Estimation method of <i>Phyllostachys edulis</i> forest canopy density based on UAV visible image

WANG Yuyang; WANG Yixiang; LI Mingzhe; LIANG Dan

doi:10.11833/j.issn.2095-0756.20210576

Volume 39 Issue 5

Sep. 2022

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Article Navigation > Journal of Zhejiang A&F University > 2022 > 39(5): 981-988

WANG Yuyang, WANG Yixiang, LI Mingzhe, LIANG Dan. Estimation method of Phyllostachys edulis forest canopy density based on UAV visible image[J]. Journal of Zhejiang A&F University, 2022, 39(5): 981-988. doi: 10.11833/j.issn.2095-0756.20210576

Citation:

WANG Yuyang, WANG Yixiang, LI Mingzhe, LIANG Dan. Estimation method of Phyllostachys edulis forest canopy density based on UAV visible image[J]. Journal of Zhejiang A&F University, 2022, 39(5): 981-988. doi: 10.11833/j.issn.2095-0756.20210576

Estimation method of Phyllostachys edulis forest canopy density based on UAV visible image

doi: 10.11833/j.issn.2095-0756.20210576

WANG Yuyang^{1, 2, 3
,},
WANG Yixiang^{1, 2, 3},
LI Mingzhe^{1, 2, 3},
LIANG Dan^{1, 2, 3
,
,}

1.
College of Environment and Resources, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China
2.
State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China
3.
Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China

Received Date: 2021-08-19
Accepted Date: 2022-06-15
Rev Recd Date: 2022-05-20

Available Online: 2022-09-22

Publish Date: 2022-10-20

Abstract

Objective Due to the growth and management characteristics of Phyllostachys edulis forest, the canopy density of Ph. edulis forest is a very important factor in the management. This study aims to explore the estimation method of canopy density of Ph. edulis forest based on unmanned aerial vehicle (UAV) visible image, which can achieve real-time and rapid acquisition of Ph. edulis forest canopy density. Method The visible light image of Ph. edulis forest of common rotor UAV was taken as the research object. 4 mature digital image processing methods were adopted, namely, threshold classification based on pixel, supervised classification based on pixel, threshold classification based on multi-scale segmentation, and supervised classification based on multi-scale segmentation. 36 sample plots with different truncation conditions and canopy density were selected. Using the existing software and MATLAB programming, the Ph. edulis canopy area in each sample plot was rapidly extracted, and then the canopy density was estimated. The estimation accuracy of the canopy density of each method was compared with the true value calculated by visual interpretation, and the performance of the 4 methods under different truncation and different canopy density conditions was compared and analyzed. Result The overall accuracy of threshold classification based on pixel, supervised classification based on pixel, threshold classification based on multi-scale segmentation, and supervised classification based on multi-scale segmentation was 91.81%, 92.96%, 93.47% and 98.86%, respectively. The absolute error of the estimated value of canopy density of the 4 methods was 0.038, 0.030, 0.024 and 0.004, respectively. Truncation condition and canopy density had no significant effect on the extraction results. Conclusion The supervised classification method based on multi-scale segmentation has the highest overall accuracy and the smallest absolute error. It can quickly and accurately extract and estimate the canopy density of Ph. edulis forest, and is suitable for different management types of Ph. edulis forest. [Ch, 2 fig. 6 tab. 28 ref.]
- crown density,
- truncation,
- multi-scale segmentation,
- Phyllostachys edulis forest,
- unmanned aerial vehicle (UAV)

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林分郁闭度是指林木树冠垂直投影面积占林地总面积的比例，是反映林分密度的重要指标，在森林资源调查中尤为重要。在传统的森林资源调查中，获取林分郁闭度的主要方法为抬头望法，该方法耗时耗力，获取的数据精度较低，且只适用于面积小、地势缓的林地，难以在地理环境复杂的大区域内应用。随着数字成像技术的发展，HERBERT^[1]提出利用鱼眼相机拍摄全天空相片进行郁闭度估测，扩大了拍摄的林冠范围；濮毅涵等^[2]基于普通可见光照片中的树叶、树干和天空进行分类，将估测结果与抬头望法结果进行模型比较，得到决定系数(R²)为0.77，但该方法适用于地形平坦的林区，在地形复杂的山地难以应用。为克服地形因子的限制，不少学者基于卫星遥感影像对乔木林林分郁闭度的提取进行了研究。高云飞等^[3]基于SPOT 5影像，对各波段的遥感影像像元亮度值进行组合，建立反演模型，最终得到最佳组合模型的R²为0.66；李擎等^[4]为提高模型精度，基于高分二号(GF-2)遥感影像，结合光谱信息、纹理特征和地形因子构建郁闭度估测模型，模型精度达89.82%。然而卫星遥感成本高、时效低、灵活性低^[5]，相比之下无人机可见光遥感在中小型遥感区域作业中能发挥更大的优势，满足动态森林资源监测的条件和需求，并提供更多可能性^[6-8]。苏迪等^[9]基于无人机可见光影像，利用主成分分析确定模型的主成分变量，建立郁闭度回归方程，估算精度为83.18%；汪霖^[10]结合无人机可见光影像得到的数字正射影像(DOM)和冠层高度模型(CHM)，使用阈值法对树冠区域进行提取并计算郁闭度，平均精度为92.93%。以上关于乔木林分郁闭度遥感估测的研究都取得了一定的成果，能够满足林业调查需求，然而，目前基于无人机可见光影像的毛竹林林分郁闭度估测研究为数不多。

毛竹作为中国南方重要的笋、竹两用竹种，是最重要的森林资源之一。第九次全国森林清查结果显示：毛竹林面积为467 hm²，占竹林总面积的73%^[11]。由于毛竹林的生长特点(扩鞭)^[12-13]和经营特点(择伐)，使得林分郁闭度在毛竹林经营中尤为重要，出笋率和采伐结果都将反映在郁闭度上，只有保持适宜毛竹生长的郁闭度，才能提高毛竹生产力。毛竹林多分布在山地丘陵地区，传统的实地调查工作量大、效率低、工作周期长、精确度低。随着无人机技术的发展，无人机林业遥感为实现低成本、高时效、高分辨率、高灵活性的动态森林资源监测提供了技术基础。基于无人机和图像识别技术的单木树冠提取已有一定进展。然而相比其他乔木林，毛竹林竹冠具有特殊性，分为钩梢与未钩梢2种形态，且竹冠鲜少呈单个状态，具有“重叠性”，目前还没有利用现有方法直接获取单株毛竹竹冠的研究。本研究对无人机可见光影像的毛竹林郁闭度估测方法进行了分析，以期为快速获取毛竹林的郁闭度提供参考。

3. 讨论

本研究4种方法提取的毛竹林竹冠区域精度均在90%以上，而汪霖等^[25]通过提取单个树冠面积，得到提取树冠区域的总体精度为93.09%，高于本研究基于像元的阈值分类法和基于像元的监督分类法，但低于基于多尺度分割的阈值分类法和基于多尺度分割的监督分类法，证明在分类前执行分割能有效提高提取精度。4种方法在竹冠区域提取精度上，基于像元的提取精度小于基于多尺度的提取精度。可能是相比于乔木林，毛竹林由于其扩鞭生长的特殊性，竹冠部分难以单个呈现，具有很强的整体性，使用传统基于像元的提取方法难以达到对整体性的要求。基于多尺度分割的监督分类法表现最好的原因是在保证对象内部同质性最大的基础上进行的分类，且结合了光谱、纹理等信息进行分类，分类依据更全面；而相较于基于多尺度分割的监督分类法，基于多尺度分割的阈值分类法由于分类规则只基于光谱信息，较为单一，故精度较低。

在基于像元的阈值分类法、基于像元的监督分类法和基于多尺度分割的阈值分类法中，是否钩梢对竹冠区域提取的影响存在较大影响。其一是由于未钩梢毛竹林郁闭度过高，林隙较小且颜色与竹冠接近，导致光学影像的饱和度增高，对特征量的敏感度随之下降，造成误差^[26-27]；其二是由于未钩梢毛竹林一般处于无人经营状态，存在毛竹倒伏现象和新老竹之间的光谱差异，造成误差。但是，基于多尺度分割的监督分类法则不受毛竹林是否钩梢的影响，其原因是多尺度分割得到内部同质性最大、外部同质性最小的斑块，很好保留了分类必需的有效信息，且避免了高分辨率影像中过于丰富的光谱与纹理信息所带来的干扰，减少错分与漏分的情况，基于此进行的监督分类能在保证竹冠整体性的前提下，通过训练样本分别提取钩梢与未钩梢毛竹林的特征参数，从而提取毛竹林竹冠区域。

在4种方法中郁闭度对竹冠区域的提取均不存在显著影响，说明4种方法对不同郁闭度的毛竹林竹冠提取都有较好的适用性。原因是不同郁闭度的毛竹林遥感影像的光谱与纹理特征差别不大，这4种方法都足以从影像中提取稳定的特征量对竹冠区域进行提取。

就郁闭度估测结果而言，4种方法提取的郁闭度误差都小于0.04，其中基于多尺度分割的监督分类法的误差最低，郁闭度误差仅为0.004，说明基于无人机的可见光影像可以用于毛竹林的郁闭度提取，具有很高的应用价值。苏迪等^[9]基于冠层高度模型数据进行主成分确定自变量建立郁闭度模型，通过检验得到模型精度为83.18%，低于本研究的估测精度，说明基于影像提取的郁闭度优于建模所得的郁闭度。严羽^[28]使用标记控制分水岭算法对树冠区域与非树冠区域进行分割，基于分割得到的树冠区域面积与实测面积对比，得到郁闭度估测误差为2.33%，高于本研究基于像元的阈值分类法、基于像元的监督分类法和基于多尺度分割的阈值分类法，但低于基于多尺度分割的监督分类法。

4. 结论

在提取毛竹林竹冠区域和郁闭度的中，本研究基于像元的阈值分类、基于像元的监督分类、基于多尺度分割的阈值分类、基于多尺度分割的监督分类等4种方法都达到了较高精度，其中基于多尺度分割的监督分类法的提取结果精度最高，竹冠区域总体精度为98.86%，郁闭度误差为0.004，显著优于其他3种方法，而且是否钩梢对于基于多尺度分割的监督分类法提取竹冠区域没有影响。在4种方法中，郁闭度对竹冠区域的提取均不存在显著影响。总体上，低廉轻便的无人机搭载可见光相机节省了大量的调查时间与精力，提高了调查效率与精度，可以用于大面积毛竹林竹冠区域和郁闭度的提取。

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类型	郁闭度	样地/ 个	平均胸径/cm	平均树高/m	坡度/ (°)	坡向
钩梢	0~0.7	13	8.91	9.9	11.6	北坡
钩梢	0.7~0.8	8	9.03	9.4	16.1	西北坡
钩梢	0.8~1.0	3	8.65	9.2	13.3	北坡
未钩梢		12	8.58	9.6	17.1	东南坡

类型	郁闭度	分割尺度	形状因子	紧致度因子
	0~0.7	29	0.3	0.5
钩梢	0.7~0.8	31	0.3	0.5
	0.8~1.0	29	0.3	0.5
未钩梢		41	0.3	0.5

方法	总体精度/%	生产者精度/%	用户精度/%	郁闭度误差
TP	91.81±3.08 c	93.34±3.37 c	96.24±2.03 b	0.038±0.026 c
SP	92.96±3.66 bc	95.47±3.29 b	95.64±2.64 b	0.030±0.026 ab
TM	93.47±2.53 b	96.10±2.24 b	95.57±2.57 b	0.024±0.018 b
SM	98.86±0.53 a	99.15±0.40 a	99.36±0.53 a	0.004±0.003 a
说明：同列不同小写字母表示差异显著(P＜0.05)

类型	方法	总体精度/%	生产者精度/%	用户精度/%
钩梢	TP	92.82±2.57 Ab	93.85±3.11 Ac	95.96±2.32 Ab
	SP	93.95±2.74 Ab	96.67±1.39 Ab	95.00±2.98 Ab
	TM	94.05±2.25 Ab	96.15±2.04 Ab	95.49±2.90 Ab
	SM	98.9±0.59 Aa	99.14±0.45 Aa	99.27±0.61 Aa

未钩梢	TP	89.80±3.12 Bb	92.33±3.77 Ac	96.81±1.15 Ab
	SP	90.97±4.52 Bb	93.07±4.57 Bc	96.92±0.98 Bb
	TM	92.30±2.77 Bb	96.01±2.70 Ab	95.73±1.81 Ac
	SM	98.78±0.37 Aa	99.16±0.29 Aa	99.55±0.22 Aa
说明：同列不同小写字母表示相同林分类型在不同方法之　　　　　间差异显著(P＜0.05)；同列不同大写字母表示相同　　　　　方法在不同林分类型之间差异显著(P＜0.05)

类型	方法	郁闭度误差	类型	方法	郁闭度误差
钩梢	TP	0.029±0.021 Ab	未钩梢	TP	0.052±0.029 Bc
	SP	0.023±0.015 Ab		SP	0.042±0.039 Bbc
	TM	0.022±0.016 Ab		TM	0.026±0.023 Ab
	SM	0.003±0.003 Aa		SM	0.004±0.003 Aa
说明：同列不同小写字母表示相同林分类型在不同方法之　　　　　间差异显著(P＜0.05)；同列不同大写字母表示相同　　　　　方法在不同林分类型之间差异显著(P＜0.05)

Estimation method of Phyllostachys edulis forest canopy density based on UAV visible image

doi: 10.11833/j.issn.2095-0756.20210576

Abstract

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通讯作者: 陈斌, bchen63@163.com

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Estimation method of Phyllostachys edulis forest canopy density based on UAV visible image

doi: 10.11833/j.issn.2095-0756.20210576

HTML

1.1. 研究区概况

1.2. 数据获取

1.2.1. 样地划分与地面数据获取

1.2.2. 无人机系统及数据采集

1.2.3. 影像预处理

1.3. 研究方法

1.3.1. 多尺度分割

1.3.2. 分类

1.3.3. 郁闭度计算

1.3.4. 精度评价

1.3.5. 单因素方差分析

2.1. 毛竹林竹冠区域提取

2.2. 钩梢与未钩梢对毛竹林竹冠区域和郁闭度提取精度的影响

2.3. 郁闭度对毛竹林竹冠区域提取精度的影响

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郁闭度	方法	总体精度/%	生产者精度/%	用户精度/%
0~0.7	TP	93.70±2.44 Ab	95.04±2.50 Ab	95.26±2.69 Ab
	SP	94.98±2.11 Ab	97.02±1.37 Ab	95.33±2.90 Ab
	TM	93.89±2.66 Ab	96.15±2.52 Ab	94.70±3.32 Ab
	SM	98.82±0.69 Aa	99.11±0.53 Aa	99.09±0.68 Aa

0.7~0.8	TP	92.21±2.65 Ab	92.43±3.83 Ac	97.03±1.68 Ab
	SP	93.62±4.31 Ab	96.97±1.43 Ab	95.22±4.78 Ab
	TM	92.71±1.40 Ab	96.11±1.43 Ab	95.19±1.90 Ab
	SM	98.88±0.48 Aa	99.08±0.32 Aa	99.42±0.48 Aa

0.8~1.0	TP	90.68±1.46 Ac	92.46±1.39 Ac	96.14±0.74 Ab
	SP	93.62±4.31 Ab	96.97±1.43 Ab	95.22±4.78 Ab
	TM	92.71±1.40 Ab	96.11±1.43 Ab	95.19±1.90 Ab
	SM	99.28±0.33 Aa	99.48±0.22 Aa	99.66±0.33 Aa
说明：同列不同小写字母表示相同郁闭度在不同方法之　　　　　间差异显著(P＜0.05)；同列不同大写字母表示相同　　　　　方法在不同郁闭度之间差异显著(P＜0.05)

Estimation method of Phyllostachys edulis forest canopy density based on UAV visible image

doi: 10.11833/j.issn.2095-0756.20210576

Abstract

Relative Article

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Related

Proportional views

Estimation method of Phyllostachys edulis forest canopy density based on UAV visible image

doi: 10.11833/j.issn.2095-0756.20210576

HTML

1.1. 研究区概况

1.2. 数据获取

1.2.1. 样地划分与地面数据获取

1.2.2. 无人机系统及数据采集

1.2.3. 影像预处理

1.3. 研究方法

1.3.1. 多尺度分割

1.3.2. 分类

1.3.3. 郁闭度计算

1.3.4. 精度评价

1.3.5. 单因素方差分析

2.1. 毛竹林竹冠区域提取

2.2. 钩梢与未钩梢对毛竹林竹冠区域和郁闭度提取精度的影响

2.3. 郁闭度对毛竹林竹冠区域提取精度的影响

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