lars eklundh dept. of physical geography and ecosystem
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Lund University / Department of Physical Geography and Ecosystem Science
Remote sensing of vegetation production
Lars EklundhDept. of Physical Geography and Ecosystem Science
Contents
• Vegetation production
• Time series analysis
• Plant Phenology Index (PPI)
• High-resolution seasonality mapping with Sentinel-2
Copernicus Training and Information Session, Lund, May 10-11, 2017
Lund University / Department of Physical Geography and Ecosystem Science
Global warming and vegetation
2016Effects on:
• Vegetation growth
• Vegetation zones
• Growing seasons
• Carbon and water cycles
• Vegetation disturbances
• Human livelihood
Lund University / Department of Physical Geography and Ecosystem Science
Time series analysis in remote sensing
Output maps
Time series of images
Pixel based analysis
Lund University / Department of Physical Geography and Ecosystem Science
Extracting vegetation seasonality parameters
startend
length
smallintegral
peak datepeak value
base level
amplitude
largeintegral
TIMESAT for data smoothing, outlier removal, and seasonality extraction
Eklundh, L., and Jönsson, P., 2015, TIMESAT: A software package for time-series processing and assessment ofvegetation dynamics. In Kuenzer et al. (eds), Remote Sensing Time Series, Remote Sensing and Digital ImageProcessing. Springer.
Lund University / Department of Physical Geography and Ecosystem Science
Mapping regional growing seasons
Jin et al., 2017, Manuscript in revision
Based on PPI and MODIS NBAR 500m data
Start of season End of season
Lund University / Department of Physical Geography and Ecosystem Science
Trends across in seasonality across 15 yearsSOS trend EOS trend LOS trend
Spatial relation SOS – mean T Sensitivity of LOS to temp and precip
Hongxiao Jin et al. in prep.
Lund University / Department of Physical Geography and Ecosystem Science
Satellite vegetation indexPPI: Plant Phenology Index
Jin and Eklundh, 2014, A physically based vegetation index for improved
monitoring of plant phenology. Rem Sens Env, 152.
• Has a biophysical basis
• Linearly related to green leaf biomass variations
• Works in dense canopies, e.g. coniferous forests
• Only slightly affected by snow
• Well correlated with carbon uptake
Carbon uptake PPI
Lund University / Department of Physical Geography and Ecosystem Science
Time-series of satellite data showing insect damage
-0,2
0
0,2
0,4
0,6
0,8
1
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
PP
I
Abisko, N. Sweden birch moth outbreak
Birch moth(Epirrita autumnata,
Operophtera brumata)
birch moth outbreak
Lund University / Department of Physical Geography and Ecosystem Science
Near real-time monitoring of vegetation loss
Insect outbreak, Abisko
Decrease in carbon uptake
GPP model
Olsson et al., 2016, Rem Sens Env, 181
Olsson et al., 2017, Biogeosciences, 14
• Based on MODIS 250 m resolution data
• 76% of the 100 km2 defoliated
• Annual carbon uptake loss nearly 50%
(20 Gg C) in 2012
Lund University / Department of Physical Geography and Ecosystem Science
Mapping forest growing seasons at high spatial resolution from Landsat
False colour Seasonal maximum Day of
composite NDVI start-of-season
Lund University / Department of Physical Geography and Ecosystem Science
Effect of clear-cutting of a coniferous pixel
regrowthclear felling
Lund University / Department of Physical Geography and Ecosystem Science
High-resolution growing seasons from Sentinel-2a
Built-up area
Courtesy: Zhanzhang Cai
Lund University / Department of Physical Geography and Ecosystem Science
Mapping Sentinel-2 seasons using TIMESAT
Lund University / Department of Physical Geography and Ecosystem Science
Thank you!
E-mail: lars.eklundh@nateko.lu.se
Special thanks to:
Zhanzhang Cai, Hongxiao Jin, Per Jönsson, Per-Ola Olsson
Web: http://www.nateko.lu.se/
TIMESAT: http://www.nateko.lu.se/timesat
Lund Earth Observation Group, the Geocenter in Lund
Lund University / Department of Physical Geography and Ecosystem Science
PPI formulation
DVI = difference VI
M = max of DVI over a time period
K = extinction coefficient
φ = canopy leaf filling factor
QE = canopy light extinction efficiency
G = leaf angular distribution function
θi = solar zenith angle
dc = diffuse fraction of solar radiation
χ = ratio of mean projected leaf area onto
horizontal and vertical planes
Jin, H. and Eklundh, L. (2014), A physically based vegetation index for improved
monitoring of plant phenology. Remote Sensing of Environment ,152:512-525.
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