Predicting DBH of a single <i>Ginkgo biloba</i> tree based on UAV images

JIA Penggang; XIA Kai; DONG Chen; FENG Hailin; YANG Yinhui

doi:10.11833/j.issn.2095-0756.2019.04.016

Volume 36 Issue 4

Jul. 2019

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JIA Penggang, XIA Kai, DONG Chen, FENG Hailin, YANG Yinhui. Predicting DBH of a single Ginkgo biloba tree based on UAV images[J]. Journal of Zhejiang A&F University, 2019, 36(4): 757-763. doi: 10.11833/j.issn.2095-0756.2019.04.016

Citation:

JIA Penggang, XIA Kai, DONG Chen, FENG Hailin, YANG Yinhui. Predicting DBH of a single Ginkgo biloba tree based on UAV images[J]. Journal of Zhejiang A&F University, 2019, 36(4): 757-763. doi: 10.11833/j.issn.2095-0756.2019.04.016

Predicting DBH of a single Ginkgo biloba tree based on UAV images

doi: 10.11833/j.issn.2095-0756.2019.04.016

JIA Penggang^{1,2
,},
XIA Kai^{1,2
,
,},
DONG Chen^1,2,
FENG Hailin^1,2,
YANG Yinhui^1,2

1.
School of Information Engineering, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China
2.
Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China

Received Date: 2018-08-29
Rev Recd Date: 2018-12-03
Publish Date: 2019-08-20

Abstract

To efficiently calculate and predict forest stock and biomass, diameter at breast height (D_BH), a basic factor of a tree, was used in a regression model. In this study, Ginkgo biloba was used as the research object. Image data was obtained with an unmanned aerial vehicle (UAV), and using the method of structure from motion (SFM), a digital surface model and an orthophoto map were generated. Next, the canopy area (A_C), crown width (W_C) and tree height (H) of G. biloba were extracted. Then, one-way regression models (A_C-D_BH, WC-D_BH, H-D_BH), binary regression models (A_C&W_C-D_BH, A_C&H-D_BH, W_C&H-D_BH), and a ternary regression model (A_C&W_C&H-D_BH) were established. Results of 52 groups of fitted samples showed that the A_C&W_C&H-D_BH model had the highest coefficient of determination (R² = 0.825 0) and the lowest root mean square error (E_RMS = 0.959 1). Results of 19 groups of test samples showed that the D_BH error rate for the A_C&W_C&H-D_BH model was 4.20%, which was less than the allowable error value (5%) for the A-type forest resource D_BH factor. Thus, a high precision D_BH value could be calculated using the three parameters of canopy area, crown width, and tree height, thereby providing a new idea for automated forest resource surveying and monitoring.
- forest mensuration,
- unmanned aerial vehicle (UAV),
- diameter at breast height (DBH),
- crown area,
- crown width,
- height,
- inversion model

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References

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[2]	PANAGIOTIDIS D, ABDOLLAHNEJAD A, SUROVY P. Determining tree height and crown diameter from high-resolution UAV imagery[J]. Int J Remote Sensing, 2017, 38(8/10):2392-2410.
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胸径（D_BH），树高（H），树冠面积（即树冠投影面积^[1]A_C）及冠幅（W_C）是进行森林资源清查的重要参数，是计算森林生物量、蓄积量的基础。传统森林资源调查主要通过人工检尺获得胸径值，这种方法具有成本高、效率低、数据滞后等弊端。遥感技术和激光雷达技术的发展与完善为森林信息的提取评估提供了新的方法^[2]。研究人员可以利用光检测和激光测量来估算林分层次上个体树的信息^[3]，新设备如激光测距仪和智能手机^[4]的使用提高了数据收集的效率和精度。步国超等^[5]为了自动化地、准确地从单站地面激光雷达数据中提取一定范围内的树木胸径，提出一种基于点云切片的圆形-椭圆自适应胸径估计方法。该方法可以快速有效地进行自适应胸径估计。国外学者较早通过建立线性或非线性预估模型的方法来反演单木参数^[6-8]，如DE’ATH^[9]通过建立树高—胸径回归方程预测不同样地树高。近年来，随着森林价值的不断挖掘，建立单木树冠面积、树高等因子与胸径的反演模型成为国内学者的研究重点。王冬至等^[10]建立了树高—胸径生长关系非线性混合效应模型，为研究混交林多树种生长规律提供参考依据。董晨等^[11]以杉木Cunninghamia lanceolata为研究对象，使用参数预估法构造胸径和树高的参数化预估模型。何游云等^[12]采用人机交互的方式提取单木树冠面积数据，并结合野外实测的胸径，建立树冠面积—胸径估算模型。结果显示：两者存在较好的非线性相关关系。无人机（UAV）是利用无线电遥控设备和自备的程序控制装置操纵的不载人飞行器，具有体积小、速度快、使用方便等优势，便于进行跟踪、定位、遥测和数字传输等工作，成为传统航空摄影测量手段的有效补充^[13-14]。由于无人机可以携带高分辨率相机，能够满足森林资源调查中高空间分辨率的要求，学者开始关注利用无人机获取具体林木信息以及森林信息量化等方面的工作。无人机遥感的影像分辨率大大超越了卫星遥感，在获取单木参数上具有天然优势，相关研究不断涌现。HERNANDEZ等^[15]基于无人机影像采用混合像元法估测出了单木树高、冠幅等参数；DEMETRIOS等^[16]使用无人机搭载的激光雷达对树冠覆盖度高的雨林进行树高测量。刘文萍等^[17]通过拟合无人机正射图像中的单木树冠面积与胸径的关系预测树木胸径值。前述研究表明：以树高，冠幅等参数反演胸径的方法基本成立，但传统手工量测提取单木参数的方法工作量大，难以应用于实践，而目前基于无人机影像的胸径预测研究较少，也不够全面。基于此，本研究以银杏Ginkgo biloba为研究对象，采用无人机获取样地影像数据，以树冠面积，冠幅和树高为自变量分别建立一元回归模型、二元回归模型和三元回归模型，目的是证明无人机遥感影像数据可用于胸径预估，并探索最佳的反演模型，推进森林资源调查研究的自动化。

1. 研究方法

1.1. 研究区概况

研究区位于浙江省杭州市浙江农林大学校园内，30.25°~30.26°N，119.72°~119.73°E，亚热带季风气候，四季分明，夏季温润多雨，冬季干燥少雨；校园内有300余种乔木，以亚热带常绿阔叶林为主。从谷歌地球（Google Earth）上截取研究区范围，共选取4块银杏样地作为试验区，每块样地为15 m × 150 m。其中样地1，样地2和样地3用来做模型拟合，共52组有效数据；样地4用来做模型反演精度检验，共19组有效数据。

1.2. 无人机系统

本研究所使用的无人机为大疆悟INSPIRE 2系列，主要由4部分组成：飞行器、遥控器、云台相机、电源。飞行器的型号为T650，质量为3 290 g（含2块电池），适用工作环境温度：-10~40 ℃，双电池系统可实现最长飞行时间约25 min。无人机搭载的云台相机型号为大疆ZenmuseX5S，镜头规格为DJI MFT 15 mm，有效像素为2 080万（表 1）。在飞行中搭配DJI GO PRO软件使用，主要是用于样地飞行路线规划。

	指标	参数
飞行器	类型	四轴飞行器
	飞行载质量	4 kg
	飞行速度	最大水平飞行速度：108 km·h^-1
	飞行高度	最大起飞海拔高度：2 500 m
遥控器	工作频率	2.400~2.483 GHz；5.725~5.850 GHz
	控制距离	2.4 GHz：7.0 km(FCC)；3.5 km(CE)；4.0 km(SRRC) 5.8 GHz：7.0 km(FCC)；2.0 km(CE)；5.0 km(SRRC)
云台相机	型号	大疆ZenmuseX5S
	外形尺寸	140 mm × 98 mm × 132 mm
	产品质量	461 g
	有效像素	2 080万
	图像分辨率	4:3 5 280 × 3 956
		16:9 5 280 × 2 970
	可控转动范围	俯仰：30°一90°

Table 1. Dajiang Wu INSPIRE 2 UAV parameters

1.3. 技术路线

本研究基于无人机遥感影像研究单木胸径反演模型。具体流程为：首先利用无人机进行样地影像的拍摄，同时实测样地内单木的胸径；利用运动恢复结构（structure from motion，SFM）方法进行三维重建并生成数字表面模型（digital surface model，DSM）和正射影像图（digital orthophoto map，DOM）；利用ENVI Lidar读取数字表面模型点云数据并提取树高；通过Arc GIS 10.1获得树冠的面积值和冠幅值，进而根据胸径、树高、冠幅和冠幅面积4个林木因子，分别建立一元回归模型（A_C-D_BH, W_C-D_BH, H-D_BH），二元回归模型（A_C&W_C-D_BH, A_C&H-D_BH, W_C&H-D_BH）和三元回归模型（A_C&W_C&H-D_BH），最后通过误差检验获得最佳反演模型。

4. 结论

本研究以银杏为研究对象，通过无人机获取影像数据并提取树冠面积、冠幅和树高3个参数，与胸径建立了多个反演模型。其中以冠幅、树冠面积和树高为自变量反演胸径的三元一次模型的决定系数为0.825 0，均方根误差为0.959 1，19组验证数据的平均误差值为0.76 cm，误差率为4.2%，是最优的模型，满足A类森林资源调查胸径因子误差小于5%的规定，可以应用到森林资源调查的实践中。另一方面，二元二次模型W_C&H-D_BH的拟合精度较高，反演误差率较低，在实际应用中，可根据客观条件选择合适的模型。本研究证明了无人机遥感影像数据可以用于胸径预估，并找到了单木银杏胸径的最佳反演模型。下一步的工作是通过编程实现树冠面积、冠幅和树高值的提取，进而推动森林资源调查的自动化。

由于本研究是在特定立地条件下进行的，而复杂地形（例如山区）或者具体密集林分地区可能会在一定程度上影响实验结果，如何提高复杂地形或者具体密集林分地区单木信息的提取精度是需要进一步解决的问题。同时也要指出，本研究仅针对较小区域内的一个树种进行多元胸径反演模型研究，不同区域或者不同树种可能会影响研究结果。

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[7]	SOARES P, TOMÉ M. Height-diameter equation for first rotation eucalypt plantations in Portugal[J]. For Ecol Manage, 2002, 166(1): 99-109.
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项目	树冠面积（提取)/m²	冠幅（提取)/m	树高（提取)/m	树高（实测)/m	胸径（实测)/cm
最小值	4.08	3.02	4.8	5.2	13.8
最大值	21.78	6.25	13.9	12.3	23.1
平均值	12.97	4.28	9.5	8.7	18.5
标准差	4.84	0.74	1.81	1.39	2.08

	模型	类型	表达式	决定系数(R²)	均方根误差(E_RMS)
	A_C-D_BH	指数函数	D_BH=14.11e(0.020 89A_C)	0.729 9	1.168 0
		幂函数	D_BH=9.423A_C^0.2704	0.758 7	1.100 0
		一次多项式	D_BH=0.401 7A_C+13.37	0.743 2	1.140 0
		二次多项式	D_BH=-0.013 42A_C²+0.769 6Ac+11.15	0.761 8	1.100 0
	H-D_BH	指数函数	D_BH=12.56e(0.042 03H)	0.546 1	1.514 0
		幂函数	D_BH=7.615H^{0.403 7}	0.569 2	1.480 0
一元模型		一次多项式	D_BH=0.817 3H+11.02	0.560 4	1.490 0
		二次多项式	D_BH=-0.043 95H²+1.66H+7.16	0.575 3	1.480 0
	W_C-D_BH	指数函数	D_BH=11.78e^{(0.106 2W_C)}	0.534 1	1.534 0
		幂函数	D_BH=8.98W_C^{0.503 8}	0.579 3	1.458 0
		一次多项式	D_BH=2.141W_C+9.448	0.557 8	1.495 0
		二次多项式	D_BH=-0.928 1W_C²+10.68W_C-9.583	0.648 9	1.354 0
	A_C&W_C-D_BH	一次多项式	D_BH=0.613A_C-1.389W_C+16.58	0.772 3	1.084 0
		二次多项式	D_BH=-0.004 122A_C²+0.818 8A_C-0.631 1W_C-0.031 71A_CW_C+13.31	0.780 2	1.087 0
			D_BH=0.046 69W_C²-0.566 4W_C+0.841 3A_C-0.063 62A_CW_C+12.96	0.780 1	1.087 0
	W_C&H-D_BH	一次多项式	D_BH=0.447 5H+2.114W_C+5.624	0.807 6	1.069 0
二元模型		二次多项式	D_BH=-0.025 26H²+1.117H+2.519W_C-0.048 53HW_C+1.912	0.813 4	1.074 0
			D_BH=0.098 34W_C²+2.621W_C+1.062H-0.149W_CH+1.929	0.812 3	1.077 0
	A_C&H-D_BH	一次多项式	D_BH=0.310 7A_C+0.3H+11.75	0.780 6	1.064 0
		二次多项式	D_BH=-0.005 197A_C²+0.619 1A_C+0.517 8H-0.017 21A_CH+8.909	0.795 1	1.050 0
			D_BH=0.041 04H²+0.132H+0.762 5A_C-0.047 16HA_C+9.747	0.796 7	1.045 0
三元模型	A_C&W_C&H-D_BH	一次多项式	D_BH=0.55A_C-1.695W_C+0.374H+15.226	0.825 0	0.959 1
说明：A_C为树冠面积（m²），W_C为冠幅（m），H为树高（m）

	模型	表达式	平均误差率/I
	A_C-D_BH	D_BH=-0.0134 2A_C²+0.769 6A_C+11.15	5.34
一元模型	H-D_BH	D_BH=-0.043 95H²+1.66H+7.16	7.40
	W_C-D_BH	D_BH=-0.928 1W_C²+10.68W_C-9.583	6.74
	A_C&W_C-D_BH	D_BH=-0.004 122A_C²+0.818 8A_C-0.631 1W_C-0.0317 1A_CW_C+13.31	4.90
二元模型	W_C&H-D_BH	D_BH=-0.025 26H²+1.117H+2.519W_C-0.048 53HW_C+1.912	4.95
	A_C&H-D_BH	D_BH=0.041 04H²+0.132H+0.762 5A_C-0.047 16HA_C+9.747	4.27
三元模型	A_C&W_C&H-D_BH	D_BH=0-55A_C-1.695W_C+0.374H+15.226	4.20

Predicting DBH of a single Ginkgo biloba tree based on UAV images

doi: 10.11833/j.issn.2095-0756.2019.04.016