Quantifying Forest Cover Loss as a Response to Drought and Dieback of Norway Spruce and Evaluating Sensitivity of Various Vegetation Indices Using Remote Sensing

Although the NDVI, EVI, TVI, SAVI, TCG, DSWI, and TCW revealed a large-scale drop in vegetation vigor and canopy water content all over the analyzed area, that is, the response of Norway spruce to severe drought occurred in 2012 (Figure 4), not all VIs predicted forest cover loss in 2015 (Figure 5). Besides TCW, Cohen’s d showed that other VIs, which did not show any response of Norway spruce to severe drought in 2012 (MSI, NDMI, and NMDI), had large and very large effects in predicting forest cover loss in 2015. A similar result was found for 2011, which was a year with less severe drought occurrence. Although the MSI [46] and NDMI [49] are considered to be highly effective in assessments of moisture stress in plants, this was not the case in our study. Based on such results, we can assume that NIR-SWIR1 ratio-based Water Vis, such as the MSI and NDMI, indicated only different soil water retaining capacities in areas where forest cover loss occurred and where it did not. We found the base for this assumption in a conclusion in Welikhe et al.’s research [104], where it was reported that MSI is strongly correlated to soil moisture at 20 cm depth. On the other hand, in a review study, Le et al. [49] summarized findings from other studies [98,116,117], concluding that the NDWI method (in our research named NDMI) yielded unsatisfactory results when applied to forest objects for water stress monitoring. Worth noticing is the large effect of the pre-drought (2009) results of the EVI, SAVI, TCG, and TCW in predicting the forest cover loss in 2015, as such a state points to pre-drought differences, and possibly the susceptibility of different Norway spruce populations, or their respective habitats, to drought events in the Kopaonik NP. The cause of this may be found in the research of Rehschuh et al. [118], in which they reported that Norway spruce trees growing on shallow, well-drained soil expressed a relatively higher drought sensitivity compared to trees from a site with deep, silty soil. The practically non-existent ability to predict the forest cover loss in 2015, with the post-drought data (2013 and 2014) using the NDVI and its modified version TVI, should not be considered unusual. Although these VIs showed strong sensitivity in the detection of Norway spruce response to severe drought, they cannot be used in predicting forest cover loss, as they do not exhibit any statistically significant difference between VI values in the area of forest and non-forest cover (forest cover loss). As such, we agree with Le et al.’s [49] conclusion stating that the NDVI cannot be effectively used in the early detection of drought effects. On the contrary, other “drought-sensitive” VIs, such as the EVI, SAVI, TCG, and TCW, showed a large (2013) to very large effect (2014) in predicting forest cover loss in 2015, indicating that the post-drought period is crucial in predicting drought effects, as it can strongly suggest where forest cover loss might occur. In contrast, these VIs, except for the TCW, did not perform well in predicting forest cover loss in 2017 (Figure 6), indicating that the primary cause of Norway spruce dieback after 2015 was mainly driven by pest outbreaks. As seen in Figure 2, forest cover loss doubled from 2015 to 2017. Such a finding goes in line with an earlier report from Matović et al. [31], where it was stated that, in those years, bark beetle began to act as a primary pest. What challenges this conclusion is a post-drought medium (2013) to a large effect (2014) of the DSWI and a large (2013) to a very large effect (2014) of the TCW in predicting forest cover loss in 2017 (Figure 6), which may indicate a direct influence of drought on the loss of forest cover in 2017. Nevertheless, so-called Water VIs (MSI, NDMI, and NMDI) performed almost the same as for 2015 forest cover loss prediction—having a large (2012) to very large effect (2013 and 2014) in predicting forest cover loss. Considering these results together with previous conclusions, where we stated that such results only indicated different soil water retaining capacities in areas where forest cover loss occurred and where it did not, we can only confirm such assumptions.