基于无人机高分辨率影像的油松新造林健康树冠提取

doi:10.11707/j.1001-7488.20221011

Abstract

Abstract:

Objective: Based on the spectral characteristics and spacial interlacing of healthy tree crowns in new afforestation, the spectral enhancement method and multi-scale segmentation thresholds of healthy tree crowns under complex ground vegetation conditions were discussed to provide technical support for the daily monitoring of afforestation verification. Method: The UAV images of newly planted trees in the core area of the Winter Olympics were chosen as experimental data. Firstly, based on the different color characteristics of healthy tree crowns and other disturbances, the images were enhanced by homomorphic filtering and transformed by ExG spectral index. Then, the Otsu method was used to obtain the binary image, and the multi-scale morphological filtering method was used for segmentation and fusion to segment the interlaced crown areas, correspondingly extract the possible healthy crown areas in the original image. Finally, based on the feature vector constructed by the color vector, the GLCM and the LBP, the random forest was used to classify the extracted area to detect the healthy tree crowns in the image. Result: The method based on spectral index transformation and multi-scale morphological filtering was able to effectively segment the interlaced and continuous crown areas, exclude other interference objects those were similar in color to healthy trees and accurately extract the areas those might be crowns. The 17 UAV orthophoto images with varying stand densities and lighting conditions were tested, and the crown centers were marked by visual interpretation. Furthermore, the three evaluation indexes: precision, recall and F1 score were used to quantitatively compare and analyze the recognition effects of random forest and SVM. The experimental result showed that 96.78% of the crowns were extracted using the multi-scale morphological filtering method, and the F1 score of random forest was higher than 97%, while the recall of support vector machine was significantly lower than that of random forest. Conclusion: Our result showed that the crown extraction method based on spectral index transformation and multi-scale morphological filtering could be able to extract the healthy crown quickly and accurately in the UAV images and effectively complete the afforestation verification.

Key words: UAV image, homomorphic filtering, spectral index, morphological filtering, random forest

CLC Number:

S758

Xuzhan Guo,Qiao Chen,Xiaofang Zhang,Liang Hong,Yuanyuan You,Shouzheng Tang,Liyong Fu. Extraction of Healthy Canopy of New Afforestation for Pinus tabulaeformis Based on UAV High-Resolution Image[J]. Scientia Silvae Sinicae, 2022, 58(10): 111-120.

Figures/Tables 8

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 1

References 0

	卜帆, 石玉立. 机载LiDAR高差和高分影像的城市树冠提取比较. 遥感技术与应用, 2017, 32 (5): 875- 882.
	Bu F , Shi Y L . The comparison of urban tree crown extraction based on airborne LiDAR elevation difference and high resolution imagery. Remote Sensing Technology and Application, 2017, 32 (5): 875- 882.
	程浈浈, 祁力钧, 程一帆, 等. 基于M-LP特征加权聚类的果树冠层图像分割方法. 农业机械学报, 2020, 51 (4): 191- 198.191-198, 260
	Cheng Z Z , Qi L J , Cheng Y F , et al. Fruit tree canopy image segmentation method based on M-LP features weighted clustering. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51 (4): 191- 198.191-198, 260
	董雪, 林明星. 基于变分结构引导滤波的低照度图像增强算法. 山东大学学报(理学版), 2020, 55 (9): 72- 80.
	Dong X , Lin M X . Low-light image enhancement algorithm based on variational structure guided filtering. Journal of Shandong University(Natural Science), 2020, 55 (9): 72- 80.
	冯静静, 张晓丽, 刘会玲. 基于灰度梯度图像分割的单木树冠提取研究. 北京林业大学学报, 2017, 29 (3): 16- 23.
	Feng J J , Zhang X L , Liu H L . Single tree crown extraction based on gray gradient image segmentation. Journal of Beijing Forestry University, 2017, 29 (3): 16- 23.
	金忠明, 曹姗姗, 王蕾, 等. 天山云杉林无人机可见光影像树冠信息提取方法研究. 林业资源管理, 2020, (1): 125- 135.
	Jin Z M , Cao S S , Wang L , et al. Study on extraction of tree crown information from UAV visible light image of Picea schrenkiana var. tianschanica forest. Forest Resources Management, 2020, (1): 125- 135.
	刘清旺, 李世明, 李增元, 等. 无人机激光雷达与摄影测量林业应用研究进展. 林业科学, 2017, 53 (7): 134- 148.
	Liu Q W , Li S M , Li Z Y , et al. Review on the applications of UAV-based LiDAR and photogrammetry in forestry. Scientia Silvae Sinicae, 2017, 53 (7): 134- 148.
	宋以宁, 刘文萍, 骆有庆, 等. 基于线性谱聚类的林地图像中枯死树监测. 林业科学, 2019, 55 (4): 187- 195.
	Song Y N , Liu W P , Luo Y Q , et al. Monitoring of dead trees in forest images based on linear spectral clustering. Scientia Silvae Sinicae, 2019, 55 (4): 187- 195.
	Allison T D , Root T L , Frumhoff P C . Thinking globally and siting locally-renewable energy and biodiversity in a rapidly warming world. Climatic Change, 2014, 126 (1/2): 1- 6.
	Csillik O , Cherbini J , Johnson R , et al. Identification of citrus trees from unmanned aerial vehicle imagery using convolutional neural networks. Drones, 2018, 2 (4): 39.
	Eklundh L , Johansson T , Solberg S . Mapping insect defoliation in scots pine with MODIS time-series data. Remote Sensing of Environment, 2009, 113 (7): 1566- 1573.
	Garg M , Dhiman G . A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Computing and Applications, 2021, 33 (4): 1311- 1328.
	Guo X , Liu Q , Sharma R P , et al. Tree recognition on the plantation using UAV Images with ultrahigh spatial resolution in a complex environment. Remote Sensing, 2021, 13 (20): 4122.
	Hirschmugl H , Ofner M , Raggam J , et al. Single tree detection in very high resolution remote sensing data. Remote Sensing of Environment, 2007, 110 (4): 533- 544.
	Jing L , Hu B , Noland T , et al. An individual tree crown delineation method based on multi-scale segmentation of imagery. ISPRS Journal of Photogrammetry & Remote Sensing, 2012, 70, 88- 98.
	Kyere-Boateng R , Marek M V . Analysis of the social-ecological causes of deforestation and forest degradation in ghana: application of the DPSIR framework. Forests, 2021, 12 (4): 409.
	Malambo L , Popescu S C , Murray S C , et al. Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery. International Journal of Applied Earth Observation and Geoinformation, 2018, 64, 31- 42.
	Midhun M S , Carlos K , Carine J , et al. Individual tree detection from unmanned aerial vehicle(UAV) derived canopy height model in an open canopy mixed conifer forest. Forests, 2017, 8 (9): 340.
	Ojala T , Pietikainen M , Maenpaa T . Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis, 2002, 24 (7): 971- 987.
	Pan Y D , Birdsey R A , Fang J Y , et al. A large and persistent carbon sink in the world's forests. Science, 2011, 333 (6045): 988- 993.
	Secord J , Zakhor A . Tree detection in urban regions using aerial LiDAR and image data. IEEE Geoscience Remote Sensing Letters, 2007, 4 (2): 196- 200.
	Selim S , Sonmez N K , Coslu M , et al. Semi-automatic tree detection from images of unmanned aerial vehicle using object-based image analysis method. Journal of the Indian Society of Remote Sensing, 2019, 47 (2): 193- 200.
	Seow M J, Asari V K. 2004. Homomorphic processing system and ratio rule for color image enhancement. IEEE International Conference on Neural Networks- Conference Proceedings, 4: 2507-2512.
	Song W , Mu X , Yan G , et al. Extracting the green fractional vegetation cover from digital images using a shadow-resistant algorithm(SHAR-LABFVC). Remote Sensing, 2015, 7 (8): 10425.
	Sunday J M . The pace of biodiversity change in a warming climate. Nature, 2020, 580 (7804): 460- 461.
	Tseng C C, Lee S L. 2017. A weak-illumation image enhancement method uisng homomorphic filter and image fusion. IEEE 6^th Global Conference on Consumer Electronics(GCCE), 1-2.
	Woebbecke D M , Meyer G E , Von Bargen K , et al. Color Indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE, 1995, 38 (1): 259- 269.
	Wulder M A , White J C , Coops N C , et al. Multi-temporal analysis of high spatial resolution imagery for disturbance monitoring. Remote Sensing of Environment, 2008, 112 (6): 2729- 2740.
	Xu W , Deng S , Liang D , et al. A crown morphology-based approach to individual tree detection in subtropical mixed broadleaf urban forests using UAV LiDAR data. Remote Sensing, 2021, 13 (7): 1278.
	Yuan Y , Hu X . Random forest and objected-based classification for forest pest extraction from UAV aerial imagery. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, 1093- 1098.
	Yun T , Jiang K , Li G , et al. Individual tree crown segmentation from airborne LiDAR data using a novel Gaussian filter and energy function minimization-based approach. Remote Sensing of Environment, 2021, 256, 112307.

识别方法Method	标定树冠Marked crowns	提取树冠Extracted crowns	精确率Precision(%)	召回率Recall(%)	F1分数F1 core(%)
随机森林Random forest	1 804	1 746	94.77	99.58	97.32
支持向量机SVM	1 804	1 746	93.41	74.56	82.93

[1]	Huiling Tian,Jianhua Zhu,Xiao He,Xinyun Chen,Zunji Jian,Chenyu Li,Xueyuan Guo,Guosheng Huang,Wenfa Xiao. Projected Biomass Carbon Stock of Arbor Forest of Three Provinces in Northeastern China Based on Random Forest Model [J]. Scientia Silvae Sinicae, 2022, 58(4): 40-50.
[2]	Xinyuan Liu,Guang Yang,Jibin Ning,Daotong Geng,Hongzhou Yu,Xueying Di. Quality and Influencing Factors of Particulate Matter Released by Surface Fuel Combustion in Korean Pine Plantation [J]. Scientia Silvae Sinicae, 2022, 58(3): 97-106.
[3]	Yonglei Shi,Zhihui Wang,Shiming Li,Chunyi Li,Peiqing Xiao,Pan Zhang,Xiaoge Chang. A Method of Estimation Aboveground Biomass of Sparse Tree-Shrub Using Optical Remote Sensing [J]. Scientia Silvae Sinicae, 2022, 58(2): 13-22.
[4]	Zhongqiu Sun,Jinping Gao,Fayun Wu,Xianlian Gao,Yang Hu,Jianxin Gao. Estimating Forest Stock Volume via Small-Footprint LiDAR Point Cloud Data and Random Forest Algorithm [J]. Scientia Silvae Sinicae, 2021, 57(8): 68-81.
[5]	Jiaqi You,Mingze Li,Wenyi Fan,Ying Quan,Bin Wang,Zhukun Mo,Zixiao Zhu. Stand Type Identification Based on Hyperspectral and LiDAR Data [J]. Scientia Silvae Sinicae, 2021, 57(5): 119-129.
[6]	Ying Pan,Mingming Ding,Jie Lin,Qiao Dai,Geng Guo,Linlin Cui. Inversion of Forest Leaf Area Index Based on PROSAIL Model and Multi-Angle Remote Sensing Data [J]. Scientia Silvae Sinicae, 2021, 57(4): 90-106.
[7]	Wenbo Li,Zhengang Lü,Xuanrui Huang,Zhidong Zhang. Predicting Spatial Distribution of Site Index for Larix principis-rupprechtii Plantations in the Northern Hebei Province [J]. Scientia Silvae Sinicae, 2021, 57(3): 79-89.
[8]	Jiaqi Ding,Wenli Huang,Yingchun Liu,Yang Hu. Estimation of Forest Aboveground Biomass in Northwest Hunan Province Based on Machine Learning and Multi-Source Data [J]. Scientia Silvae Sinicae, 2021, 57(10): 36-48.
[9]	Lei Zhang, Pengsen Sun, Shirong Liu. Growing-Season Transpiration of Typical Forests in Different Succession Stages in Subalpine Region of Western Sichuan, China [J]. Scientia Silvae Sinicae, 2020, 56(1): 1-9.
[10]	Hou Xiaojing, Ming Jinke, Qin Rongshui, Zhu Jiping. Analysis of the Fire Risk in Wildland-Urban Interface with Random Forest Model [J]. Scientia Silvae Sinicae, 2019, 55(8): 194-200.
[11]	Li Dan, Zhang Junjie, Zhao Mengxi. Extraction of Stand Factors in UAV Image Based on FCM and Watershed Algorithm [J]. Scientia Silvae Sinicae, 2019, 55(5): 180-187.
[12]	Jie Lan,Xiangdong Lei,Yutao Zhang. Analysis on Trade-Offs and Synergies of Multiple Functions of Picea schrenkiana Forests in Central Tianshan Mountains [J]. Scientia Silvae Sinicae, 2019, 55(11): 9-18.
[13]	Yingkai Zhang,Pengju Liu,Changchun Liu,Yi Ren. Prediction Method of Cunninghamia lanceolata Growth Based on Spatial Clustering [J]. Scientia Silvae Sinicae, 2019, 55(11): 137-144.
[14]	Chen Liang, Zhou Guomo, Du Huaqiang, Liu Yuli, Mao Fangjie, Xu Xiaojun, Li Xuejian, Cui Lu, Li Yangguang, Zhu Di. Simulation of CO₂ Flux and Controlling Factors in Moso Bamboo Forest Using Random Forest Algorithm [J]. Scientia Silvae Sinicae, 2018, 54(8): 1-12.
[15]	Li Xiaohong, Chen Erxue, Li Zengyuan, Li Shiming. Object Based Land Cover Classification Method Integrating Multi-Source Remote Sensing Data [J]. Scientia Silvae Sinicae, 2018, 54(2): 68-80.

Extraction of Healthy Canopy of New Afforestation for Pinus tabulaeformis Based on UAV High-Resolution Image

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 0

Related Articles 15

Recommended Articles

Metrics

Comments