Page 1 Combine satellite-based geoinformation, ground measurements and statistical methods to assess...

Combine satellite-based geoinformation, ground measurements and statistical

methods to assess green-up trends in earth observational studies

Doktor, D. Helmholtz Centre for Environmental Research – UFZ

Contact: [email protected]

Two observational sets:Green-up dates dGU derived from 1 [km] NDVI vs ground observations

of budburst dBB

How can the two sets be compared ?

mailto:[email protected]@

Why are we interested in trends in green-up dates ?

Plant Phenology, what is it ?

• Phenology is the study of the timing of recurring life cycle events

• For plants such events are budburst, leaf unfolding, blossoming, fruit ripening, leaf colouring etc.

History: Time series since ~815 AD (cherry blossom in Japan)Phenological observation network started in 1882Modern phenological networks include the International Phenological Gardens and National Networks

Why is phenology important ?

• Track biotic response to climate change

• Strongly influences atmospheric dynamics in the lower boundary layer

• Strong biophysical control on climate model simulations

• Estimating biological productivity

• Understanding land-atmosphere interactions

• Modelling vegetative inputs into biogeochemical cycles

Sets of geoinformation

• Satellite data: Daily observations of the Normalized Difference Vegetation Index. Influenced by clouds, atmospheric haze, viewing angle, sun position etc.

• German Weather Service (DWD) phenological database, ground observations: Budburst dates of wildgrowing species

• CORINE (Coordinated Information on the Environment) land-use cover map

• Digital elevation model (DEM), 1*1km resolution over Germany

Satellite products

• Range of satellite products => Landsat, AVHRR, MODIS, Spot with all different spectral, temporal and spatial resolutions

• Terra-MODIS data downloadable at no costs => Surface reflectance, NDVI, EVI, surface temperature etc.

• Longer time series (since 1981) are provided by NOAA-AVHRR, but lower resolution and data quality

Satellite derived vegetation information - NDVI

• Daily observations or composites of 1*1 km spatial resolution

• Based on reflection differences in the near-infrared red wave lengths

• Values generally between 0-1RNIR

RNIRNDVI

NDVI composites over the Hainich from 1991

Range of methods to compute green-up dates => half-way between min and max, steepest slope, inflexion point of sigmoidal curve

Extract green-up date

Satellite and ground observational data

NDVI, 1989-2004, NOAA/AVHRR, 1 km

Atmospheric filtering and corrections

(Koslowsky et al. 2001, Koslowsky et al. 2003)

Dynamic filtering BISE (Viovy et al. 1992)

Ground observations,

Observational network of German Weather Service (DWD)

For Beech and Oak (N = 35195)

(Schaber & Badeck 2002, 2005)

CORINE land-use data set

Scaling ground observations to the scale observed by satellites

• In contrast to remote sensing ground observations do generally not provide spatially integrated data => individual plant vs canopy

• Low resolution, cloud contamination, atmospheric interferences, slight variation inthe geolocation

• Green-up of understorey, influence of surrounding pixels (sensor‘s spatialreponsitivity)

• Missing habitat maps for potential decomposition of the satellite signal

Geostatistical interpolation

• External Drift Kriging (EDK) using a Digital Elevation Model of Germany (1*1 km) as external variable

• Physical relationship between external variable and variable to estimated => Phenological pace dependent on tem-perature

Comparison of the two sets – a statistical approach

• Geographically co-incident data of ground and satellite observations are not directly compared. Statistical approach comparing the parameters of the probability density function of the respective set (Doktor et al. 2005; Badeck et al. 2006)

• Generation of subsets with different proportions of non-forest landuse according to CORINE (resolution of 0.25 x 0.25 km). Those subsets of 1 x 1 km forest pixels are

classified with up to 0 through 9 non-forest 0.25 x 0.25 subpixels

• Additionally, a subset is produced of which the mid-point of the NDVI pixels is inside a CORINE deciduous forest subpixel. This subpixel has to be surrounded by at least

three rows of deciduous forest on any side

Expectation: slightly later green-up date than observed budburst

Result: generally substantially earlier green-up than budburstHypotheses: strong impact of non-tree vegetation elements

on the signal, even in mainly forested landscapes

At 1 km resolution, satellite derived green-up dates for pixels classified as deciduous forest are very sensitive

to admixture of other vegetation elements

Heterogeneity is a major cause for discrepancies

µ Q1 Q3 SD

1989 0.99 0.98 0.99 0.85

1990 0.99 0.99 0.96 0.75

1991 0.97 0.84 0.97 0.28

1992 0.59 0.86 0.00 0.74

1993 0.99 0.98 0.95 0.64

1994 0.11 0.46 0.00 0.81

1995 0.98 0.99 0.99 0.72

1996 0.71 0.87 0.45 0.89

1997 0.73 0.75 0.75 0.73

1998 0.96 0.94 0.99 0.62

1999 0.97 0.90 0.99 0.33

2000 0.99 0.98 0.82 0.85

2001 0.98 0.91 0.99 0.67

2002 0.92 0.89 0.96 0.60

2003 0.76 0.71 0.71 0.55

2004 0.96 0.97 0.93 0.05

Differences between the pdf's parameters of dGU and dBB are calculated. The relationship of these differences and the number of non-forest sub-pixels is modelled using a linear regression baxy

Green-up dates on a continental scale

Observed green-up trends

Myneni et. al (1997): increased plant growth in high northern latitudesSchwartz et. al (2006) : earlier SOS over the Northern HemisphereStöckli et. al (2004): Trend for earlier green-upDelbart et. al (2004,2005): Earlier green-up over Siberia

• Studies based on satellite data with different spatial and spectral resolution• Use of different methods to determine green-up dates• Mostly done on continental scale with homegeneous land-use

Trend of green-up dates

Conclusions I

The Statistical approach most promising because remote sensing provides a statistical distribution of a random variable, not an exact representation of the state of the land surface or atmosphere at

a particular pixel. The same holds true for ground observations as they sample from biological variability and landscapes with heterogeneous microclimates

At 1 km resolution and in highly fragmented landscapes, a heterogeneous vegetation cover is a major cause for differences between satellite derived green-up dates and

date of budburst of the main vegetation element observed on the ground.

Therefore, at this spatial scale, green-up dates derived from satellite data and ground observations record two intrinsically different but related traits.

Both can be linked potentially only through the use of high resolution land cover data and thus ground observation can be used for validation purposes with such an analysis of mixed signals

Conclusions II

Ground observational networks still provide unique information about plant phenology => go out and observe

Detected green-up trends based on satellite are of course valid but results have to be

evaluated carefully

Carbon Sequestration dependent on changing temperature patterns and land-use change

Earlier start of the season increases sequestration, 16 % more absorbed PAR (negative feedback)

C-sequestration varies with land-use, i.e. forest se- questers more than crops

IPCC Guidelines for National Greenhouse Gas tories (GNGG)

Thank you for your attention !

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