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Remote Sens. 2016, 8(8), 639; doi:10.3390/rs8080639

Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series

1
Institute of Remote Sensing and Photogrammetry, Graz Technical University, Steyrergasse 30/I, Graz 8010, Austria
2
Institute for Applied Remote Sensing, European Academy of Bozen/Bolzano (EURAC), Viale Druso 1, Bolzano 39100, Italy
3
European Environment Agency (EEA), Kongens Nytorv 6, Copenhagen K 1050, Denmark
*
Author to whom correspondence should be addressed.
Academic Editors: Angela Lausch, Marco Heurich, Lars T. Waser and Prasad S. Thenkabail
Received: 26 April 2016 / Revised: 24 July 2016 / Accepted: 27 July 2016 / Published: 5 August 2016
(This article belongs to the Special Issue Remote Sensing of Forest Health)
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Abstract

Observed alternation of global and local meteorological patterns governs increasing drought impact, which puts at risk ecological balance and biodiversity of the alpine forest. Despite considerable attention, drought impact on forest ecosystems is still not entirely understood, and comprehensive forest drought monitoring has not been implemented. In this study, we proposed to bridge this gap exploiting a time-domain synergetic use of medium resolution MODSI NDVI (Normalized Difference Vegetation Index) and NDII7 (Normalized Difference Infrared Index band 7) time series as well as on-station temperature and precipitation measures combined in the scPDSI (self-calibrated Palmer Drought Severity Index) datasets. Analysis employed the S-mode Principal Component Analysis (PCA) examined under multiple method settings and data setups. The investigation performed for South Tyrol (2001–2012) indicated prolonged meteorological drought condition between 2003 and 2007, as well as general drying tendencies. Corresponding temporal variability was identified for local mountain forest. The former response was fostered more often by NDII7, which is related to foliage water content, whereas NDVI was more prone to report on an overall downturn and implied drop in forest photosynthetic activity. Among tested approaches, the covariance-matrix based S-mode PCA of z-score normalized vegetation season NDVI and NDII7 time series ensured the most prominent identification of drought impact. Consistency in recognized temporal patterns confirms integrity of the approach and aptness of used remote-sensed datasets, suggesting great potential for drought oriented environmental analyses. View Full-Text
Keywords: S-mode Principal Component Analysis (PCA); drought; forest; MODIS; NDVI; NDII7; scPDSI; the Alps; South Tyrol S-mode Principal Component Analysis (PCA); drought; forest; MODIS; NDVI; NDII7; scPDSI; the Alps; South Tyrol
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lewińska, K.E.; Ivits, E.; Schardt, M.; Zebisch, M. Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series. Remote Sens. 2016, 8, 639.

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