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

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.

Drought Impact on Phenology and Green Biomass Production of Alpine Mountain Forest—Case Study of South Tyrol 2001–2012 Inspected with MODIS Time Series

“…Both were in strong accordance with local events as well as Pan-European meteorological conditions [60,69].…”

“…As discussed in [60], the first PC (1scPDSI, Figure 1a correspond with panels (a-d), and accounted for 63%, 9.95%, 7.36% and 5.61% of the total scPDSI data variance, respectively. Middle row: changes in forest canopy photosynthetic activity ('greenness') rendered in the first four PCs from covariance-matrix based PCA of the nNDVI (z-score normalized vegetation season NDVI) time series.…”

“…Being a statistical method, physical meaning of PCA results should be evaluated against known or expected variability or process [68]. Following findings from [60], NDVI and NDII7 time series were z-score normalized from a composite mean, and truncated to annual vegetation season time-windows corresponding with a period between April and mid October. Each resulting time series (nNDVI and nNDII7, respectively) was introduced into the covariance-matrix S-mode PCA under the forest mask.…”

“…Due to a mismatch in temporal resolution of both datasets (vegetation season vs. complete year, and 16-day vs. month, respectively), correlation was performed for yearly averages. A visual comparison between temporal pattern complemented the statistics [60] and allowed to determine NDVI and NDII7 based PCs with the strongest linear relation to identified meteorological drought conditions, hence representing forest response to drought. Selected PCs were subsequently correlated (pixel based Pearson's correlation) with their respective vegetation index time-series, resulting in a correlation map (as many maps as PCs selected).…”

“…Panels (i-l) depict PCs 1st to 4th that accounted for 11.98%, 3.39%, 2.23% and 1.66% of the total nNDII7 time series variance, respectively. Figure modified from [60].…”