地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (8): 1133-1140.doi: 10.3724/SP.J.1047.2016.01133
鲁林1(), 周小成1,*(
), 余治忠1, 韩尚2, 汪小钦1
收稿日期:
2015-10-26
修回日期:
2016-02-23
出版日期:
2016-08-10
发布日期:
2016-08-10
通讯作者:
周小成
E-mail:sirc_lulin@163.com;zhouxc@fzu.edu.cn
作者简介:
作者简介:鲁 林(1990-),男,硕士生,研究方向为林业遥感。E-mail:
基金资助:
LU Lin1(), ZHOU Xiaocheng1,*(
), YU Zhizhong1, HAN Shang2, WANG Xiaoqin1
Received:
2015-10-26
Revised:
2016-02-23
Online:
2016-08-10
Published:
2016-08-10
Contact:
ZHOU Xiaocheng
E-mail:sirc_lulin@163.com;zhouxc@fzu.edu.cn
摘要:
采用机载LiDAR数据估算森林结构参数是当前林业遥感中的研究热点。本文以福建省长汀县朱溪河流域为示范区,探讨了随机森林算法(RF)在机载LiDAR数据林分平均树高估测中的适用性。首先,通过渐进三角网(TIN)算法进行点云滤波并获取相应林分样地的植被点云子集和高程归一化的植被点云;然后,从归一化后的植被点云提取出高度分位数变量以及点云统计特征值等24个变量参数;最后,根据提取的变量参数和野外实测林分均高数据建立研究区林分平均高随机森林回归估测模型。研究结果表明,模型估测的样地平均树高与实测值具有明显线性相关关系,线性回归系数为0.938,相关系数达到0.968。对样地的估测精度都在86%以上,总体平均精度达到了93.17%。研究认为,基于植被点云变量参数的随机森林模型估测林分平均树高具有较高的可靠性。
鲁林, 周小成, 余治忠, 韩尚, 汪小钦. 随机森林算法在机载LiDAR数据林分平均树高 估算中的应用研究[J]. 地球信息科学学报, 2016, 18(8): 1133-1140.DOI:10.3724/SP.J.1047.2016.01133
LU Lin,ZHOU Xiaocheng,YU Zhizhong,HAN Shang,WANG Xiaoqin. Plot-level Forest Height Inversion Using Airborne LiDAR Data Based on the Random Forest[J]. Journal of Geo-information Science, 2016, 18(8): 1133-1140.DOI:10.3724/SP.J.1047.2016.01133
表2
检验样地的树高反演结果与实测对比"
样地编号 | 实测平均树高/m | 估测平均树高/m | 树高差值/m | 精度/(%) |
---|---|---|---|---|
30 | 9.37 | 8.51 | -0.86 | 90.82 |
31 | 7.11 | 7.06 | -0.05 | 99.29 |
32 | 15.54 | 15.16 | -0.38 | 97.55 |
33 | 8.07 | 7.58 | -0.49 | 93.92 |
34 | 14.94 | 15.57 | 0.63 | 95.78 |
35 | 6.26 | 7.09 | 0.83 | 86.74 |
36 | 10.74 | 11.77 | 1.03 | 90.41 |
37 | 9.57 | 11.12 | 1.55 | 83.80 |
38 | 8.73 | 8.28 | -0.45 | 94.84 |
39 | 7.57 | 7.48 | -0.09 | 98.81 |
40 | 14.52 | 13.50 | -1.02 | 92.97 |
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