Remote sensing for monitoring forests
in northern Russia, results and current
challenges
Selected outcomes of the research project on Multiplatform remote sensing of
the impact of climate change on northern forests of Russia
Olga Tutubalina1, Gareth Rees2, Sergey Bartalev3, Andrey Medvedev4,
Gareth Marshall5, Vasily Zharko3, Nikolay Shabanov3, Elena Golubeva1,
Oleg Zheleznyy1
1 M.V. Lomonosov Moscow State University, Moscow, Russia
2 Scott Polar Research Institute, University of Cambridge, Cambridge, United Kingdom
3 Institute of Space Research of the Russian Academy of Sciences, Moscow, Russia
4 Institute of Geography of the Russian Academy of Sciences, Moscow, Russia
5 British Antarctic Survey, Cambridge, United Kingdom

Japan-Russia Scientific Forum. Online Workshop “Japan-Russia Collaboration for Ecosystem Monitoring”
15-16 September 2021

Background
Numerous studies point to forest and shrub tundra advance, and to the Arctic amplification—a more intense warming in the Arctic than over the rest of the globe
Since 2000, Terra/Aqua MODIS provides near-daily (weekly if account for clouds) imagery at
250 m resolution to monitor Arctic and subarctic vegetation, a high quality dataset to analysephenological changes. But MODIS maps should be adapted for high latitudes
Multi-scale validation methods need to be developed for MODIS-derived maps (field-highresolution images-MODIS images)

Behaviour of the circumarctic forest-tundra
Ecotone in 19th- early 21st century. Rees, Hofgaard et al., 2020. Is
subarctic forest advance able to keep pace with climate change?
Global Change Biology

2Project goal:

The multiscale concept

to develop a methodology for assessing the dynamics of phytomass ofnorthern forests of Russia since 2000 due to climate change, toprovide such an assessment with validation at key sites, based onprocessing of multiplatform satellite and aerial imagery, groundsurveysAnalysis of MODIS LAI (Leaf Area Index), GSV (Growing Stock Volume) data
series since 2000 г., comparison with climate dynamics for the whole ofnorthern Russia. Determining the role of climate and other factors withinmodel territories
Scaling up LAI and GSV data from the ground and UAV surveys to MODIS(through intermediate imagery from Sentinel 2 satellites), to validate
MODIS maps

Assessing tree stand parameters (including LAI, GSV) within model
territories using unmanned aerial vehicle (UAV) survey (from 50 and 100m), field measurements and descriptions
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Datasets and methods
Leaf area index (LAI)=
one-sided green leaf area
per unit surface area

Temperature dataset: ERA Interim
reanalysis
selected by comparing vs. station data.
Less noisy than ERA5

Cumulative sums SAT∑ of t > 5 C

Daily MODIS LAI based ona physical model

7-day cloud-masked
composites withmedian LAI value
Temporally interpolated
7-day LAI to fill gaps
and smooth seasonal profiles

LAI dynamics: regression ofwhole seasonal profiles
Regressed LAI
profile for 2019

Regressed LAI
profile for 2000

(earlierspring)(earlierfall)
DOY

4Seasonal trends for 8 dates, ∆LAI, ∆(SAT∑) in 2000-2019

Seasonal trends for 8 dates,
correlation (∆ LAI, ∆ SAT∑)

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MODIS LAI trends and cumulative temperature SAT∑ for 17 June, 2000- 2019
LAI trends were assessed
throughout all seasonal
profile with a 7-day
interval. A linear
regression was performed
for each interval for the 20years of MODIS
observations (2000-2019).
Results are shown as
relative change in LAI(∆LAI, %).

Cumulative temperature is
calculated since 1 Marchand only for average day
temperatures above +5С.
Then linear trends ∆SAT∑
are calculated. Timeintervals correspond to theLAI product. ERA Interimdataset.

In spring the green leaf biomass is conditioned by cumulative temperature. The same is true for trends.
Therefore the spatial distribution of LAI trends is very well explained by the SAT trends.

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LAI & SAT∑, Taimyr peninsula, 01 July
∆LAITMRLC

LAI in 2003

LAISTARTLAIEND77

Key field regions
Will show central Kola Peninsula and centralKola
Sakha (detailed field data), Noril’sk region (oldfield data, RS data analysis)
Norilsk
Sakha8

111

Image courtesy: A.Tyagny-Ryadno

Noril’sk key region, area of largescale industrial degradation
since 1960s

Maximum degradation is recordedin the Rybnaya River valley(SE from the metallurgical plants):
death of larch forests, degradinglichen cover in boggy areas,
expansion of grasses and sedges

Figure from Kirdyanov et al., 2020

9Changes of Normalised Difference Vegetation Index (NDVI) for Noril’sk in
1985-2019, imagery from Landsat satellites, Google Earth Engine
5/14•Imagery screened for cloud cover•Computed maximum NDVI for summer period (July 1st – August10th) for each year from Landsat 5-8 Surface Reflectance;•max NDVI changes show overall increase, small areas ofdecrease, in the mountains and industrial built-up areas.

•How to determine statistically significant change?

-> analysis of the whole dataset: statistical testing of every pixel.

1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019

80
60
40
200

Landsat 5Landsat 7

Landsat 8

Availability of Landsat imagery for the AOI 1985-201910

6/144/1

Mann-Kendall Trend Analysis

Normalised difference
between the numbersof each of following
cases:

Yx+1 > Yx
andYx+1 < Yx
Where Y is max NDVI each year

* Area showing
significant trends hasenough observations

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The greatest NDVI growth: small valleys

10/14
8/0

1.00
0.80

R² = 0.7383

0.60
0.40

R² = 0.8309

0.20
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
2015
2017
2019

0.00

Pollution-resisitant grasses (Equisetum sp., Graminea grasses)
and shrubs (willow, alder) are present and regenerating.
However, regeneration of larch trees is only happening in someareas further away from the pollution source (field data of 2021).

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Comparison of SAT∑ and max NDVI trends
13/14 1200.00

0.7

y = 0.0022x + 0.5156
R² = 0.1781
0.6

1000.00

0.5
800.00600.00y = 6.2111x + 635.45
R² = 0.2232

0.4

0.3
400.000.2

200.00

0.00

0.1

0

Cumulative summer temperatures above +5°С between May 1st – August 10th 1985 –2019 within 68-70°N and 85-90°N based on ERA-Interim reanalysis data (blue line, leftaxis) and maximum NDVI averaged over the key region (red line, right axis).

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Future work and challenges• Case-study time series for other key regions (Sakha, Kola)• Analysis of exceptional forest fire activity and recent climate inSakha• National-scale analysis of precipitation (snow and rain) trends
vs. forest biomass trends. Presumably trends and correlations
less evident than for temperatures, area-by-area analyses may
be required

THANK YOU FOR YOUR ATTENTION!
This research has been supported by the Institutional Links programme, funded by theBritish Council (Grant no. 352397111) and the Ministry of Science and Higher
Education of the Russian Federation (Grant Agreement No. 14.616.21.0099 dated 27
February 2018, project RFMEFI61618X0099).
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