Publication: Land cover mapping in cloud-prone tropical areas using Sentinel-2 data: Integrating spectral features with Ndvi temporal dynamics
Issued Date
2020-04-01
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ISSN
20724292
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2-s2.0-85084267159
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Mahidol University
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SCOPUS
Bibliographic Citation
Remote Sensing. Vol.12, No.7 (2020)
Suggested Citation
Chong Huang, Chenchen Zhang, Yun He, Qingsheng Liu, He Li, Fenzhen Su, Gaohuan Liu, Arika Bridhikitti Land cover mapping in cloud-prone tropical areas using Sentinel-2 data: Integrating spectral features with Ndvi temporal dynamics. Remote Sensing. Vol.12, No.7 (2020). doi:10.3390/rs12071163 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/56168
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Title
Land cover mapping in cloud-prone tropical areas using Sentinel-2 data: Integrating spectral features with Ndvi temporal dynamics
Abstract
© 2020, by the authors. Accurate remote sensing and mapping of land cover in the tropics remain difficult tasks since data gaps and a heterogenic landscape make it challenging to perform land cover classification. In this paper, we proposed a multi-feature classification method to integrate temporal statistical features with spectral and textural features. This method is designed to improve the accuracy of land cover classification in cloud-prone tropical regions. Sentinel-2 images were used to construct an NDVI stack for a time-series statistical analysis to characterize the temporal variance of land cover. Two statistical indices were calculated and used to represent the variation in annual vegetation. These indices included the mean (NDVI_mean) and coefficient of variation (NDVI_cv) for the NDVI time series. The temporal statistical features were then integrated with spectral and textural features extracted from high-quality Sentinel-2 imagery for Random Forest classification. The performance and contribution of different combinations were assessed based on their classification accuracies. Our results show that the time-series statistical analysis is an effective way to represent land cover category information contained in annual NDVI variance. The method uses clear pixels from dense low-quality images to obtain the NDVI statistical characteristics, thus, to reduce the influence of random factors such as weather conditions on single-date image. The addition of NDVI_mean and NDVI_cv can improve the separability among most types of land cover. The overall accuracy and the kappa coefficient reached values of 0.8913 and 0.8514 when NDVI_mean and NDVI_cv were integrated. Furthermore, the time-series statistical analysis has less stringent requirements regarding image quality and features a high computational efficiency, which shows its great potential to improve the overall accuracy of land cover classification at regional scales in cloud-prone tropical regions.