检索结果-南通市图书馆

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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