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Human activities have transformed natural land surface to artificial constructions, which causes the urban regions increasingly expanding. Urban development can be reflected by the dynamic changes of impervious surfaces, which can be assessed using time-series satellite data. The Pearl River Delta (PRD) metropolitan has witnessed fast urban development over past decades. Even though many studied have focused on monitoring the urbanization of megacities, there is still a lack of comprehensive understanding about the long-term annual dynamics of urban impervious surfaces at city level in PRD. In this study, MOD09A1 (MODIS Surface Reflectance 8 Day L3 Global 500 m) products were chosen to assess the annual dynamics of impervious surface at city level in PRD. Temporal filtering was utilized to improve the accuracy of the impervious surface estimation. Moreover, a new index, normalized difference urban development index (NDUDI) was proposed to investigate relative development of impervious surface in PRD. Additionally, the economy factor, Gross Domestic Product (GDP) was introduced to reveal the correlation with the impervious surfaces. Results demonstrated that the overall accuracy for impervious surface extraction was about 91.00-94.00%. The impervious surfaces area in PRD dramatically increased from 6000 km2 to 115,840 km2 in the past 16 years. Guangzhou and Foshan have the fastest developing speed, while Dongguan, Shenzhen and Foshan sacrificed much more lands to the construction of PRD than other cities. Besides, areas of impervious surface per city show very good correlations with GDP in city of Guangzhou, Huizhou, Foshan, Zhongshan, Hong Kong and Zhuhai-Macau. The scatter plot between NDUDI and GDP data display different clustering pattern between different cities. [ABSTRACT FROM AUTHOR]

Integrating synthetic aperture radar (SAR) and optical data to improve urban land cover classification has been identified as a promising approach. However, which integration level is the most suitable remains unclear but important to many researchers and engineers. This study aimed to compare different integration levels for providing a scientific reference for a wide range of studies using optical and SAR data. SAR data from TerraSAR-X and ENVISAT ASAR in both WSM and IMP modes were used to be combined with optical data at pixel level, feature level and decision levels using four typical machine learning methods. The experimental results indicated that: 1) feature level that used both the original images and extracted features achieved a significant improvement of up to 10% compared to that using optical data alone; 2) different levels of fusion required different suitable methods depending on the data distribution and data resolution. For instance, support vector machine was the most stable at both the feature and decision levels, while random forest was suitable at the pixel level but not suitable at the decision level. 3) By examining the distribution of SAR features, some features (e.g., homogeneity) exhibited a close-to-normal distribution, explaining the improvement from the maximum likelihood method at the feature and decision levels. This indicated the benefits of using texture features from SAR data when being combined with optical data for land cover classification. Additionally, the research also shown that combining optical and SAR data does not guarantee improvement compared with using single data source for urban land cover classification, depending on the selection of appropriate fusion levels and fusion methods. [ABSTRACT FROM AUTHOR]