Mapping large-scale distribution and changes of aquatic vegetation in Honghu Lake, China, using multitemporal satellite imagery

Fangfang Li; Chang Li; Benlin Xiao; Yanzhen Wang

doi:10.1117/1.JRS.7.073593

22 January 2013 Mapping large-scale distribution and changes of aquatic vegetation in Honghu Lake, China, using multitemporal satellite imagery

Fangfang Li, Chang Li, Benlin Xiao, Yanzhen Wang

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 073593 (January 2013). https://doi.org/10.1117/1.JRS.7.073593

Abstract

Honghu Lake, one of the seven largest fresh-water lakes in China, is well known for its ecological and economic importance, as well as its rapid changes in recent years. This study investigates the potential of using remote sensing to map and monitor aquatic vegetation changes in Honghu Lake on a large scale. Landsat TM/ETM+ images dated July 27, 2000, July 9, 2002, and July 17, 2008, and CBERS image dated August 12, 2005, are employed to map the aquatic vegetation distribution in the lake. A hybrid classiﬁcation method, combining the power of the decision tree classifier, naïve Bayes classifier, and supporting vector machine classifier is used to distinguish different wetland types. A novel polar coordinate map method is proposed to map the changes of aquatic vegetation on a large scale. The map demonstrates vegetation patch size changes and percentage changes in the whole lake directions during four periods. Validation using in situ surveys and historical ancillary data suggests that this approach could map the distribution and monitor the changes of aquatic vegetation on a large scale efficiently.

Citation Download Citation

Fangfang Li, Chang Li, Benlin Xiao, and Yanzhen Wang "Mapping large-scale distribution and changes of aquatic vegetation in Honghu Lake, China, using multitemporal satellite imagery," Journal of Applied Remote Sensing 7(1), 073593 (22 January 2013). https://doi.org/10.1117/1.JRS.7.073593

Published: 22 January 2013

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