Changes of Wetland in Xiamen City Based on Remote Sensing and GIS

DOI

10.2991/icadme-16.2016.124How to use a DOI?

Keywords

Wetland; decision tree classify method; Dynamic changes

Abstract

Wetland is a valuable natural resource, with irreplaceable ecological functions. With the development of industrialization, urbanization and population growth, wetland area is decreasing. And ecological functions gradually weakened. Based on TM remote sensing image from 2000 to 2011, this paper built decision tree model of wetland classification to extract wetland by classify. The paper has an research on the dynamic change of wetland of Xiamen city. The results show a growing trend between 2000 and2011, wetland area of Xiamen city increased 583.16 hm2. About 448.93 hm2 area is changed from wetland to non-wetland, which 440.73 hm2 area of waters into non-wetland. About 583.16 hm2 area is changed from non-wetland to wetland. The total area of natural wetlands is increasing 239.39 hm2; including 477.15 hm2 non-wetland turn into natural wetlands and 237.76 hm2 natural wetlands turn into artificial wetlands. This shows that human activities lead to a large of natural wetlands of the study area had transformed into wetlands. According to the wetland area and the average annual rate of change, it is easily seen that tidal wetlands, mangroves, farms and salt wetland are large impacted by human activities or other factors.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Zongmei Li
AU  - Qiaoling Huang
AU  - Wang Man
AU  - Qin Nie
PY  - 2017/07
DA  - 2017/07
TI  - Changes of Wetland in Xiamen City Based on Remote Sensing and GIS
BT  - Proceedings of the 2016 6th International Conference on Advanced Design and Manufacturing Engineering (ICADME 2017)
PB  - Atlantis Press
SP  - 698
EP  - 702
SN  - 2352-5401
UR  - https://doi.org/10.2991/icadme-16.2016.124
DO  - 10.2991/icadme-16.2016.124
ID  - Li2017/07
ER  -