森林与环境学报2018，Vol.38Issue(1)：44-49,6.DOI:10.13324/j.cnki.jfcf.2018.01.008
毛竹林分冠层叶面积指数高光谱估测
Hypesrpecrtal estimation of Phyllostachys edulis forest canopy LAI
摘要
Abstract
Leaf area index ( LAI) is an important parameter to embody forest canopy structure and its accurate estimation is a great significance for implementation of precision forestry.For monitoring Phyllostachys e dulis LAI rapidly and non-destructively, ISI921VF-256 field spectral radiometer and LAI-2200 canopy analyzer were used to acquire P.edulis canopy and LAI value in the northwest of Fujian Province, respectively.Sensitive bands were selected to construct 8 new vegetation indexes and the correlation between LAI and its vegetation indexes was analyzed.And then random forest regression (RFR), support vector regression (SVR) and back propagation (BP) were used to construct hyperspectral estimation models of P.edulis forest canopy LAI that the coefficient of determination (R2 ), root mean square error (ERMS), the mean absolute error (EMA ), and the slope of the regression line between the estimated and actual value were as evaluation indexes and applied to compare model prediction accuracy.Results showed that 8 new built vegetation indexes which were NDVI674, NDVI687, GNDVI563, GRVI563, RVI674,RVI 687, DVI674, DVI687 were significantly correlated with LAI (P<0.01).The coefficient of determination (R2) was 0.7323 by using RFR model, which improved 01.066 and 0.2470 than SVR model and BP model, respectively.TheE MA was 0.4062 by using RFR model, which decreased 0.0448 and 0.4811 than SVR model and BP model, respectively.The ERMS was 0.4062 by using RFR model, which was slightly more than SVR model and much smaller than BP model, respectively.The slope of the regression line between the estimated and actual value was close to 1 by using RFR model, superior to the SVR and BP model.Hyperspectral estimation effect of RFR model for hyperspectral estimation of P. edulis forest canopy LAI had an advantage to SVR model and BP model, suggesting RFR model can be applied to region-wide hyperspectral estimation of P.edlu is forest canopy LAI.关键词
叶面积指数/毛竹林/高光谱/估测Key words
leaf area index/Phyllostachys edulis forest/hyperspectral/estimation分类
信息技术与安全科学引用本文复制引用
姚雄,曾琪,刘健,郑文英,余坤勇..毛竹林分冠层叶面积指数高光谱估测[J].森林与环境学报,2018,38(1):44-49,6.基金项目
国家自然科学基金项目(31770760 （）
41401385) （）
福建省科技厅项目(2016N0003). （2016N0003）
