Developing kNN forest data imputation for Catalonia

The combined use of LiDAR(Light Detection And Ranging)scanning and field inventories can provide spatially continuous wall-to-wall information on forest characteristics.This information can be used in many ways in forest mapping,scenario analyses,and forest manage-ment planning.This study aimed to find the optimal way to obtain continuous forest data for Catalonia when using kNN imputation(kNN stands for“k nearest neighbors”).In this method,data are imputed to a certain location from k field-measured sample plots,which are the most similar to the location in terms of LiDAR metrics and topographic variables.Weighted multidimensional Euclidean distance was used as the similarity measure.The study tested two different methods to optimize the distance measure.The first method optimized,in the first step,the set of LiDAR and topographic variables used in the measure,as well as the transformations of these variables.The weights of the selected variables were optimized in the second step.The other method optimized the variable set as well as their transformations and weights in one single step.The two-step method that first finds the variables and their transfor-mations and subsequently optimizes their weights resulted in the best imputation results.In the study area,the use of three to five nearest neighbors was recommended.Altitude and latitude turned out to be the most important variables when assessing the similarity of two locations of Catalan forests in the context of kNN data imputation.The optimal distance measure always included both LiDAR metrics and topographic variables.The study showed that the optimal similarity measure may be different for different regions.Therefore,it was suggested that kNN data imputation should always be started with the optimization of the measure that is used to select the k nearest neighbors.

Assessing Post-Storm Forest Dynamics in the Pyrenees Using High-Resolution LIDAR Data and Aerial Photographs

We evaluated how historical storm events have shaped the current forest landscape in three Pyrenean subalpine forests(NE Spain).For this purpose we related forest damage estimations obtained from multi-temporal aerial photographic comparisons to the current forest typology generated from airborne Li DAR data, and we examined the role of past natural disturbance on the current spatial distribution of forest structural types.We found six forest structural types in the landscape: early regeneration(T1 and T2), young even-aged stands(T3), uneven-aged stands(T4) and adult stands(T5and T6).All of the types were related to the timing and severity of past storms, with early-regeneration structures being found in areas markedly affected in recent times, and adult stands predominating in those areas that had suffered lowest damage levels within the study period.In general, landscapes where high or medium levels of damage were recurrent also presented higher levels of spatial heterogeneity,whereas the opposite pattern was found in the less markedly affected landscape, characterized by thepresence of large regular patches.Our results show the critical role that storm regimes in terms of timing and severity of past storms can play in shaping current forest structure and future dynamics in subalpine forests.The knowledge gained could be used to help define alternative forest management strategies oriented toward the enhancement of landscape heterogeneity as a measure to face future environmental uncertainty.