Single Tree Parameters Extraction of Broad-Leaved Forest Based on UAV Tilting Photography

doi:10.13466/j.cnki.lyzygl.2022.01.016

Abstract

Abstract:

Extracting single tree parameters based on UAV tilting photography data is a hot topic in forestry research. Taking ginkgo(Ginkgo biloba L.) broad-leaved forest as the research object,based on UAV tilting photography data,this paper used the local maximum based algorithm to identify the single tree vertex and extract the single tree height under three detection windows(3m×3m、5m×5m、7m×7m),and the accuracies of recognition and extraction were verified respectively. Then,this paper applied the seed region growth algorithm and marker-controlled watershed algorithm for tree canopy extraction,and the extraction accuracies of two algorithms were compared. The results showed that: 1) Under the three single tree detection windows,the F scores of ginkgo tree vertex recognition were 0.87,0.88 and 0.83 respectively. The 5×5m window had the best recognition effect,and in its tree height prediction fitting equation,R² reached 0.99 and RMSE was 1.91m;2) When the relative error threshold of predicted canopy was 30%,the accuracies of seed region growth algorithm and marker-controlled watershed algorithm in extracting crown area were 73.14% and 63.43% respectively. In establishing the linear regression relationship between predicted and measured canopy,the R ² of the two algorithms were 0.98 and 0.97,and the RMSE were 1.79m ² and 2.44m² respectively. In general,the single tree canopy segmentation accuracy of seed region growth algorithm was higher than that of marker-controlled watershed algorithm. This study points out that the UAV tilting photography technology is feasible in the automatic and accurate single tree identification and segmentation of ginkgo broad-leaved forest,thus it has great application potential in forestry investigation.

Key words: tilting photography, tree height, canopy, local maximum algorithm, region growth algorithm, watershed algorithm

CLC Number:

S771

CHEN Zhoujuan, CHENG Guang, BU Yuankun, HUANG Wei, CHEN Jiahui, LI Weizhong. Single Tree Parameters Extraction of Broad-Leaved Forest Based on UAV Tilting Photography[J]. FOREST RESOURCES WANAGEMENT, 2022, (1): 132-141.

Figures/Tables 11

Tab.1

Fig.1

Tab.2

Fig.2

Fig.3

Tab.3

Accuracy assessment of tree top detection results

窗口大小/(m×m)	样本数/株	$TP$	$FN$	$FP$	r/%	p/%	F
3×3	184	142	0	42	77.17	100.00	87.12
5×5	184	141	3	35	80.11	97.92	88.13
7×7	184	124	19	32	79.49	86.71	82.94

Tab.3

Fig.4

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Fig.7

Fig.8

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相机项目	相机参数	无人机项目	无人机参数
传感器尺寸/mm	23.5×15.6	飞行高度/m	64
航飞设置焦距/mm	25	飞行速度/m/s	7.9
快门速度/s	1/1000	飞行时间/min	8
最小光圈/m/s	7.9	图像尺寸/px	6000×4000
分辨率/px	6000×4000	地面分辨率/cm	1
感光度	100-800	旁向重叠率/%	75
最小定时拍照间距/s	1	航向重叠率/%	80

变量	最小值	最大值	平均值
树高/m	7.5	14.9	11.5
冠幅/m	4.0	9.0	5.8
树冠面积/m²	1.9	34.6	12.8