[1]姚雄,曾琪,刘健,等.毛竹林分冠层叶面积指数高光谱估测[J].森林与环境学报,2018,38(01):44-49.[doi:10.13324/j.cnki.jfcf.2018.01.008]
 YAO Xiong,ZENG Qi,LIU Jian,et al.Hyperspectral estimation of Phyllostachys edulis forest canopy LAI[J].,2018,38(01):44-49.[doi:10.13324/j.cnki.jfcf.2018.01.008]
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毛竹林分冠层叶面积指数高光谱估测()
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《森林与环境学报》[ISSN:2096-0018/CN:35-1327/S]

卷:
38
期数:
2018年01期
页码:
44-49
栏目:
出版日期:
2018-01-15

文章信息/Info

Title:
Hyperspectral estimation of Phyllostachys edulis forest canopy LAI
作者:
姚雄12 曾琪2 刘健12 郑文英2 余坤勇12
1. 3S技术与资源优化利用福建省高校重点实验室, 福建 福州 350002;
2. 福建农林大学林学院, 福建 福州 350002
Author(s):
YAO Xiong12 ZENG Qi2 LIU Jian12 ZHENG Wenying2 YU Kunyong12
1. University Key Lab for Geomatics Technology and Optimize Resources Utilization in Fujian Province, Fuzhou, Fujian 350002, China;
2. College of Forestry, Fujian Agriculture and Forestry University, Fuzhou, Fujian 350002, China
关键词:
叶面积指数毛竹林高光谱估测
Keywords:
leaf area indexPhyllostachys edulis foresthyperspectralestimation
分类号:
TP79
DOI:
10.13324/j.cnki.jfcf.2018.01.008
摘要:
叶面积指数(leaf area index,LAI)是体现林分冠层结构的一项重要参数,其准确估测对于精准林业的实施具有重要意义。为了快速、无损地监测毛竹林LAI,采用ISI921VF-256野外地物光谱辐射计和LAI-2200冠层分析仪获取福建省西北部毛竹林分冠层光谱反射率和LAI值,通过敏感波段的选取,新建了8类植被指数,分析了LAI值与对应植被指数的相关性,进而利用随机森林回归、支持向量回归和反向传播神经网络法构建了毛竹林分冠层LAI高光谱估测模型,以决定系数(R2)、均方根误差(ERMS)、平均绝对误差(EMA)和估测值与实测值的回归线斜率为指标评价并比较了模型预测精度。结果表明:新建的NDVI674、NDVI687、GNDVI563、GRVI563、RVI674、RVI687、DVI674、DVI687八类植被指数与LAI均呈极显著相关(P<0.01)。建立的RFR模型中,决定系数R2达到0.732 3,分别比SVR模型和BP模型提高了0.106 6和0.247 0;其EMA为0.406 2,分别比SVR模型和BP模型减少了0.044 8和0.481 1;其ERMS为0.646 3,略高于SVR模型,但远小于BP模型;其实测值与估测值的回归线斜率接近1,优于SVR模型和BP模型的回归线斜率。RFR模型对毛竹林分冠层LAI的高光谱估测效果优于SVR模型和BP模型,可用于大区域范围毛竹林冠LAI的高光谱估测。
Abstract:
Leaf area index (LAI) is an important parameter to embody forest canopy structure and its accurate estimation is a great significance for implementation of precision forestry. For monitoring Phyllostachys edulis LAI rapidly and non-destructively, ISI921VF-256 field spectral radiometer and LAI-2200 canopy analyzer were used to acquire P. edulis canopy and LAI value in the northwest of Fujian Province, respectively. Sensitive bands were selected to construct 8 new vegetation indexes and the correlation between LAI and its vegetation indexes was analyzed. And then random forest regression (RFR), support vector regression (SVR) and back propagation (BP) were used to construct hyperspectral estimation models of P. edulis forest canopy LAI that the coefficient of determination (R2), root mean square error (ERMS), the mean absolute error (EMA), and the slope of the regression line between the estimated and actual value were as evaluation indexes and applied to compare model prediction accuracy. Results showed that 8 new built vegetation indexes which were NDVI674, NDVI687, GNDVI563, GRVI563, RVI674, RVI687, DVI674, DVI687 were significantly correlated with LAI (P<0.01). The coefficient of determination (R2) was 0.732 3 by using RFR model, which improved 0.106 6 and 0.247 0 than SVR model and BP model, respectively. The EMA was 0.406 2 by using RFR model, which decreased 0.044 8 and 0.481 1 than SVR model and BP model, respectively. The ERMS was 0.406 2 by using RFR model, which was slightly more than SVR model and much smaller than BP model, respectively. The slope of the regression line between the estimated and actual value was close to 1 by using RFR model, superior to the SVR and BP model. Hyperspectral estimation effect of RFR model for hyperspectral estimation of P. edulis forest canopy LAI had an advantage to SVR model and BP model, suggesting RFR model can be applied to region-wide hyperspectral estimation of P. edulis forest canopy LAI.

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备注/Memo

备注/Memo:
收稿日期:2017-03-17;改回日期:2017-10-18。
基金项目:国家自然科学基金项目(31770760;41401385);福建省科技厅项目(2016N0003)。
作者简介:姚雄(1990-),男,博士研究生,从事3S技术应用及资源监测研究。Email:424532024@qq.com。
通讯作者:余坤勇(1980-),男,副教授,从事资源监测及3S技术应用。Email:yuyky@126.com。
更新日期/Last Update: 1900-01-01