一种新的地面激光点云中树木叶面积计算方法

doi:10.12302/j.issn.1000-2006.202210012

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南京林业大学学报（自然科学版） ›› 2023, Vol. 47 ›› Issue (5): 28-38.doi: 10.12302/j.issn.1000-2006.202210012

所属专题：林草计算机应用研究专题

• 专题报道：林草计算机应用研究专题（执行主编李凤日） • 上一篇下一篇

一种新的地面激光点云中树木叶面积计算方法

李双娴¹(), 陆鑫¹, 多杰才仁², 张怀清³, 薛联凤¹, 云挺¹^,²^,^*()

1.南京林业大学信息科学技术学院,江苏南京 210037
2.南京林业大学林草学院、水土保持学院,江苏南京 210037
3.中国林业科学研究院资源信息研究所,北京100091

收稿日期:2022-10-10 修回日期:2023-01-02 出版日期:2023-09-30 发布日期:2023-10-10
作者简介:李双娴(541045309@qq.com)。
基金资助:
国家自然科学基金项目(31770591);国家自然科学基金项目(32071681);江苏省自然科学基金面上项目(BK20221337);中国林科院资源信息研究所基本科研业务费专项项目(CAFYBB2019SZ004)

A novel approach for leaf area retrieval from terrestrial laser scanned points

LI Shuangxian¹(), LU Xin¹, Duojie Cairen², ZHANG Huaiqing³, XUE Lianfeng¹, YUN Ting¹^,²^,^*()

1. College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
2. College of Forestry and Grassland, College of Soil and Water Conservation, Nanjing Forestry University, Nanjing 210037, China
3. Institute of Forest Resource Information Techniques CAF, Beijing 100091, China

Received:2022-10-10 Revised:2023-01-02 Online:2023-09-30 Published:2023-10-10

摘要/Abstract

摘要：

【目的】地面激光扫描仪(terrestrial laser scanning, TLS)通过获取植物的稠密激光点云,以精细刻画森林的结构参数,如树木骨架和叶面积等。真实叶面积是林学和植物学中表型研究的一个重要参数,而目前在植物科学领域,还没有很好的表型特征测量手段。因此,本研究提出了一种新颖的地面激光点云的叶面积估算方法来评估树木的表型特征指标。【方法】首先,设计了一种基于小平面区域定位与生长的植物点云的单叶分割算法,实现精准的单叶点云分割提取;其次,以每片叶片法向量与扫描仪入射激光线的夹角、扫描仪与叶片的距离和单叶的点云数量3个参量为输入特征,并结合训练样本与L1+L2正则化多元回归方法获取拟合系数,以反演树冠内所有叶片的面积。最后,将校园内实验树(紫薇、樱花、银杏和香樟)作为研究对象,并将计算结果与实测值进行比对。【结果】本研究方法相较最小二乘拟合算法,取得了更优的叶面积反演结果。对于两棵小树而言,本研究方法与实测值比对取得了较好的结果:紫薇(R²=0.95,RMSE为0.42 cm²)、樱花(R²=0.92,RMSE为1.87 cm²);对于两棵具有更大树冠和枝叶的大树,本研究方法也取得了较好的结果,分别为银杏(R²=0.83,RMSE为1.24 cm²)、香樟(R²=0.86,RMSE为1.10 cm²)。【结论】本研究方法面向林木激光点云数据,运用计算机视觉与机器学习技术准确计算树冠内叶片面积,为林木的叶面积计量提供新颖的思路。

关键词: 地面激光扫描(TLS), 单叶分离, L1+L2正则化多元回归, 真实叶面积计算

Abstract:

【Objective】Terrestrial laser scanning involves collecting dense laser point clouds of plants to finely characterize the structural parameters of a forest, such as tree skeletons and true leaf area. True leaf area is an important index for phenotypic studies in forestry and botany. At present, there are no well-evidenced methods for measuring phenotypic traits in plant science. Here we develop a novel approach for true leaf area retrieval from terrestrial laser scanned points to appraise key phenotypic parameters. 【Method】 First we designed an individual leaf segmentation algorithm based on the small plane locating and region growing for plant point clouds, to achieve an accurate single-leaf point cloud segmentation. Second, we input three parameters: the angle between the normal vector of a single leaf and the incident laser beam from the scanner, the distance between the scanner and leaf, and the number of point clouds of a single leaf. Training samples combined L1 and L2 regularized multiple regression methods to realize inverse calculations of the total leaf area of all leaf elements in a tree canopy. Finally, we chose four individual trees on our campus(crape myrtle, cherry, ginkgo and camphor) to verify the effectiveness of our results by comparison with field measurements. 【Result】Leaf area retrieval results indicated the superiority of our approach over existing least-square fitting methods. Compared with field measurements, we saw better performance for two small trees: the crape myrtle [coefficient of determination (R²) was 0.95 and root mean square error (RMSE) was 0.42 cm²] and the cherry (R² = 0.9 and RMSE was 0.42 cm²). Appreciable results were achieved for the ginkgo (R² = 0.83 and RMSE was 1.24 cm²) and camphor trees (R² = 0.86; RMSE was 1.10 cm²); these are larger trees with extended crowns and more vegetative elements in the canopy. 【Conclusion】 This method synergistically employed a computer vision and machine learning to accurately calculate the leaf area of canopies using scanned points, yielding novel perspectives for assessing the true leaf area of canopies.

Key words: terrestrial laser scanning (TLS), individual leaf segmentation, L1+L2 regularized multiple regression, leaf area retrieval

中图分类号:

S758
TP391

李双娴,陆鑫,多杰才仁,等. 一种新的地面激光点云中树木叶面积计算方法[J]. 南京林业大学学报（自然科学版）, 2023, 47(5): 28-38.

LI Shuangxian, LU Xin, Duojie Cairen, ZHANG Huaiqing, XUE Lianfeng, YUN Ting. A novel approach for leaf area retrieval from terrestrial laser scanned points[J].Journal of Nanjing Forestry University (Natural Science Edition), 2023, 47(5): 28-38.DOI: 10.12302/j.issn.1000-2006.202210012.

图/表 9

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参考文献 37

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树种 species	树高/m tree height	冠幅(南北× 东西)/m×m crown width (north-south× east-west)	枝下高/m clear bole height	单叶长/ cm leaf length	单叶宽/ cm leaf width
紫薇 Lagerstroemia indica	2.50	2.04×2.24	0.67	5.29±1.02	3.46±0.52
樱花 Cerasus spp.	3.50	2.95×2.54	1.29	11.51±2.35	5.91±2.02
银杏 Ginkgo biloba	10.35	4.96×4.26	1.75	4.78±0.96	7.33±1.47
香樟 Cinnamomum camphora	16.31	7.16×6.06	3.67	7.05±1.87	4.40±0.72

树种 species	分割叶片点云数量 leaf point clouds		分割枝干点云数量 branch point clouds		枝叶分离总体精度/% total accuracy of branch and leaf separation	分割单叶数量 number of single leaves divided				平均叶面积/cm² mean leaf area		树冠总叶面积/m² total crown leaf area
树种 species	分割叶片点云数量 leaf point clouds		计算机 network	人工 manual	枝叶分离总体精度/% total accuracy of branch and leaf separation	计算机 network	人工 manua	算法[37] algorithm[37]		平均叶面积/cm² mean leaf area		计算机 network		人工 manua		算法[37] algorithm[37]
紫薇 Lagerstroemia indica	15 480	16 080	4 644	4 824	94.81	387	402	351	12.21±1.83		0.47		0.42		0.49
樱花 Cerasus spp.	35 814	37 768	9 244	9 531	92.36	485	497	468	45.35±8.07		2.15		2.11		2.19
银杏 Ginkgo biloba	424 983	426 153	84 996	85 230	91.56	10 132	10 927	9 542	23.36±4.67		23.46		22.03		25.53
香樟 Cinnamomum camphora	397 350	402 630	119 205	120 789	92.18	12 197	13 421	11 586	20.68±4.76		25.39		24.12		27.75

树种 species	枝叶分离 separation of branches and leaves	叶叶分离 leaf separation	拟合训练 fitting training	拟合测试 fitting test	总时间 total time
紫薇 Lagerstroemia indica	4	2	0.3	0.5	6.8
樱花 Cerasus spp.	6	3	0.5	0.7	10.2
银杏 Ginkgo biloba	15	11	2	2.3	30.3
香樟 Cinnamomum camphora	17	14	2.2	3	36.2

一种新的地面激光点云中树木叶面积计算方法

A novel approach for leaf area retrieval from terrestrial laser scanned points

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