Extraction of individual tree information based on remote sensing images from an Unmanned Aerial Vehicle

Xinyu DONG; Jiaguo LI; Hanyue CHEN; Lei ZHAO; Liming ZHANG; Shihe XING

doi:10.11834/jrs.20198073

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Extraction of individual tree information based on remote sensing images from an Unmanned Aerial Vehicle
Vol. 23, Issue 6, Pages: 1269-1280(2019)
Received：10 February 2018，

Accepted：06 March 2018，

Published：2019-11
DOI： 10.11834/jrs.20198073
稿件说明：

移动端阅览

董新宇, 李家国, 陈瀚阅, 赵磊, 张黎明, 邢世和. 2019. 无人机遥感影像林地单株立木信息提取. 遥感学报, 23(6): 1269–1280 DOI： 10.11834/jrs.20198073.

Dong X Y, Li J G, Chen H Y, Zhao L, Zhang L M and Xing S H. 2019. Extraction of individual tree information based on remote sensing images from an Unmanned Aerial Vehicle. Journal of Remote Sensing, 23(6): 1269–1280 DOI： 10.11834/jrs.20198073.

摘要

针对无人机遥感技术在提取单株立木信息的限制性问题，提出一种新的自动单株立木信息提取方法。对原始无人机影像进行光谱信息增强处理以突出局部细节特征；通过引入DBI指数自动化确定K-means聚类方法的最优聚类数目，进而对影像像素进行标记；通过利用高斯马尔可夫随机场模型进一步对影像进行分割；使用数学形态学算子等方法对分割结果进行后处理得到单株立木树冠信息，通过图像几何矩原理计算得到单株立木位置以作为其识别的依据。结果表明，应用该提取方法，油松林区和樟子松林区单株立木识别总体精度分别为89.52%和95.65%、单木树冠提取精度分别为81.90%和95.65%，均具有较好地适用性。该方法不需要大量的人工干预和先验知识的输入，大大提高提取方法的自动化程度。

Abstract

Extraction of individual tree information is significant in managing forestry resources and protecting the ecological environment. In addition

this strategy has been extensively applied to forests using high spatial resolution satellite images or Lidar data in recent years. The rapid development of unmanned aerial vehicle remote sensing makes itself play an important role in this field. This study uses high spatial resolution aerial remote sensing images to extract individual tree information automatically. The individual tree information includes individual tree extraction and crown delineation. A novel extraction method of individual tree information was proposed in this study. First

the spectral information of the original images was enhanced through decorrelation stretch. Spectral information enhancement aimed to expand the coupling degree of image information by stretching the principal component information of the bands that are correlated

thus increasing the color saturation of the image. Second

the optimal cluster number of K-means clustering algorithm was obtained by introducing a DBI index

then the image pixels were marked based on the K-means clustering algorithm. On this basis

a GMRF model was constructed for image segmentation. Finally

the segmentation results were post-processed through mathematical morphology to complete individual tree crown delineation

and individual tree detection was based on the position of individual tree calculated by image geometric moment. This study acquired the reference data through visual interpretation to evaluate the result. The verification result showed that the overall accuracies of individual tree detection through this method are 89.52% and 95.65% for medium and low canopy density forests

correspondingly. The accuracies of individual tree crown delineation achieved 81.90% and 95.65% for medium and low canopy density forests

respectively. Therefore

the proposed method is better than the object-oriented method in terms of extraction effect through comparison and analysis. The proposed method can be used to obtain the individual tree information of forestry management rapidly and has high extracting precision based on the verification of results. The proposed method does not require considerable manual intervention and prior knowledge

thus significantly improving the degrees of automation.

关键词

Keywords

references

Bruce Blundell S. 2008. Local gradient and local maximum analysis of lidar data for tree crown identification//Proceedings of ASPRS 2008 Annual Conference. Portland, Oregon: ASPRS: 90-101

卜帆, 石玉立. 2017. 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, 32(5): 875–882

Bu F and Shi Y L. 2017. The comparison of urban tree crown extraction based on airborne LiDAR elevation difference and high resolution imagery. Remote Sensing Technology and Application, 32(5): 875–882

Bunting P, Lucas R M, Jones K and Bean A R. 2010. Characterisation and mapping of forest communities by clustering individual tree crowns. Remote Sensing of Environment, 114(11): 2536–2547

Culvenor D S. 2002. TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery. Computers and Geosciences, 28(1): 33–44

Deng G, Li Z Y, Wu H G and Zhang X. 2011. Automated extracting tree crown from quickbird stand image//Proceedings of the 4th IFIP TC 12 Conference on Computer and Computing Technologies in Agriculture. Nanchang, China: Springer: 304-311 [DOI: 10.1007/978-3-642-18333-1_36 http://dx.doi.org/10.1007/978-3-642-18333-1_36 ]

邓睿, 黄敬峰, 王福民. 2011. 基于去相关拉伸光谱增强的HJ-1影像水体信息提取方法研究. 光谱学与光谱分析, 31(11): 3064–3068

Deng R, Huang J F and Wang F M. 2011. Research on extraction method of water body with DS spectral enhancement based on HJ-1 images. Spectroscopy and Spectral Analysis, 31(11): 3064–3068

Erikson M. 2003. Segmentation of individual tree crowns in colour aerial photographs using region growing supported by fuzzy rules. Canadian Journal of Forest Research, 33(8): 1557–1563

Erikson M and Olofsson K. 2005. Comparison of three individual tree crown detection methods. Machine Vision and Applications, 16(4): 258–265

Fang F, Im J, Lee J and Kim K. 2016. An improved tree crown delineation method based on live crown ratios from airborne LiDAR data. GIScience and Remote Sensing, 53(3): 402–419

冯超. 2007. K-means聚类算法的研究[D]. 大连: 大连理工大学

Feng C. 2007. Research of K-means Clustering Algorithm[D]. Dalian: Dalian University of Technology

Gougeon F A. 1995. A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Canadian Journal of Remote Sensing, 21(3): 274–284

Gougeon F A and Leckie D G. 2006. The individual tree crown approach applied to Ikonos images of a coniferous plantation area. Photogrammetric Engineering and Remote Sensing, 72(11): 1287–1297

Hung C, Bryson M and Sukkarieh S. 2012. Multi-class predictive template for tree crown detection. ISPRS Journal of Photogrammetry and Remote Sensing, 68: 170–183

Jing L H, Hu B X, Noland T and Li J L. 2012. An individual tree crown delineation method based on multi-scale segmentation of imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 70: 88–98

Karlson M, Reese H and Ostwald M. 2014. Tree crown mapping in managed woodlands (Parklands) of semi-arid West Africa using WorldView-2 imagery and geographic object based image analysis. Sensors (Basel), 14(12): 22643–22669

Ke Y H and Quackenbush L J. 2007. Forest species classification and tree crown delineation using quickbird imagery//Proceedings of ASPRS 2007 Annual Conference. Tampa, Florida: ASPRS: 9

Lamar W R, McGraw J B and Warner T A. 2005. Multitemporal censusing of a population of eastern hemlock ( Tsuga canadensis L.) from remotely sensed imagery using an automated segmentation and reconciliation procedure. Remote Sensing of Environment, 94(1): 133–143

Larsen M, Eriksson M, Descombes X, Perrin G, Brandtberg T and Gougeon F A. 2011. Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions. International Journal of Remote Sensing, 32(20): 5827–5852

李丹, 柯樱海, 宫辉力, 李小娟, 邓曾. 2016. 基于高分辨率遥感影像的城市典型乔木树种分类研究. 地理与地理信息科学, 32(1): 84–89

Li D, Ke Y H, Gong H L, Li X J and Deng Z. 2016. Urban tree species classification with machine learning classifier using worldview-2 imagery. Geography and Geo-Information Science, 32(1): 84–89

Lin Y, Jiang M, Yao Y J, Zhang L F and Lin J Y. 2015. Use of UAV oblique imaging for the detection of individual trees in residential environments. Urban Forestry and Urban Greening, 14(2): 404–412

Pollock R J. 1996. The Automatic Recognition of Individual Trees in Aerial Images of Forests Based on a Synthetic Tree Crown Image Model. Vancouver, Canada: University of British Columbia

Rango A, Laliberte A, Herrick J E, Winters C, Havstad K, Steele C and Browning D. 2009. Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management. Journal of Applied Remote Sensing, 3(1): 033542

沈利强, 姜仁荣, 王培法. 2017. 一种高分辨率遥感图像单木树冠信息提取方法. 遥感信息, 32(3): 142–148

Shen L Q, Jiang R R and Wang P F. 2017. A method for individual tree-crown extraction from high spatial resolution imagery. Remote Sensing Information, 32(3): 142–148

王利民, 刘佳, 杨玲波, 陈仲新, 王小龙, 欧阳斌. 2013. 基于无人机影像的农情遥感监测应用. 农业工程学报, 29(18): 136–145

Wang L M, Liu J, Yang L B, Chen Z X, Wang X L and Ouyang B. 2013. Applications of unmanned aerial vehicle images on agricultural remote sensing monitoring. Transactions of the Chinese Society of Agricultural Engineering, 29(18): 136–145

Wulder M, Niemann K O and Goodenough D G. 2000. Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery. Remote Sensing of Environment, 73(1): 103–114

张凝, 冯跃文, 张晓丽, 樊江川. 2015. 结合航空影像纹理和光谱特征的单木冠幅提取. 北京林业大学学报, 37(3): 13–19

Zhang N, Feng Y W, Zhang X L and Fan J C. 2015. Extracting individual tree crown by combining spectral and texture features from aerial images. Journal of Beijing Forestry University, 37(3): 13–19

张齐勇, 岑敏仪, 周国清, 杨晓云. 2009. 城区LiDAR点云数据的树木提取. 测绘学报, 38(4): 330–335

Zhang Q Y, Cen M Y, Zhou G Q and Yang X Y. 2009. Extracting trees from LiDAR data in urban region. Acta Geodaetica et Cartographica Sinica, 38(4): 330–335

周艳飞, 张绘芳, 李霞, 杨帆, 丁程锋. 2014. 不同方法提取无人机影像树冠信息效果分析. 新疆农业大学学报, 37(3): 231–235

Zhou Y F, Zhang H F, Li X, Yang F and Ding C F. 2014. Analysis on effects of tree crown information from unmanned aerial vehicle image extraction by different methods. Journal of Xinjiang Agricultural University, 37(3): 231–235

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