Analysis to Urban Landscape Pattern Change Based on Multi-Temporal CBERS Imagery

Abstract

The landscape pattern change of Xuzhou city was and longitude 116°22' to 118°40' E. Plain covers main land analyzed based on multi-temporal CBERS images by (about 900/O) of Xuzhou city as an important part of Huaihai quantitative analysis to landscape pattern index of Xuzhou city plain. The annual average temperature is 14 degree Celsius in 2001, 2005 and 2007. Image classification and data fusion * * * -were conducted at first, and then the multi-temporal and multianderainfall is about 900 miiers Tecitye foest scale data of CBERS sensors was used for landscape pattern coverage rate is 22.50, and the main crops include wheat, analysis and comparison. The changes of vegetation landscape corn, rice, beans, etc, and the economic crops include cotton, were discussed based on NDVI data as a special interest. It peanuts, sesames, etc. A lot of changes have taken place in proves that CBERS data is suitable for urban landscape analysis past 20 years with the rapid urbanization process. Landscape as the effective complementarity of other information sources. pattern is one of the most effective indicators to urban growth and environmental evolution [2]. Multi-temporal and multi-

Dynamic monitoring of land use/cover changes based on CBERS imagery

“…Support Vector Machine (SVM) is the best statistical learning algorithm, and its criterion is Structural Risk Minimization (SRM). When the sample error minimize, last boundary of model generalization error is reduced, so generally the model is advanced [2]. The maximum likelihood (ML) classifier is a parametric classifier that relies on the second-order statistics of a Gaussian Probability Density Function (PDF) model for each class [2].…”

“…When the sample error minimize, last boundary of model generalization error is reduced, so generally the model is advanced [2]. The maximum likelihood (ML) classifier is a parametric classifier that relies on the second-order statistics of a Gaussian Probability Density Function (PDF) model for each class [2]. The neural network (NN) implemented in this research is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation [12].…”

“…It remained functional until August 2003. The second satellite, CBERS-2, was successfully launched on October 21, 2003, and the third one, CBERS-2B, was also successfully launched on September 19, 2007 [1,2].…”