Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purpl
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ORIGINAL PAPER
Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains, a national scenic spot in Nanjing, China Fangyan Zhu1 • Wenjuan Shen1 • Jiaojiao Diao1 • Mingshi Li1,2 • Guang Zheng3
Received: 3 August 2018 / Accepted: 11 April 2019 The Author(s) 2019, corrected publication 2019
Abstract Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures. High spatial resolution remote sensing images can be used to detect subtle vegetation changes. The major objective of this study was to map and quantify forest vegetation changes in a national scenic location, the Purple Mountains of Nanjing, China, using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11676-019-00978-x) contains supplementary material, which is available to authorized users. Project funding: The work was supported by the National Natural Science Foundation of China (31670552), the PAPD (Priority Academic Program Development) of Jiangsu provincial universities and the China Postdoctoral Science Foundation funded project. Additionally, this work was performed while the corresponding author acted as an awardee of the 2017 Qinglan Project sponsored by Jiangsu Province. The online version is available at http://www.springerlink.com. Corresponding editor: Tao Xu. & Mingshi Li [email protected] 1
College of Forestry, Nanjing Forestry University, Nanjing 210037, People’s Republic of China
2
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, People’s Republic of China
3
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Science, Nanjing University, Nanjing 210023, People’s Republic of China
vegetation changes and provide a reference for sustainable management. We used Quickbird images acquired in 2004, IKONOS images acquired in 2009, and WorldView2 images acquired in 2015. Four pixel-based direct change detection methods including the normalized difference vegetation index difference method, multi-index integrated change analysis (MIICA), principal component analysis, and spectral gradient difference analysis were compared in terms of their change detection performances. Subsequently, the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes. An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results. The results showed that the MIICA method was the best pixel-based change detection method. And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior
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