Comparison of individual tree crown extraction method and suitability of airborne and spaceborne high-resolution remote sensing images

Sun Zhenfeng; Zhang Xiaoli; Li Niwen

doi:10.13332/j.1000-1522.20180446

Journal of Beijing Forestry University > 2019 > 41(11): 66-75. > DOI: 10.13332/j.1000-1522.20180446

Sun Zhenfeng, Zhang Xiaoli, Li Niwen. Comparison of individual tree crown extraction method and suitability of airborne and spaceborne high-resolution remote sensing images[J]. Journal of Beijing Forestry University, 2019, 41(11): 66-75. DOI: 10.13332/j.1000-1522.20180446

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Comparison of individual tree crown extraction method and suitability of airborne and spaceborne high-resolution remote sensing images

School of Forestry, Key Laboratory of Precision Forestry, Provincial Key Laboratory of Forest Cultivation and Conservation, Beijing Forestry University, Beijing 100083, China

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Received Date: December 30, 2018
Revised Date: February 28, 2019
Available Online: September 19, 2019
Published Date: October 31, 2019

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Abstract

Abstract

ObjectiveThis method applies high resolution remote sensing image to extract individual tree crown information quickly and accurately, which can have important significance for modern forest management. Object-oriented multi-scale image segmentation method can effectively solve the limitations of pixel feature analysis and is an important technical approach to individual tree crown extraction. This paper compares and analyzes the tree crown segmentation accuracy of different remote sensing platforms and artificial forest species, explores the advantages and applicability of the experimental methods for different scale image data and tree species, and provids reference for the assortment of image data combined with the purpose of investigation.
MethodTaking Gaofeng Forest Farm of Guangxi Zhuang Autonomous Region as the research area, the UAV CCD, airborne CCD and spaceborne GF-2 remote sensing image data were selected. Aiming at the poor contrast effect between the crown area and the background area, the image enhancement processing was firstly performed by wavelet transform to remove the image noise, enhance the contrast between the crown and the background, and then apply the object-oriented multi-scale segmentation method to eliminate the interference of the background area. Rapid extraction of single tree crown for canopy areas was taken. Finally, the process and method of extracting single tree crown of Eucalyptus robusta and Cunninghamia lanceolata plantation under three kinds of images, and the accuracy of crown extraction were studied and analyzed.
ResultWavelet transform is effective in enhancing UAV and airborne images. The individual tree crown segmentation accuracy of Eucalyptus robusta and Cunninghamia lanceolate in UAV platform was 87%, 93.3%, with tree crown estimation accuracy of 84.2%, 85.1%, respectively. The individual tree crown segmentation accuracy of Eucalyptus robusta and Cunninghamia lanceolate in airborne platform was 89%, 91.1%, with tree crown estimation accuracy of 83.9%, 84.4%, respectively. However, wavelet transform is not appropriate for image enhancement of spaceborne platform. The crown segmentation accuracy of Eucalyptus robusta and Cunninghamia lanceolata in spaceborne platform was 82%, 89%, with tree crown estimation accuracy of 72.3%, 73.3%, respectively.
ConclusionIn UAV and airborne platform, the precision of tree crown extraction by multi-scale segmentation is close. In spaceborne platform, the extraction accuracy of the individual tree crown is lower than that of the former two platforms because of the limitation of spatial resolution of the image, and the direct application of multi-scale segmentation to single tree crown extraction. But it can also meet the basic needs of forest survey.
- high-resolution image,
- wavelet transform,
- image enhancement,
- object-oriented segmentation,
- tree crown extraction

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References (26)

References

[1]	Ni W, Li X, Woodcock C E, et al. Transmission of solar radiation in boreal conifer forests: measurements and models[J]. Journal of Geophysical Research Atmospheres, 1997, 102(24): 29555−29566.
[2]	Song C, Band L E. MVP: a model to simulate the spatial patterns of photosynthetically active radiation under discrete forest canopies[J]. Revue Canadienne De Recherche Forestière, 2004, 34(34): 1192−1203.
[3]	梁文海, 刘吉凯, 张伟, 等. 基于面向对象方法的GF-2影像桉树林信息提取[J]. 浙江农林大学学报, 2017, 34(4):721−729. doi: 10.11833/j.issn.2095-0756.2017.04.019 Liang W H, Liu J K, Zhang W, et al. Extracting eucalyptus information using GF-2 images based on an object-oriented method[J]. Journal of Zhejiang A&F University, 2017, 34(4): 721−729. doi: 10.11833/j.issn.2095-0756.2017.04.019
[4]	Pouliot D A, King D J, Bell F W, et al. Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration[J]. Remote Sensing of Environment, 2002, 82(2): 322−334.
[5]	Wang L, Gong P, Biging G S. Individual tree-crown delineation and treetop detection in high-spatial-resolution aerial imagery[J]. Photogrammetric Engineering & Remote Sensing, 2004, 70(3): 351−358.
[6]	Francis A G. A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images[J]. Canadian Journal of Remote Sensing, 1995, 21(3): 274−284. doi: 10.1080/07038992.1995.10874622
[7]	Pollock R J. The automatic recognition of individual trees in aerial images of forests based on a synthetic tree crown image model[C]. Vancouver: The University of British Columbia, 1996: 172.
[8]	Leckie D G, Gougeon F A, Tinis S, et al. Automated tree recognition in old growth conifer stands with high resolution digital imagery[J]. Remote Sensing of Environment, 2005, 94(3): 311−326. doi: 10.1016/j.rse.2004.10.011
[9]	Wang L. A multi-scale approach for delineating individual tree crowns with very high resolution imagery[J]. Photogrammetric Engineering & Remote Sensing, 2010, 76(4): 371−378.
[10]	王宗梅, 徐天蜀, 岳彩荣. 基于高分辨率遥感影像的森林信息提取方法综述[J]. 林业调查规划, 2016, 41(4):1−6. doi: 10.3969/j.issn.1671-3168.2016.04.001 Wang Z M, Xu T S, Yue C R. Extraction methods of forest information based on high resolution remote sensing images[J]. Forest Inventory and Planning, 2016, 41(4): 1−6. doi: 10.3969/j.issn.1671-3168.2016.04.001
[11]	Skurikhin A N, Garrity S R, McDowell N G, et al. Automated tree crown detection and size estimation using multi-scale analysis of high-resolution satellite imagery[J]. Remote Sensing Letters, 2013, 4(5): 465−474. doi: 10.1080/2150704X.2012.749361
[12]	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[J]. 北京林业大学学报, 2017, 39(3):16−23. Feng J J, Zhang X L, Liu H L. Single tree crown extraction based on gray gradient image segmentation[J]. Journal of Beijing Forestry University, 2017, 39(3): 16−23.
[13]	崔少伟. 基于高分辨率遥感数据单木树冠提取研究[D]. 哈尔滨: 东北林业大学, 2011. Cui S W. Study on individual tree crown extraction based on high resolution remote sensing[D]. Harbin: Northeast Forestry University, 2011.
[14]	刘晓莉, 任丽秋, 李伟, 等. 阈值优化的遥感影像小波去噪[J]. 遥感信息, 2016, 31(2):109−113. doi: 10.3969/j.issn.1000-3177.2016.02.020 Liu X L, Ren L Q, Li W, et al. Threshold optimized wavelet for remotely sensed image denoising[J]. Remote Sensing Information, 2016, 31(2): 109−113. doi: 10.3969/j.issn.1000-3177.2016.02.020
[15]	张霞, 汤森林. 一种去除遥感图像条带噪声的小波矩匹配方法[J]. 遥感技术与应用, 2018, 33(2):305−312. Zhang X, Tang S L. Destriping remote sensing imagery by wavelet moment matching algorithm[J]. Remote Sensing Technology and Application, 2018, 33(2): 305−312.
[16]	李超炜, 邓新蒲, 赵昊宸. 基于小波分析的遥感影像薄云去除算法研究[J]. 数字技术与应用, 2017(6):137−139, 143. doi: 10.3969/j.issn.1007-9416.2017.06.080 Li C W, Deng X P, Zhao H C. Thin cloud removal algorithm based on wavelet analysis for remote sensing images[J]. Digital Technology and Application, 2017(6): 137−139, 143. doi: 10.3969/j.issn.1007-9416.2017.06.080
[17]	陈杰, 邓敏, 肖鹏峰, 等. 利用小波变换的高分辨率多光谱遥感图像多尺度分水岭分割[J]. 遥感学报, 2011, 15(5):908−926. doi: 10.11834/jrs.2011280 Chen J, Deng M, Xiao P F, et al. Multi-scale watershed segmentation of high-resolution multi-spectral remote sensing image using wavelet transform[J]. Journal of Remote Sensing, 2011, 15(5): 908−926. doi: 10.11834/jrs.2011280
[18]	王学成, 杨飞. 基于面向对象分类方法的Rapideye桉树提取[J]. 桉树科技, 2014(3):10−16. doi: 10.3969/j.issn.1674-3172.2014.03.002 Wang X C, Yang F. Extraction of eucalyptus plantations metrics using rapid-eye imagery with object-oriented classification method[J]. Eucalypt Science & Technology, 2014(3): 10−16. doi: 10.3969/j.issn.1674-3172.2014.03.002
[19]	郭昱杉, 刘庆生, 刘高焕, 等. 基于标记控制分水岭分割方法的高分辨率遥感影像单木树冠提取[J]. 地球信息科学学报, 2016, 18(9):1259−1266. Guo Y S, Liu Q S, Liu G H, et al. Individual tree crown extraction of high resolution image based on marker-controlled watershed segmentation method[J]. Journal of Geo-Information Science, 2016, 18(9): 1259−1266.
[20]	沈利强, 姜仁荣, 王培法. 一种高分辨率遥感图像单木树冠信息提取方法[J]. 遥感信息, 2017, 32(3):142−148. doi: 10.3969/j.issn.1000-3177.2017.03.023 Shen L Q, Jiang R R, Wang P F. A method for individual tree-crown extraction from high spatial resolution imagery[J]. Remote Sensing Information, 2017, 32(3): 142−148. doi: 10.3969/j.issn.1000-3177.2017.03.023
[21]	Ballanti L, Blesius L, Hines E, et al. Tree species classification using hyperspectral imagery: a comparison of two classifiers[J]. Remote Sensing, 2016, 8(6): 445−462.
[22]	Roth K L, Roberts D A, Dennison P E, et al. The impact of spatial resolution on the classification of plant species and functional types within imaging spectrometer data[J]. Remote Sensing of Environment, 2015, 171: 45−57. doi: 10.1016/j.rse.2015.10.004
[23]	韩鹏, 龚健雅, 李志林, 等. 遥感影像分类中的空间尺度选择方法研究[J]. 遥感学报, 2010, 14(3):507−518. Han P, Gong J Y, Li Z L, et al. Selection of optimal scale in remotely sensed image classification[J]. Journal of Remote Sensing, 2010, 14(3): 507−518.
[24]	Wang G, Gertner G, Anderson A B. Up-scaling methods based on variability-weighting and simulation for inferring spatial information across scales[J]. International Journal of Remote Sensing, 2004, 25(22): 4961−4979. doi: 10.1080/01431160410001680428
[25]	胡云锋, 徐芝英, 刘越, 等. 空间尺度上推方法的精度评价: 以内蒙古锡林郭勒盟土地利用数据为例[J]. 地理研究, 2012, 31(11):1961−1972. Hu Y F, Xu Z Y, Liu Y, et al. Accuracy analysis of up-scaling data: a case study with land use data in Xilin Gol of Inner Mongolia, China[J]. Geographical Research, 2012, 31(11): 1961−1972.
[26]	张凝, 冯跃文, 张晓丽, 等. 结合航空影像纹理和光谱特征的单木冠幅提取[J]. 北京林业大学学报, 2015, 37(3):13−19. Zhang N, Feng Y W, Zhang X L, et al. Extracting individual tree crown by combining spectral and texture features from aerial images[J]. Journal of Beijing Forestry University, 2015, 37(3): 13−19.

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Comparison of individual tree crown extraction method and suitability of airborne and spaceborne high-resolution remote sensing images

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