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

Author

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  • Shiwei Dong

    (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)

  • Hong Li

    (Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Danfeng Sun

    (College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China)

Abstract

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.

Suggested Citation

  • Shiwei Dong & Hong Li & Danfeng Sun, 2017. "Fractal Feature Analysis and Information Extraction of Woodlands Based on MODIS NDVI Time Series," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:7:p:1215-:d:104536
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    References listed on IDEAS

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    1. Shiwei Dong & Yuchun Pan & Hui Guo & Bingbo Gao & Mengmeng Li, 2021. "Identifying Influencing Factors of Agricultural Soil Heavy Metals Using a Geographical Detector: A Case Study in Shunyi District, China," Land, MDPI, vol. 10(10), pages 1-15, September.
    2. Inés Gazzano & Marcel Achkar & Ismael Díaz, 2019. "Agricultural Transformations in the Southern Cone of Latin America: Agricultural Intensification and Decrease of the Aboveground Net Primary Production, Uruguay’s Case," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    3. Qi Wang & Peng Guo & Shiwei Dong & Yu Liu & Yuchun Pan & Cunjun Li, 2023. "Extraction of Cropland Spatial Distribution Information Using Multi-Seasonal Fractal Features: A Case Study of Black Soil in Lishu County, China," Agriculture, MDPI, vol. 13(2), pages 1-19, February.
    4. Vaclav Beran & Marek Teichmann & Frantisek Kuda & Renata Zdarilova, 2020. "Dynamics of Regional Development in Regional and Municipal Economy," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
    5. Le’an Qu & Zhenjie Chen & Manchun Li, 2019. "CART-RF Classification with Multifilter for Monitoring Land Use Changes Based on MODIS Time-Series Data: A Case Study from Jiangsu Province, China," Sustainability, MDPI, vol. 11(20), pages 1-23, October.

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