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. Full article

The performance of a new algorithm for binary snow cover monitoring based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images at 250 m resolution is validated using snow cover maps (SCA) based on Landsat 7 ETM+ images and in situ snow depth measurements from ground stations in selected test sites in Central Europe. The advantages of the proposed algorithm are the improved ground resolution of 250 m and the near real-time availability with respect to the 500 m standard National Aeronautics and Space Administration (NASA) MODIS snow products (MOD10 and MYD10). It allows a more accurate snow cover monitoring at a local scale, especially in mountainous areas characterized by large landscape heterogeneity. The near real-time delivery makes the product valuable as input for hydrological models, e.g., for flood forecast. A comparison to sixteen snow cover maps derived from Landsat ETM/ETM+ showed an overall accuracy of 88.1%, which increases to 93.6% in areas outside of forests. A comparison of the SCA derived from the proposed algorithm with standard MODIS products, MYD10 and MOD10, indicates an agreement of around 85.4% with major discrepancies in forested areas. The validation of MODIS snow cover maps with 148 in situ snow depth measurements shows an accuracy ranging from 94% to around 82%, where the lowest accuracies is found in very rugged terrain restricted to in situ stations along north facing slopes, which lie in shadow in winter during the early morning acquisition. Full article

A new algorithm for snow cover monitoring at 250 m resolution based on Moderate Resolution Imaging Spectroradiometer (MODIS) images is presented. In contrast to the 500 m resolution MODIS snow products of NASA (MOD10 and MYD10), the main goal was to maintain the resolution as high as possible to allow for a more accurate detection of snow covered area (SCA). This is especially important in mountainous regions characterized by extreme landscape heterogeneity, where maps at a resolution of 500 m could not provide the desired amount of spatial details. Therefore, the algorithm exploits only the 250 m resolution bands of MODIS in the red (B1) and infrared (B2) spectrum, as well as the Normalized Difference Vegetation Index (NDVI) for snow detection, while clouds are classified using also bands at 500 m and 1 km resolution. The algorithm is tailored to process MODIS data received in real-time through the EURAC receiving station close to Bolzano, Italy, but also standard MODIS products are supported. It is divided into three steps: first the data is preprocessed, including reprojection, calculation of physical reflectance values and masking of water bodies. In a second step, the actual classification of snow, snow in forested areas, and clouds takes place based on MODIS images both from Terra and Aqua satellites. In the third step, snow cover maps derived from images of both sensors of the same day are combined to reduce cloud coverage in the final SCA product. Four different quality indices are calculated to verify the reliability of input data, snow classification, cloud detection and viewing geometry. Using the data received through their own station, EURAC can provide SCA maps of central Europe to end users in near real-time. Validation of the algorithm is outlined in a companion paper and indicates good performance with accuracies ranging from 94% to around 82% compared to in situ snow depth measurements, and around 93% compared to SCA derived from Landsat ETM+ images. Full article