By Topic

Selection of an appropriate classification technique for coastal biomass mapping using high and low resolution dataset

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)

Mangrove forest is an integral part of inter-tidal zone of the coastal environment extending throughout the tropics and subtropics of the world. There is a wide spectrum of economic and ecological utility of Indus delta mangrove forests, sixth largest man groves forest of 92 countries. Over the last decades, numerous classification techniques/software have been used to extract deltaic vegetation information through remotely sensed data. In the present research paper, two different techniques (maximum likelihood classification and subpixel classification) were applied at high and low resolution satellite data. The aim was to propose best classification technique for the mapping and management of coastal green gold. Study highlights the draw backs of traditional classification results at low resolution satellite dataset. Subpixel classification results applied on medium resolution image were satisfactory and comparable with maximum likelihood classification results at high resolution satellite data.

Published in:

Advances in Space Technologies, 2008. ICAST 2008. 2nd International Conference on

Date of Conference:

29-30 Nov. 2008