2 EC JRC, IES, Land Resource Management Unit, Via E. Fermi 1, 21027 Ispra, Italy ... Arising aridity and droughts become severe threat to the environment.
Drought assessment in the alpine forest of South Tyrol – EOF analyses of MODIS derived time series of vegetation indices Katarzyna Ewa Lewińska1,3, Eva Ivits2, Mathias Schardt3, Marc Zebisch1 1 2 3
Institute for Applied Remote Sensing, EURAC, Viale Druso 1, 39100 Bolzano, Italy EC JRC, IES, Land Resource Management Unit, Via E. Fermi 1, 21027 Ispra, Italy / EEA, Kongens Nytorv 6, 1050 Copenhagen K, Denmark Graz Technical University, Institute of Remote Sensing and Photogrammetry, Steyrergasse 30/I, 8010 Graz, Austria
Introduction Observed recently changes in global and local patterns of temperature, precipitation and insolation lead to increasing number of extreme events. Arising aridity and droughts become severe threat to the environment. The Alps, and in particular alpine forest are one of the most ecologically vulnerable regions in Europe, which has already been subjected to climate change induced uphill shift of vegetation belts and alternation in phenological dynamism. In order to understand and mitigate ongoing changes extensive forest monitoring is needed.
Results Eigenvectors identified as carrying a potential physical meaning of vegetation response to prolonged drought conditions (Figure 3): • 3CORNDVI8-18 (3rd loading correlation based EOF of veg. season NDVI) • 2COVnNDVI8-18 (2nd loading covariance based EOF of normalized veg. season NDVI) • 4COVnNDII8-18 (4th loading covariance based EOF of normalized veg. season NDII) Each was pixel-based correlated with the appropriate time series (NDVI or NDII) and the 95th percentile was recognized as drought affected zone (Figure 3).
Comparing with traditional forest inventory procedures remote sensing techniques provide a robust and fast approach for time and space related vegetation analyses. In our study we propose an omnibus method for medium to large scale long-term vegetation drought stress monitoring.
Study site and data South Tyrol (Figure 1) – a typical alpine region with complex orography. Forest: 42.8% (3170.5 km²) of the area: coniferous: 90.1% mixed: 7.3% broadleaved 2.6% Complex vegetation structure due to the elevation gradient and resulting climatic conditions.
Figure 3 Temporal (right) and spatial (left) representations of the identified drought affected forest regions in South Tyrol Figure 1 South Tyrol. Location of meteorological stations marked in black
Long time series (min. 25 years) of on-station meteorological monthly precipitation and temperature records (Figure 1)
scPDSI (2001-2012) (self-calibrated Palmer Drought Severity Index2)
MOD13Q1 2000-2013 time series (250 m resolution, 16-day product)
• NDVI (2001-2013) • NDII (2001-2013) • Phenological indicators (2001-2012) [Phenolo algorithm1]
Drought impact on phenology within each strata was investigated by the means of repeated ANOVA measures (Figure 4). • below 1400 m asl • dominates hardwood forest • lower parts of south exposed slopes • coniferous and mixed forest • coniferous stands • 700-2100m asl
Meteorological conditions Figure 4 Phenological response within drought affected sites
Conclusions • intense meteorological drought conditions between 2002 and 2007 • the most severe meteorological drought observed in the central part of the region
Figure 2 scPDSI 2001-2012 on-station time series (left; numbers consistent with Figure 1), and three dominant variability patterns identified through the EOF decomposition of the scPDSI data (right). Explained variance in brackets.
Empirical Orthogonal Function (EOF) EOF (or PCA – Principal Component Analysis) is a statistical approach for analyzing variance of a signal in data. It convolutes the original time series into the new orthogonal time and space patterns (eigenvectors and their spatial representations), where the variance of each component is maximized. We tested multiple EOF approaches and data setups: • NDVI and NDII time series • correlation & covariance matrix based EOF • not-normalized & normalized data (normalization from the composite mean) • complete (Jan-Dec), vegetation season (Apr-Oct) and high-season (Aug-Sep) time series • raw data & after Savitzky-Golay3 time domain filtering • secondary rotation approach (Promax4 and Varimax5) 20 setup combinations + rotations Eigenvectors evaluated against the scPDSI temporal variability in search for coincide temporal patterns of drought impact.
• EOF proved to be a reliable method for identification of subtle vegetation changes in the complex and diverse alpine forest environment • MODIS time series provided good overview of the drought impact • NDVI and NDII time series ensured diverse and complementary results • vegetation season time series (Apr-Oct) performed the best • • • • • • •
strong response to the heatwave of 2003 vegetation type dependent responses to the prolonged dry spell 2003-2007 drought impact and phenology response differ within elevation and aspect classes the strongest drought impact observed on the lowest elevations hardwood species benefited from the dry spell increasing seasonal dynamism (CF) and trend for earlier vegetation onset (SBD) significant long term trends (possible snapshot of longer transformation processes)
Bibliography 1 2 3 4 5
- Ivits, E., et al., 2013, Ecol. Indic., 27, 17–28. - Wells, N., et al., 2004, J. Clim., 17, 2335–2351 - Chen, J., et al., 2004, Remote Sens. Environ., 91(3-4), 332–344. – Kaiser, H., 1958, Psychometrika, 23, 187-200 – Hendrickson, A.E., White, P.O., 1964, Br. J. Stats. Psychol., 17, 65-70
Acknowledgements The study was carried out at EURAC (European Academy of Bolzano) as a part of the GMES/Copernicus EU-FP7 EUFODOS project (European Forest Downstream Service; http://www.eufodos.info).
