欢迎访问《应用生态学报》官方网站,今天是 分享到:

应用生态学报 ›› 2018, Vol. 29 ›› Issue (7): 2391-2400.doi: 10.13287/j.1001-9332.201807.011

• 研究报告 • 上一篇    下一篇

基于MODIS时间序列反射率数据的雷竹林LAI反演

朱迪恩, 徐小军*, 杜华强, 周国模, 毛方杰, 李雪建, 李阳光   

  1. 1亚热带森林培育国家重点实验室/浙江省森林生态系统碳循环与固碳减排重点实验室/浙江农林大学环境与资源学院, 浙江临安 311300
  • 收稿日期:2017-11-06 出版日期:2018-07-18 发布日期:2018-07-18
  • 通讯作者: *E-mail: xuxiaojun3115371@163.com
  • 作者简介:朱迪恩, 男, 1991年生, 硕士研究生. 主要从事森林资源遥感监测与信息技术研究. E-mail: 1096178563@qq.com
  • 基金资助:

    本文由国家自然科学基金项目(31500520,31670644)、浙江省竹资源与高效利用协同创新中心项目(S2017011)和浙江省与中国林业科学研究院省院合作林业科技项目(2017SY04)资助.

Retrieval of leaf area index of Phyllostachys praecox forest based on MODIS reflectance time series data.

ZHU Di-en, XU Xiao-jun*, DU Hua-qiang, ZHOU Guo-mo, MAO Fang-jie, LI Xue-jian, LI Yang-guang   

  1. State Key Laboratory of Subtropical Silviculture/Zhejiang Province Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration/School of Environmental and Resources Science, Zhejiang A&F University, Lin’an 311300, Zhejiang, China
  • Received:2017-11-06 Online:2018-07-18 Published:2018-07-18
  • Contact: *E-mail: xuxiaojun3115371@163.com
  • Supported by:

    The work was supported by the National Natural Science Foundation of China (31500520, 31670644), the Zhejiang Provincial Collaborative Innovation Center for Bamboo Resources and High-efficiency Utilization (S2017011), and the Joint Forestry Research Fund of Department of Forestry of Zhejiang Province and Chinese Academy of Forestry (2017SY04).

摘要: 本文以雷竹林为研究对象,基于MODIS地表反射率数据构建了归一化植被指数(NDVI)、比值植被指数(SR)、Gitelson绿色植被指数(GI)、增强型植被指数(EVI)和土壤调整植被指数(SAVI)5种植被指数,并将其与MODIS 7个波段原始反射率数据作为遥感变量,采用逐步回归和相关分析两种方法进行变量筛选,结合LAI实测数据构建了逐步回归和BP神经网络两种模型,对雷竹林生态系统观测站点2014年1月—2017年3月LAI时间系列数据进行反演,并将反演结果与同时期MOD15A2 LAI产品进行对比分析.结果表明: SR为唯一入选逐步回归模型的变量;b1、b2、b3和b7以及5种植被指数与LAI之间的相关性均达到显著水平,可作为BP神经网络模型的输入变量.使用BP神经网络反演得到的LAI与实测LAI之间的相关性显著,R2为0.71,RMSE为0.34,RMSEr为13.6%,其R2比逐步回归模型提高了10.9%,RMSE降低了5.6%,RMSEr降低了12.3%,与MODIS LAI相比,其R2提高了54.5%,RMSE降低了79.3%,RMSEr降低了79.1%.结合MODIS时间序列反射率和BP神经网络模型能够精确地反演雷竹林LAI,为实现基于遥感技术快速监测区域雷竹林LAI提供可行的方法.

Abstract: Based on the MODIS surface reflectance data, five vegetation indices, including norma-lized difference vegetation index (NDVI), simple ratio index (SR), Gitelson green index (GI), enhanced vegetation index (EVI) and soil adjusted vegetation index (SAVI) were constructed as remote sensing variables, coupled with the seven original spectral reflectance bands of MODIS. Stepwise regression and correlation analysis were used to select the variables, and the stepwise regression and Back Propagation (BP) neural network models were constructed based on the measured LAI to retrieve the LAI time series data of Phyllostachys praecox (Lei bamboo) forest during the period from January 2014 to March 2017. The retrieval results were compared with MOD15A2 LAI products during the same period. The results showed that SR was the single variable selected for the stepwise regression model. The correlations of LAI with bands b1, b2, b3, b7 and five vegetation indices were significant, which could be used as input variables of BP neural network model. There was a significant correlation between the LAI estimated from BP neural network and measured LAI, with the R2 of 0.71, RMSE of 0.34, and RMSEr of 13.6%. R2 was increased by 10.9%, RMSE decreased by 5.6%, and RMSEr decreased by 12.3% compared with LAI estimated from stepwise regression method. R2 was increased by 54.5%, RMSE decreased by 79.3%, and RMSEr decreased by 79.1% compared with MODIS LAI. The LAI of Lei bamboo forest could be accurately retrieved using BP neural network method based on MODIS reflectance time series data, which would be a feasible method for rapid monitoring of LAI in Lei bamboo forest.