[1]周蔚,吕延杰*,林起楠. 联合光学和合成孔径雷达数据的太平湖森林地上生物量反演研究[J].西北林学院学报,2023,38(2):193-200.[doi:10.3969/j.issn.1001-7461.2023.02.27]
　ZHOU Wei,LV Yan-jie*,LIN Qi-nan. Retrieval of Above Ground Biomass in Taiping Lake Forests Using Optical and SAR Dataset[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2023,38(2):193-200.[doi:10.3969/j.issn.1001-7461.2023.02.27]
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 联合光学和合成孔径雷达数据的太平湖森林地上生物量反演研究()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第38卷
期数:
2023年第2期
页码:
193-200
栏目:
出版日期:
2023-03-31

文章信息/Info

Title:
 Retrieval of Above Ground Biomass in Taiping Lake Forests Using Optical and SAR Dataset
文章编号:
1001-7461(2023)02-0193-08
作者:
 周蔚1; 吕延杰1*; 林起楠2
 （1.国家林业和草原局 华东调查规划设计院，浙江 杭州 310019；2.浙江农林大学，浙江 杭州 311300）
Author(s):
 ZHOU Wei1; LV Yan-jie1*; LIN Qi-nan2
 (1.East China Forestry Inventory and Planning Institute,Hangzhou 310019,Zhejiang,China; 2.Zhejiang Agriculture and Forestry University,Hangzhou 311300,Zhejiang,China)
关键词:
 ; Sentinel-1; Sentinel-2; 随机森林回归算法; 地上生物量; 植被指数
Keywords:
 ; Sentinel-1;  Sentinel-2;  random forest regression algorithm;  above ground biomass;  vegetation index
分类号:
S771.8
DOI:
10.3969/j.issn.1001-7461.2023.02.27
文献标志码:
A
摘要:
 光学和合成孔径雷达（SAR）多源传感器数据融合对提高森林地上生物量（AGB）提取精度具有重要意义。以太平湖森林为研究对象，以Sentinel-1 SAR数据和Sentinel-2光学数据为数据源，利用随机森林回归算法系统性地评估光学和SAR数据对AGB反演的互补优势和策略选择。采用Sentinel-2光学数据的AGB反演精度（R2=0.63，RMSE=37.05 mg/hm2，sMAPE=0.56）优于采用Sentinel-1 SAR数据的AGB反演精度（R2=0.37，RMSE=52.25 mg/hm2，sMAPE=0.65），联合两者数据的AGB估算精度最高（R2=0.69，RMSE=34.17 mg/hm2，sMAPE=0.55）；基于不同策略构建的AGB估计模型当中，植被指数（RVI、NDVI和红边相关的NDVIre）和纹理变量（NDVIre_Mea）的重要性高于光谱波段和后向散射系数。联合Sentinel-1和Sentinel-2数据的光谱波段、植被指数、纹理信息和后向散射系数，能够有效的缓和遥感信息饱和性问题和提高AGB反演精度。
Abstract:
 Multi-source data fusion of optical and synthetic aperture radar (SAR) is of great significance to improve the extraction accuracy of forest aboveground biomass (AGB).This study explored the complementary advantages and strategy selection of SAR (Sentinel-1) and optical (Sentinel-2) data for AGB inversion in Taiping Lake forests using random forest regression algorithm.The AGB inversion accuracy using sentinel-2 optical data (R2=0.63,RMSE=37.05 mg/hm2,sMAPE=0.56) was better than that using sentinel-1 SAR data (R2=0.37,RMSE=52.25 mg/hm2,sMAPE=0.65).The AGB estimation accuracy combining the two data was the highest (R2=0.69,RMSE=34.17 mg/hm2,sMAPE=0.55).For different strategies,in the construction of AGB model,vegetation indices (e.g.,RVI,NDVI and red-edge related index NDVIre) and texture variables (e.g.,NDVI_Mea) were more important than spectral band and backscattering coefficient.This study concluded that combining the spectral band,vegetation index,texture information and backscattering coefficient of sentinel-1 and sentinel-2 data could effectively alleviate the problem of remote sensing information saturation and improve the estimation accuracy in AGB mapping.

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备注/Memo

备注/Memo:
 收稿日期：2022-01-20修回日期：2022-04-01
基金项目：安徽省森林资源年度监测及“森林资源一张图”应用试点。
第一作者：周蔚，工程师。研究方向：林业遥感。E-mail:zhouwei8502@163.com
*通信作者：吕延杰，工程师。研究方向：森林经营管理与林业信息化应用。E-mail:1095369262@qq.com
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