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

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