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.Sustainability 2017, 9, 1215 2 of 17 practices. Therefore, it is important to have a rapid and effective information extraction method to support woodland management and sustainable use.The management objectives of woodlands vary at different scales. Global, intercontinental, and national scale studies mainly focus on long-term monitoring and integrated research on the major problems affecting woodland to support decision-making for ecological restoration, environmental protection, and policy needs, for example, the effect of large-scale afforestation on land surface temperature [12] and the response to ecosystem degradation from rapid economic development in China [13]. Regional and provincial scale research mainly concentrates on key ecological engineering and environmental issues related to forestry and the ecological elements or mechanistic studies of woodland systems, for example, the ecological effects of government projects [11]. Studies of specific applications of woodland monitoring methods are considered at city or county scales, and most of these address technical methods [14]. Traditional woodland parameters are usually obtained by field surveys and investigations based on monitoring stations or standard samples, and the process is expensive, time-consuming, labor-intensive, and lacks timeliness. Remote sensing technology with a large coverage, short revisit time, and low cost can rapidly and accurately detect the type, distribution, area, stru...

No abstract