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Vegetation change detection using artificial neural networks with ancillary data in Xishuangbanna, Yunnan Province, China

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Chinese Science Bulletin

Abstract

Timely and accurate change detection of the Earth’s surface features provides the foundation for better planning, management and environmental studies. In this study ANN change detection was used to perform vegetation change detection, and was compared with post-classification method. Before the post-classification was performed the ANN classification was used to yield multitemporal vegetation maps. ANN were also used to perform a one-pass classification for the images in 2003 and 2004. DEM and slope were used as two extra channels. During the training stage, the training data was separated into 82 subclasses including 36 change subclasses and 46 no change subclasses. Moreover NDVI differencing methods were used to develop the change mask. The result showed that combining the NDVI differencing method with visual interpretation when identifying reference areas can produce more accurate change detection results for the ANN one pass change classification. Moreover, it is effective to use elevation and slope as extra channels together with PCA components, to perform ANN-based change detection in mountainous study areas. It is also important to separate the vegetation transition classes into subclasses based on spectral response patterns, especially for mountainous terrains. This processing can reduce the topographic effect and improve the change detection accuracy.

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Authors and Affiliations

  1. Institute of Ecology and Geobotany, Yunnan University, Kunming, 650091, China

    Zhang ZhiMing & Ou XiaoKun

  2. Laboratory of Forest Management and Spatial Information, Ghent University, Ghent, 9000, Belgium

    Zhang ZhiMing, Lieven Verbeke, Eva De Clercq & Robert De Wulf

Authors
  1. Zhang ZhiMing
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  2. Lieven Verbeke
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  3. Eva De Clercq
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  4. Ou XiaoKun
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  5. Robert De Wulf
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Corresponding author

Correspondence to Ou XiaoKun.

Additional information

Supported by the National Key Project for Basic Research on Ecosystem Changes in Longitudinal Range-Gorge Region and Transboundary Eco-security of Southwest China (Grant No. 2003CB415102), Vlaamse Interuniversitaire Raad (VLIR ZEIN2002PR264-886), Belgium and Foundation of Provincial Education, Yunnan Province (Grant No. 04Y220B)

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Zhang, Z., Verbeke, L., De Clercq, E. et al. Vegetation change detection using artificial neural networks with ancillary data in Xishuangbanna, Yunnan Province, China. Chin. Sci. Bull. 52 (Suppl 2), 232–243 (2007). https://doi.org/10.1007/s11434-007-0711-1

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  • DOI: https://doi.org/10.1007/s11434-007-0711-1

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