## 1 Introduction  
The last century, and in particular the past three decades, brought a noticeable change in local and global weather patterns [1; 2]. Alternation of temperature, precipitation and insolation that result in prolonged climatic anomaly has recently been highlighted in numerous studies (e.g., [3; 4; 5; 6; 7]). Among others, the Alps experience accelerated climate change [8], with two-fold increase of temperature [9] and transformation of precipitation patterns [8; 10], which further enhance substantial dryness of the local climate [11]. Despite considerable resistance [12], the alpine environment is affected by climate change, which has triggered uphill shift of vegetation belts [13; 14; 15], local dieback processes (e.g., [12; 14; 16]) and overall sway of ecological balance [17; 18]. Consequently, alpine forest, which can be deemed as the largest and the most biodverse land cover of the Alps ([19; 20] (p. 13)) is subjected to increasing drought implications. Drought impact on forest, although seemingly apparent, is still entangled and loaded with uncertainty [21]. Forest drought influence is complex because even though a primary dry spell does not always directly lead to a dieback (e.g., [16; 22; 23]), it makes trees more vulnerable to secondary damages and disturbances such as windfalls, pest outbreaks [24], fire [25], fungus, parasites and pathogens infestations [14]. Furthermore, trees response to drought is governed by timing and intensity of an event, site-specific environmental conditions [26; 27] as well as inter- and intra-species differences [14; 28]. This intricacy, still not investigated in depth, is a great hindrance not only for sustainable forest management and silviculture [21], but also for carbon sink efficiency modeling [29; 30] and related climate change scenarios [31]. A reliable inspection of drought impact on the alpine forest ecosystem poses major challenges. Traditional dendrochronological surveys are limited to small sites (e.g., [27; 32; 33; 34]) or transects [28] which makes the perception of a drought phenomenon at larger scale very difficult. Moreover, recent studies showed that climate change introduces unsystematic shifts to tree growth patterns [33; 35], which presents a new hindrance for forest analyses. Alternative approaches based on flux measurements provide information on forest productivity and drought impact [36] only for small sites with still relatively short time series of observations. Furthermore, flux measurements are aimed at forest stand as a whole and cannot distinguish between forest canopy and understory. In comparison, remote sensing ensures a unique opportunity for reliable acquisition of wall to wall information on environmental conditions at relatively low costs. Abundance of datasets with different spatial and spectral resolution makes remote sensing a versatile and accurate source of forest related information with enhanced temporal resolution [37]. Although optical sensors render only information on the top of the forest canopy, tree foliage was proven to be a good indicator for the assessment of tree condition (e.g., [38; 39; 40]). Among others, NDVI (Normalized Difference Vegetation Index [41]) and NDII/ (Normalized Difference Infrared Index based on MODIS band 7 [42],aka NBR--Normalized Burn Ratio [43]) gained considerable popularity in the analyses of forest status. While NDVI provides assessment of canopy chlorophyll content, hence photosynthetic activity ('greenness'), NDII/ 2 approximates foliage spectral properties governed by water content and cell structure. Both indices can be derived using MODIS (Moderate Resolution Imaging Spectroradiometer) datasets that demonstrated overall high potential for forest monitoring (e.g., [39; 40; 44]), including forest drought analyses [45; 46]. Moreover, NDVI time series can be exploited to extract biomass and phenological parameters (e.g., [47; 48; 49]), shedding more light on vegetation condition.