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林业科学 ›› 2017, Vol. 53 ›› Issue (12): 62-72.doi: 10.11707/j.1001-7488.20171207

• 论文与研究报告 • 上一篇    下一篇

基于ICESat-GLAS数据的波形去噪算法优选与森林冠顶高反演模型建立

王新闯, 吴金汝, 陆凤连, 焦海明, 张合兵   

  1. 河南理工大学测绘与国土信息工程学院 焦作 454000
  • 收稿日期:2017-03-27 修回日期:2017-06-30 出版日期:2017-12-25 发布日期:2018-01-13
  • 基金资助:
    国家自然科学基金项目(41401500);中国博士后科学基金项目(2015M580629,2016M590679);河南省高等学校重点科研项目(16A420003,17A420001);河南省高校科技创新团队(18IRTSTHN008);河南省高校基本科研业务费专项资金(NSFRF1630);河南理工大学创新型科研团队(B2017-16)。

Optimal Selection of Algorisms for Denoising ICESat-GLAS Waveform Data and Development of a Forest Crown Height Estimation Model

Wang Xinchuang, Wu Jinru, Lu Fenglian, Jiao Haiming, Zhang Hebing   

  1. School of Surveying and Land Information Engineering, Henan Polytechnic University Jiaozuo 454000
  • Received:2017-03-27 Revised:2017-06-30 Online:2017-12-25 Published:2018-01-13

摘要: [目的]比较基于不同窗函数的GLAS数据去噪算法和多种森林冠顶高反演模型的精度,优选波形去噪算法并确立对森林冠顶高估算精度较高的反演模型,为森林生物量估测等研究奠定数据基础。[方法]首先基于布莱克曼窗函数和高斯窗函数对GLAS数据进行去噪处理,采用RMSE和SNR定量比较2种波形去噪方法的去噪效果;然后对去噪效果最好的窗函数去噪后的波形提取波形参数,并分针叶林、阔叶林、针阔混交林和不分林型4种情况,采用线性回归方法,以波形长度为参数建立波形参数模型,以波形长度、地形指数为参数建立地形因子模型,在地形因子模型基础上,逐步引入波形半能量高、波形前缘长度和波形后缘长度等参数建立全模型,比较3类模型的模拟效果。[结果]高斯窗函数去噪后的RMSE较低、SNR较高,去噪效果较优;冠顶高反演模型中,分林型和不分林型情况下,全模型模拟效果均优于其他2类模型。其中地形因子模型中针叶林效果较好:R2=0.853,RMSE=2.519 7 m;全模型中针阔混交林效果最好:R2=0.972,RMSE=1.001 4 m。[结论]高斯窗函数对GLAS波形去噪能力较强,且在复杂地形情况下,当引入多种波形参数结合地形因子建立多元线性回归模型时,模型对各林型最大冠顶高的解释能力显著提高,可在一定程度上克服地形因子模型在坡度较大地区对冠顶高解释困难的问题,实现复杂地形情况下森林冠顶高的精确估算。

关键词: 大光斑激光雷达, 布莱克曼窗去噪, 高斯窗去噪, 森林冠顶高反演模型

Abstract: [Objective] By comparing the GLAS data denoising algorithm based on different window functions and the accuracy of different inversion models of forest crowns, the waveform denoising algorithm is optimized and the inversion model with high estimation accuracy is obtained, which can lay the foundation for the study of forest biomass estimation.[Method] Firstly, the GLAS data are denoised by the Blackman window function and the Gaussian window function, and the effects of two kinds of waveform denoising method is quantitatively compared with the root mean square error(RMSE)and the signal to noise ratio(SNR). Then, the waveform data denoised by the window function that produced a better denoising effect were used to extract waveform data parameters. Four forest types (coniferous forests, broadleaf forests, mixed coniferous and broadleaf forests, and all forests)were included in this study. The linear regression method was used to develop a waveform-parameter model with the waveform length and a topographic-index model with the waveform length and the topographic-index. A full-parameter model was based on the topographic-index model with the height of median energy, the waveform leading edge, and the waveform trailing edge. Finally, the result of the crown height estimations of these three models were compared.[Result] The result show that the Gaussian window function gave a lower RMSE and a higher SNR than the Blackman window function. This indicates that use of the Gaussian window function for denoising provided more accurate data. For all forest types, the predictions of crown height given by the full-parameter model were more accurate than those given by other two models. The predictions of crown height for the coniferous forest given by the topographic-index model(R2=0.853, RMSE=2.519 7 m) were more accurate than the predictions of that model for other forest types. The predictions given for the mixed coniferous and broadleaf forest by the full-parameter model(R2=0.972,RMSE=1.001 4 m)were more accurate than the predictions of that model for other forest types.[Conclusion] In conclusion, the Gaussian window function performed better than the Blackman window function in denoising the GLAS waveform data. For the waveforms reflected from complex terrain, when the new multiple linear regression model was developed that included several waveform data parameters and the topographic-index, the performance of the model in interpreting the maximum crown height was significantly improved. This makes our model overcome the difficulty of interpreting the maximum crown height of forest on the highly sloping terrain, and thus an accurate estimation of forest crown height on complex terrains can be achieved.

Key words: large footprint laser radar, Blackman window denoising, Gaussian window denoising, forest crown height inversion model

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