Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS

This paper reports a semi-automated workflow for detection and quantification of forest damage from windthrow in an Alpine region, in particular from the Vaia storm in October 2018. A web-GIS platform allows to select the damaged area by drawing polygons; several vegetation indices (VIs) are automatically calculated using remote sensing data (Sentinel-2A) and tested to identify the more suitable ones for quantifying forest damage using cross-validation with ground-truth data. Results show that the mean value of NDVI and NDMI decreased in the damaged areas, and have a strong negative correlation with severity. RGI has an opposite behavior in contrast with NDVI and NDMI, as it highlights the red component of the land surface. In all cases, variance of the VI increases after the event between 0.03 and 0.15. Understorey not damaged from the windthrow, if consisting of 40% or more of the total cover in the area, undermines significantly the sensibility of the VIs to detecting and predicting severity. Using aggregational statistics (average and standard deviation) of VIs over polygons as input to a machine learning algorithm, i.e., Random Forest, results in severity prediction with regression reaching a root mean square error (RMSE) of 9.96, on a severity scale of 0–100, using an ensemble of area averages and standard deviations of NDVI, NDMI, and RGI indices. The results show that combining more than one VI can significantly improve the estimation of severity, and web-GIS tools can support decisions with selected VIs. The reported results prove that Sentinel-2 imagery can be deployed and analysed via web-tools to estimate forest damage severity and that VIs can be used via machine learning for predicting severity of damage, with careful evaluation of the effect of understorey in each situation

Functional balance between leaf and xylem tissues is maintained under different soil water availability in Pinus sylvestris and Picea abies

Plants are composed by tight connected different tissues and organs that work in synchrony among each other guiding all physiological processes. The functionality of the entire organism depends on the resources allocation for the different organs that have to balance the cost and the benefit of the plant. The ratio between leaf biomass and xylem biomass is an extremely important plant trait since it links photosynthesis to transpiration efficiency and to respiratory costs. Resource availability have been reported to significantly affect the growth of trees. In limited resource environment trees present smaller leaves and shorter braches than plants grown in non-limiting conditions. The aim of this work is to evaluate if the resource allocation is maintained constant during ontogeny and if the ratio between leaf and xylem is preserved in different environmental conditions to guarantee the functionality of the system. We sampled branches of Pinus sylvestris and Picea abies grown on arid and mesic soils. For each the branch we measured the xylem volume and the leaf biomass produced each year and how they cumulate from the branch apex to the base. Our results showed that the cumulated leaf biomass and xylem volume scale linearly with the distance from the branch apex and the branches from the wet site, especially P. sylvestris\u2019 s, had more leaf biomass and xylem volume. This confirms that carbon allocation is conserved during the ontogeny and that the trees grown in non-limiting conditions have higher production. Moreover, the relation among leaf biomass and xylem volume showed a conserved allocation pattern in the two species with no effect of the environmental conditions. This demonstrates that these two traits are highly correlated and dependent on each other and that their functional balance is highly preserved to sustain the functionality of the tree independently by the resource availability