B8, 2012
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
ANALYSING AND QUANTIFYING VEGETATION RESPONSES TO RAINFALL WITH
HIGH RESOLUTION SPATIO-TEMPORAL TIME SERIES DATA FOR DIFFERENT
ECOSYSTEMS AND ECOTONES IN QUEENSLAND
M. Schmidt , T. Udelhoven?
2 Department of Science, Information Technology, Innovation and the Arts, Remote Sensing Centre, 4001 Brisbane, Australia —
Michael .schmidt@derm.qld.gov.au
^ Remote Sensing and Geoinformatics, University of Trier, 54286 Trier, Germany - udelhoven@uni-trier. de
Commission VI, WG VI/4
ABSTRACT:
: KEY WORDS: Landsat, MODIS, NDVI, distributed lag models, time series
Vegetation responses and ecosystem function are spatially variable and influenced by climate variability. The Spatial and Temporal
Adaptive Reflectance Fusion Model (STARFM) was used to combine MODIS (Moderate Resolution Imaging Spectrometer) and
Landsat TM/ETM+ (Thematic Mapper/ Enhanced Thematic Mapper plus) imagery for an 8 year dataset (2000-2007) at 30m spatial
resolution with 8 day intervals. This dataset allows for a functional analysis of ecosystem responses, suitable for heterogeneous
landscapes. Derived vegetation index information in form of the NDVI (Normalised Difference Vegetation Index) was used to
investigate the relationship between vegetation responses and gridded rainfall data for regional ecosystems. A hierarchical
decomposition of the time series has been carried out in which relationships among the time-series were individually assessed for
deterministic time-series components (trend component and seasonality) as well as for the stochastic seasonal anomalies. While no
common long-term trends in NDVI and rainfall data in the time period considered exist, there is however, a strong concurrence in
the seasonally of NDVI and rainfall data. This component accounts for the majority of variability in the time-series. On the level of
seasonal anomalies, these relationships are more subtle. The statistical analysis required, among others, the removal of temporal
autocorrelation for an unbiased assessment of significance. Significant lagged correlations between rainfall and NDVI were found in
complex Queensland savannah vegetation communities. For grasslands and open woodlands, significant relationships with lag times
between 8 and 16 days were found. For denser, evergreen vegetation communities greater lag times of up to 2.5 months were found.
The derived distributed lag models may be used for short-term NDVI and biomass predictions on the spatial resolution scale of
Landsat (30m).
1. INTRODUCTION
The vegetative surface cover has an important function in the
earth system (Steffen and Tyson 2001) which is linked via
several feedback mechanisms to hydrological and
climatological processes. Identifying and quantifying these
linkages delivers important insight for environmental
modelling, management and informed decision making.
Satellite earth observation data with high temporal repeat
intervals such as AVHRR (Advanced Very High Resolution
Radiometer) or MODIS (Moderate Resolution Imaging
Spectroradiometer) deliver spatially dense information about
the earth surface and are well-suited for monitoring continental
scale surface processes.
Vegetation indices derived from earth observation systems have
proved to be useful to describe surface vegetation behaviour
(Tucker 1979). The most prominent of these is the Normalised
Difference Vegetation Index (NDVI) which is proportional to
the amount of photosynthetically active radiation absorbed by
green vegetation (Asrar et al. 1984). Spectral information from
the AVHRR sensor in channel 1 (0.58 to 0.68 micrometers) and
channel 2 (0.75 to 1.1 micrometers) are combined to formulate
the NDVI as follows:
NDVI=(ch2-ch1)/ch1+ch2
The NDVI is widely used to monitor vegetation (Tucker 1979)
and ecosystem processes (Pettorelli et al. 2005). For example,
Lotsch et al. (2005) used AVHRR time series data (1981-1999)
to monitor responses of terrestrial ecosystems to drought in the
northern hemisphere. Nemani et al. (2003) have studied the
climate-driven increase in global terrestrial net primary
production using AVHRR data, building bioclimatic indices
and vegetation growth limiting factors. Examples where NDVI
and rainfall were related are widespread in the literature (e.g.
Ecklundh, 1998, Anayamba & Tucker, 2005; Udelhoven et al.,
2009; Schmidt et al., 20102).
At regional or local scales are data with higher spatial
resolution required. Landsat Thematic Mapper (TM) imagery
have proven to be useful in many vegetation monitoring
applications at regional scale (Xie et al, 2008, Danaher, 2010).
A combination of MODIS and Landsat imagery via data fusion
have successfully be used to establish a temporally dense (e.g.
