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遥感技术与应用  2019, Vol. 34 Issue (4): 847-856    DOI: 10.11873/j.issn.1004-0323.2019.4.0847
遥感应用     
基于光学与SAR因子的森林生物量多元回归估算
苏华1(),张明慧1,李静1,2,陈修治2,汪小钦1
1. 福州大学 卫星空间信息技术综合应用国家地方联合工程研究中心、空间数据挖掘与;信息共享教育部重点实验室,福建 福州 350116
2. 中国科学院 华南植物园,广东 广州 510650
Forest Biomass Estimation Using Multiple Regression with Optical and SAR Features: A Case Study in Fujian Province
Hua Su1(),Minghui Zhang1,Jing Li1,2,Xiuzhi Chen2,Xiaoqin Wang1
1. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou 350116, China
2. South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
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摘要:

基于福建省Landsat-8 OLI影像,利用混合像元分解模型从实测样地数据中筛选出“纯净”的植被像元,并将筛选出的样地分为针叶林、阔叶林和混交林3种植被类型,依次提取3种不同植被类型“纯净”植被像元的树高、林龄、坡度属性信息以及对应的光学NDVI、RVI植被因子和合成孔径雷达(SAR)HH、HV极化后向散射因子,分别构成不同植被类型的“含光学特征多元因子”(NDVI、RVI、树高、林龄、坡度)和“含SAR特征多元因子”(HH、HV、树高、林龄、坡度),开展对比研究。采用含光学特征的多元因子回归模型先估测不同植被类型的森林叶生物量,然后根据叶生物量与地上生物量的关系间接估测森林地上生物量。同时,采用含SAR特征的多元因子回归模型直接估测森林的地上生物量。最后,对比分析这两组多元回归模型的估测精度。结果表明:不同植被类型的含光学特征多元回归模型的验证精度(针叶林:R2为0.483,RMSE为29.522 t/hm2;阔叶林:R2为0.470,RMSE为21.632 t/hm2;混交林:R2为0.351,RSME为25.253 t/hm2)比含SAR特征多元回归模型的验证精度(针叶林:R2为0.319,RMSE为28.352 t/hm2;阔叶林:R2为0.353,RMSE为18.991t/hm2;混交林:R2为0.281,RMSE为26.637 t/hm2)略高,说明在福建省森林生物量估算中采用含光学特征的多元回归模型(先估测叶生物量进而间接估测地上生物量)比利用含SAR特征的多元回归模型(直接估测地上生物量)更具优势。

关键词: 地上生物量叶生物量光学特征SAR特征多元因子    
Abstract:

Using Landsat-8 OLI images and 296 survey samples in Fujian province, we extracted pure vegetation pixels biased on pixel unmixing models, and divided the samples into coniferous forest, broad-leaved forest and mixed forest, then employed tree height, plantation age and slope as attribute information from pure vegetation samples, and also extracted NDVI, RVI form Landsat8 OLI, and HV, HH backscatter coefficient form SAR image, so as to compose multiple factors with optical features (NDVI, RVI, tree height, plantation age, slope) and SAR features (HH, HV, tree height, plantation age, slope) for comparison study. Since optical remote sensing can only observe vegetation canopy information rather than the whole vegetation information, we firstly estimated the leaf biomass by using multiple regression with optical features, then estimated the above-ground biomass indirectly in line with the relationship between above-ground biomass and leaf biomass. Since SAR L-band with long wavelength can penetrate the canopy and directly observe the whole vegetation information above the ground, we used multiple regression with SAR features to directly estimate the above-ground biomass. Finally, we analyzed and compared the estimation accuracy from the two regression methods. The result shows that the estimation accuracy from multiple regression with optical features (coniferous forest: R2=0.483, RMSE=29.522 t/hm2; broad-leaved forest: R2=0.470, RMSE=21.632 t/hm2; mixed forest: R2=0.351, RSME=25.253 t/hm2) is higher than that from multiple regression with SAR features (coniferous forest: R2=0.319, RMSE=28.352 t/hm2; broad-leaved forest: R2=0.353, RMSE=18.991 t/hm2; mixed forest: R2=0.281, RMSE=26.637 t/hm2), suggesting the indirect above-ground biomass estimation from multivariate regression with optical information is more suitable than direct above-ground estimation from multivariate regression with SAR information in Fujian Province.

Key words: Above-ground biomass    Leaf biomass    Optical features    SAR features    Multivariate factor
收稿日期: 2018-06-15 出版日期: 2019-10-16
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目(41971384);福建省高校杰出青年科研人才培育计划(KJ2017-17);福建省自然科学基金(2017J01657);海西政务大数据应用协同创新中心资助(2015750401);中央引导地方科技发展专项(2017L3012)
作者简介: 通信作者:苏华(1985—),男,福建福清人,博士,副研究员,主要从事环境遥感研究。E?mail:suhua@fzu.edu.cn
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引用本文:

苏华,张明慧,李静,陈修治,汪小钦. 基于光学与SAR因子的森林生物量多元回归估算[J]. 遥感技术与应用, 2019, 34(4): 847-856.

Hua Su,Minghui Zhang,Jing Li,Xiuzhi Chen,Xiaoqin Wang. Forest Biomass Estimation Using Multiple Regression with Optical and SAR Features: A Case Study in Fujian Province. Remote Sensing Technology and Application, 2019, 34(4): 847-856.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.4.0847        http://www.rsta.ac.cn/CN/Y2019/V34/I4/847

图1  研究区域及样地分布
图2  研究技术路线图
图3  不同植被类型的叶生物量估测结果
图4  不同植被类型的实测叶生物量与实测地上生物量散点图
图5  不同植被类型的估测地上生物量与实测地上生物量散点图
图6  不同植被类型估测地上生物量与实测地上生物量散点图
图7  多元回归方法中不同植被类型的估测地上生物量归一化值与实测地上生物量归一化值散点图
图8  多元回归方法中不同植被类型的估测地上生物量与实测地上生物量散点图
植被类型R2RMSE/(t/hm2)
针叶林0.48329.522
阔叶林0.47021.632
针阔混交林0.35125.253
表1  3种植被多元回归模型精度评价
图9  含SAR多元回归方法中各植被类型的估测地上生物量归一化值与实测地上生物量归一化值散点图
图10  含SAR多元回归方法中各植被类型的估测地上生物量与实测地上生物量散点图
植被类型R2RMSE/(t/hm2)
针叶林0.39128.352
阔叶林0.35318.991
针阔混交林0.28126.637
表2  3种植被含SAR多元回归模型精度评价
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