DocumentCode :
3400489
Title :
Dynamic Monitoring of Changing and Evolvement in Kumtag Desert Based on Remote Sensing Images
Author :
Zhang Huaiqing ; Ling Cheng-xing
Author_Institution :
Inst. of Forest Resource Inf. Tech., Chinese Acad. of Forestry, Beijing, China
Volume :
3
fYear :
2010
fDate :
23-24 Oct. 2010
Firstpage :
336
Lastpage :
339
Abstract :
Lansat MSS and Land sat TM data of the Kumtag desert research area in four periods from 1975, 1990, 2000 to 2007 were chosen to dynamic monitor the changes of desert area. An images interpretation system was established after data preprocessing. The desert changing data for the Kumtag desert research area in the past 32 years were acquired through the combination of maximum likelihood classification with visual interpretation. The results showed that the desert area of the Kumtag research area kept increasing from 1975 to 2007, specifically, the area rose from 22,712.58km2 in 1975 to 23,352.33km2 in 2007. The period from 2000 to 2007 was the period with the maximum dynamic desert change in the research area. The comprehensive analysis of the transition probability matrixes of desert, gobi, saline land, vegetation area, mountain bodies and water bodies in these three periods reflected that the dynamic-degree variation trend and the transition characteristics in the Kumtag desert area were mainly resulted from the laws of nature evolution, involving temperature, precipitation, evaporation, humidity, the changing characteristics of vegetation distribution and surface wind-erosion. From integrated analysis, it shows that main reason of desert development characteristic is natural selection and evolution.
Keywords :
geomorphology; geophysical image processing; image classification; maximum likelihood estimation; remote sensing; AD 1975; AD 1990; AD 2000; AD 2007; Kumtag desert change dynamic monitoring; Kumtag desert evolution dynamic monitoring; Landsat MSS data; Landsat TM data; data preprocessing; desert area changes; dynamic degree variation trend; evaporation; humidity; image interpretation system; maximum likelihood classification; precipitation; remote sensing images; surface wind erosion; temperature; transition probability matrix; vegetation distribution; visual interpretation; Atmospheric modeling; Cities and towns; Data mining; Monitoring; Remote sensing; Vegetation; Vegetation mapping; Kumtag desert; dynamic monitoring; remote sensing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence and Computational Intelligence (AICI), 2010 International Conference on
Conference_Location :
Sanya
Print_ISBN :
978-1-4244-8432-4
Type :
conf
DOI :
10.1109/AICI.2010.308
Filename :
5655620
Link To Document :
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