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Scientia Silvae Sinicae ›› 2021, Vol. 57 ›› Issue (7): 80-91.doi: 10.11707/j.1001-7488.20210709

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Multi-Feature Classification of Optical and SAR Remote Sensing Images for Typical Tropical Plantation Species

Chong Huang1,Chenchen Zhang1,2,Qingsheng Liu1,He Li1,Xiaomei Yang1,Gaohuan Liu1   

  1. 1. State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences Beijing 100101
    2. University of Chinese Academy of Sciences Beijing 100049
  • Received:2020-01-19 Online:2021-07-25 Published:2021-09-02

Abstract:

Objective: Accurate plantation map plays an important role in sustainable forest resource management. In this paper,a multi-feature classification method based on Sentinel-2 optical and Sentinel-1 SAR data combination was proposed for discriminating and mapping typical plantation species in the tropical region. Method: 43 spectral features were extracted from a scene of Sentinel-2 image. Their importance was sorted through the out-of-bag(OOB) error estimation,to select out the optimal spectral features for plantation species classification. In addition,4 textural features were also calculated from the Sentinel-2 image to represent the spatial information of each plantation species,and 4 backscattering coefficient features were extracted from Sentinel-1 SAR image to represent the structural characteristics. Then,different combinations using the selected spectral features,together with textural and backscattering coefficient features were input to the random forest classifier for plantation species classification. The performance and contribution of different combinations were assessed based on their classification accuracies. Result: 1) The OOB score reaches the highest value of 0.947 2 when the top 17 important Sentinel-2 spectral features are employed. Among them,blue,red-edge,NIR spectral bands,and vegetation indices based on NIR and red-edge bands have a high importance for plantation species identification. 2) Eucalyptus,oil palm and rubber plantations show some differences in different textural features. Among the three plantations,oil palm has the largest CON(contrast) and ENT(entropy),and the smallest ASM(angular second moment) value,while eucalyptus has the largest ASM and COR(correlation),and the smallest ENT value. For rubber,the values of CON and COR are the smallest,while the values of ASM and ENT are between those of eucalyptus and oil palm. The backscattering coefficients of the three plantations also exhibit much separability,especially for the ratio of VV to VH. 3) When only the Sentinel-2 spectral features were used for plantation species classification,the F1 values of eucalyptus,oil palm and rubber plantations were 0.61,0.74 and 0.70,respectively. However,the combination of spectral features,textural features and backscattering coefficient features have been seen the highest classification accuracy,with the overall accuracy and Kappa coefficient of 85% and 0.83,respectively. The F1 values of eucalyptus,oil palm and rubber plantation increase by 0.19,0.15 and 0.10,respectively. Conclusion: The spectral bands of Sentinel-2 optical image could provide an important basis for plantation species recognition,while the derived textural features and the backscattering coefficient features extracted from Sentinel-1 SAR image may also provide supplementary information which contributes a lot to the identification of different plantation species. Considering the high temporal and spatial resolution of Sentinel-2 and Sentinel-1,the integration of spectral,textural and structural information might show great potentials to improve the accuracy of plantation species discriminating at a fine scale.

Key words: plantation, Sentinel-2, synthetic aperture radar(SAR), Sentinel-1, plantation species classification, red-edge index, multi-features

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