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Sustainability 2017, 9(7), 1215;

Fractal Feature Analysis and Information Extraction of Woodlands Based on MODIS NDVI Time Series

College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
Author to whom correspondence should be addressed.
Received: 2 June 2017 / Revised: 26 June 2017 / Accepted: 6 July 2017 / Published: 13 July 2017
(This article belongs to the Special Issue Geospatial Technologies for Sustainable Natural Resources)
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The quick and accurate extraction of information on woodland resources and distributions using remote sensing technology is a key step in the management, protection, and sustainable use of woodlands. This paper presents a low-cost and high-precision extraction method for large woodland areas based on the fractal features of the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for Beijing, China. The blanket method was used for computing the upper and lower fractal signals of each pixel in the NDVI time series images. The fractal signals of woodlands and other land use/land cover types at corresponding scales were analyzed and compared, and the attributes of woodlands were enhanced at the fifth lower fractal signal. The spatial distributions of woodlands were extracted using the Iterative Self-Organizing Data Analysis technique (ISODATA), and an accuracy assessment of the extracted results was conducted using the China Land Use and Land Cover Data Set (CLUCDS) from the same period. The results showed that the overall accuracy, kappa coefficient, and error coefficient were 90.54%, 0.74, and 8.17%, respectively. Compared with the extracted results for woodlands using the MODIS NDVI time series only, the average error coefficient decreased from 30.2 to 7.38% because of these fractal features. The method developed in this study can rapidly and effectively extract information on woodlands from low spatial resolution remote sensing data and provide a robust operational tool for use in further research. View Full-Text
Keywords: fractal features; information extraction; accuracy assessment; MODIS NDVI; woodland fractal features; information extraction; accuracy assessment; MODIS NDVI; woodland

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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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Dong, S.; Li, H.; Sun, D. Fractal Feature Analysis and Information Extraction of Woodlands Based on MODIS NDVI Time Series. Sustainability 2017, 9, 1215.

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