[1]王璟睿,沈文娟,李卫正,等. 基于RapidEye的人工林生物量遥感反演模型性能对比[J].西北林学院学报,2015,30(06):196-202.[doi:doi:10.3969/j.issn.1001-7461.2015.06.36]
 WANG Jing-rui,SHEN Wen-juan,LI Wei-zheng,et al. Performances Comparison of Multiple Non-linear Models for Estimating Plantations’ Biomass Based on RapidEye Imagery[J].JOURNAL OF NORTHWEST FORESTRY UNIVERSITY,2015,30(06):196-202.[doi:doi:10.3969/j.issn.1001-7461.2015.06.36]
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 基于RapidEye的人工林生物量遥感反演模型性能对比()
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《西北林学院学报》[ISSN:1001-7461/CN:61-1202/S]

卷:
第30卷
期数:
2015年06期
页码:
196-202
栏目:
出版日期:
2015-11-30

文章信息/Info

Title:
 Performances Comparison of Multiple Non-linear Models for Estimating Plantations’ Biomass Based on RapidEye Imagery
文章编号:
1001-7461(2015)06-0196-07
作者:
 王璟睿12沈文娟12李卫正12李明诗12*郑光3
 (1.南京林业大学 林学院,江苏 南京 210037;2.南方林业协同创新中心,南京林业大学,江苏 南京 210023;3.南京大学 国际地球系统科学研究所,江苏 南京 210023)
Author(s):
 WANG Jing-rui12 SHEN Wen-juan12 LI Wei-zheng12 LI Ming-shi12* ZHENG Guang3
 (1.College of Forestry, Nanjing Forestry University, Nanjing, Jiangsu 210037, China;2. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu 210037, China; 3. International Institute for Earth System Science Nanjing University, Nanjing, Jiangsu 210037, China)
关键词:
 生物量RapidEyeBP神经网支持向量机随机森林
Keywords:
 biomass RapidEye BP neural networks support vector machines random forest
分类号:
S750
DOI:
doi:10.3969/j.issn.1001-7461.2015.06.36
文献标志码:
A
摘要:
 利用2012年RapidEye高空间分辨率遥感影像并结合野外样方数据,采用多种建模技术进行生物量的反演和制图。先依据RapidEye光谱数据发展出包括NDVI、RVI等多种植被指数、光谱特征图像及纹理特征,再通过相关分析筛选建模所需因变量,采用支持向量机、BP神经网络和随机森林算法建立森林生物量估测模型并进行精度验证。结果表明,基于支持向量机的建模R2为0.687,验证R2为0.641,平均相对误差为0.306;基于BP神经网的建模R2为0.552,验证R2为0.358,平均相对误差为0.525;基于随机森林的建模R2为0.850,验证R2为0.324,平均相对误差为0.468。采用支持向量机算法所制作的空间意义明确的森林生物量分布图,为制定合理的森林经营措施提供有益指导。
Abstract:
 The major objective of the current study was to use RapidEye imagery in synergy with filed inventories to create and validate three empirical modeling algorithms (Support vector machines-SVM, BP networks and Random forests-RF) for mapping plantations’ biomass at a local scale. The predicting variables involved diverse vegetation indices, spectral features, textures and topographic attributes. The results showed that for SVM-based model, the regression R2 was 0.687 and the corresponding validation R2 was 0.641 with an average relative error of 0.306; for BP network-based model, the regression R2 was 0.552 and the validation R2 was 0.358 with an average relative error of 0.525; for RF-based model, the regression R2 was 0.850 while the validation R2 was 0.324 with an average relative error of 0.468. The optimal model, SVM-based model was finalized to retrieve biomass spatial patterns, which provide insights and implications for local forest management practices and planning.

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备注/Memo

备注/Memo:
 收稿日期:2015-04-01 修回日期:2015-05-13
基金项目:林业公益性行业专项(201304208);国家林业局“948”项目(2014-04-25);国家自然科学基金(31270587);江苏高校优势学科建设工程资助项目(PAPD)和江苏省普通高校研究生科研创新计划项目(kylx15-0908)。
作者简介:王璟睿,女,在读硕士,研究方向:森林资源。E-mail:wangjingrui59@sina.com
*通信作者:李明诗,男,教授,研究方向:森林资源。E-mail:nfulms@aliyun.com
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