Developing kNN forest data imputation for Catalonia  

作　　者：Timo Pukkala Núria Aquilué Ariadna Just Jordi Corbera Antoni Trasobares 

机构地区：[1]Forest Science and Technology Centre of Catalonia(CTFC),Crta.de St.Llorençde Morunys,Km 2,25280 Solsona,Spain [2]Cartographic and Geological Institute of Catalonia(ICGC),08038 Barcelona,Spain

出　　处：《Journal of Forestry Research》2024年第4期142-155,共14页林业研究（英文版）

基　　金：This work was supported by a Juan de la Cierva fellowship of the Spanish Ministry of Science and Innovation(FCJ2020-046387-I);the Spanish Ministry of Science,Innovation and Universities(PID2020-120355RB-IOO).

摘　　要：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.

关 键 词：Forest inventory Differential evolution Simulated annealing LIDAR 

分 类 号：S757.2[农业科学—森林经理学] TN958.98[农业科学—林学]