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Developing kNN forest data imputation for Catalonia

Journal of Forestry Research 2024年第4期35卷 142-155页

作者： Timo Pukkala Núria Aquilué ariadna just Jordi Corbera Antoni Trasobares Forest Science and Technology Centre of Catalonia(CTFC) Crta.de St.Llorençde MorunysKm 225280 SolsonaSpain Cartographic and Geological Institute of Catalonia(ICGC) 08038 BarcelonaSpain

The combined use of LiDAR(Light Detection And Ranging)scanning and field inventories can provide spatially continuous wall-to-wall information on forest *** information can be used in many ways in forest mapping,scenario analyses,and forest manage-ment *** 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 *** multidimensional Euclidean distance was used as the similarity *** study tested two different methods to optimize the distance *** 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 *** weights of the selected variables were optimized in the second *** other method optimized the variable set as well as their transformations and weights in one single *** two-step method that first finds the variables and their transfor-mations and subsequently optimizes their weights resulted in the best imputation *** the study area,the use of three to five nearest neighbors was *** 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 *** optimal distance measure always included both LiDAR metrics and topographic *** study showed that the optimal similarity measure may be different for different ***,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.

关键词： Forest inventory Differential evolution Simulated annealing LiDAR

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

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Journal of Mountain Science 2015年第4期12卷 841-853页

作者： ángela BLáZQUEZ-CASADO José R.GONZáLEZ-OLABARRIA Santiago MARTíN-ALCóN ariadna just Mariló CABRé Lluís COLL Forest Sciences Center of Catalonia (CTFC) Institut Cartogràfic i Geològic de Catalunya (ICGC)

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 *** 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 *** 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 *** 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 *** 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.

关键词： Storm regime Forest succession Forest structure Airborne Li DAR Spatial patterns Pyrenean Subalpine forests

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