MONITORING SEABIRDS AND MARINE MAMMALS BY GEOREFERENCED AERIAL PHOTOGRAPHY

Abstract

The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of biogeographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction, in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital imagery, which can be carried out at higher (safer) flight altitudes (beyond the rotor-swept zone of the wind turbines) have become a mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC) and exposure times ranging between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance (GSD) of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the human eye guided by purpose-programmed software to distinguish biological from non-biological signals. Each detected seabird or marine mammal signal is identified to species level or assigned to a species group and automatically saved into a geo-database for subsequent quality assurance, geo-statistical analyses and data export to third-party users. The relative size of a detected object can be accurately measured which provides key information for species-identification. During the development and testing of this system until 2015, more than 40 surveys have produced around 500.000 digital aerial images, of which some were taken in specially protected areas (SPA) of the Baltic Sea and thus include a wide range of relevant species. Here, we present the technical principles of this comparatively new survey approach and discuss the key methodological challenges related to optimizing survey design and workflow in view of the pending regulatory requirements for effective environmental impact assessments.

Full text

MONITORING SEABIRDS AND MARINE MAMMALS BY GEOREFERENCED AERIAL
PHOTOGRAPHY
G. Kemper a, *, A. Weidauer b, T. Coppack b, c
a GGS - Büro für Geotechnik, Geoinformatik und Service, Speyer, Germany - kemper@ggs-speyer.de
b Institute of Applied Ecology (IfAÖ GmbH), Rostock, Germany - weidauer@ifaoe.de
c Current affiliation: APEM Ltd, Manchester, United Kingdom - t.coppack@apemltd.co.uk
KEY WORDS: Biological Monitoring, Digital Surveys, Environmental Impact Assessment, Marine Wildlife, Offshore Wind
ABSTRACT:
The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of bio-
geographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction,
in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population
estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard
environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore
wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb
sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital
imagery, which can be carried out at higher (safer) flight altitudes (beyond the rotor-swept zone of the wind turbines) have become a
mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system
including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital
images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a
flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC) and exposure times ranging
between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance
(GSD) of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further
editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the
human eye guided by purpose-programmed software to distinguish biological from non-biological signals. Each detected seabird or
marine mammal signal is identified to species level or assigned to a species group and automatically saved into a geo-database for
subsequent quality assurance, geo-statistical analyses and data export to third-party users. The relative size of a detected object can
be accurately measured which provides key information for species-identification. During the development and testing of this system
until 2015, more than 40 surveys have produced around 500.000 digital aerial images, of which some were taken in specially
protected areas (SPA) of the Baltic Sea and thus include a wide range of relevant species. Here, we present the technical principles of
this comparatively new survey approach and discuss the key methodological challenges related to optimizing survey design and
workflow in view of the pending regulatory requirements for effective environmental impact assessments.
* Corresponding author
1. INTRODUCTION
1.1 General Introduction
The marine environment is subject to a variety of anthropogenic
influences, including global climate change. The environmental
changes caused by climate change and the actions taken by
humans to mitigate and to adapt to these changes are currently
leading to an increased exploitation of marine resources with
presumed (although largely unquantified) effects on marine life.
The development of the offshore wind industry, for example, in
combination with shipping and fisheries potentially leads to a
reduction undisturbed wintering and resting areas for seabirds
(Mendel & Garthe, 2010; Dierschke et al., 2012). Furthermore,
the increasing demand for marine gravels and sands intended
for coastal protection is in potential conflict with the habitat
requirements of some benthivorous duck species, which feed
from molluscs and other invertebrates on the seabed
(Müncheberg et al., 2012).
In order to determine the state of the marine environment in a
rapidly changing world, as well as to assess the conservation
status of its inhabitants, an increasingly accurate information
base on the distribution and abundance of marine species is
required. This entails effective monitoring schemes that provide
meaningful data and detailed vulnerability assessment maps to
inform policy decisions and to guide spatial planning
procedures.
An objective evaluation of the environmental consequences of
human activities for marine organisms has often been hampered
by the lack of precise data in the affected areas. Here, we review
recent developments to improve data acquisition by using high-
resolution georeferenced digital photography to map seabirds
and marine mammals.
1.2 Limitations of visual survey methods
The assessment of the effects of offshore wind farms, for
example, on seabirds and marine mammals are typically based
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B8-689-2016
689

on large-scale surveys that take place on a monthly or seasonal
basis before, during and after construction of the wind farm. In
the past, birds and mammals were usually counted by observers
from ships (Garthe et al., 2002) or low-flying aircraft
(Diederichs et al., 2002; Thomsen et al., 2004) along linear
transects, thereby recording the frequency of observed objects
and estimating the distance of each object from the point of
observation. While ship-based counts are associated with
considerable costs for logistics, airborne observations are
comparatively time- and cost-effective, however pose
methodological problems. Due to the relatively high airspeed of
the deployed aircraft (about 100 kn; ~50 m/s) flying at around
250 ft (~80 m) to ensure that bird species can be identified by
the human eye, observer-based aerial surveys can provide only
rough population estimates, especially in bird species that
aggregate in large numbers. This is the case, for example, in the
Baltic Sea where large groups of sea ducks occur. Low-flying
aircraft also scare-off sensitive bird species to a non-negligible
extent, such that quantitative estimates are additionally biased.
Furthermore, the detection probability decreases with the
distance between the bird and the observer. The resulting
quantitative estimates and the quality of species identification
can also vary widely between individual observers. Overall,
these sources of variance that impact quantitative population
estimates can only be compensated by synthetic correction
factors. However, such corrections do not enhance the quality
of the raw data per se and limit the comparability of data sets
collected under different sampling configurations and
conditions.
Assessing the environmental impacts of an offshore wind farm
on birds essentially requires an unbiased detection of the
distribution of individuals relative to the turbines’ coordinates.
If sensitive bird species are systematically disturbed by the
detection method itself (low-flying aircraft, ship) cause-effect
relationships between wind turbines and birds cannot be
reliably addressed. Raw data resulting from observer-based
surveys cannot be quality-assured retrospectively and used to
secure evidence. Finally, there is a great safety risk for the
human observer flying at low altitude past operational wind
turbines.
1.3 Aerial digital survey methods
For the reasons mentioned above, survey methods based on
aerial digital imaging by means of aircraft flying at significantly
higher altitude are an effective alternative and are more and
more replacing traditional observer-based survey techniques.
The current version of the standard investigation concept
(StUK4) of the German Federal Maritime and Hydrographic
Agency (BSH) in fact recommends the use of aerial digital
imaging methods for assessing the impact of offshore wind
turbines on seabirds and marine mammals which has stimulated
further refinement of this method.
In the early 1950s, the first attempts were made to quantify
large aggregations of birds by conventional aerial photography.
It was none other than the famous zoologist Bernhard Grzimek
who demonstrated together with his son that large flocks of
flamingos in Africa could only be quantified exactly on aerial
photographs (Grzimek & Grzimek, 1960). For the large-scale
quantification of pelagic marine species, however, it was for a
long time not feasible to use (analogue) aerial photography
because of the sheer number of required images and the costs
involved in image archiving. With the advent of digital camera
technology and the availability of large digital archives, these
problems are now manageable and the costs have become
moderate. In addition, digital images provide the possibility of
automated data analysis via image processing applications.
Meanwhile, the development of digital aerial photography and
the corresponding storage and computing capacities are so
advanced that digital aerial imaging can be used standardized
and cost-effectively.
Digital survey techniques have been increasingly used since
about 2007 in environmental impact studies for the offshore
wind industry, most notably in Denmark and the United
Kingdom (Thaxter & Burton, 2009). Both videographic and
photographic techniques are deployed. Videography benefits
from a higher frame rate though at the cost of a smaller frame
(foot-print) size per camera unit. As a consequence, multiple
parallel video streams are recorded to achieve sufficient areal
coverage. High-resolution digital photography, on the other
hand, compromises frame rate in favour of a larger aspect ratio
per camera unit. Currently, the use of high-resolution medium-
format cameras with up to 80 megapixels is possible (Coppack
et al. 2015). This enables photographic flights at altitudes over
1300 ft (~400 m) with a ground sampling distance (GSD) of 2
cm, depending on the specifications of the lens. At these
altitudes, which are well above the rotor-swept zone of the wind
turbines, displacement effects on sensitive bird species through
the presence of the aircraft are significantly reduced.
Furthermore, bird distributions and individual distances to man-
made structures (wind turbines, vessels) can be accurately
measured and stored digitally for further GIS analyses. This is a
major advantage over observer-based protocols that involve
voice recordings of quantitative estimates.
2. DEVELOPMENT OF A METHOD BASED ON
GEOREFERENCED PHOTOGRAPHY
2.1 Methodological principles
Comparisons of simultaneous observer-based aerial surveys
with digital aerial surveys generally show that relevant bird and
marine mammal species can be detected and classified to
species level in digital images (Thaxter & Burton, 2009;
Kulemeyer et al. 2011; Taylor et al. 2014, Dittmann et al.
2014). Although digital still images of birds and marine
mammals initially showed significant motion blur due to long
exposure times relative to the required airspeed and the lack of
forwards motion compensation (FMC), an evaluation of the
quantitative outcomes of conventional and digital surveys was
possible. Results from a pilot study carried out by Kulemeyer et
al. (2011) suggested that the numbers of three sea duck species
had been significantly underestimated by the observer-based
method, i.e., frequencies of individuals were 15% (Common
Eider), 69% (Long-tailed Duck), and 98% (Common Scoter)
lower than frequencies determined by the aerial digital method
(Kulemeyer et al., 2011). Such differences may be partly
explained by the time lag between surveys and differences in
coverage between photographic and visual methods. However,
comparative results strongly suggest that photographic methods
provide more accurate population estimates than conventional
observer-based aerial surveys that are more susceptible to
species-specific variation in flush behaviour and observation
bias. To calibrate both methods accurately, it would be
necessary to repeat such parallel flights under a range of
different weather conditions.
2.2 Camera technology
Airborne image acquisition for biological monitoring is
presently based on commercially available, photogrammetric
components. The requirements to be met by a purpose-
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B8-689-2016
690

assembled camera system include a flight altitude of at least
1300 ft (taking human flight safety, average cloud base and the
escape distance of birds into account), a GSD of 2 - 3 cm, a
covered strip width of about 400 m, and the option for a 30% to
40% image overlap to compensate for loss of effective coverage
due to glint and glare (reflections from the sea surface), and
vignetting (Figure 1; cf. Groom et al., 2013).
Figure 1. Example of a series of georeferenced, overlapping
aerial images shown in QGIS
A typical camera system to monitor marine wildlife may consist
of two or more medium format cameras (e.g. PhaseOne
IXA180) mounted onto a gyroscopically stabilized platform
(Figure 2).
Figure 2. (a) Equipping a twin-engine aircraft for aerial digital
surveys of marine wildlife, (b) View of a crosswise mounted
tandem camera (PhaseOne IXA180) within a gyroscopically
stabilized platform (GGS Speyer), (c) Exterior view of the
camera system through the hatch of the aircraft
The gyro-static camera platform is connected with a computer
system that triggers shutter release, stores the incoming image
files and, at the same time, operates the flight management
system (AeroTopoL) and the logging of geographic position
and altitude from the GNSS-INS sensors (Kemper, 2012).
The sensors of each camera unit (PhaseOne IXA180) includes
10,320 x 7,752 cells at a resolution of 5.2 micron. Equipped
with 110 mm lenses, this camera setup covers a strip width of
407 m from an altitude of 1400 ft (423 m) with 2 cm GSD. The
minimal reading interval is 1.5 s and enables a theoretical image
overlap of 48% at an airspeed of 100 kn (~50 m/s) and an image
length in the direction of flight of 155 m. Realistically, frame
rates of 1.8 - 2.0 s are feasible and an image overlap of 30%
sufficient under normal weather conditions. In addition, the
camera system has implemented FMC. Trials have shown that
details of birds and marine mammals are depicted best with
sensitivity set to ISO 100, an aperture of 3.2 and an exposure of
<1/1,000 s. In general, modular imaging systems based on
medium format camera units are smaller and lighter and can be
installed in small aircraft, including unmanned aerial vehicles
(UAV). A current disadvantage of the applied camera unit is the
unavailability of a near infra-red (NIR) band, which could be
advantageous to compensate glare at sensor level. This option
currently has to be solved by including a third dedicated NIR
camera.
2.3 Data storage and processing
The monitoring guideline of the German Federal Maritime and
Hydrographic Agency (BSH) currently recommend study areas
of at least 2000 km² of which normally 10% are to be sampled
within one day. The volume of data (raw image files) arising
from such surveys may reach around 5,000 – 8,000 frames per
flight and camera. Each camera can store about 500 - 800 GB
on two separate solid-state disks during the flight. The image
transfer rate lies at 40 - 70 MB/s via USB 3.0 interface. Each
raw image is unpacked and converted into orthorectified block
oriented 500 - 800 MB large 16-bit RGB TIFF. The image files
are augmented with acquisition-related metadata. After this
georeferencing, the overlapping image areas are marked and
edited, and series of analysable images are compiled. From this
point on, visual screening of the image files with commercially
available or open-source geographic information systems is
possible. The sheer extent of the image database (a single flight
takes up to 9 TB) and the need to incorporate image processing
algorithms into the workflow, puts forth the development and
use of purpose-programmed software applications.
2.4 Image analysis for object recognition
There are basically two (not mutually exclusive) approaches to
analysing and evaluating the resultant extensive image material:
(1) automated image pre-processing (e.g. by eCognition; cf.
Groom et al., 2013), including information from a visually
screened subsample of images; (2) visual pre-processing of the
complete image material with the help of purpose-programmed
GIS-based screening applications (e.g. by combining QGIS and
OpenCV; cf. Coppack et al. 2015, Figure 3).
Both approaches involve image pre-processing, in which the
images without biological positive signals are sorted out and the
geo-positions of all potential biological signals are collected in
a database for subsequent identification and quality assurance.
The visual pre-selection of entire sets of images is presently the
more robust approach, yet requires a lot of manpower. Visual
screening is facilitated by purpose-programmed software
applications, by which each image is divided into equal
segments (50 - 80 segments per image, depending on effective
footprint size) that can be tabbed through in a logical sequence
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B8-689-2016
691

(Figure 3). All objects that could potentially be a bird or a
marine mammal (and any other conspicuous object) are marked
manually on the screen and automatically stored in a database,
thereby saving an image identification number, the flight
transect number, the camera identification number, the object
class and the geo-position of the object. The object class is
roughly defined as: (1) swimming bird; (2) flying bird; (3)
marine mammal; (4) conspicuous unknown object; (5)
glare/glint; (6) wave/spray. For quality assurance, a sub-set of
the images is screened twice. All selected objects from the
visual screening process are then classified by an experienced
analyst to reject objects that are not birds or marine mammals.
Figure 3. Screenshot of a software interface for the systematic
visual screening of geo-referenced aerial photographs (example
image shows a harbour porpoise)
2.5 Species identification (ID) and quality assurance
This subsequent step includes the identification of objects down
to species or species-group level and the quantification of
identified birds or marine mammals. Ideally, two experts
classify each object independently, using a specially developed
identification tool which is linked with the geo-database to
facilitate the retrieval of individual objects and accompanying
metadata (Figure 4).
Figure 4. Screen shot of a software interface used for species
identification (example image shows a juvenile gannet)
This ID tool also enables the measurement and storage of
morphometric parameters, which is important supplementary
information for identifying bird or mammal species (and which
can be used to calculate the flight heights of birds, if relevant).
If there are discrepancies in species identification between the
two experts (as indicated automatically following a database
query), a third expert can be consulted to review the case and
issue a final decision. Typical features for the identification of
bird species in aerial images have been described by Dittmann
et al. (2014). All birds and marine mammals that are identified
to genus or species level receive an attribute of accuracy. In the
case of birds, individuals are further categorized (where
possible) according to (1) sex, (2) age class (adult, immature,
juvenile, K1-K4), and (3) behavioural traits (swimming, diving,
flying, direction of flight). For marine mammals, additional
information may be noted, e.g. the presence of calves.
After the ID process, the census information is transferred into
standard data tables and used for the calculation of population
densities at grid-cell or at survey-area level, and for geo-
statistical analyses related to habitat use (e.g. Skov et al., 2016)
and potential displacement effects. Randomized samples of the
photographic material containing classified georeferenced
objects can be reciprocally quality controlled by external,
independent reviewers. The geo-spatial positioning of
individuals allows a precise measurement of their distance to
anthropogenic structures (e.g. pipelines, offshore wind turbines)
and relating species-specific distributions to functional
ecological parameters (e.g. water depth), habitat features, and
associated food resources.
Figure 5. The distributions of three sea duck species based on
gapless aerial photos taken on March 12 2014 in the German
Baltic Sea (Bay of Wismar) in relation to different water depth
classes. Maps are based on data by Steffen (2014).
The examples given in Figure 5 show the specific distributional
patterns of three species of sea ducks in the Baltic Sea derived
from aerial images covering almost 100% of the area (Steffen
2014). The morphological characteristics of these species as
seen in aerial images (cf. Figure 5) are described by Dittmann et
al. (2014) as follows:
Common Eider, Somateria mollissima. ♂ (nuptial plumage):
white back, white scapulars and leg stain form typical "fish
tail" shape; black tail, rump and flanks merge with dark
background of the water surface; the black does not appear
very clearly. ♀: body is light brown on the light-facing side,
the sides of the head contrasting in brighter beige.
Long-tailed Duck, Clangula hyemalis. ♂ (nuptial plumage):
white plumage parts on head, nape and rump, and pale grey
back plumage outshine darker plumage parts that merge
partly with the background, giving the impression of a figure
eight-type shape. ♀: white plumage parts on head and rump,
which are separated by the dark back plumage, appear as two
distinct spots.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B8-689-2016
692

Common Scoter, Melanitta nigra. ♂ (nuptial plumage):
completely black; yellow patch on beak mostly invisible, feet
as opposed to the Velvet Scoter Melanitta fuscata dark, but
not always visible. ♀: body completely dark brown, bright
head sides contrasting strongly.
Databases containing this species-specific information coupled
with individual biometric measurements will be useful when
training automated object identification algorithms in order to
accelerate the pre-screening process, which currently limits the
overall workflow.
3. CONCLUSIONS AND OUTLOOK
The use of high-resolution aerial imagery to map marine
wildlife shows several advantages over conventional observer-
based methods. Site-specific frequencies of individuals can be
determined without correcting for distance-related observation
bias (Buckland et al., 2001), and the resulting population
estimates remain verifiable at raw-data level. The method can
also be applied to complement land-based waterbird counts, e.g.
in protected areas, and to advance national or international
monitoring schemes.
However, there are still a number of methodological challenges
both in image acquisition an image analysis that need to be
overcome in the future (Buckland et al., 2012; Taylor et al.,
2014; Coppack et al. 2015). From a logistical and economic
perspective, it is reasonable attempting to capture the full range
of seabirds and marine mammal species with the same sensor
technique, under the same sampling regime, and within the
shortest possible time window. However, while white seabirds,
gulls for example, are clearly visible against the dark
background of the sea surface (in areas that are free of glare,
glint, and spray), marine mammals often merge with their
environment and may become visible only when they emerge
from the sea surface and reflect the sunlight. These varying
signal-to-noise relationships between biota require a high
quantization of the imaging channels and filters, imposing
equivalent requirements on the image processing software.
Up to now, seabirds and marine mammals have been usually
targeted manually in aerial digital images. With the increasing
regular use of aerial digital methods, image pre-processing
(object recognition) should be automated in order to accelerate
and standardise the entire workflow. Automated object
recognition based on deep machine learning is subject to current
research. Moreover, there is a need for further optimising
sampling design and effort in view of the associated survey
costs.
In principle, the following three parameters would need to be
addressed: (1) the required minimum number of seasonal
surveys to distinguish phenological and stochastic fluctuations
from population changes (cf. Maclean et al. 2012); (2) the
required size of a survey area to characterize habitat clines and
their associated populations; (3) the minimum effective
coverage of a survey area in conjunction with the optimum
sampling design to obtain statistically robust results. These
parameters are being continually discussed in several national
and international research projects in order to define the
minimum regulatory requirements for effective environmental
impact assessments.
As mentioned above, glare is a critical factor that limits
effective coverage, potentially producing many false positives.
Glare effects show up in the RGB bands but not in the NIR.
Current trials have therefore included a supplementary camera
in the NIR band to obtain more information on glare detection
and compensation. This could facilitate the automated
separation of signals from background noise in order to detect
objects of interest with higher precision and consistency.
Beside this improvement, a better image-to-image correlation as
well as a better image rectification will be required. To achieve
this with the setup of three cameras (two RGB and one NIR),
further camera-calibrations using photogrammetric software are
intended. We propose an accurate calibration of the cameras
(calibration of focal length, PPS/PPA, radial distortion) and of
the relative offset angles (relative boreside angles). A significant
improvement could be achieved by using precise GNSS-INS to
receive direct referencing values for the projection centres and
the rotation angles Omega, Phi, and Kappa.
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B8-689-2016
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B8, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B8-689-2016
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