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Forests 2019, 10(2), 139;

The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data

School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
Ministry of Education Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Hohai University, Nanjing 210098, China
Institute of remote sensing and digital Earth, Chinese Academy of Sciences, Beijing 100101, China
Author to whom correspondence should be addressed.
Received: 25 December 2018 / Revised: 2 February 2019 / Accepted: 5 February 2019 / Published: 8 February 2019
PDF [7801 KB, uploaded 19 February 2019]


Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability. View Full-Text
Keywords: coefficient of variation; evergreen trees; NDVI; remote sensing; time-series coefficient of variation; evergreen trees; NDVI; remote sensing; time-series

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Yang, Y.; Wu, T.; Wang, S.; Li, J.; Muhanmmad, F. The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data. Forests 2019, 10, 139.

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