[1]龙依,蒋馥根,孙华,等.基于HLS数据的森林蓄积量遥感反演[J].森林与环境学报,2021,41(06):620-628.[doi:10.13324/j.cnki.jfcf.2021.06.008]
 LONG Yi,JIANG Fugen,SUN Hua,et al.Remote sensing inversion of forest volume based on HLS data[J].,2021,41(06):620-628.[doi:10.13324/j.cnki.jfcf.2021.06.008]
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基于HLS数据的森林蓄积量遥感反演()
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《森林与环境学报》[ISSN:2096-0018/CN:35-1327/S]

卷:
41卷
期数:
2021年06期
页码:
620-628
栏目:
出版日期:
2021-11-13

文章信息/Info

Title:
Remote sensing inversion of forest volume based on HLS data
作者:
龙依12 蒋馥根12 孙华12 邱湘龙3 顾兴贵3
1. 中南林业科技大学林业遥感信息工程研究中心, 湖南 长沙 410004;
2. 南方森林资源经营与监测国家林业与草原局重点实验室, 湖南 长沙 410004;
3. 中南林业科技大学芦头实验林场, 湖南 岳阳 414000
Author(s):
LONG Yi12 JIANG Fugen12 SUN Hua12 QIU Xianglong3 GU Xinggui3
1. Research Center of Forestry Remote Sensing & Information Engineering, Changsha, Hunan 410004, China;
2. Key Laboratory of State Forestry & Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, Hunan 410004, China;
3. Lutou Experimental Forest Farm, Central South University of Forestry and Technology, Yueyang, Hunan 414000, China
关键词:
森林蓄积量遥感反演协同陆地卫星和哨兵2号随机森林
Keywords:
forest volumeremote sensing inversionharmonized Landsat and Sentinel-2random forest
分类号:
S758.51
DOI:
10.13324/j.cnki.jfcf.2021.06.008
摘要:
通过对比陆地卫星8号(Landsat 8)与协同陆地卫星和哨兵2号(HLS)数据在波段信息及森林蓄积量建模效果的差异,探索HLS数据源在森林蓄积量反演中的应用潜力。以内蒙古自治区旺业甸林场为研究区,以Landsat 8和HLS为数据源提取遥感变量,结合森林蓄积量样地实测数据,利用线性逐步回归(LSR)和逐步随机森林(SRF)进行特征变量筛选,分别构建线性和非线性模型开展森林蓄积量反演,并进行精度验证及对比分析。结果表明:HLS影像中的7个波段与Landsat 8影像各对应波段均表现为极显著相关,相关系数绝对值均大于0.7(P<0.01),在波段信息上具有较好的一致性;基于 Landsat 8及HLS影像数据构建多元线性回归和随机森林模型,其最优模型决定系数(R2)分别为0.55和0.54,均方根误差(RMSE)分别为65.66和68.15 m3·hm-2,结果无显著性差异(P>0.05)。利用HLS影像进行森林蓄积量估测,可得到与Landsat 8相似的效果,且HLS数据无需进行繁琐的预处理,能显著提高森林蓄积量估测效率,为大尺度森林蓄积量遥感反演和森林资源监测提供参考。
Abstract:
By comparing the differences between Landsat 8 and Harmonized Landsat and Sentinel-2 (HLS) data in terms of band information and forest volume modeling, the application potential of the HLS source in forest volume inversion was explored. Considering Wangyedian Forest Farm in the Inner Mongolia Autonomous Region as the study area, Landsat 8 and HLS images were used as data sources to extract remote sensing variables. In combination with the measured data of forest volume sample plots, linear stepwise regression and stepwise random forest were used to screen characteristic variables. Linear and nonlinear models were constructed to perform forest volume inversion, followed by accuracy verification and comparative analysis. The results showed that the seven bands in HLS were highly significantly correlated with each corresponding band of the Landsat 8 image, and the absolute values of the correlation coefficients were all greater than 0.7 (P<0.01), indicating good consistency in terms of band information. Based on the Landsat 8 and HLS images, multiple linear regression and random forest models were constructed. The R2 values of these optimized models were 0.55 and 0.54, while the RMSE values were 65.66 and 68.15 m3·hm-2, respectively, indicating no significant differences (P>0.05).Therefore, using HLS imagery for forest volume estimation can yield results similar to those of Landsat 8; further, HLS data do not require tedious preprocessing, which can significantly improve the efficiency of forest volume estimation. Thus, this study provides a reference for large-scale forest volume remote sensing inversion and forest resource monitoring.

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相似文献/References:

[1]蒋馥根,孙华,ZHAO Feng,等.基于方差优化k最近邻法的森林蓄积量估测[J].森林与环境学报,2019,39(05):497.[doi:10.13324/j.cnki.jfcf.2019.05.008]
 JIANG Fugen,SUN Hua,ZHAO Feng,et al.Forest stock volume estimation based on a variance-optimized kNN model[J].,2019,39(06):497.[doi:10.13324/j.cnki.jfcf.2019.05.008]

备注/Memo

备注/Memo:
收稿日期:2021-07-20;改回日期:2021-08-28。
基金项目:国家自然科学基金面上项目(31971578);"十三五"国家重点研发计划项目:人工林资源监测关键技术研究(2017YFD0600900);湖南省教育厅科学研究重点项目(17A225)。
作者简介:龙依(1999-),女,硕士研究生,从事林业遥感研究。Email:20201100036@csuft.edu.cn。
通讯作者:孙华(1979-),男,教授,从事林业遥感研究。Email:sunhua@csuft.edu.cn。
更新日期/Last Update: 1900-01-01