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Can multiscale roughness help computer-assisted identification of coastal habitats in Florida?


Abstract and Figures

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.
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Can multiscale roughness help computer-assisted
identification of coastal habitats in Florida?
Vincent Lecours§ & Michael C. Espriella
School of Forest Resources & Conservation
University of Florida
7922 NW 71st Street, Gainesville, Florida, USA, 32606
AbstractCoastal 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.
Coastal habitats like oyster reefs and salt marshes provide
economic opportunities as well as vital ecosystem services such as
shoreline erosion control, habitat and nursery for a variety of
species, and water filtration. However, many of these ecosystem
services are threatened by natural and anthropogenic factors (e.g.,
coastal development, sea-level rise, hurricanes). Mapping and
monitoring coastal habitats are critical to improving scientific
understanding of the complex dynamics of coastal ecosystems, to
better inform management, planning, and conservation efforts.
Florida’s coastal waters are the most economically valuable,
have the highest recreational use, and have one of the highest
concentrations of coastal communities in the United States. At a
2007 workshop, regional, state, and federal partners concluded that
although mapping coastal resources was a top priority, the lack of
a standard, reproducible approach was hindering broad-scale
efforts [1]. With the increased likelihood of extreme weather
events [2] that have the potential to impact coastal habitats in
Florida [3], there is a critical need to develop an effective and
reproducible mapping and monitoring workflow that can be used
to answer questions in a variety of contexts (e.g., sea-level rise,
community resilience, hurricane impact assessments).
In a recent article, Espriella et al. [4] proposed a reproducible
approach to detect and delineate three types of coastal habitats
oyster reefs, salt marshes, and mudflats in imagery collected with
Unoccupied Aircraft Systems (UAS). The approach is centered on
a two-level Geographic Object-Based Image Analysis (GEOBIA)
[5] that first identifies and extracts water areas from the data before
classifying the remaining objects into their respective habitats.
Both the RGB mosaic and the Digital Surface Model (DSM),
produced using structure-from-motion photogrammetry, were
used as inputs. However, with an overall classification accuracy of
79%, that GEOBIA alone may not be robust enough for accurate
temporal monitoring. Oysters had the lowest overall separability
from the other habitats, which is problematic from a management
perspective; oysters are one of the most important living coastal
resources actively managed in the Gulf of Mexico, are suffering
from major declines in the area, and thus are of particular interest.
In nature, oyster reefs are more structurally complex than
marsh and mudflats, at multiple scales. Therefore, we hypothesize
that geomorphometry can provide a means to help differentiate
these habitats from each other. While Espriella et al. [4] derived
local measures of terrain attributes (e.g., rugosity, relative
position) at multiple independent spatial scales [6] using relatively
few search neighborhoods [7], their feature-space optimization to
select the variables best fit to recognize the different habitats did
not identify any DSM-derived variables as being relevant. Here,
we evaluate the potential of a multiscale measure of roughness [8]
as opposed to independent measures of roughness derived at
multiple scales to help distinguish oyster reefs, salt marshes, and
mudflats from each other.
Vincent Lecours and Michael Espriella (2020) Can multiscale roughness help computer-assisted identification of coastal habitats in Florida?:
in Massimiliano Alvioli, Ivan Marchesini, Laura Melelli & Peter Guth, eds., Proceedings of the Geomorphometry 2020 Conference, doi:10.30437/GEOMORPHOMETRY2020_24.
UAS imagery was collected on December 8th, 2018 at low tide,
at the mouth of Little Trout Creek (29° 15 34.98” N, 83° 4 29.68”
W), on the west coast of Florida (Fig. 1). The imagery was
collected at nadir using a DJI Inspire 2 equipped with a Zenmuse
X7 35 mm RGB sensor. The UAS was flown 60 m above ground
level, with an 80% along-track overlap and 75% across-track
overlap. Four checkered targets were evenly distributed across the
scene and located using a Trimble 5800 real-time kinematic
positioning system. In addition to the orthomosaic, a DSM was
produced using structure-from-motion photogrammetry in Pix4D
Mapper v. 4.2.27. The total area surveyed covered approximately
0.116 km2 and provided data with a 0.66 cm spatial resolution
(Fig. 2), with a root mean square error of 0.3 cm in longitude and
latitude, and 0.1 cm in elevation for the residuals of control points.
The GEOBIA ruleset of Espriella et al. [4] was applied to the
data. The resulting classification was used to extract 37 oyster
reefs, 22 salt marshes, and 33 mudflats areas from the DSM. The
areas were selected because they were fully encompassed within
the extent of the data (i.e., no areas from the boundaries of the
mosaic). Each extracted area was entered in the “Multiscale
Roughness” tool of WhiteboxTools v. 1.1.0 [9], with search
neighborhood radii ranging from 1 grid cell to 9,751 grid cells,
which corresponds to the length of the longest feature (a salt marsh
of about 64 m; cf. center of Fig. 2 and Fig. 3). The tool produced
two main outputs: a raster that indicates, for each pixel, the size of
the search neighborhood at which the measured roughness was the
highest, and a raster displaying the magnitude of the measured
roughness at that scale. Descriptive statistics of the two output
types were calculated for each habitat type.
Figure 3 presents the GEOBIA classification results and the
scale and magnitude outputs for all the studied habitats. The
spatial distributions of the scale and magnitude values seem to be
influenced by the geometry of the features and the quality of the
DSM. For instance, high-magnitude values were found on long
and narrow features, and broader-scale values were found in areas
of interpolation artifacts where the presence of water affected
DSM production. In general, magnitude is the most promising
output to differentiate the three studied coastal habitats (Fig. 3);
patches of mud displayed a much lower magnitude than other
habitat types, which was expected considering the less complex
nature of mudflats, and salt marshes displayed a much higher
magnitude than other habitat types, likely because of the presence
of a vegetation cover captured in the DSM. Oyster reefs, which
are the most heterogeneous habitats, had intermediate magnitudes
of roughness at intermediate scales.
These observations are confirmed by the analysis of the
statistical distributions of scale and magnitude (Tab. 1). On
Geomorphometry 2020 Lecours and Espriella
average, the scale of maximum roughness was broader for
mudflats than for oysters and marshes. However, averages are
likely biased by outliers caused by edge effects: the skewness of
the distributions for scale shows that they are highly skewed for
marsh and oysters, and moderately skewed for mudflats.
Distributions of scale for marshes and oysters are leptokurtic, and
that of mudflats is platykurtic. The distributions of magnitudes for
mudflats and oysters are heavily skewed, with a high and sharp
peak and long and fat tails caused by many outliers. The
distribution of magnitudes for marshes is relatively symmetrical
but platykurtic, with a short and thin tail. Given these results, we
do not expect that the averages presented in Tab. 1 are fully
representative. We adjusted them by manually removing outliers
from the distribution and obtained revised averages for scale of
1,231.66 (≈9.4 m) for marshes, 1,980.06 (≈19.6 m) for mudflats,
and 1879.68 (≈15.0 m) for oysters. However, the median values
suggest that patterns of highest roughness can be found at about
1.5 m for marshes, 12.9 m for mudflats, and 5.3 m for oysters,
which is more consistent with what can be observed in the field
in terms of habitat complexity and habitat patch size. Setting these
results into the natural context can thus serve as additional
evidence that edge effects influenced some of the statistics (e.g.,
average, standard deviation). In fact, the cumulative distributions
of scale for all habitat types showed a stabilization in slope
between 125 and 400, which corresponds to 0.8 to 2.6 m,
indicating that most of the high measured roughness would be
found at scales finer than 3 m.
It is noteworthy that all habitat types reached a local peak of
maximum roughness at search radii of 30 cells (oysters and mud)
or 33 cells (marsh), which corresponds to 21±1 cm. While this is
an interesting result, it should be interpreted with caution: given
the different natures of the habitat types, it is improbable that they
display local roughness at almost the same exact scale (with a
precision of 6 mm). A possible explanation is that intrinsic noise
is present in the DSM at this specific scale range and captured by
the analysis. In terms of magnitude, the statistical distributions
confirm that magnitudes of roughness are generally smaller for
mudflats and higher for marshes.
This work is an initial exploration of the potential of measures
of multiscale topographic patterns to help identify coastal
habitats. However, limitations include the use of the results of an
imperfect spectral-based GEOBIA classification to guide the
selection of habitat features for this analysis. For instance, Fig. 4
shows that one of the objects identified as marsh is partly
misclassified: only the central section of this object is a vegetated
salt marsh the surrounding habitat is oysters. However, the
entirety of this area was considered as marsh for the analysis
because it was based on the objects defined and classified by the
GEOBIA workflow. Both the scale and magnitude of the
multiscale roughness captured that difference, with the marsh
having a finer-scale roughness of higher magnitude than the
surrounding oysters. This directly highlights the potential of these
Marsh Mud Oyster Marsh Mud Oyster
Number of Cells 154,945,348 238,774,206 438,236,795 154,945,348 238,774,206 438,236,795
Minimum 1 1 1 3.90 2.18 3.31
Maximum 9,750 9,750 9,750 108.65 120.51 101.21
Average 1,428.40 2,973.09 2,276.70 21.34 7.90 10.77
Median 229 1,948 804
25.76 6.30 9.82
Standard Deviation 2,287.13 3,127.44 2,943.04 11.01 5.64 5.47
Variance 5,230,967.94 9,780,854.37 8,661,511.55 121.33 31.76 29.88
Skewness 2.00 0.87 1.30 -0.10 2.91 3.15
Kurtosis 6.19 2.51 3.48 1.77 14.55 17.43
Geomorphometry 2020 Lecours and Espriella
measures to augment the GEOBIA and differentiate coastal
habitats in Florida.
Another limitation of this work is that each of the 92 features
studied was analyzed independently, which artificially increased
edge effects. The complex dynamics of this coastal ecosystem
mean that oysters can be directly adjacent to mudflats and
marshes. As such, analyzing a patch of multiple habitats as one,
then separating it into different habitats post-analysis before
computing statistics could have reduced the influence of edge
effects. However, this would be very computationally intensive
given the size of the DSM (24 GB).
Coastal geomorphometry has recently been identified as a
future application of geomorphometry that will present challenges
due to the presence of features both over and under the waterline
[10]. Here we presented such an application, and these challenges
were highlighted by a strong influence of edge effects and feature
geometry that artificially increased the average scale at which
maximum roughness was observed and the average magnitude of
that roughness. However, we concluded that mudflats display
relatively smaller amplitudes of roughness over broader scales
and that salt marshes display the highest roughness over relatively
finer scales. Oyster reefs showed intermediate patterns of
roughness, with both amplitudes and scales between those of the
two other habitat types. While we hypothesized that oyster reefs
would show the highest roughness at the finest scales, the finer-
scale patterns of salt marshes may be explained by the presence
of characteristic vegetation on the marshes, which creates
relatively high roughness patterns in the DSM. Future work
should repeat the analyses using a Digital Terrain Model (DTM)
instead of a DSM. In theory, the DTM would preserve the
complex fine-scale structures of oyster reefs while omitting the
vegetation over salt marshes that created local roughness.
Denoising algorithms could also be applied to the models to
ensure that the multiscale analyses capture the scales at which
patterns are observed rather than the noise in the data. Finally,
because we demonstrated that multiscale roughness shows
potential to help differentiate coastal habitat types from each
other, we recommend evaluating the suitability of other
multiscale geomorphometric measures, such as multiscale
topographic position [11], multiscale maximum spherical
standard deviation [8], multiscale maximum difference from
mean elevation [11], and multiscale topographic anisotropy [12]
for the identification of coastal habitats.
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Geomorphometry 2020 Lecours and Espriella
... DSMs were first smoothed using a low-pass filter (3 × 3 neighborhood window) and clipped using a negative 1 m buffer from the edge to remove potential artifacts and outliers. Terrain metrics, which are frequently used to quantify structural complexity and shape biotic communities on reefs or indicate ecosystem health and stability, e.g., [17,38,40,[73][74][75], were derived from the DSMs using a 3 × 3 pixels (4 mm) neighborhood window ( Table 1). The vector ruggedness measure (VRM) was also derived based on a 5 cm neighborhood window [37]. ...
... Multiscale metrics such as MR mitigate the shortcomings associated with arbitrary scale selection. [38,40,77] Roughness scale ...
... The second output of MultiscaleRoughness, which determines the filter radius (spatial scale; r) associated with the greatest roughness value that identifies the spatial scale at which σ max is expressed. [38,40,77] Profile curvature ...
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The complexity of tropical reef habitats affects the occurrence and diversity of the organisms residing in these ecosystems. Quantifying this complexity is important to better understand and monitor reef community assemblages and their roles in providing ecological services. This study employed structure-from-motion photogrammetry to produce accurate 3D reconstructions of eight reefs in Guam and quantified the structural complexity of these sites using seven terrain metrics: rugosity, slope, vector ruggedness measure (VRM), multiscale roughness (magnitude and scale), plan curvature, and profile curvature. The relationships between terrain complexity, benthic community diversity, and coral cover were investigated with generalized linear models. While the average structural complexity metrics did not differ between most sites, there was significant variation within sites. All surveyed transects exhibited high structural complexity, with an average rugosity of 2.28 and an average slope of 43 degrees. Benthic diversity was significantly correlated with the roughness magnitude. Coral cover was significantly correlated with slope, roughness magnitude, and VRM. This study is among the first to employ this methodology in Guam and provides additional insight into the structural complexity of Guam's reefs, which can become an important component of holistic reef assessments in the future.
... Conversely, marsh and oyster habitats produced the highest user accuracies at coarser resolutions of 23 and 29 cm, respectively. Selecting a proper observational scale is an ongoing area of research as no one scale is appropriate for the study of all ecological processes [19,[55][56][57]. Multiscale workflows can help address the limitations of single-scale workflows by highlighting those that are most informative, a consideration that is as important as variable selection [19]. ...
... Multiscale workflows can help address the limitations of single-scale workflows by highlighting those that are most informative, a consideration that is as important as variable selection [19]. Lecours and Espriella (2020) suggest that multiscale roughness metrics can help delineate intertidal habitats [57]. The study finds that mudflats present a comparatively lower magnitude of roughness, with the highest roughness values captured at coarse scales. ...
... Multiscale workflows can help address the limitations of single-scale workflows by highlighting those that are most informative, a consideration that is as important as variable selection [19]. Lecours and Espriella (2020) suggest that multiscale roughness metrics can help delineate intertidal habitats [57]. The study finds that mudflats present a comparatively lower magnitude of roughness, with the highest roughness values captured at coarse scales. ...
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... These metrics produced rasters which were derived using the geodiv, glcm, MultiscaleDTM, spatialEco, terra, and whitebox packages in R v4.1 (R core team 2021). Complexity rasters were derived at the reef extent (i.e., using the DSM of the entire reef), mitigating concerns of using discrete and discontinuous habitat patches and minimizing edge effect ( Fig. 4; Lecours and Espriella, 2020). Each quadrat subsection was then extracted from the surface complexity raster using RTK recorded corners and midpoints between corners, as done above. ...
... Multiscale metrics mitigate the shortcomings of arbitrarily selecting a scale of analysis (Lecours et al., 2015;Misiuk et al., 2018). Lecours and Espriella (2020) used the 'multiscale roughness' tool (Lindsay, 2016) at the reef scale (i.e., allowing for analysis scales up to the length of the reef) and found that the highest magnitude Table 3 Top six models generated from the model selection procedure. AICc refers to Akaike Information Criterion adjusted for small sample size, SE refers to standard error, and Pr(<z) reports the p-value. ...
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... Similarly, several studies have applied geostatistical methods to examine the relationship between physical parameters (relief) and seafloor biological features to define habitats with similar relief characteristics, such as flat areas, slopes, caves, and depressions, allowing association with distribution patterns of communities and species. (Brown et al., 2011;Lecours and Espriella, 2020;Menandro et al., 2020). ...
... In the race to best characterize and track all fine-scale 3D processes occurring in dynamic habitats such as biogenic formations [38], structure-for-motion (SfM) photogrammetry [35] is an essential tool. However, topographic analysis has not been applied frequently to entire intertidal reefs, with notable exception of [39], even though topographic metrics have been successfully applied to small-scale (meter) reef hummocks [40]. Metrics based on landform analysis [41][42][43][44] could be useful in quantifying reef topographic complexity, which is influenced by reef health and dynamic status. ...
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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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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.
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Several landforms are known to exhibit topographic anisotropy, defined as a directional inequality in elevation. The quantitative analysis of topographic anisotropy has largely focused on measurements taken from specific landforms, ignoring the surrounding landscape. Recent research has made progress in measuring topographic anisotropy as a distributed field in natural landscapes. However, current methods are computationally inefficient, as they require specialized hardware and computing environments, or have a limited selection of scales that undermines the feasibility and quality of multiscale analyses by introducing bias. By necessity, current methods operate with a limited set of scales, rather than the full distribution of possible landscapes. Therefore, we present a method for measuring topographic anisotropy in the landscape that has the computational efficiency required for hyperscale analysis by using the integral image filtering approach to compute oriented local topographic position (LTP) measurements, coupled with a root-mean-square deviation (RMSD) model that compares directional samples to an omnidirectional sample. Two tools were developed: One to output a scale signature for a single cell, and the other to output a raster containing the maximum anisotropy value across a range of scales. The performances of both algorithms were tested using two data sets containing repetitive, similarly sized and oriented anisotropic landforms, including a dune field and a drumlin field. The results demonstrated that the method presented has the robustness and sensitivity to identify complex hyperscale anisotropy such as nested features (e.g., a drumlin located within a valley).
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Geomorphometry, the science of quantitative terrain characterization, has traditionally focused on the investigation of terrestrial landscapes. However, the dramatic increase in the availability of digital bathymetric data and the increasing ease by which geomorphometry can be investigated using geographic information systems (GISs) and spatial analysis software has prompted interest in employing geomorphometric techniques to investigate the marine environment. Over the last decade or so, a multitude of geomorphometric techniques (e.g. terrain attributes, feature extraction, automated classification) have been applied to characterize seabed terrain from the coastal zone to the deep sea. Geomorphometric techniques are, however, not as varied, nor as extensively applied, in marine as they are in terrestrial environments. This is at least partly due to difficulties associated with capturing, classifying, and validating terrain characteristics underwater. There is, nevertheless, much common ground between terrestrial and marine geomorphometry applications and it is important that, in developing marine geomorphometry, we learn from experiences in terrestrial studies. However, not all terrestrial solutions can be adopted by marine geomorphometric studies since the dynamic, four-dimensional (4-D) nature of the marine environment causes its own issues throughout the geomorphometry workflow. For instance, issues with underwater positioning, variations in sound velocity in the water column affecting acoustic-based mapping, and our inability to directly observe and measure depth and morphological features on the seafloor are all issues specific to the application of geomorphometry in the marine environment. Such issues fuel the need for a dedicated scientific effort in marine geomorphometry. This review aims to highlight the relatively recent growth of marine geomorphometry as a distinct discipline, and offers the first comprehensive overview of marine geomorphometry to date. We address all the five main steps of geomorphometry, from data collection to the application of terrain attributes and features. We focus on how these steps are relevant to marine geomorphometry and also highlight differences and similarities from terrestrial geomorphometry. We conclude with recommendations and reflections on the future of marine geomorphometry. To ensure that geomorphometry is used and developed to its full potential, there is a need to increase awareness of (1) marine geomorphometry amongst scientists already engaged in terrestrial geomorphometry, and of (2) geomorphometry as a science amongst marine scientists with a wide range of backgrounds and experiences.
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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).
Technical Report
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The importance of mapping habitats and bioregions as a means to improve resource management has become increasingly clear. Large areas of the waters surrounding Florida are unmapped or incompletely mapped, possibly hindering proper management and good decision-making. Mapping of these ecosystems is among the top priorities identified by the Florida Oceans and Coastal Council in their Annual Science Research Plan. However, lack of prioritization among the coastal and marine areas and lack of coordination of agency efforts impede efficient, cost–effective mapping. A workshop on Mapping of Florida’s Coastal and Marine Resources was sponsored by the U.S. Geological Survey (USGS), Florida Department of Environmental Protection (FDEP), and Southeastern Regional Partnership for Planning and Sustainability (SERPPAS). The workshop was held at the USGS Florida Integrated Science Center (FISC) in St. Petersburg, FL, on February 7-8, 2007. The workshop was designed to provide State, Federal, university, and non-governmental organizations (NGOs) the opportunity to discuss their existing data coverage and create a prioritization of areas for new mapping data in Florida. Specific goals of the workshop were multifold, including to: provide information to agencies on state-of-the-art technology for collecting data; inform participants of the ongoing mapping programs in waters off Florida; present the mapping needs and priorities of the State and Federal agencies and entities operating in Florida; work with State of Florida agencies to establish an overall priority for areas needing mapping; initiate discussion of a unified classification of habitat and bioregions; discuss and examine the need to standardize terminology and data collection/storage so that data, in particular habitat data, can be shared; identify opportunities for partnering and leveraging mapping efforts among agencies and entities; identify impediments and organizational gaps that hinder collection of data for mapping; seek innovative solutions to the primary obstacles identified; identify the steps needed to move mapping of Florida’s oceans and coasts forward, in preparation for a better coordinated, more cost-effective mapping program to allow State and Federal agencies to make better decisions on coastal-resource issues. Over 90 invited participants representing more than 30 State and Federal agencies, universities, NGOs, and private industries played a large role in the success of this two-day workshop. State of Florida agency participants created a ranked priority order for mapping 13 different regions around Florida. The data needed for each of the 13 priority regions were outlined. A matrix considering State and Federal priorities was created, utilizing input from all agencies. The matrix showed overlapping interests of the entities and will allow for partnering and leveraging of resources. The five most basic mapping needs were determined to be bathymetry, high-vertical resolution coastline for sea-level rise scenarios, shoreline change, subsurface geology, and benthic habitats at sufficient scale. There was a clear convergence on the need to coordinate mapping activities around the state. Suggestions for coordination included: creating a glossary of terms: a standard for specifying agency data-mapping needs; creating a geographic information officer (GIO) position or permanent organizing group to maintain communications established at this workshop and to maintain progress on the issues identified during the workshop. The person or group could develop a website, maintain a project-status matrix, develop a list of contacts, create links to legislative updates and links to funding sources; developing a web portal and one-stop/clearinghouse of data. There was general consensus on the need to adopt a single habitat classification system and a strategy to accommodate existing systems smoothly. Unresolved aspects of the systems warrant that a separate workshop would be needed to work out details. Participants recognized that the State priority list would necessarily be updated periodically. An annual review of priorities would facilitate information exchange, mapping activities updates, and re-allocation of funding among changing priorities. It was recognized that mapping of State waters would take billions of dollars and in light of tightening budgets there was need for processes that could be used to appropriate or leverage monies for mapping and reduce data-collection costs. Fourteen different avenues were explored. There was a clear consensus that the linking of public to private partnerships to support mapping was imperative, and ways to achieve this were discussed.
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Mountain peaks are mapped as multi‐scale entities with modifiable boundaries and variable contents. Four semantic meanings are imported and quantified to first characterize peaks at a range of spatial scales and then evaluate the multi‐criteria ‘peakness’ at each scale. Peakness is defined as the prototypicality of identified summits and as the similarity of each point (cell) to summits. The procedure then summarizes the individual‐scale peakness across considered spatial scales into a univariate membership surface. This allows mapping of vague peak entities as non‐homogeneous peak regions whose boundaries depend on user‐specified peakness thresholds. This procedure was applied in a case study to tackle several challenges in landform delineation, including boundary, spatial continuity, spatial scale, topographic context, and multi‐criteria definition.
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Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of 'grey' literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.
CASQUS is a numerical simulation tool to model the feedback mechanism between surface and tectonic processes. It includes the surface processes model CASCADE into the finite element solver ABAQUS/Standard(TM). The finite element method allows for geomechanical ...