基于Landsat 8 OLI的云南元江流域森林生物量光学遥感估测及其饱和点分析  

Optical Remote Sensing Estimation and Saturation Point Analysis of Forest Biomass in Yuanjiang Basin,Yunnan Province Based on Landsat 8 OLI

作　　者：李紫荆 刘彦枫 冉娇 欧光龙 胥辉 Li Zijing;Liu Yanfeng;Ran Jiao;Ou Guanglong;Xu Hui(College of Forestry,Key Laboratory of State Forestry Administration on Biodiversity Conservation in Southwest China,Southwest Forestry University,Kunming Yunnan 650233,China)

机构地区：[1]西南林业大学林学院,西南林业大学西南地区生物多样性保育国家林业局重点实验室,云南昆明650233

出　　处：《西南林业大学学报（自然科学）》2023年第1期126-136,共11页Journal of Southwest Forestry University：Natural Sciences

基　　金：国家自然科学基金项目(31760206,31770677,31660202)资助;云南省王广兴专家工作站(2018IC100)资助;云南省万人计划青年拔尖人才专项(YNWR–QNBJ–2018–184)资助。

摘　　要：以云南省元江流域11种优势树种作为研究对象,基于森林资源二类调查数据和同期Landsat 8 OLI遥感影像,采用多元线性逐步回归和支持向量机回归的方法分别建立遥感生物量估测模型,进而反演流域森林生物量,同时确定光学遥感生物量饱和点阈值。结果表明:11种优势树种地上生物量遥感估测的光饱和值分别为云南松83 t/hm^(2)、思茅松79 t/hm^(2)、华山松125 t/hm^(2)、杉木68 t/hm^(2)、其他针叶树89 t/hm^(2)、桉树74 t/hm^(2)、橡胶66 t/hm^(2)、常绿阔叶117 t/hm^(2)、落叶阔叶56 t/hm^(2)、其他阔叶85 t/hm^(2)、其他乔木经济树55 t/hm^(2);支持向量机模型的平均相对误差绝对值和决定系数R^(2)均优于多元线性回归模型,支持向量机模型中11种优势树种平均残差均小于多元线性回归模型。研究结果可为提高元江流域森林生物量的估测精度提供参考。Based on the second-class survey data of forest resources and Landsat 8 OLI remote sensing images in the same period,taking 11 dominant tree species in Yuanjiang basin of Yunnan Province as the research object,the remote sensing biomass estimation models are established by using the methods of multiple linear stepwise regression and support vector machine regression,and then the forest biomass in the basin is retrieved,and the threshold of optical remote sensing biomass saturation point is determined at the same time.The results showed that the light saturation values estimated by remote sensing of aboveground biomass of 11 dominant tree species were 83 t/hm^(2)of Yunnan pine,79 t/hm^(2)of Simao Pine,125 t/hm^(2)of Huashan pine,68 t/hm^(2)of Cunninghamia lanceolata,89 t/hm^(2)of other conifers,74 t/hm^(2)of Eucalyptus,66 t/hm^(2)of rubber,117 t/hm^(2)of evergreen broad-leaved leaves,56 t/hm^(2)of deciduous broad-leaved leaves,85 t/hm^(2)of other broad-leaved trees and 55 t/hm^(2)of other economic trees;Absolute value of average relative error and determination coefficient R^(2) of support vector machine model.The average residuals of 11 dominant tree species in support vector machine model are less than those in multiple linear regression model.This study can provide a reference for improving the estimation accuracy of forest biomass in Yuanjiang River Basin.

关 键 词：森林生物量 支持向量机 遥感估测模型 元江流域 

分 类 号：S757[农业科学—森林经理学]