Fractal Feature Analysis and Information Extraction of Woodlands Based on MODIS NDVI Time Series
文献类型： 外文期刊
作者： Dong, Shiwei 1 ; Li, Hong 2 ; Sun, Danfeng 1 ;
作者机构： 1.China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Beijing 100097, Peoples R China
3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
关键词： fractal features;information extraction;accuracy assessment;MODIS NDVI;woodland
期刊名称：SUSTAINABILITY （ 影响因子：3.251； 五年影响因子：3.473 ）
ISSN： 2071-1050
年卷期： 2017 年 9 卷 7 期
页码：
收录情况： SCI
摘要： 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.
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