Crown segmentation of CHM based on the enhanced frost local filtering and distance map reconstruction

doi:10.12302/j.issn.1000-2006.202209011

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2023, Vol. 47 ›› Issue (5): 9-18.doi: 10.12302/j.issn.1000-2006.202209011

Special Issue: 林草计算机应用研究专题

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Crown segmentation of CHM based on the enhanced frost local filtering and distance map reconstruction

ZHANG Huacong¹^,²^,³(), TAN Xinjian³(), YU Longhua³, LI Yueqiao³, CHEN Yongfu¹^,², LIU Ren³, ZHANG Huaiqing¹^,²^,^*()

1. Institute of Forest Resource Information Techniques, CAF, Beijing 100091, China
2. Key Laboratory of Remote Sensing and Information, NFGA, Beijing 100091, China
3. Experimental Enter of Subtropical Forestry, CAF, Xinyu 336600, China

Received:2022-09-05 Revised:2022-10-25 Online:2023-09-30 Published:2023-10-10

Abstract

Abstract:

【Objective】We used the enhanced Frost local filtering and single-tree distance map reconstruction marking technology to segment a canopy height model (CHM), improving the accuracy and efficiency of unpiloted aerial system (UAV)-light detection and ranging (LiDAR) segmentation in single-tree crowns.【Method】 We selected three forest types - coniferous mixed, coniferous-broad mixed, and broad-leaved mixed - in the Shanxia Experimental Forest Farm of Fenyi, Jiangxi Province. We then used UAV-LiDAR data to construct the CHM. To combat the increased pores in the crown area of the high-resolution CHM, we used the enhanced Frost local filtering to optimize the CHM and results were compared with different filtering methods. Next we applied the distance map reconstruction marker segmentation technology to segment and analyze the CHM-with resolutions of 0.1, 0.2, 0.5 and 1.0 m after optimization of the enhanced Froest local filter. Finally, we determined the CHM with the optimal resolution, and compared segmentation results with that of a watershed algorithm with the same resolution and mean-shift segmentation algorithm. 【Result】 Applying the enhanced Frost local filter indeed optimized the CHM-preserving image details while suppressing phase crown noise. A resolution of 0.2 m performed best for the CHM segmentation. An overall accuracy of 0.96, 0.84 and 0.75 was observed for coniferous mixed, coniferous-broad mixed, and broad-leaved mixed forests, respectively. The crown width of a single tree was calculated according to crown segmentation results, and the R² estimated at 0.83, 0.82 and 0.71, respectively. 【Conclusion】Through the enhanced Frost local filtering and distance map reconstruction marking technology, the single-tree segmentation and crown estimation of laser point cloud CHMs can be realized, meeting key requirements of forest surveys and monitoring.

Key words: UAV laser point cloud, cannopy height model(CHM), enhance Frost, distance map marker and reconstruction, crown segmentation

CLC Number:

S758.5

ZHANG Huacong, TAN Xinjian, YU Longhua, LI Yueqiao, CHEN Yongfu, LIU Ren, ZHANG Huaiqing. Crown segmentation of CHM based on the enhanced frost local filtering and distance map reconstruction[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(5): 9-18.

Figures/Tables 7

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指标 index	冠层类型 canopy layer type	原始图像 original image	增强Frost enhanced frost	增强Lee enhanced Lee	局部Sigma local sigma	中值滤波 median filtering	本研究 this research
平滑指数(SI) smoothness index	A阔叶混交林	2.38	2.56	2.83	2.36	2.53	2.63
	B针叶混交林	3.06	3.39	3.39	3.13	3.34	3.31
	C针阔混交	2.48	2.66	2.65	2.52	2.58	2.60
	均值	2.64	2.87	2.96	2.67	2.82	2.85
信噪比(SNR) signal to noise ratio	A阔叶混交林	14.33	14.83	14.71	14.33	14.61	14.53
	B针叶混交林	13.25	14.05	14.05	13.35	13.79	13.61
	C针阔混交	13.66	14.11	14.08	13.73	14.17	13.91
	均值	13.75	14.33	14.28	13.77	14.19	14.02
等效视数(ENL) equivalent number of looks	A阔叶混交林	5.70	6.59	8.06	5.63	6.47	6.98
	B针叶混交林	9.46	11.55	11.55	9.90	11.24	11.04
	C针阔混交	6.22	7.14	7.09	6.38	6.72	6.81
	均值	7.13	8.43	8.90	7.31	8.14	8.28
边缘保持指数(EPI) edge protect index	A阔叶混交林		0.44	0.51	0.91	0.45	0.89
	B针叶混交林		0.47	0.48	0.86	0.43	0.85
	C针阔混交		0.48	0.49	0.89	0.44	0.85
	均值		0.46	0.49	0.89	0.44	0.86

冠层类型 canopy layer type	样地序号 sample No.	实测株树 quantity of tree	总体精度(OA) overall accuracy			误判误差 (CE) commission error			漏判误差(OE) omission error
冠层类型 canopy layer type	样地序号 sample No.	实测株树 quantity of tree	本研究 this paper	分水岭 watershed	均值漂移 mean shift	本研究 this paper	分水岭 watershed	均值漂移 mean shift	本研究 this paper	分水岭 watershed	均值漂移 mean shift
A阔叶混交林 broad-leaved mixed forest	1	41	0.71	0.37	0.37	0.27	0.61	0.61	0.02	0.02	0.02
	2	52	0.79	0.42	0.37	0.21	0.54	0.58	0.00	0.04	0.05
	3	41	0.76	0.44	0.32	0.22	0.56	0.66	0.02	0.00	0.02
	均值mean	45	0.75	0.41	0.35	0.23	0.57	0.62	0.02	0.02	0.03
B针叶混交林 coniferous mixed forest	4	67	0.97	0.78	0.66	0.03	0.21	0.31	0.00	0.01	0.03
	5	62	0.94	0.81	0.74	0.05	0.18	0.23	0.02	0.02	0.03
	6	56	0.98	0.84	0.71	0.02	0.16	0.24	0.00	0.00	0.05
	均值mean	62	0.96	0.81	0.70	0.03	0.18	0.26	0.01	0.01	0.04
C针阔混交林 coniferous broad- leaved mixed forest	7	59	0.86	0.69	0.54	0.10	0.29	0.41	0.03	0.02	0.05
	8	65	0.82	0.72	0.58	0.14	0.25	0.38	0.05	0.03	0.04
	9	57	0.84	0.68	0.56	0.11	0.30	0.40	0.05	0.02	0.04
	均值mean	60	0.84	0.70	0.57	0.12	0.28	0.40	0.04	0.02	0.03
总体均值total mean		56	0.85	0.64	0.55	0.13	0.34	0.43	0.02	0.02	0.02

Crown segmentation of CHM based on the enhanced frost local filtering and distance map reconstruction

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