机载LIDAR数据的树高识别算法与应用分析

doi:10.3724/SP.J.1047.2014.00958

地球信息科学学报 ›› 2014, Vol. 16 ›› Issue (6): 958-964.doi: 10.3724/SP.J.1047.2014.00958

机载LIDAR数据的树高识别算法与应用分析

王轶夫^1,²(), 岳天祥^1,^*(), 赵明伟^1,², 杜正平¹, 刘向锋³, 刘爽³, 宋二非³, 孙文正³, 张彦丽⁴

1. 中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2. 中国科学院大学,北京 100049
3. 同济大学测绘与地理信息学院,上海 200092
4. 西北师范大学地理与环境科学学院,兰州 730070

收稿日期:2014-02-20 修回日期:2014-03-20 出版日期:2014-11-10 发布日期:2014-11-01
通讯作者: 岳天祥 E-mail:wangyf@lreis.ac.cn;yue@lreis.ac.cn
作者简介:
作者简介：王轶夫(1990-),男,安徽安庆人,博士生,研究方向为资源环境与生态系统模拟。E-mail:wangyf@lreis.ac.cn
基金资助:
国家自然科学基金重点项目(91325204);国家高技术研究发展计划项目(2013AA122003);科技基础性工作专项(2013FY111600-4)

Study of Factors Impacting the Tree Height Extraction Based on Airborne LIDAR Data

WANG Yifu^1,²(), YUE Tianxiang^1,^*(), ZHAO Mingwei^1,², DU Zhengping¹, LIU Xiangfeng³, LIU Shuang³, SONG Erfei³, SUN Wenzheng³, ZHANG Yanli⁴

1. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
4. College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China

Received:2014-02-20 Revised:2014-03-20 Online:2014-11-10 Published:2014-11-01
Contact: YUE Tianxiang E-mail:wangyf@lreis.ac.cn;yue@lreis.ac.cn
About author:
*The author: CHEN Nan, E-mail:fjcn99@163.com

摘要/Abstract

摘要：

利用机载激光雷达数据提取天然次生林的树高,旨在探索影响树高提取精度的主要因素。首先,采用高精度曲面建模平差算法(Adjustment Computation of High-accuracy Surface Modeling,HASM-AD)生成研究区不同空间分辨率的数字高程模型(Digital Elevation Model,DEM)、数字地表模型(Digital Surface Model,DSM)和冠层高度模型(Canopy Height Model,CHM);其次,用树顶点识别算法提取林木树高,设置不同树高识别范围,对比分析不同CHM分辨率和不同树高识别范围对树高提取精度的影响;最后,以天涝池流域30个实测样地数据为样本,对提取精度进行检验。结果显示：提取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0.694;树高识别范围是影响树高提取精度的重要因素,CHM分辨率对其影响较小。研究表明,采用高采样密度的雷达点云数据、正确选择CHM生成方法和改进树顶点识别算法是提高天然次生林树高提取精度的有效途径。

关键词: 机载激光雷达, HASM, 树高, 天涝池, 天然次生林

Abstract:

The purpose of this study is to evaluate the accuracy of extracting average height of natural secondary forest using airborne LIDAR data and to explore the problems that accompany. The DSMs and DEMs with differentspatial resolutions were simulated, by applying HASM-AD algorithm. DSM minus DEM gives CHM, and the tree heights were extracted from CHM. We applied tree vertex recognition algorithm with different recognition scopes. Using 30 measured plot data for verification, we tried to express how CHM spatial revolutionand recognition scope could affect tree height extraction accuracy. Firstly, we produced the 0.5 m resolution of CHM and gave 3 trials with setting the recognition scope radius as 0.5 m, 1.0 m and 1.5 m consecutively. The contrast between the results showed that the number of tree vertices extracted was the largest when the recognition scope radius was set as 0.5 m. The algorithm's ability to recognize tree vertex decreases as recognition scope radius increases. Then, we set the recognition scope radius as 0.5 m unchanged and gave 3 trials in which we extracted tree vertex from different CHM with 3 different resolutions (0.1 m, 0.25 m, 0.5 m). The results showed that the number of tree vertices extracted in 3 trials were close. In other words, the recognition scope radius could hardly influence tree vertex extraction. Finally, we compared the average value of the extracted tree heights in each plot to the average of the measured values. The result showed that they were highly correlated with each other, and the regression coefficient between them was 0.694. In conclusion, the recognition scope radius has great influence on tree vertex extraction, while resolution of CHM has little influence on tree vertex extraction. Increasing the sampling density of LIDAR data, choosing an appropriate CHM simulation method and improving the tree vertex recognition algorithm can increase the accuracy of tree height extraction.

Key words: airborne LIDAR, HASM, tree height, Tianlaochi, natural secondary forest

王轶夫, 岳天祥, 赵明伟, 杜正平, 刘向锋, 刘爽, 宋二非, 孙文正, 张彦丽. 机载LIDAR数据的树高识别算法与应用分析[J]. 地球信息科学学报, 2014, 16(6): 958-964.DOI:10.3724/SP.J.1047.2014.00958

WANG Yifu,YUE Tianxiang,ZHAO Mingwei,DU Zhengping,LIU Xiangfeng,LIU Shuang,SONG Erfei,SUN Wenzheng,ZHANG Yanli. Study of Factors Impacting the Tree Height Extraction Based on Airborne LIDAR Data[J]. Journal of Geo-information Science, 2014, 16(6): 958-964.DOI:10.3724/SP.J.1047.2014.00958

图/表 10

表1

图1

图2

图3

表2

图4

图5

图6

表3

图7

参考文献 23

[1]	高广磊,信忠保,丁国栋,等.基于遥感技术的森林健康研究综述[J].生态学报,2013,33(6):1675-1689.
[2]	Fang J Y, Chen A P, Peng C H, et al.Changes in forest biomass carbon storage in china between 1949 and 1998[J]. Science, 2001,292(5525):2320-2322.
[3]	Foody G M, Boyd D S and Cutler M E J. Predictive relations of tropical forest biomass from landsat tm data and their transferability between regions[J]. Remote Sensing of Environment, 2003,85(4):463-474.
[4]	王立海, 赵正勇,杨旗.利用GIS对吉林针阔混交林TM遥感图像分类方法的初探[J].应用生态学报, 2006,17(4): 4577-4582.
[5]	Pant P, Heikkinen V, Hovi A, et al.Evaluation of simulated bands in airborne optical sensors for tree species identification[J]. Remote Sensing of Environment, 2013,138:27-37.
[6]	Hirata Y, Tabuchi R, Patanaponpaiboon P, et al.Estimation of aboveground biomass in mangrove forests using high-resolution satellite data[J]. Journal of Forest Research, 2014,19(1):34-41.
[7]	庞勇,赵峰,李增元,等.机载激光雷达平均树高提取研究[J].遥感学报,2008,12(1):152-158.
[8]	Clark M L, Clark D B, Roberts D A.Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape[J]. Remote Sensing of Environment, 2004,91(1):68-89.
[9]	Nilsson M.Estimation of tree heights and stand volume using an airborne LiDAR system[J]. Remote Sensing of Environment, 1996,56(1):1-7.
[10]	Hudak A T, Lefsky M A, Cohen W B, et al.Integration of lidar and landsat ETM plus data for estimating and mapping forest canopy height[J]. Remote Sensing of Environment, 2002,82(2-3):397-416.
[11]	St-Onge B, Jumelet J, Cobello M, et al.Measuring individual tree height using a combination of stereophotogrammetry and LiDAR[J]. Canadian Journal of Forest Research-Revue Canadienne De RechercheForestiere, 2004,34(10):2122-2130.
[12]	Koukoulas S and Blackburn G A. Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LiDAR and multi-spectral remotely sensed data[J]. International Journal of Remote Sensing, 2005,26(3):431-455.
[13]	Suarez J C, Ontiveros C, Smith S, et al.Use of airborne LiDAR and aerial photography in the estimation of individual tree heights in forestry[J]. Computers & Geosciences, 2005,31(2):253-262.
[14]	Edson C and Wing M G. Airborne light detection and ranging (LiDAR) for individual tree stem location, height, and biomass measurements[J]. Remote Sensing, 2011,3(11):2494-2528.
[15]	Mora B, Wulder M A, White J C, et al.Modeling stand height, volume, and biomass from very high spatial resolution satellite imagery and samples of airborne LiDAR[J]. Remote Sensing, 2013,5(5):2308-2326.
[16]	岳天祥,杜正平.高精度曲面建模与经典模型的误差比较分析[J].自然科学进展,2006,16(8):986-991.
[17]	Yue T X, Du Z P, Song D J, et al.A new method of surface modeling and its application to demconstruction[J]. Geomorphology,2007,91(1-2):161-172.
[18]	Chen C F, Yue T X.A method of dem construction and related error analysis[J]. Computers & Geosciences,2010,36(6):717-725.
[19]	Yue T X, Wang S H.Adjustment computation of HASM: A high-accuracy and high-speed method[J]. International Journal of Geographical Information Science,2010,24(11):1725-1743.
[20]	赵明伟,岳天祥,赵娜.改进的HASM-AD算法及在空间变量模拟的应用分析[J].地球信息科学学报,2013,15(5): 655-661.
[21]	肖青,闻建光.黑河生态水文遥感试验:天姥池小流域机载激光雷达DEM点云数据. 中国科学院遥感与数字地球研究所,2012.doi:10.3972/hiwater.009.2013.db
[22]	Koch B, Heyder U,Weinacker H.Detection of individual tree crowns in airborne LiDAR data[J]. Photogrammetric Engineering and Remote Sensing, 2006,72(4):357-363.
[23]	刘清旺,李增元,陈尔学,等.利用机载激光雷达数据提取单株木树高和树冠[J].北京林业大学学报,2008,30(6):83-89.

样地号	森林类型	株数	平均树高(m)	最大树高(m)	最小树高(m)	平均胸径(cm)	样地号	森林类型	株数	平均树高(m)	最大树高(m)	最小树高(m)	平均胸径(cm)
1	祁连圆柏	30	7.3	11.3	2.2	29.7	16	青海云杉	34	11.0	21.2	2.6	15.6
2	祁连圆柏	13	8.8	12.7	2.5	33.5	17	青海云杉	27	13.0	21.6	2.4	19.8
3	青海云杉	60	3.4	19.3	0.5	9.9	18	青海云杉	40	12.8	26.3	2.0	16.1
4	青海云杉	17	11.1	18.6	0.8	21.7	19	青海云杉	39	10.4	21.6	2.3	15.0
5	混交林	36	4.0	10.7	1.3	8.2	20	青海云杉	46	10.7	20.8	2.9	13.7
6	青海云杉	106	7.1	16.9	1.8	9.9	21	青海云杉	108	9.0	13.6	4.4	10.0
7	青海云杉	21	9.9	20.4	1.7	20.1	22	青海云杉	20	13.5	22.8	3.5	18.8
8	青海云杉	65	8.0	17.7	2.0	13.2	23	青海云杉	25	10.8	20.0	4.5	15.6
9	青海云杉	44	7.7	17.0	2.2	12.6	24	青海云杉	37	7.7	17.3	2.6	10.4
10	青海云杉	22	7.1	20.1	1.5	13.8	25	青海云杉	26	11.4	21.8	3.2	17.6
11	青海云杉	16	10.7	25.0	2.5	21.3	26	青海云杉	26	12.4	22.1	3.9	16.9
12	青海云杉	16	11.7	25.3	1.8	25.3	27	青海云杉	34	10.4	22.4	3.5	14.6
13	青海云杉	6	21.1	24.4	15.1	39.1	28	青海云杉	11	20.3	31.1	4.8	30.2
14	青海云杉	49	9.4	19.9	2.0	14.2	29	青海云杉	29	14.6	26.6	4.6	20.4
15	青海云杉	17	14.0	21.8	3.0	23.5	30	混交林	19	10.2	23.4	2.8	22.5

实验	分辨率(m)	搜索半径(m)
1	0.1×0.1	0.5
2	0.25×0.25	0.5
3	0.5×0.5	0.5
4	0.5×0.5	1.0
5	0.5×0.5	1.5

线性回归方程	R²	说明
y₁=0.819x₁+5.032	0.694	y₁、x₁分别为提取的样地平均树高和实测值
y₂=0.845x₂+2.390	0.850	y₂、x₂分别为提取的样地最大树高和实测值
y₃=1.582x₃+2.340	0.111	y₃、x₃分别为提取的样地最小树高和实测值
y₄=0.145x₄+10.01	0.339	y₄、x₄分别为提取的样地株数和实测值

机载LIDAR数据的树高识别算法与应用分析

Study of Factors Impacting the Tree Height Extraction Based on Airborne LIDAR Data

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 23

相关文章 12

Metrics

本文评价

推荐阅读 0

[1]	张永军, 黄星北, 刘欣怡. 一种地形自适应的机载LiDAR点云正则化TPS滤波方法[J]. 地球信息科学学报, 2020, 22(4): 898-908.
[2]	刘羽, 虢建宏, 岳天祥, 赵娜. 地表CO₂浓度分布模拟试验及分析[J]. 地球信息科学学报, 2017, 19(2): 197-204.
[3]	李晗, 赵娜, 岳天祥, 申维, 刘羽. 基于HASM方法的黑河流域潜在蒸发量的模拟[J]. 地球信息科学学报, 2017, 19(11): 1466-1474.
[4]	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.
[5]	张涛, 李宝林, 赵娜, 许丽丽. 结合TRMM数据的区域降水高精度曲面建模研究[J]. 地球信息科学学报, 2015, 17(8): 895-901.
[6]	赵明伟, 岳天祥, 赵娜. 改进的HASM-AD算法及在空间变量模拟的应用分析[J]. 地球信息科学学报, 2013, 15(5): 655-661.
[7]	赵娜, 岳天祥. 高精度曲面建模的一种快速算法[J]. 地球信息科学学报, 2012, 14(3): 281-285.
[8]	闫长青 , 岳天祥. 基于GPU的HASM动态模拟与实时渲染方法[J]. 地球信息科学学报, 2012, 14(2): 149-157.
[9]	张昊, 岳天祥, 李昌华, 杜正平, 范泽孟, 孙晓芳, 赵娜. 利用三元组稀疏矩阵技术改进HASM算法——以全球平均气温模拟为例[J]. 地球信息科学学报, 2012, 14(2): 158-164.
[10]	范泽孟, 岳天祥, 陈传法, 孙晓芳. 中国气温与降水的时空变化趋势分析[J]. 地球信息科学学报, 2011, 13(4): 526-533.
[11]	宋敦江, 岳天祥, 杜正平. 由等高线建立DEM的YUE-HASM方法研究[J]. 地球信息科学学报, 2009, 11(3): 325-332.
[12]	宋敦江, 岳天祥, 龚云, 杜正平. 散乱数据插值的HASM方法[J]. 地球信息科学学报, 2007, 9(3): 45-51.