BGBiogeosciencesBGBiogeosciences1726-4189Copernicus PublicationsGöttingen, Germany10.5194/bg-15-1549-2018Technical note: A simple approach for efficient collection of field
reference data for calibrating remote sensing mapping of northern wetlandsCalibrating remote sensing mapping of northern wetlandsGålfalkMagnusmagnus.galfalk@liu.seKarlsonMartinCrillPatrickhttps://orcid.org/0000-0003-1110-3059BousquetPhilippeBastvikenDavidhttps://orcid.org/0000-0003-0038-2152Department of Thematic Studies – Environmental Change,
Linköping University, 58183 Linköping, SwedenDepartment
of Geological Sciences, Stockholm University, 10691 Stockholm, SwedenLaboratoire des Sciences du Climat et de l'Environnement (LSCE),
Gif-sur-Yvette, FranceMagnus Gålfalk (magnus.galfalk@liu.se)15March20181551549155722October201726October20173February20187February2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://bg.copernicus.org/articles/15/1549/2018/bg-15-1549-2018.htmlThe full text article is available as a PDF file from https://bg.copernicus.org/articles/15/1549/2018/bg-15-1549-2018.pdf
The calibration and validation of remote sensing land cover products are
highly dependent on accurate field reference data, which are costly and
practically challenging to collect. We describe an optical method for
collection of field reference data that is a fast, cost-efficient, and robust
alternative to field surveys and UAV imaging. A lightweight, waterproof,
remote-controlled RGB camera (GoPro HERO4 Silver, GoPro Inc.) was used to
take wide-angle images from 3.1 to 4.5 m in altitude using an extendable
monopod, as well as representative near-ground (< 1 m) images to
identify spectral and structural features that correspond to various land
covers in present lighting conditions. A semi-automatic classification was
made based on six surface types (graminoids, water, shrubs, dry moss, wet
moss, and rock). The method enables collection of detailed field reference
data, which is critical in many remote sensing applications, such as
satellite-based wetland mapping. The method uses common non-expensive
equipment, does not require special skills or training, and is facilitated by
a step-by-step manual that is included in the Supplement. Over time a global
ground cover database can be built that can be used as reference data for
studies of non-forested wetlands from satellites such as Sentinel 1 and 2
(10 m pixel size).
Introduction
Accurate and timely land cover data are important for, e.g., economic,
political, and environmental assessments, and for societal and landscape
planning and management. The capacity for generating land cover data products
from remote sensing is developing rapidly. There has been an exponential
increase in launches of new satellites with improved sensor capabilities,
including shorter revisit time, larger area coverage, and increased spatial
resolution (Belward and Skøien, 2015). Similarly, the development of land
cover products is increasingly supported by the progress in computing
capacities and machine learning approaches.
At the same time it is clear that the knowledge of the Earth's land cover is
still poorly constrained. A comparison between multiple state-of-the-art land
cover products for West Siberia revealed disturbing uncertainties (Frey and
Smith, 2007) as estimated wetland areas ranged from 2 to 26 % of the
total area, and the correspondence to in situ observations for wetlands was
only 2–56 %. For lakes, all products revealed similar area cover
(2–3 %), but the agreement with field observations was as low as
0–5 %. Hence, in spite of the progress in technical capabilities and
data analysis progress, there are apparently fundamental factors that still
need consideration to obtain accurate land cover information.
The West Siberia example is not unique. Current estimates of the global
wetland area range from 8.6 to 26.9 × 106 km2, with great
inconsistencies between different data products (Melton et al., 2013). The
uncertainty in wetland distribution has multiple consequences, including
being a major bottleneck for constraining the assessments of global methane
(CH4) emissions (Crill and Thornton, 2017), which was the motivation for
this area comparison. Wetlands and lakes are the largest natural CH4
sources (Saunois et al., 2016) and available evidence suggests that these
emissions can be highly climate sensitive, particularly at northern latitudes
predicted to experience the highest temperature increases and melting
permafrost – both contributing to higher CH4 fluxes (Schuur et al.,
2009; Yvon-Durocher et al., 2014).
CH4 fluxes from areas with different plant communities in northern
wetlands can differ by orders of magnitude (in the following, northern
wetlands refer to non-forested boreal, subarctic, and arctic wetlands). Small
wet areas dominated by emergent graminoid plants account for by far the
highest fluxes m-2, while the more widespread areas covered by, e.g.,
Sphagnum mosses have much lower CH4 emissions m-2 (e.g.,
Bäckstrand et al., 2010). The fluxes associated with the heterogeneous
and patchy (i.e., mixed) land cover in northern wetlands are well understood
on the local plot scale, whereas the large-scale extrapolations are very
uncertain. The two main reasons for this uncertainty are that the total
wetland extent is unknown and that present map products do not distinguish
between different wetland habitats which control fluxes and flux regulation.
As a consequence the whole source attribution in the global CH4 budget
remains highly uncertain (Kirschke et al., 2013; Saunois et al., 2016).
Improved land cover products relevant to CH4 fluxes and their regulation
are therefore needed to resolve this. The detailed characterization of
wetland features or habitats requires the use of high-resolution satellite
data and sub-pixel classifications that quantify percent, or fractional, land
cover. A fundamental bottleneck for the development of fractional land cover
products is the quantity and quality of the reference data used for
calibration and validation (Foody 2013; Foody et al., 2016). In fact,
reference data can often be any data available at higher resolution than the
data product, including other satellite imagery and airborne surveys, in
addition to field observations. In turn, the field observations can range
from rapid landscape assessments to detailed vegetation mapping in inventory
plots, where the latter yields high-resolution and high-quality data but is
very expensive to generate in terms of time and manpower (Frey and Smith,
2007; Olofsson et al., 2014). Ground-based reference data for fractional land
cover mapping can be acquired using traditional methods, such as visual
estimation, point frame assessment or digital photography (Chen et al.,
2010). These methods can be applied using a transect approach to increase the
area coverage in order to match the spatial resolutions of different
satellite sensors (Mougin et al., 2014).
The application of digital photography and image analysis software has shown
promise for enabling rapid and objective measurements of fractional land
cover that can be repeated over time for comparative analysis (Booth et al.,
2006a). While several geometrical corrections and photometric setups are
used, nadir (downward facing) and hemispherical view photography is most
common, and the selected setup depends on the height structure of the
vegetation (Chen et al., 2010). Most previous research has however focused on
distinguishing between major general categories, such as vegetation and
non-vegetation (Laliberte et al., 2007; Zhou and Liu, 2015), and is typically
not used to characterize more subtle patterns within major land cover
classes. Many applications in the literature have been in rangeland, while
there is a lack of wetland classification. Furthermore, images have mainly
been close-up images taken from a nadir view perspective (Booth et al.,
2006a; Chen et al., 2010; Zhou and Liu, 2015), thereby limiting the spatial
extent to well below the pixel size of satellite systems suitable for
regional-scale mapping.
From a methano-centric viewpoint, accurate reference data at high enough
resolution, being able to separate wetland (and upland) habitats with
differing flux levels and regulation, are needed to facilitate progress with
available satellite sensors. The resolution should preferably be better than
1 m2 given how the high emitting graminoid areas are scattered on the
wettest spots where emergent plants can grow. Given this need, we propose a
quick and simple type of field assessment adapted for the 10 × 10 m
pixels of the Sentinel 1 and 2 satellites.
Our method uses true color images of the ground, followed by image analysis
to distinguish fractional cover of key land cover types relevant to CH4
fluxes from northern wetlands, where we focus on a few classes that differ in
their CH4 emissions. We provide a simple manual allowing anyone to take
the photos needed in a few minutes per field plot. Land cover classification
can then be made using the red–green–blue (RGB) field images (sometimes
also converting them to the intensity–hue–saturation (IHS) color space) by
software such as, e.g., CAN-EYE (Weiss and Baret, 2010), VegMeasure (Johnson
et al., 2003), SamplePoint (Booth et al., 2006b), or eCognition (Trimble
commercial software). With this simple approach it would be quick and easy
for the community to share such images online and to generate a global
reference database that can be used for land cover classification relevant to
wetland CH4 fluxes, or other purposes,
depending on the land cover classes used. We use our own routines written in
Matlab due to the large field of view used in the method, in order to correct
for the geometrical perspective when calculating areas (to speed up the
development of a global land cover reference database, we can do the
classification on request if all necessary parameters and images are
available as given in our manual).
A remotely controlled wide-field camera mounted on a long monopod
captures the scene in one shot, from above the horizon down to nadir. After
using the horizon image position to correct for the camera angle, a 10 × 10 m area close to the camera is used for classification.
Correction of lens distortion. (a) Raw wide-field camera image.
(b) After correction.
Modeling of lens distortion. Checkboard pattern used for
calibrations. Red circles are used to mark automatically detected square
corners, while the yellow square marks the origin of the coordinate system.
Images of the pattern are taken from 10 to 20 different camera angles.
Fieldwork
The camera setup is illustrated in Fig. 1, with lines showing the spatial
extent of a field plot. Our equipment included a lightweight RGB camera
(GoPro 4 Hero Silver; other types of cameras with remote control and a
suitable wide field of view would also work) mounted on an extendable monopod
that allows imaging from a height of 3.1–4.5 m. The camera had a resolution
of 4000 × 3000 pixels with a wide field of view (FOV) of
122.6 × 94.4∘ and was remotely controlled over Bluetooth
using a cellphone application that allows a live preview, making it possible
to always include the horizon close to the upper edge in each image (needed
for image processing later – see below). The camera had a waterproof casing
and could therefore be used in rainy conditions, making the method robust to
variable weather conditions. Measurements were made for about 200 field plots
in northern Sweden in the period 6–8 September 2016.
For each field plot, the following were recorded.
One image taken at > 3.1 m height (see illustration in Fig. 1)
which includes the horizon coordinate close to the top of the image
Three to four close-up images of the most common surface cover types in the plot (e.g., typical
vegetation) and a very short note for each image indicating what is shown,
e.g., whether a close-up image shows dry or wet moss (two of our classes), as
there can be different colors within a class.
GPS position of the camera location (reference point)
Notes of the image direction relative to the reference point
A long modified monopod with a GoPro camera mounted at the end was used for
the imaging. The geographic coordinate of the camera position was registered
using a handheld Garmin Oregon 550 GPS with a horizontal accuracy of
approximately 3 m. The positional accuracy of the images can be improved by
using a differential GPS and by registering the cardinal direction of the
FOV. The camera battery lasted for a few hours after a full charge, but was
charged at intervals when not used, e.g., when moving between different field
sites, making it possible to do all the imaging using only one
camera.
Illustration using Matlab's Camera calibration application of the
camera positions used when taking the calibration images.
Calibration of projected geometry using an image corrected for lens
distortion. Model geometry is shown as white numbers and a white grid, while
green and red numbers are written on the ground using chalk (red lines at 2
and 4 m left of the center line were strengthened for clarity). The camera
height in this calibration measurement is 3.1 m.
Image processing and models
As the camera had a very wide FOV, the raw images do have a strong lens
distortion (Fig. 2). This can be corrected for many camera models (e.g., the
GoPro series) using ready-made models in Adobe Lightroom or Photoshop, or by
modeling the distortion for any camera using the camera calibrator
application in Matlab's computer vision system toolbox as described below. A
checkboard pattern is needed for the modeling (Fig. 3), which should consist
of black and white squares with an even number of squares in one direction
and an odd number of squares in the other direction, preferably with a white
border around the pattern. The next step is to take images of this pattern
from 10 to 20 unique camera positions, providing many perspectives of the
pattern with different angles for the distortion modeling (Fig. 3). In order
to make accurate models, it is important both to have sharp images with no
motion blur (e.g., due to movement or poor lighting) and to include several
images where the pattern is close to the edges of the image, as this is where
the distortion is greatest. An alternative but equivalent method, preferably
used for small calibration patterns printed on a standard sheet of paper
(e.g., letter or A4), is to mount the camera and instead move the paper to
10–20 unique positions with different angles to the camera. A quick way to
do this with only one person present is by recording a video while moving the
paper slowly and then selecting calibration images from the video afterwards.
The illustration of camera positions used (Fig. 4) can also be displayed in
the application as a mounted camera with different positions of the
calibration pattern. The next step is to enter the size of a checkerboard
square (mm, cm, or in) which is followed by an automatic identification of
the corners of squares in the pattern (Fig. 3). Images with bad corner
detection can now be removed (optional) to improve the modeling. As a last
step, camera parameters can now be calculated with the press of a button and
be saved as a variable in Matlab (cameraParams). This whole procedure only
has to be done once for each camera and FOV setting used, meaning once for a
data collection campaign or a project if the same camera model and FOV are
used. Applying the correction to images is done using a single command in
Matlab: img_corr = undistortImage(img, cameraParams). Here img and
img_corr are variables for the uncorrected and corrected versions,
respectively.
One of our field plots. (a) Image corrected for lens distortion,
with a projected 10 × 10 m grid overlaid. (b) Image after recalculation to
overhead projection (10 × 10 m).
Using a distortion-corrected calibration image, we then developed a model of
the ground geometry by projecting and fitting a 10 × 10 m grid on a
parking lot with measured distances marked using chalk (Fig. 5). Such
calibration of projected ground geometry only needs to be done when changing
the camera model or field of view setting, and is valid for any camera height
as long as the heights used in the field and in the calibration imaging are
known. It is done quickly by drawing short lines every meter for distances up
to 10 m, and some selected perpendicular lines at strategic positions to
obtain the perspective. At least one, and preferably two, perpendicular
distances should be marked at a minimum of two different distances along the
central line (in Fig. 5 at 2 and 4 m left of the central line at distances
along the central line of 0 and 2.8 m). The geometric model uses the camera
FOV, camera height, and vertical coordinate of the horizon (to obtain the
camera angle). We find excellent agreement between the modeled and measured
grids (fits are within a few centimeters) for both camera heights of 3.1 and
4.5 m.
The vertical angle α from nadir to a certain point on the grid with
ground distance Y along the center line is given by α=arctan(Y/h), where h is the camera height. For distance points
in our calibration image (Fig. 5), using 0.2 m steps in the range 0–1 m
and 1 m steps from 1 to 10 m, we calculate the nadir angles α(Y)
and measure the corresponding vertical image coordinates
ycalib(Y).
In principle, for any distortion corrected image there is a simple
relationship yimgα=(αY-α0)/PFOV, where yimg is the image
vertical pixel coordinate for a certain distance Y, PFOV the pixel field of
view (deg pixel-1), and α0 the nadir angle of the bottom
image edge. In practice, however, correction for lens distortion is not
perfect, so we have fitted a polynomial in the calibration image to obtain
ycalibα from the known α and measured
ycalib. Using this function we can then obtain the
yimg coordinate in any subsequent field image using
yimg=ycalibα+PFOVhor×yimghor-ycalibhor
where yimghor and ycalibhor are
the vertical image coordinates of the horizon in the field and calibration
image, respectively. As the PFOV varies by a small amount across the image
due to small deviations in the lens distortion correction, we have used
PFOVhor, which is the pixel field of view at the horizon
coordinate. In short, the shift in horizon position between the field and
calibration images is used to compensate for the camera having different
tilts in different images. In order to obtain correct ground geometry it is
therefore important to always include the horizon in all images.
The horizontal ground scale dx (pixels m-1) varies linearly with
yimg, making it possible to calculate the horizontal image coordinate
ximg using
ximg=xc+X×dx=xc+X×yimghor-yimg×dx0ycalibhor×hcalibhimg
where dx0 is the horizontal ground scale at the bottom edge of the
calibration image, xc the center line coordinate (half the horizontal
image size), X the horizontal ground distance, and hcalib and
himg the camera heights in the calibration and field image,
respectively.
Thus, using Eqs. (1) and (2) we can calculate the image coordinates
(ximg, yimg) in a field image from any ground coordinates (X,
Y). A model grid is shown in Fig. 5 together with the calibration image,
illustrating their agreement.
For each field image, after correction for image distortion, our Matlab
script asks for the y-coordinate of the horizon (which is selected using a
mouse). This is used to calculate the camera tilt and to over-plot a distance
grid projected on the ground (Fig. 6a). Using Eqs. (1) and (2) we then
recalculate the image to an overhead projection of the nearest
10 × 10 m area (Fig. 6b). This is done using interpolation, where a
(ximg, yimg) coordinate is obtained from each (X,
Y) coordinate, and the brightness in each color channel (R, G, B)
calculated using sub-pixel interpolation. The resulting image is reminiscent
of an overhead image, with equal scales in both axes. There is however a
small difference, as the geometry (due to the line of sight) does not provide
information about the ground behind high vegetation in the same way as an
image taken from overhead. In cases with high vegetation (which is some of
our 200 field plots), mostly high grass, we used a higher camera altitude to
decrease obscured areas. Another possibility is to direct the camera towards
nadir (see the manual in Supplement S1) to image areas -5 to +5 m from the center of
a plot, further decreasing the viewing angles from nadir. We did not have any
problems with shrub or brushwood as it was only a couple of dm high, and
birch trees did not grow on the mires. We also recommend using a camera
height of about 6 m to decrease obscuration and to increase the mapped area.
Close-up images in one of our 10 × 10 m field plots
(Fig. 6).
Classification of a field plot image (Fig. 6b) into the six main
surface components. All panels have an area of 10 × 10 m.
(a) Graminoids, (b) water, (c) shrubs,
(d) dry moss, (e) wet moss, (f) rock.
Image classification
After a field plot has been geometrically rectified, so that the spatial
resolution is the same over the surface area used for classification, the
script distinguishes land cover types by color, brightness and spatial
variability. Aided by the close-up images of typical surface types also taken
at each field plot (Fig. 7) and short field notes about the vegetation,
providing further verification, a script is applied to each
overhead-projected calibration field (Fig. 6b) that classifies the field plot
into land cover types. This is a semi-automatic method that can account for
illumination differences between images. In addition, it facilitates
identification, as there can for instance be different vegetation with
similar color, and rock surfaces that have a similar appearance to water or
vegetation. After an initial automatic classification, the script has an
interface that allows manual reclassification of areas between classes. The
close-up images have high detail richness, allowing identification, and color
and texture assignment of the different land cover classes during similar
light and weather conditions as when the whole-plot image is taken. This
makes results robust regarding different users collecting data, with respect
to light conditions, times of day, etc. The sensitivity is instead affected
by the person defining the classes, just as with normal visual inspection.
For calculations of surface color we filter the overhead projected field
images using a running 3 × 3 pixel mean filter, providing more
reliable statistics. Spatial variation in brightness, used as a measurement
of surface roughness, is calculated using a running 3 × 3 pixel
standard deviation filter. Denoting the brightness in each (red, green, and
blue) color channel R, G and B, respectively, we could for instance
find areas with green grass using the green filter index 2G/(R+B), where a
value above 1 indicates green vegetation. In the same way, areas with water
(if the close-up images show blue water due to clear sky) can be found using
a blue filter index 2B/(R+G). If the close-up images show dark or gray
water (cloudy weather), it can be distinguished from rock and white
vegetation using either a total brightness index (R+G+B)/3 or an index that
is sensitive to surface roughness, involving σ(R), σ(G), or
σ(B), where σ denotes the 3 × 3 pixel standard
deviation centered on each pixel, for a certain color channel.
In this study we used six different land cover types of relevance to CH4
regulation: graminoids, water, shrubs, dry moss, wet moss, and rock. Examples
of classified images are shown in Fig. 8. Additional field plots and
classification examples can be found in Supplement S2. Compared to the
corrections for lens distortion and geometrical projection, the
classification part often takes the longest time, as it is semi-automatic and
requires trial and error testing of which indices and class limits to use for
each image as vegetation and lighting conditions might vary. After a number
of images with similar vegetation and conditions have been classified, the
process goes much faster as the indices and limits will be roughly the same.
One may also need to reclassify parts manually by moving a square region from
one class to another based on visual inspection. The main advantage with this
method of obtaining reference data is however that it is very fast in the
field and works in all weather conditions. In a test study, we were able to
make classifications of about 200 field plots in northern Sweden in a 3-day
test campaign despite rainy and windy conditions. For each field plot,
surface area (m2) and coverage (%) were calculated for each class.
The geometrical correction models (lens distortion and ground projection)
were made in about an hour, while the classifications for all plots took a
few days.
Conclusions
This study describes a quick method to document ground surface cover and
process the data to make them suitable as reference data for remote sensing.
The method requires a minimum of equipment that is frequently used by
researchers and persons with general interest in outdoor activities, and
image recording can be made easily and in a few minutes per plot without
requirements of specific skills or training. In addition to covering large
areas in a short time, it is a robust method that works in any weather using
a waterproof camera. This provides an alternative to, e.g., using small
unmanned aerial vehicles (UAVs) which are efficient at covering large areas
but have the drawbacks of being sensitive to both wind and rain and typically
having flight times of about 20 min (considerably lower if many takeoffs and landings are needed when moving between plots). The presented
photographic approach is also possible using a mobile phone camera, although
such cameras usually have a very small field of view compared to many
adventure cameras (such as the GoPro, which is also cheaper than a
cellphone). We recommend using a higher camera
altitude; a height of 6 m would make mobile phone imaging of
10 × 10 m possible (using a remote Bluetooth controller) and
20 × 20 m mapping using a camera with a large field of view such as
the GoPro. Hence, if the method becomes widespread and a fraction of those
who visit northern wetlands (or other environments without dense tall
vegetation where the method is suitable) contribute images and related
information, there is a potential for rapid development of a global database
of images and processed results with detailed land cover for individual
satellite pixels. In turn, this could become a valuable resource supplying
reference data for remote sensing. To facilitate this development,
Supplement S1 includes a complete manual and the authors will assist with
early stage image processing and initiate database development.
The data used in this article are a small subset of our
approximately 200 field plots, only used as examples to illustrate the steps of the
method. More examples are given in the Supplement. The full dataset of field
reference data will be published in a future database.
The supplement related to this article is available online at: https://doi.org/10.5194/bg-15-1549-2018-supplement.
The authors declare that they have no conflict of
interest.
Acknowledgements
This study was funded by a grant from the Swedish Research Council VR to
David Bastviken (ref. no. VR 2012-48). We would also like to acknowledge the
collaboration with the IZOMET project (ref. no. VR 2014-6584) and IZOMET
partner Marielle Saunois (Laboratoire des Sciences du Climat et de
l'Environnement (LSCE), Gif-sur-Yvette, France). Edited by: Paul Stoy Reviewed by: two
anonymous referees
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