摘要
以四川省道孚县木茹林场亚高山森林为研究对象,在针叶林、阔叶林和灌木林3种森林类型中各设置15个标准样地,利用标准地实测森林植被生物量数据建立了基于遥感信息的川西亚高山该三种森林的最佳森林植被生物量遥感估算模型,并估算了森林植被生物量。结果表明,在建立的一元线性回归、一元非线性回归和多元线形回归生物量模型中,以多元线性回归模型拟合度最高。
The study site is located in the subalpine forest in Muru forest farm,Daofu county,western Sichuan in China.Fifteen plots（30m ×30m）were established in coniferous forest,broad-leaved forest and shrubbery,respectively.Based on actual investigation values and remote sensing information,the optimal fitting-models of biomass were established for coniferous forest,broad-leaved forest and shrubbery,respectively.Furthermore,vegetation biomasses were calculated using these models.Multiple linear regression models were found to be more suitable in assessing the forest biomass than the non-linear regression and monadic linear models.The following models were the optimal fitting-models of biomass were established for coniferous forest,broad-leaved forest and shrubbery,respectively：Y=127.3TM2＋93.8TM3＋344.5TM5-75.5WVI＋0.339V13-226.3BVI＋9.7,Y=-204.7TM2＋105.4TM3-58.1WVI-37.9DVI＋57.3BVI＋13.3,Y=-49.5TM2-141.2TM3＋0.056BVI＋16.5.The total forest biomass was 3.67×106tin the study region.Coniferous forest biomass is the dominant component of forest biomass in this region,which reached 2.68×106t,accounted for 73.0% of the total regional forest biomass.
出处
《物探化探计算技术》
CAS
CSCD
2016年第5期699-707,共9页
Computing Techniques For Geophysical and Geochemical Exploration
基金
"十一五"国家科技支撑计划课题(2006BAD23B05)
关键词
亚高山森林
生物量
遥感
相关分析
回归模型
sub-alpine forest
biomass
remote sensing
correlation analysis
multi-regression model
