Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data:a case study in the Sierra Nevada Mountains,California

作     者：Qin Ma Yanjun Su Shengli Tao Qinghua Guo 

作者机构：School of EngineeringSierra Nevada Research InstituteUniversity of California at MercedMercedCAUSA State Key Laboratory of Vegetation and Environmental ChangeChinese Academy of SciencesInstitute of BotanyBeijingPeople’s Republic of China Department of EcologyCollege of Urban and Environmental Sciencesand Key Laboratory for Earth Surface Processes of the Ministry of EducationPeking UniversityBeijingPeople’s Republic of China 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2018年第11卷第5期

页      面：485-503页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This study is supported by the National Natural Science Foundation of China[project numbers 41471363 and 31270563] National Science Foundation[DBI 1356077] the USDA Forest Service Pacific Southwest Research Station 

主　　题：Airborne Laser Scanning change detection tree growth tree competition Sierra Nevada 

摘      要：Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth *** Laser Scanning(ALS)can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy *** study proposed an ALSbased framework to quantify tree growth and ***-temporal ALS data were used to quantify tree growth in height(ΔH),crown area(ΔA),crown volume(ΔV),and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada *** analyzed the correlations between tree growth attributes and controlling factors(*** sizes,competition,forest structure,and topographic parameters)at multiple *** the individual tree level,ΔH had no consistent correlations with controlling factors,ΔA andΔV were positively related to original tree sizes(R0.3)and negatively related to competition indices(R0.7),ΔV was positively related to original tree sizes(|R|0.8).Multivariate regression models were simulated at individual tree level forΔH,ΔA,andΔV with the R2 ranged from 0.1 to *** ALS-based tree height estimation and growth analysis results were consistent with field measurements.