Semisupervised GDTW kernel-based fuzzy c-means algorithm for mapping vegetation dynamics in mining region using normalized difference vegetation index time series

Duo Jia; Cangjiao Wang; Shaogang Lei

doi:10.1117/1.JRS.12.016028

2 March 2018 Semisupervised GDTW kernel-based fuzzy c-means algorithm for mapping vegetation dynamics in mining region using normalized difference vegetation index time series

Duo Jia, Cangjiao Wang, Shaogang Lei

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 016028 (March 2018). https://doi.org/10.1117/1.JRS.12.016028

Abstract

Mapping vegetation dynamic types in mining areas is significant for revealing the mechanisms of environmental damage and for guiding ecological construction. Dynamic types of vegetation can be identified by applying interannual normalized difference vegetation index (NDVI) time series. However, phase differences and time shifts in interannual time series decrease mapping accuracy in mining regions. To overcome these problems and to increase the accuracy of mapping vegetation dynamics, an interannual Landsat time series for optimum vegetation growing status was constructed first by using the enhanced spatial and temporal adaptive reflectance fusion model algorithm. We then proposed a Markov random field optimized semisupervised Gaussian dynamic time warping kernel-based fuzzy c-means (FCM) cluster algorithm for interannual NDVI time series to map dynamic vegetation types in mining regions. The proposed algorithm has been tested in the Shengli mining region and Shendong mining region, which are typical representatives of China’s open-pit and underground mining regions, respectively. Experiments show that the proposed algorithm can solve the problems of phase differences and time shifts to achieve better performance when mapping vegetation dynamic types. The overall accuracies for the Shengli and Shendong mining regions were 93.32% and 89.60%, respectively, with improvements of 7.32% and 25.84% when compared with the original semisupervised FCM algorithm.

Citation Download Citation

Duo Jia, Cangjiao Wang, and Shaogang Lei "Semisupervised GDTW kernel-based fuzzy c-means algorithm for mapping vegetation dynamics in mining region using normalized difference vegetation index time series," Journal of Applied Remote Sensing 12(1), 016028 (2 March 2018). https://doi.org/10.1117/1.JRS.12.016028

Received: 12 October 2017; Accepted: 23 January 2018; Published: 2 March 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available