide a wide range of critical ecosystem services, for example by
protecting from erosion and storms, providing opportunities for tourism and outdoor activities,
providing habitats for other species, and enhancing local fisheries. Many of these habitats and the
services they provide are facing extreme pressure, such as unsustainable tourism and coastal
development, inadequate protection and management, and pollution. Ongoing restoration and
monitoring efforts often do not have baseline data against which to quantify success or failure.
There is therefore a need for frequent, effective, and comprehensive mapping and monitoring
methods, which marine geomatics can provide. One of the primary challenges when trying to
monitor intertidal habitats is that sometimes they are under the waterline, and sometimes they are
above the waterline, meaning that multiple approaches must be combined to provide a compre-
hensive understanding. In the past few years, our program has contributed to statewide efforts to
develop monitoring approaches that combine optical and acoustic remote sensing for coastal
habitat monitoring.
We have studied many estuaries and creeks off an area of the west coast of Florida called the
Nature Coast, off the towns of Cedar Key and Suwannee. A combination of intertidal oyster reefs,
salt marshes, and mudflats is found in these areas (Figure 5). The different habitat types form a
dynamic ecosystem in which unhealthy oyster reefs can transform into mudflats that salt marshes
can then populate, and accurate temporal monitoring is critical to capture these subtle changes.
Our work combines UAS-derived multispectral imagery (up to ten spectral bands), structure-from-
motion (SfM) photogrammetry digital surface and terrain models (DSMs and DTMs), and lidar da-
ta. So far, we have focused on object-based image analysis for identifying and delineating habitat
types (e.g., Espriella et al., 2020) and on understanding the topographic structure of the different
habitat types using geomorphometry (e.g., Lecours & Espriella, 2020, Figures 6 and 7). Our cur-
rent semi-automated classification efforts reach accuracies in the low 90%, and the integration of
topographic and bathymetric information is likely to improve these even more. The use of DSMs
and DTMs is critical to these efforts as the spectral signature of the different habitat types can of-
ten be similar and vary with wetness levels (e.g., if the tide just retreated). We also demonstrated
that the vertical structure and complexity of intertidal oyster reefs are linked to their health, which
highlights the importance of collecting topographic and bathymetric data. In order to test whether
what we observed in intertidal reefs is also true in subtidal reefs (i.e., those that are permanently
submerged), we have mapped and imaged the Suwannee Reef, an important yet potentially de-
clining reef complex on the west coast of Florida, with an EdgeTech 2205 mounted on a small
autonomous surface vehicle (Figure 2). These hydrographic surveys will provide baseline data to
state partners and contribute to their monitoring efforts while allowing us to test our hypotheses
related to the topographic/bathymetric structure of the reefs. In 2022, we plan to collect underwa-
ter lidar, apply SfM photogrammetry techniques on underwater videos, and apply SDB methodol-
ogies on UAS multispectral data to produce UAS-derived bathymetry. These parallel data collec-
tion efforts will allow us to directly compare fine-scale bathymetric measurements in a structurally
complex environment from many different technologies.
Figure 5: Example of UAS-collected photomosaic showing salt marshes, oyster reefs, and mudflats in Little Trout
Creek, FL. This particular mosaic has a spatial resolution of 6 mm, enabling the capture of fine-scale habitat infor-
mation. Data collected and processed by Andrew Ortega.
Figure 6: A measure of multiscale roughness developed by Lindsay et al. (2019) and applied to a DSM of a
mudflat. The analysis reports the scale at which maximum rugosity levels are quantified and the magnitude of that ru-
gosity. This analysis highlighted individual oysters on top of the mudflat that were not visible solely from the UAS im-
agery and DSM.
Figure 7:A measure of multiscale topographic position (Newman et al., 2018) that characterizes areas that are higher
or lower than their surroundings at various spatial scales captured individual oyster clusters, and highlighted a scar in
the reef caused by a boat propeller, which was not as obvious in the UAS imagery.
While most of our methodological developments occur while studying oyster reefs (due to their
relative proximity to our campus), many of the workflows we develop are transferable to other
habitat-forming species, such as coral reefs. In collaboration with the Florida Fish and Wildlife
Conservation Commission’s (FWC) Coral Reef Evaluation and Monitoring Program, our team is
investigating the use of SfM photogrammetric reconstruction of coral reefs from historical aerial
imagery. The produced DSMs are then used to derive complexity metrics to relate to coral colony
success. The general goal of this work is to fate-track coral colonies through historical imagery to
characterize survivorship and fine-scale topography in chronically disturbed coral reefs in the Dry
Tortugas National Park while also demonstrating the feasibility of using geomatics tools for an-
swering regional-scale ecological questions.
While there has recently been an increase in seafloor mapping efforts in Florida by governments,
academic, and non-profit organizations, there is currently no standardized framework for marine
spatial data and metadata integration and distribution. In 2020, the Florida RESTORE Act Cen-
ters of Excellence Program, managed by the Florida Institute of Oceanography, funded the estab-
lishment of the West Florida Shelf Standardized Mapping Framework Center of Excellence in our
School. The purpose of this effort is to build a process for and get community agreement on a
framework to support effective and dynamic aggregation of current and future seafloor mapping
data. We have been reviewing existing and successful frameworks for marine data integration
and distribution around the world and existing standards, protocols, and guidelines for marine da-
ta collection, integration, and distribution. While standards for hydrographic data and metadata
have been set and are updated periodically at the international and national levels, standards for
other data types like sediment samples or ocean chemistry data are not as widely adopted. Our
work should provide a framework to ensure that all Florida marine geospatial data are digitally
resilient, i.e., accessible, interchangeable, operational, and of reasonable quality for given uses,
thus ensuring that data can be used many times for different applications.
Hydrography is an inherent component of marine geomatics. Without hydrographic data, we could
not understand the primary characteristics of marine habitats, identify navigational hazards follow-
ing hurricanes, or assess coastal vulnerability and resilience. While this note provides an over-
view of our group’s work in marine geomatics, it is important to recognize the contribution of many
other groups that contribute to understanding Florida’s coastal waters using hydrography and ma-
rine geomatics, including different groups at the University of Florida, the Florida Institute of
Oceanography, the Florida Coastal Mapping Program, the Center for Ocean Mapping and Inno-
vative Technologies at the University of South Florida, NOAA’s Mission: Iconic Reef, FWC, and
many other academic, governmental, and non-governmental organizations. Together, we grow
the potential of marine and coastal spatial data to contribute to decision-making in contexts such
as coastal zone management, ecosystem restoration, navigation, and coastal resilience planning.
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revolution and remote sensing LiDAR in Mesoamerican archaeology”, Proceedings of the Na-
tional Academy of Sciences of the United States of America, 109, pp. 12916-12921.
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(2009). “Projecting global marine biodiversity impacts under climate change scenarios”, Fish
and Fisheries, 10, pp. 235-251.
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nal of Archaeological Science, 37, pp. 1226-1232.
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da, Gainesville, FL.
- Espriella, M.C., Lecours, V., Frederick, P.C., Camp, E.V. and Wilkinson, B. (2020).
“Quantifying intertidal habitat relative coverage in a Florida estuary using UAS imagery and
GEOBIA”, Remote Sensing, 12, pp. 1-17.
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viewed 24 February 2022,
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treme weather events in the eastern United States based on a high resolution climate model-
ing system”, Environmental Research Letters, 7, pp. 1-12.
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mania’s cultural landscapes: using habitat suitability modelling of archaeology sites as a land-
scape history tool”, Journal of Biogeography, 46, pp. 2570-2582.
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ceedings of the Geomorphometry 2020 Conference, pp. 111-114.
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Florida coastal wetlands”, SSRN, pp. 1 -37.
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estimating ground elevation and vegetation characteristics in coastal salt marshes from high-
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vation and vegetation characteristics in coastal salt marshes using UAV-based LiDAR and digi-
tal aerial photogrammetry”, Remote Sensing, 13, pp. 1-30.
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ResearchGate has not been able to resolve any citations for this publication.
Full-text available
This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAEVH = 12.6 cm and RMSEVH = 17.5 cm; density: MAEVD = 6.9 stems m−2 and RMSEVD = 9.4 stems m−2) and morphology (MAEM = 4.2 cm; RMSEM = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAEVH = 31.1 cm; RMSEVH = 38.1 cm; MAEVD = 12.7 stems m−2; RMSEVD = 16.6 stems m−2; MAEM = 11.3 cm; RMSEM = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAEVH = 6.9 cm; RMSEVH = 9.4 cm; MAEVD = 10.0 stems m−2; RMSEVD = 14.0 stems m−2). However, it improves when used together with the DAP-UAV point cloud (MAEVH = 21.7 cm; RMSEVH = 25.8 cm; MAEVD = 15.2 stems m−2; RMSEVD = 18.7 stems m−2). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.
Full-text available
Salt marshes are transitional zones between ocean and land, which act as natural buffers against coastal hazards. The survival of salt marshes is governed by the rate of organic and inorganic deposition, which strongly depends on vegetation characteristics, such as height and density. Vegetation also favors the dissipation of wind waves and storm surges. For these reasons, an accurate description of both ground elevation and vegetation characteristics in salt marshes is critical for their management and conservation. For this purpose, airborne LiDAR (Light Detection And Ranging) laser scanning has become an accessible and cost‐effective tool to map salt marshes quickly. However, the limited horizontal resolution (~1 m) of airborne‐derived point clouds prevents the direct extraction of ground elevation, vegetation height, and vegetation density without the coupling with imagery datasets. Instead, due to the lower flight altitude, UAV (Unmanned Aerial Vehicle)‐borne laser scanners provide point clouds with much higher resolution (~5 cm). Although methods for estimating ground level and vegetation characteristics from UAV LiDAR have been proposed for flat ground, we demonstrate that a sloping ground increases prediction errors. Here we derive a new formulation that improves the estimation by employing a correction based on a LiDAR‐derived estimate of local ground slope. Our method directly converts the 3D distribution of UAV‐LiDAR‐derived points into vegetation density and height, as well as ground elevation, without the support of additional datasets. The proposed formulation is calibrated by using measured density and height of Spartina alterniflora in a marsh in Sapelo Island, Georgia, USA, and successfully tested on an independent dataset. Our method produces high resolution (40×40 cm2) maps of ground elevation and vegetation characteristics, thus capturing the large gradients in the proximity of tidal creeks.
Conference Paper
Full-text available
Coastal habitats are of natural, economic, and cultural importance in Florida, and there is a need for effective approaches to map and monitor them. Geographic Object-Based Image Analysis (GEOBIA) was previously applied to an orthomosaic and a Digital Surface Model (DSM) to automatically delineate oyster reef, salt marsh, and mudflat habitats in Little Trout Creek, Florida. Here we evaluated whether a multiscale measure of roughness has the potential to improve this GEOBIA workflow in this context where oysters are spectrally similar to the two other habitat types. Our results show that multiscale roughness can be used to distinguish the different coastal habitat types studied. The level of roughness of mudflats is usually higher at broader scales, and the magnitude of that roughness is relatively small. Marsh roughness was highest at finer scales, and its magnitude was higher compared to other habitat types likely due to marshes' vegetation cover, which is captured in the DSM. The highest magnitudes of roughness for oysters were smaller than, and found at slightly broader scales than, the highest roughness for marshes. Our results were strongly affected by edge effects because the studied habitats are discrete and discontinuous. Multiscale roughness has the potential to help delineate coastal habitats in Florida, but more work is needed to better understand the multiscale topographic patterns of different coastal habitats in Florida and elsewhere.
Full-text available
Intertidal habitats like oyster reefs and salt marshes provide vital ecosystem services including shoreline erosion control, habitat provision, and water filtration. However, these systems face significant global change as a result of a combination of anthropogenic stressors like coastal development and environmental stressors such as sea-level rise and disease. Traditional intertidal habitat monitoring techniques are cost and time-intensive, thus limiting how frequently resources are mapped in a way that is often insufficient to make informed management decisions. Unoccupied aircraft systems (UASs) have demonstrated the potential to mitigate these costs as they provide a platform to rapidly, safely, and inexpensively collect data in coastal areas. In this study, a UAS was used to survey intertidal habitats along the Gulf of Mexico coastline in Florida, USA. The structure from motion photogrammetry techniques were used to generate an orthomosaic and a digital surface model from the UAS imagery. These products were used in a geographic object-based image analysis (GEOBIA) workflow to classify mudflat, salt marsh, and oyster reef habitats. GEOBIA allows for a more informed classification than traditional techniques by providing textural and geometric context to habitat covers. We developed a ruleset to allow for a repeatable workflow, further decreasing the temporal cost of monitoring. The classification produced an overall accuracy of 79% in classifying habitats in a coastal environment with little spectral and textural separability, indicating that GEOBIA can differentiate intertidal habitats. This method allows for effective monitoring that can inform management and restoration efforts.
Full-text available
Aim Understanding past distributions of people across the landscape is key to understanding how people used, affected and related to the natural environment. Here, we use habitat suitability modelling to represent the landscape distribution of Tasmanian Aboriginal archaeological sites and assess the implications for patterns of past human activity. Location Tasmania, Australia. Methods We developed a RandomForest ‘habitat suitability' model of site records in the Tasmanian Aboriginal Heritage Register. We applied a best‐effort bias correction, considered 31 predictor variables relating to climate, topography and resource proximity, and used a variable selection procedure to optimize the final model. Model uncertainty was assessed via bootstrapping and we ran an analogous MaxEnt model as a cross‐validation exercise. Results The results from the RandomForest and MaxEnt models are highly congruent. The strongest environmental predictors of site occurrence include distance to coast, elevation, soil clay content, topographic roughness and distance to inland water. The highest habitat suitability scores are distributed across a wide range of environments in central, northern and eastern Tasmania, including coastal areas, inland water body margins and forests and savannas in the drier parts of Tasmania. With the exception of coastal areas much of western Tasmania has low habitat suitability scores, consistent with theories of low‐density Holocene Tasmanian Aboriginal settlement in this region. Main conclusions Our modelling suggests Tasmanian Aboriginal people occupied a heterogeneity of habitats but targeted coastal areas around the whole island, and drier, less steep and/or open forest and savanna environments in the central lowlands . The western interior was identified as being rarely used by Aboriginal people in the Holocene, with the exception of isolated pockets of habitat; yet whether this is a true reflection of Aboriginal‐resourceuse demands increased archaeological surveys, particularly in the Tasmanian Wilderness World Heritage Area.
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Surface roughness is a terrain parameter that has been widely applied to the study of geomorphological processes. One of the main challenges in studying roughness is its highly scale-dependent nature. Determining appropriate mapping scales in topographically heterogenous landscapes can be difficult. A method is presented for estimating multiscale surface roughness based on the standard deviation of surface normals. This method utilizes scale partitioning and integral image processing to isolate scales of surface complexity. The computational efficiency of the method enables high scale sampling density and identification of maximum roughness for each grid cell in a digital elevation model (DEM). The approach was applied to a 0.5 m resolution LiDAR DEM of a 210 km 2 area near Brantford, Canada. The case study demonstrated substantial heterogeneity in roughness properties. At shorter scales, tillage patterns and other micro-topography associated with ground beneath forest cover dominated roughness scale signatures. Extensive agricultural land-use resulted in 35.6% of the site exhibiting maximum roughness at micro-topographic scales. At larger spatial scales, rolling morainal topography and fluvial landforms, including incised channels and meander cut banks, were associated with maximum surface roughness. This method allowed for roughness mapping at spatial scales that are locally adapted to the topographic context of each individual grid cell within a DEM. Furthermore, the analysis revealed significant differences in roughness characteristics among soil texture categories, demonstrating the practical utility of locally adaptive, scale-optimized roughness.
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This study is the first evaluation of dynamical downscaling using the Weather Research and Forecasting (WRF) Model on a 4 km × 4 km high resolution scale in the eastern US driven by the new Community Earth System Model version 1.0 (CESM v1.0). First we examined the global and regional climate model results, and corrected an inconsistency in skin temperature during the downscaling process by modifying the land/sea mask. In comparison with observations, WRF shows statistically significant improvement over CESM in reproducing extreme weather events, with improvement for heat wave frequency estimation as high as 98%. The fossil fuel intensive scenario Representative Concentration Pathway (RCP) 8.5 was used to study a possible future mid-century climate extreme in 2057–9. Both the heat waves and the extreme precipitation in 2057–9 are more severe than the present climate in the Eastern US. The Northeastern US shows large increases in both heat wave intensity (3.05 °C higher) and annual extreme precipitation (107.3 mm more per year).