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Remote Sens. 2016, 8(2), 117; doi:10.3390/rs8020117

Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets

The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Datun Road 20, Chaoyang District, Beijing 100101, China
Institute of Geography Urban and Regional Planning, University of Peshawar, Peshawar 25120, Pakistan
Author to whom correspondence should be addressed.
Academic Editors: Jinwei Dong, Xiangming Xiao, Clement Atzberger and Prasad S. Thenkabail
Received: 9 October 2015 / Revised: 20 January 2016 / Accepted: 25 January 2016 / Published: 5 February 2016
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Eucalyptus, a short-rotation plantation, has been expanding rapidly in southeast China in recent years owing to its short growth cycle and high yield of wood. Effective identification of eucalyptus, therefore, is important for monitoring land use changes and investigating environmental quality. For this article, we used remote sensing images over 15 years (one per year) with a 30-m spatial resolution, including Landsat 5 thematic mapper images, Landsat 7-enhanced thematic mapper images, and HJ 1A/1B images. These data were used to construct a 15-year Normalized Difference Vegetation Index (NDVI) time series for several cities in Guangdong Province, China. Eucalyptus reference NDVI time series sub-sequences were acquired, including one-year-long and two-year-long growing periods, using invested eucalyptus samples in the study region. In order to compensate for the discontinuity of the NDVI time series that is a consequence of the relatively coarse temporal resolution, we developed an inverted triangle area methodology. Using this methodology, the images were classified on the basis of the matching degree of the NDVI time series and two reference NDVI time series sub-sequences during the growing period of the eucalyptus rotations. Three additional methodologies (Bounding Envelope, City Block, and Standardized Euclidian Distance) were also tested and used as a comparison group. Threshold coefficients for the algorithms were adjusted using commission–omission error criteria. The results show that the triangle area methodology out-performed the other methodologies in classifying eucalyptus plantations. Threshold coefficients and an optimal discriminant function were determined using a mosaic photograph that had been taken by an unmanned aerial vehicle platform. Good stability was found as we performed further validation using multiple-year data from the high-resolution Gaofen Satellite 1 (GF-1) observations of larger regions. Eucalyptus planting dates were also estimated using invested eucalyptus samples and the Root Mean Square Error (RMSE) of the estimation was 84 days. This novel and reliable method for classifying short-rotation plantations at small scales is the focus of this study. View Full-Text
Keywords: mosaic photograph; commission–omission error; planting date estimation; inverted triangle area methodology mosaic photograph; commission–omission error; planting date estimation; inverted triangle area methodology

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Qiao, H.; Wu, M.; Shakir, M.; Wang, L.; Kang, J.; Niu, Z. Classification of Small-Scale Eucalyptus Plantations Based on NDVI Time Series Obtained from Multiple High-Resolution Datasets. Remote Sens. 2016, 8, 117.

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