Temple University Assessing Patterns of Annual Change Article Analysis i have attached the article and the format that i want my answer in. please follow t

Temple University Assessing Patterns of Annual Change Article Analysis i have attached the article and the format that i want my answer in. please follow the format attached strictly to provide an answer. it doesn’t have to be very long 2-5 pages is enough. Geomorphology 336 (2019) 152–164
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/geomorph
Assessing patterns of annual change to permafrost bluffs along the North
Slope coast of Alaska using high-resolution imagery and
elevation models
Ann E. Gibbs a,⁎, Matt Nolan b, Bruce M. Richmond a, Alexander G. Snyder a, Li H. Erikson a
U.S. Geological Survey, Pacific Coastal and Marine Science Center, 2885 Mission Street, Santa Cruz, CA 95060, USA
Fairbanks Fodar, P.O. Box 82416, Fairbanks, AK 99708, USA
a r t i c l e
i n f o
Article history:
Received 5 October 2018
Received in revised form 20 March 2019
Accepted 25 March 2019
Available online 29 March 2019
Coastal change
Permafrost bluffs
Arctic Alaska
Beaufort Sea
a b s t r a c t
Coastal permafrost bluffs at Barter Island, on the North Slope, Beaufort Sea Coast of Alaska are among the most
rapidly eroding along Alaska’s coast, having retreated up to 132 m between 1955 and 2015. Here we quantify
rates and patterns of change over a single year using very-high resolution orthophotomosaics and coregistered surface elevation models derived from a survey-grade form of structure-from-motion photogrammetry from a fixed-wing, manned aircraft. The resulting elevation models were validated with over 10,000 ground
check points and found that 95% agreed to within 20 cm, before accounting for real differences in the ground surface due to seasonality, vegetation, and checkpoint acquisition errors. This data set provides the most detailed
and accurate measurements of coastal change to date along the Alaskan coast and the method is scalable to
more extensive coastlines. Between July 2014 and July 2015, the bluffs retreated an average of 1.3 m, and a maximum of 8.1 m, with an associated net volume loss of 38,100 ± 300 m3 (1.3 m3/m). This average retreat over this
single year was slightly less than the 60-year mean rate of change of −1.5 ± 0.1 m/yr, suggesting the 2014–
2015 year represented relatively typical to slightly below average conditions. Most of the bluff material (70%)
was lost during the 3 summer months (July to Sept) of 2014 and the remaining 30% between the late-summer
and following winter-spring. The pattern of change was predominantly landward retreat of the top of the bluffs,
removal of the debris apron and subsequent niching at the base of the bluffs during mid to late summer (July to
Sept) followed by erosion of the bluff face and deposition of debris at the base of the bluff through the remainder
of the year (Sept to the following July). Drivers of the observed change are likely a combination of thermal erosion
on the bluff face throughout the summer and episodic thermo-mechanical removal of material, niching, and undercutting of the base associated with high-water levels driven by low-pressure storms and westerly winds.
These patterns and high rates of change are believed to be broadly representative of coastal permafrost bluffs
found along many high-latitude coastlines worldwide.
Published by Elsevier B.V. This is an open access article under the CC BY license
1. Introduction
Arctic permafrost coasts represent about 1/3 of the world’s coastlines (Lantuit et al., 2012) and are unique from temperate coasts in
that their behavior is strongly influenced by the presence of interstitial
ice and permafrost, which provide cohesion to relatively unconsolidated material, and the presence, absence, and duration of seasonal
sea-ice (Aré, 1988; Reimnitz et al., 1988). Processes driving coastal
change in permafrost environments can be fundamentally reduced to
mechanical and thermal processes and shorelines anchored by
⁎ Corresponding author.
E-mail addresses: agibbs@usgs.gov (A.E. Gibbs), matt@fairbanksfodar.com (M. Nolan),
brichmond@usgs.gov (B.M. Richmond), agsnyder@usgs.gov (A.G. Snyder),
lerikson@usgs.gov (L.H. Erikson).
permafrost-rich substrates are highly vulnerable to climate change, as
increasing temperatures and duration of the ice-free season and degradation of permafrost can lead to accelerated rates of coastal erosion
(Overeem et al., 2011; Barnhart et al., 2014; Jones et al., 2018). Despite
this vulnerability, our understanding of how these coastal environments change through time is poor due to the remoteness of these locations, limited windows of opportunity for observation (limited daylight
and ice-free conditions), poor geodetic infrastructure, scarcity of historical data, and other data collection challenges. Recent advances in
coastal change and, in particular, permafrost bluff change research in
the Arctic, for example in Western Siberia (Isaev et al., 2018; Novikova
et al., 2018), Svalbard (Guégan and Christiansen, 2017), and the Canadian Arctic (Obu et al., 2017; Irrgang et al., 2018) have improved the
overall scientific understanding of the diverse forms and rates of coastal
change across the Arctic, however, high quality, highly accurate, and
0169-555X/Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
A.E. Gibbs et al. / Geomorphology 336 (2019) 152–164
temporally robust field data from which to confidently assess and/or
impacts of climate change are still lacking (Lantuit et al., 2013;
Overduin et al., 2014). Extreme erosion of the Alaskan Beaufort Sea
coast has been observed for well over a century, with estimates of highly
variable erosion rates on the order of 1 to 10 m per year (m/yr),
witnessed by Ernest Leffingwell during his 1906–1914 expeditions
(Leffingwell, 1919). More recently, using topographic maps,
orthoimagery, and lidar data, Gibbs and Richmond (2015, 2017), calculated an average long-term shoreline change rate of −1.8 ± 0.04 m/yr
along the Alaskan Beaufort Sea coast between 1947 and 2012, and
with much higher rates (up to 22 m/yr of retreat and 11 m/yr of
deposition) measured at some locations. Lantuit et al., 2012 summarized coastal change rates for the approximately 25% of the Arctic
coast where coastal change rates were measured and found that arctic
coastlines are eroding at an average rate of 0.5 m/yr, but with considerable local and regional variability. The highest mean rates of erosion
were measured in the Laptev (0.73 m/yr), East Siberian (0.87 m/yr),
and Canadian and American Beaufort Seas (1.12 and 1.15 m/yr, respectively). These long-term rates are more than three times higher than for
most other predominantly erosional, temperate coasts of the U.S.
(Hawaii, Fletcher et al., 2012; New England and Mid-Atlantic, Hapke
et al., 2011; and the Gulf and Southeast, Himmelstoss et al., 2017).
Only along the Gulf Coast of Louisiana are mean erosional long-term
shoreline change rates higher (−7.7 ± 0.9 m/yr from 1853 to 2001),
primarily due to the migration of barrier islands and permanent loss
of wetlands thought to be associated with submergence and destruction
of the Mississippi River delta plain (Reimnitz et al., 1988; Penland et al.,
1990; Morton et al., 2004; Himmelstoss et al., 2017). When comparing
annually averaged rates, however, it’s important to recognize that in
contrast to temperate environments where coastal processes can modify the coast for 12 months of the year, most of the coastal change
in high-latitude permafrost environments occurs only during the
~3-month long, ice-free season when winds, waves, and coastal currents can act directly on an exposed coast (Wiseman Jr. et al.,
1973; Hopkins and Hartz, 1978). As the duration of ice-free conditions increases, the potential for greater coastal change increases,
especially as coastal sea-ice forms later in the year, leaving the
coast vulnerable to the impacts of large fall storms.
Long-term, multi-year rate change analyses provide important information on overall trends and patterns and can be compared with
rates of change from other locations, however, details on timing and
processes and an understanding the interplay between the two in the
overall pattern of change requires detailed observation and measurements acquired at appropriate time scales. For example, is erosion gradual or episodic, and under what circumstances? Or, are erosion rates
accelerating and what are the significant processes driving the change?
The ability to quantify coastal change and understand these patterns
and drivers at appropriate time scales is important for modeling and
projecting future coastal behavior, especially as arctic air and water
temperatures increase and stronger and more frequent storms can impact the coast later in the season as seasonal sea-ice forms later and
melts earlier (Barnhart et al., 2014). Along the extensive and remote
coast of Alaska, high-quality imagery and elevation data are historically
rare, in part because traditional methods of acquiring the data are cost
prohibitive. Recent advances in digital photogrammetric technology, including improvements to consumer-grade cameras, GPS processing
techniques, desktop computer processing capabilities, and the development of powerful photogrammetric software using Structure-fromMotion (SfM) algorithms (Koenderink and van Doorn, 1991; Westoby
et al., 2012; Nolan et al., 2015; Nolan and DesLauriers, 2016), allow
for improved mapping and analysis of coastal change in 3-dimensions
for a fraction of the cost of traditional photogrammetric or lidar
The goal of this paper is to document and quantify an annual pattern
of 2- and 3-dimensional change to the coastal permafrost bluffs at Barter
Island, Alaska using repeat, airborne orthoimagery and coincident
digital surface elevation models (DSMs) derived using SfM photogrammetric methods and precise positioning. The technique is not only well
suited to rapidly acquiring high quality data in remote locations, but can
also be applied in most environments for a fraction of the cost of lidar or
traditional photogrammetric acquisition and where ground- or unmanned aircraft system (UAS)-based surveys are impractical. Using
time-lapse cameras and water level and temperature measurements
we infer the timing of observed change and discuss implications in the
context of sediment budget, annual signals, and relative mechanisms
of change. Despite the limited time span of this study, the results shed
light on the rapid rates of coastal change along high-latitude shorelines
where changing climate conditions have the potential to alter the behavior of these coastal environments, particularly through accelerated
thermal degradation and increased mechanical erosion during extended periods of ice-free, open-water conditions.
2. Study location
Barter Island, considered the gateway to the Arctic National Wildlife
Reserve (ANWR), is located on the northeast coast of Alaska approximately 120 km west of the U.S.-Canadian border on the Alaskan Beaufort Sea coast (Fig. 1). The island has a long history of Inupiat
occupation and was a major trade center until the late 19th century
(State of Alaska, 2015). The Native Village of Kaktovik on the north
shore of Barter Island was incorporated as a city in 1971 and had a population of 239 as of the 2010 census. Like other Native Villages in northern Alaska, subsistence hunting, fishing and whaling play a major role in
the local economy and lifestyle. In the 1950s, the U.S. Air Force built an
airstrip and Defense Early Warning Line (DEW) radar station on the island. The DEW site was deactivated in 1989 and in 1990 upgraded to
part of the North Warning System Long Range Radar Site (LRRS)
which continues operations today. Access to the island is limited to
boats, barges, and aircraft – there are no permanent roads leading to
the island (Fig. 1).
The village of Kaktovik and adjacent U.S. Air Force radar site are
fronted by eroding coastal permafrost bluffs that range in height from
a few meters to more than ten meters high. Barter Island, like the entire
North Slope of Alaska, is in a zone of continuous permafrost, with a shallow active layer (generally b1 m deep) that is subject to thawing during
the summer season. The coastal permafrost bluffs are poorly consolidated and consist of a complex sequence of material ranging from
dense clay, interbedded sand and gravel, massive sand, widespread
ground ice including ice wedges, segregated ice layers, massive ice,
and thermokarst cave ice, and a surface peat layer (Rawlinson, 1993;
Jorgenson and Brown, 2005; Kanevskiy et al., 2013). Aerial lidar elevation data obtained in 2009 (U.S. Geological Survey, 2015) revealed
~10 m high bluff elevations across the central portion of the island
where field observations of bluff stratigraphy showed multi-layered
Fig. 1. Lidar derived elevation map of Barter Island showing locations described in the text.
Solid white box outlines the bluff study area and location of Fig. 5. Dashed white box
outlines the boundary of Fig. 3.
A.E. Gibbs et al. / Geomorphology 336 (2019) 152–164
stratification. The lower elevation outer flanks of the exposed bluff face
consist of layers of sandy-silt below the surface peat layer. The island
has broad, low-lying (b 2 m high) sand and gravel barrier spits extending both east and west from the topographically higher tundra hinterland of the island.
The Beaufort Sea is typically covered with sea ice from approximately October through June. Prevailing winds are easterly and average
near 21 km/h (13 miles/h) with little annual variation (Western
Regional Climate Center, 2008; Zhang et al., 2016). The region is
microtidal with a mean diurnal range of 18 cm (0.6 ft.; NOAA, 2017a),
however, water levels can become elevated or depressed up to several
meters due to winds and low-pressure systems; westerly winds tend
to elevate water levels, whereas easterly winds tend to lower water
levels (Reimnitz and Maurer, 1979; Sultan et al., 2011; Erikson et al.,
in press).
Using topographic maps, orthoimagery, and lidar elevation data
from 4 times periods between 1947 and 2012, Gibbs and Richmond,
2017 found average rates of change of the shoreline fronting the Barter
Island coast of −1.2 ± 0.2 m/yr (range −1.8 to +0.2 m/yr) and bluff retreat rates averaged 1.5 ± 0.1 m/yr (range 2.2 to 0.4 m/yr) between
1955 and 2015 (Gibbs et al., 2018). The long-term rates can be
punctuated by individual years with much higher erosion rates and
over 20 m of bluff retreat has been observed by the authors in a single
year (e.g. 2008, 2017). Mechanisms for bluff failure along this coast
are poorly documented but likely include a combination of thermal
degradation and thawing of permafrost in the exposed bluff face
(thermodenudation), mechanical and thermal niching at the bluff toe,
followed by rotational slumping and block collapse (thermoabrasion;
Aré, 1988; Hoque and Pollard, 2009; Günther et al., 2013). Recession
rates appear to be largely dependent on ice content, the frequency
and intensity of storms, run-up elevation, and seawater and air temperatures (Reimnitz et al., 1988; Wobus et al., 2011; Overduin et al., 2014).
ArcMap (ESRI, 2014) and areas where snow and ice were present in
the imagery, particularly July 2014, were masked from the final DSMs
prior to volume change analyses. Permafrost bluffs along the Arctic
coast commonly have a surficial peat layer that is more cohesive and resistant to erosion than underlying, less consolidated but ice rich sands
and gravels. As bluffs erode this can result in overhanging visor morphology at the top of the bluff and a more landward (southerly) position
of the underlying face and/or base of the bluff (Fig. 2A). In this study,
bluffs were photographed from a position slightly seaward of the
beach and nearly the entire face of the bluff was imaged and real elevations calculated for both the upper sub-horizontal visored tundra surface as well as the bluff face. In some locations, this resulted in a wide
range of Z (elevation) values for the same or adjacent X and Y locations
(Fig. 2B) and a simple averaging of all the elevation points in locations
with a large Z-value range would yield lower net values than the true
value of the top of the bluff. To resolve this, a conditional raster
representing the range of elevation values contained within one square
meter was created and, using this conditional raster, the maximum
value within the specified grid cell size (upper surface) was selected
where the range of elevation values exceeded 1 m. Where range of elevation values were b1, a simple average of the points within the grid cell
was calculated.
3.2. Ground control and check points
Ground survey data were acquired in September 2014 and 2016
using dual frequency GPS receivers and post-processed relative to a
3. Methods
3.1. Image acquisition and construction of elevation models
A proprietary form of structure-from-motion photogrammetry
known as fodar was used to acquire and create orthophotomosaics
and digital elevation models. Image acquisition methods and hardware
are described fully in Nolan et al. (2015), Nolan and DesLauriers (2016),
and Gibbs et al. (2016) and did not deviate significantly for this study. A
key feature of fodar is that photo-center positions are located to within
10 cm using on-board survey-grade GPS and proprietary hardware. Acquisitions were made on 3 separate dates and flight lines, altitudes, and
resolution of the data were different for each of the surveys (Table 1).
The July 2014 survey was opportunistic and reconnaissance in design
and not optimized for resolution and precision compared to the later
surveys. Orthophotomosaics, digital surface models, raw and edited elevation point clouds, and supporting GPS data used in this study are
available at Gibbs et al., 2019).
[GCP, Ground Control Point].
Prior to DSM generation the elevation point cloud was edited to remove structures and spurious elevation values associated with moving
water surfaces. DSMs were created using a uniform 23-cm cell size, natural neighbor algorithm, and half-meter point thinning method in
Table 1
Data acquisition and adjustment parameters.
Altitude (m)
Jul 01, 2014
Sep 07, 2014
Jul 05, 2015
Resolution (cm)
Adjustment to
GCP (cm)
Fig. 2. A: Photograph of the bluffs at Barter Island showing overhanging peat visors at the
top of the bluffs underlain by a thick layer of segregated ice and unconsolidated sand and
gravel that are notched at their base and partially infilled with debris; bluff height is
approximately 8 m (Photo A. Gibbs, Sept. 2016); B: Example of the point cloud data
from July 2015 for a small section of the Barter Island Coast. The colored band shows the
range in elevation values within 1 square meter. Profile A-A’ across the point cloud data
shows that where the edge of the bluff top is overhanging, elevation data from both the
top and upper face of the bluff have significantly different Z values for shared X and Y
locations. Elevation data is missing where bluffs features were not resolvable by the SfM
A.E. Gibbs et al. / Geomorphology 336 (2019) 152–164
temporary base station on Barter Island. Data were collected at 3 photoidentifiable survey monuments in 2014 and 2016, along 9 transects
across the tundra bluff in September 2014, and in September 2016 at
additional points located on the concrete and gravel pads near the former POL (Petroleum, Oil, and Lubricants) tank farm north of the LRRS
that are presumed to have remained vertically stable over the past
few years (Fig. 3). Horizontal and vertical accuracy of the data are approximately ±0.03 m. Vertical elevations of the bluff transect data
that were acquired by walking with the survey rod held slightly above
the ground surface could be biased upward by 5 to 10 cm.
Data were shifted to best fit the coordinates of t…
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