lars eklundh dept. of physical geography and ecosystem

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


Global warming and vegetation

2016Effects on:

• Vegetation growth

• Vegetation zones

• Growing seasons

• Carbon and water cycles

• Vegetation disturbances

• Human livelihood


Time series analysis in remote sensing

Output maps

Time series of images

Pixel based analysis


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.


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


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.


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


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


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


Mapping forest growing seasons at high spatial resolution from Landsat

False colour Seasonal maximum Day of

composite NDVI start-of-season


Effect of clear-cutting of a coniferous pixel

regrowthclear felling


High-resolution growing seasons from Sentinel-2a

Built-up area

Courtesy: Zhanzhang Cai


Mapping Sentinel-2 seasons using TIMESAT


Thank you!

E-mail: [email protected]

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

mailto:[email protected]


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.

lars eklundh dept. of physical geography and ecosystem

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