=Paper=
{{Paper
|id=Vol-3006/39_regular_paper
|storemode=property
|title=Central Yamal vegetation monitoring based on Sentinel-2 and Sentinel-1 imagery
|pdfUrl=https://ceur-ws.org/Vol-3006/39_regular_paper.pdf
|volume=Vol-3006
|authors=Tatiana G. Plutalova,Kanayim Teshebaeva,Dmitry N. Balykin,Alexander V. Puzanov,Jacobus van Huissteden,Mikhail I. Koveshnikov,Olga V. Lovtskaya,Nelly M. Kovalevskaya
}}
==Central Yamal vegetation monitoring based on Sentinel-2 and Sentinel-1 imagery==
https://ceur-ws.org/Vol-3006/39_regular_paper.pdf
Central Yamal vegetation monitoring based on
Sentinel-2 and Sentinel-1 imagery
Tatiana G. Plutalova1 , Kanayim Teshebaeva2 , Dmitry N. Balykin1 ,
Alexander V. Puzanov1 , Jacobus van Huissteden2 , Mikhail I. Koveshnikov1 ,
Olga V. Lovtskaya1 and Nelly M. Kovalevskaya1
1
Institute for Water and Environmental Problems SB RAS, Barnaul, Russia
2
VU University, Amsterdam, the Netherlands
Abstract
In this study fusion of optical (Sentinel-2) and radar (Sentinel-1) imagery is presented for vegetation
cover classification in polar Arctic environment of the Western Siberia. Sentinel-1 and Sentinel-2 images
were analyzed using parametric rule classification. Results showed significantly improved land cover
classification results based on contextual analysis. Synergy of Sentinel-2 bands 4 and 3 and Sentinel-1
dual polarization VV and VH images increased the classification accuracy significantly. Specifically,
classification accuracy increased for two classes — Erect dwarf-shrub tundra with 6% and Fresh Water with
10%. The classification accuracy as well test sites were analyzed using in situ data collected during three
fieldwork campaigns in August-September (2016–2018) in the surrounding of Bovanenkovo settlement.
Keywords
Central Yamal, vegetation, Sentinel-2, Sentinel-1, classification, Maximum Likelihood, contextual analysis.
1. Introduction
Arctic vegetation cover is significantly affected by recently changing climate and environmental
conditions [1, 2]. Meanwhile, tundra vegetation is crucial for permafrost dynamics and feedbacks
between the Earth surface and the atmosphere [3]. Since a vegetation effect on heat and moisture
exchange between the soil and atmosphere is significant (in fact, “soil temperature and humidity
depend on vegetation cover” [4]), vegetation monitoring in the Arctic is essential.
Vegetation cover development (primarily, its height and density) is of particular importance.
Vegetation height is a key biophysical control on and proxy for environmental conditions. For
example, shrub height influences snow trapping and snowmelt, which impact ground thermal
conditions [5]. The mapping of areas with different height vegetation for the circumpolar Arctic
tundra biome is of interest for a wide range of applications, including biomass and habitat
studies as well as permafrost modeling in the context of climate change.
Poorly studied climate-related dynamics of vegetation is the main reason for great uncertainty
of forecasts. The most effective calculations relating to vegetation dynamics can be implemented
on the basis of remote sensing (RS) data providing the detailed information on vegetation cover
(class, thickness, height, etc.) in different spectral, spatial and temporal/seasonal aspects.
SDM-2021: All-Russian conference, August 24–27, 2021, Novosibirsk, Russia
" plutalova.tg@gmail.com (T. G. Plutalova); j.van.huissteden@vu.nl (J. v. Huissteden)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
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Tatiana G. Plutalova et al. CEUR Workshop Proceedings 330–342
Existing approaches to the vegetation cover analysis based on RS methods are primarily
focused on exploiting optical RS data.
Most studies of Arctic vegetation are based on low-resolution satellite data, such as AVHRR
(Advanced Very High Resolution Radiometer, 1.09 km nadir resolution) or MODIS (medium-
resolution spectrometer, resolution ≥250 m) ones [6, 7, 8]. However, gradual changes in
vegetation are hard to detect from coarse-resolution data due to landscape heterogeneity and
lack of pure pixels. Thus, AVHRR observations show a positive trend in tundra greening from
1982 to 2010, the overall mean greenness of northern hemisphere tundra has increased by about
8% [7]. There is, however, a considerable unexplained regional variation in the observed NDVI
trends, and the climate drivers are not well understood.
High-resolution satellites (SPOT, Landsat, RapidEye, Sentinel-2) detect vegetation variability
much better [9, 10, 11]. In particular, the European land cover mapping is based on Sentinel-2
images [12].
Sentinel-2 studies are often limited to analyzing the efficiency of individual bands and veg-
etation indices [3]. Also, because of the Arctic vegetation system complexity, the mapping
of technogeneous disturbance of tundra vegetation in Yamal-Nenets Autonomous Okrug was
based on visual interpretation of Landsat-8 and Sentinel-2 imagery [13]. Many aspects of
parametric classification of Sentinel-2 imagery as well as subsequent analysis of dynamics of
tundra vegetation classes remain poorly studied. Among major tasks is the mapping of Central
Yamal vegetation based on the parametric rule classification of multispectral Sentinel-2 imagery
and field data.
As a complement to optical RS techniques, vegetation cover maps derived from synthetic
aperture radar (SAR) systems are an important tool for monitoring and dynamics analyzing
of vegetation. SAR data independence solar irradiance and cloud cover is a significant reason
for using this technique for vegetation cover classification, especially in northern latitude
vegetation where the acquisition of optical data is hindered by frequent cloud cover. In addition,
the sensitivity of radar signals to moisture content and textural properties of vegetation may
separate vegetation classes, particularly when optical sensors are saturated over dense vegeta-
tion. Several studies have used SAR images for tundra vegetation classification. A Bayesian
Maximum Likelihood (ML) Classifier was applied to SAR imagery covering the boreal forest
region (northern Canada) during the peak of the growing season demonstrating the overall
accuracy of the classification up to 92% [14]. The usage of Radarsat-2 SAR data for the modeling
of aboveground phytomass for high arctic environment indicates that SAR data are sensitive for
specific classes of tundra cover (in particular, polar semi-desert, mesic heath, wet sedge) [15].
Mosaics of summer and winter Japanese Earth Resources Satellite 1 (JERS-1) SAR imagery
were employed for the mapping of vegetated wetland of Alaska, including moss, lichen and
shrubs [16]. The per-class average error rate for aggregate wetlands classes ranged between
5.0% and 30.5%, and the total aggregate accuracy calculated based on all classified pixels was
89.5%.
Recent advancements in imaging science have provided finer spatial, spectral, and temporal
resolution. In addition, non-optical data sources such as SAR data have been shown to add value
when combined with optical remote sensing data. In particular, for Danube delta wetlands the
results for Sentinel-2 classification performance is 87.5% of mean accuracy, synergy of Sentinel-2
and Sentinel-1 improves the classification performance up to 97.1% [17].
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A combination of Sentinel-2 and Sentinel-1 to derive tundra vegetation height based on
vegetation indices for the territory of the Yamal and Gydan Peninsulas has demonstrated that
this method is effective only for mapping vegetation of limited height [5]. It is known also that
vegetation indices developed in optical remote sensing can provide the information only on the
surface of vegetation canopy [17].
Supervised and unsupervised classifications for combining optical data (Landsat-8) with
two types of SAR data (TerraSAR-X and Radarsat) were employed for the mapping of tundra
vegetation in Richards Island, Canada [18]. The classification accuracies were analyzed using
test data collected during three fieldwork campaigns. The optical data offered an acceptable
initial accuracy for the land cover classification for five tundra cover types. The overall accuracy
was increased by the combination of SAR and optical data:
— up to 71% for unsupervised classification (Landsat 8 and TerraSAR-X);
— up to 87% for supervised classification (Landsat 8 and Radarsat-2).
It is worth noting that a new European CLC (Copernicus Land Cover) standard for land
monitoring in the next decade is being currently developed on the basis of classification of time
series of Sentinel-1 and Sentinel-2 [19].
Thus, the second task arising in the context of Central Yamal vegetation monitoring is to
determine the influence of SAR dimension (VV and VH polarizations of Sentinel-1) on vegetation
classification based on optical (Sentinel-2) data.
2. Materials and methods
2.1. Study area
The study area characterized by subarctic climate is located in the permafrost tundra zone of the
central part of Yamal Peninsula (Figures 1 and 2). The terrain is flat (its heights do not exceed
100 m a.s.l.) with extensive river network and numerous thermokarst lakes.
Figure 1: Study area.
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a
b
c
d
e
Figure 2: Field photographs of Central Yamal showing: Barrens (a); Fresh Water (b); Sedge, moss, low-
shrub wetland (c); Erect dwarf-shrub tundra (d); Low-shrub tundra (e) (photo: Balykin D., Sysoeva T.).
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2.2. Datasets
In this study, multispectral data of Sentinel-2, SAR data of Sentinel-1 and field data of August–
September (2016–2018) are used (Tables 1 and 2).
The Copernicus Sentinel Program by the European Space Agency (ESA) is a satellite program
with the open source of completely transparent real-time data (retrieved from Alaska Satellite
Facility processed by ESA-2019). Both Sentinel-1 and Sentinel-2 data are used to compute the
displacement and evaluate relationships between the displacement and environmental variables.
The Sentinel data products are made available systematically and free of charge to all data users
including the general public, scientific and commercial users.
The Sentinel-1 constellation consists of 2 satellites, Sentinel-1A and Sentinel-1B, which have
been operating since October 2014 and April 2016. The images are available in the Level 1 Ground
Range Detected (GRD). Level-1 GRD data with High Resolution HR), 250 km Interferometric
Wide (IW) swath images in C-band through all-weather conditions with 20m ground resolution.
Images are available in dual VV and VH polarizations. Sentinel-1 also has frequent imagery
over this area of Yamal; since 2017, it is a focus area for crater outburst monitoring.
The Sentinel-2 constellation consists of 2 satellites, Sentinel-2A and Sentine-2B, which
have been operating since June 2015 and March 2017. Their spatial resolution is 10, 20, or
60 m depending on the bands, providing a 5-day temporal resolution by combining the two
satellites, and a 290 km swath. Images were downloaded with processing level 1C on the
https://earthexplorer.usgs.gov/platform.
Table 1
Satellite data.
Spatial Resolution Polarization
No. Data Type Date of Acquisition
(bands) Channels/Spectral Bands
10 m (2, 3, 4, 8), 12.08.2016
1. Sentinel-2 Optical 20 m (5, 6, 7, 8a), 24.08.2017 13 multispectral bands
60 m (1, 9, 10) 21.07.2018
2. Sentinel-1 SAR 20 m 20.08.2018 VV and VH polarization
Table 2
Field data.
Field data of August–September
Symbol Class
2016 2017 2018
B Barrens 411 411 437
FW Fresh water 687 687 728
W3 Sedge, moss, low-shrub wetland 619 619 656
S1 Erect dwarf-shrub tundra 21 21 22
S2 Low-shrub tundra 249 249 264
TOTAL: 1987 1987 2107
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2.3. Methodology
2.3.1. Land cover classes of Central Yamal
Central Yamal land cover is characterized by five dominant classes [1, 2, 7, 8]: Barrens (B); Fresh
water (FW); Sedge, moss, low-shrub wetland (W3); Erect dwarf-shrub tundra (S1); Low-shrub
tundra (S2) (see Table 2).
2.3.2. Image processing and analysis
Atmospheric correction of Sentinel-2 (A, B) scenes was performed using module Sen2Cor of
SNAP software.
To map vegetation cover, we applied the supervised classification approach for both Sentinel-2
imagery and synergy of Sentinel-2 and Sentinel-1 imagery. The supervised classification was
done using ML Classifier implemented in ERDAS Imagine. This method has been shown in
previous works to be efficient for the classification of general land cover types by optical and
SAR imagery [14, 18].
Figure 3: Spectral bands of Sentinel-2.
Table 3
Combinations of Sentinel-2 bands [20] and Sentinel-1 VV/VH-polarizations.
Combination R1 G1 B1 R2 G2 B2
Natural Colors S-2: RGB= (4, 3, 2) 4 3 VV 4 3 VH
False color Infrared S-2: RGB= (8, 4, 3) 8 4 VV 8 4 VH
False color Urban S-2: RGB= (12, 11, 4)
12 11 VV 12 11 VH
Atmospheric penetration S-2: RGB= (12, 11, 8𝑎)
Vegetation Analysis S-2: RGB= (11, 8, 4)
11 8 VV 11 8 VH
Agriculture S-2: RGB= (11, 8, 2)
Healthy vegetation S-2: RGB= (8, 11, 2)
8 11 VV 8 11 VH
Land/Water S-2: RGB= (8, 11, 4)
Natural Colors with Atmospheric Removal S-2: RGB= (12, 8, 3)
12 8 VV 12 8 VH
Shortwave Infrared S-2: RGB= (12, 8, 4)
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The supervised ML classification was run in the following steps.
(1) Training data: creating spectral signature for each supervised training sample (area of
interest) derived from ground truth data.
(2) Maximum-Likelihood Classification: classification based on the minimum Mahalanobis
distance.
A preliminary visual pattern analysis was performed for combined optical and SAR data on
the basis of
1) assumption that high-resolution SAR data makes it possible to use textural (contextual)
characteristics
2) RGB combinations relying on band spectral properties of Sentinel-2 (Figure 3) and VV/VH
polarizations of Sentinel-1 (Table 3).
3. Results
3.1. Results of Sentinel-2 data processing
Figure 4 presents the results of Sentinel-2 classification for the study territory in 2016-2018.
During this period, there was the total reduction (from 28% to 9%) of open (free from vegetation)
areas (Figure 5). At the same time, there was an overall increase in areas related to vegetation:
a b c
Figure 4: ML classification results for Sentinel — 2 scenes: 12.08.2016 (a), 24.08.2017 (b) and 21.07.2018 (c).
a b c
Figure 5: Distribution of class areas based on Sentinel-2 data processing: 12.08.2016 (a), 24.08.2017 (b)
and 21.07.2018 (c).
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— the largest increase occurred for Low-shrub tundra (S2), i.e. 10 times (2016–2017);
— the increase from 3% to 27% occurred for Erect dwarf-shrub tundra (S1) (2016–2018);
— the twofold increase occurred for Sedge, moss, low-shrub wetland (W3) (2016–2017).
Fresh Water class is predominant on the territory of Central Yamal. The total area of water
bodies in 2018 was 34% (Figure 5, c). The total area of vegetation classes is 57%, 27% of which
account for Erect dwarf-shrub tundra (S1). Open areas and sands occupy 9% of the study
territory.
3.2. Results of combined Sentinel-2 and Sentinel-1 data processing
For co-processing, Sentinel-2 and Sentinel-1 scenes of 2018 were used (see Table 1).
Qualitative differences in visual representations of Sentinel-2 and Sentinel-1 synergy make
it possible to choose combinations for the most effective representation of target classes (Ta-
ble 4). For some combinations, there is decrease (−) and/or increase (+) in the highlighting
of anthropogenic classes (Barrens, Fresh Water). For example, for R1 G1 B1 = (8, 11, 𝑉 𝑉 ) the
highlighting of Barrens decreases and the highlighting of Fresh Water increases.
The results obtained for combinations R1 G1 B1 = (4, 3, VV) and R2 G2 B2 = (4, 3, VH) were
thoroughly studied for
— pixel-wise ML classification (Table 5, Figure 6);
— ML classification based on contextual analysis (Table 6, Figure 7).
It is appeared that contextual analysis can significantly improve the results compared to
pixel-by-pixel processing. For Sentinel-2 data, a significant improvement occurs for all classes,
except for Fresh Water (yet, high accuracy of 90% remains for this class). For the synergy of
Sentinel-2 and Sentinel-1 data, the improvement occurs for almost all classes (up to 100%).
Table 4
Class highlighting for synergy of Sentinel-2 and Sentinel-1.
Object highlighting efficiency
1 1 1 Natural
R G B objects (N) Fresh
Combination Barrens Water
R2 G2 B2 Anthropogenic Vegetation classes (+/−) (+/−)
objects (A)
N, A
4, 3, VV Difficult to discern,
Natural Colors (equally + +
4, 3, VH transitional areas
effective)
8, 4, VV Difficult to discern,
False Color Infrared A + −
8, 4, VH no clear contours
False Color Urban, 12, 11, VV A (water,
W3, S1 + +
Atmospheric penetration 12, 11, VH buildings)
Vegetation Analysis, 11, 8, VV
N W3, S1, S2 − +
Agriculture 11, 8, VH
Healthy Vegetation, 8, 11, VV
N W3, S1, S2 − +
Land/Water 8, 11, VH
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Table 5
Results of pixel-wise ML classification.
% of matches with test set
Class Sentinel-2 Sentinel-2 & Sentinel-1 Sentinel-2 & Sentinel-1
RGB= (4, 3, 2) R1 G1 B1 = (4, 3, VV) R2 G2 B2 = (4, 3, HV)
Sedge, moss,
36.9 11.6 11.5
low-shrub wetland
Erect dwarf-shrub tundra 73.7 80.7 80.5
Low-shrub tundra 66.8 15.4 12.6
Barrens 66.5 41.3 41.3
Fresh water 93.2 67 63.3
a
b
Figure 6: Visual representation of synergy Sentinel-2 and Sentinel-1: R1 G1 B1 = (4, 3, VV) (a);
R2 G2 B2 = (4, 3, VH) (b).
Table 6
Results of ML classification based on contextual analysis.
% of matches with test set
Class Sentinel-2 Sentinel-2 & Sentinel-1 Sentinel-2 & Sentinel-1
RGB= (4, 3, 2) R1 G1 B1 = (4, 3, VV) R2 G2 B2 = (4, 3, HV)
Sedge, moss,
60.7 23.6 23.5
low-shrub wetland
Erect dwarf-shrub tundra 94.2 100 100
Low-shrub tundra 97.9 50.3 47.6
Barrens 99.6 76.9 76.9
Fresh water 90.5 100 100
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a b
Figure 7: Synergy of Sentinel-1 and Sentinel-2 classification results: R1 G1 B1 = (4, 3, VV) (a);
R2 G2 B2 = (4, 3, VH) (b).
In case of pixel-wise classification, the use of VV and VH polarizations increases the accuracy
by 6% for the Erect dwarf-shrub, while in case of contextual analysis — for classes Erect dwarf-
shrub tundra and Fresh Water up to 100% (Erect dwarf-shrub and Fresh Water — by 6% and 10%,
respectively).
4. Discussion
For the period 2016–2018 in the Bovanenkovo surroundings, there is a general reduction of
areas unoccupied with vegetation that is consistent with the studies demonstrating Arctic
greening [7, 8, 21].
It is obvious that even for a band combination, which is inefficient from the point of view
of clear distinction of vegetation classes, an additional use of SAR data increases the results
of one of the vegetation classes. This is consistent with other studies [17, 18] in the sense that
co-processing of optical and SAR data provides the improved results as compared to the use of
only optical data.
Therefore, to obtain the comprehensive assessment of synergy of Sentinel-1 and Sentinel-2
data in the context of Sentinel-2 band combinations for Central Yamal, it is necessary to classify
the study territory scenes for cases of all possible band combinations and full set of Sentinel-2
bands.
5. Conclusion
Due to rapidly advancing climate change, there is a great need for monitoring Arctic vegetation.
Substantial changes in vegetation cover and its composition have been observed in many areas
across the Arctic. Because of regional variations in vegetation dynamics, there is a need for the
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detailed analysis of vegetation patterns and changes, which may be driven by local factors (i.e.,
disturbance regimes). Our goal was to develop remote-sensing tools for monitoring (and better
understanding) of variations in the vegetation system of Central Yamal.
ML classification based on high-resolution multispectral data as well as visual pattern analysis
of combined multispectral and SAR data show high-performance in obtaining the information
on five land cover classes of Central Yamal:
1. Open spaces (free from vegetation).
2. Open aquatic areas.
3. Sedge, moss, shrub wetlands.
4. Tundra vegetation with a height of less than 40 cm.
5. Tundra vegetation with a height of more than 40 cm.
The use of Sentinel-2 and Sentinel-1 synergy in various band combinations may be efficient for
identifying anthropogenic (Fresh Water, Barrens) and natural objects (Sedge, moss, low-shrub
wetland, Erect dwarf-shrub tundra, Low-shrub tundra).
In particular, the combination of high-resolution spectral (10 m, Red, Green) and SAR (VV
and VH polarizations) data increases the classification accuracy for Fresh Water and Erect
dwarf-shrub tundra classes up to 100% (by 10% and 6%, respectively). The impact of VV and VH
polarizations differs insignificantly.
The study findings may contribute to the mapping of arctic vegetation, since the methodology
improves the recognition accuracy. The constructed maps are of interest for assessing the
ecological state and dynamics of vegetation cover.
Acknowledgments
This study was carried out as a part of State Task (Projects 0306-2021-0007, 121031200178-8) of
the Institute for Water and Environmental Problems SB RAS with the financial support of the
Non-Profit Partnership “Russian Center for Arctic Development” (Salekhard).
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