## 1 Introduction  
Impervious surface coverage is increasingly being acknowledged to go hand in hand with urban sprawl while influencing ground runoff, jeopardizing water quality, destroying ecosystem functions and causing the urban heat island (UHI) effect [1; 2; 3]. Various methodologies have been developed to estimate and map the percentage of impervious surface using different spatial resolution imagery [4; 5]. Although the high resolution satellite images, such as IKONOS, Worldwide data have been applied in impervious surface mapping [4; 6], they are still not readily available and are very expensive in large scale studies. Therefore, the medium (10-100 m) spatial resolution satellite images are the most popular data that have been successfully applied in many previous impervious surface analyses [7; 8; 9]. Review of impervious surface mapping studies based on medium resolution images has revealed that the linear spectral mixture analysis (LSMA) is one of the effective approaches for estimating ISA [10; 11]. With the advantages of extracting sub-pixel information and dealing with the spectral mixture problem effectively, different optimized algorithms based on LSMA model have been developed to improve the pixel-wise ISA estimation [12; 13]. Nevertheless, these LSMA models have one problem in common, that the similarity in spectral properties among vegetation, soil, and other impervious surface materials results in a large number of mixed pixels in classification 0. Therefore, the impervious surface tend to be overestimated in regions with small ISA and underestimated in regions with large ISA [14; 15]. The Object-Based Image Analysis (OBIA), which focuses on objects and allows combining characteristics of shape, texture, context and relationships with neighbors in the image analyses [16], can provide an alternative to estimate the impervious surface. OBIA aims to improve the image interpretation by identifying structural features of land cover and thereby reducing pixel-wise spectral heterogeneity [17; 18]. Thus, it is an important issue in ISA to consider multi-scale texture information jointly to prevent the loss of information and to reduce the misclassifications in ISA analyses. ISA depicts the differences between urban and non-urban activities, which are closely related to land cover, population distribution, and urban thermal environment. Many previous researches have investigated the relationships between impervious surface and surface features (e.g., land surface temperature, vegetation abundance) [19; 20; 21]. Most of them, however, only stay at the pixel level. At the same time, land surface temperature (LST) estimates obtained from airborne or satellite thermal infrared remote sensing, have been recognized as a typical characterized feature of measuring urban heat island [22; 23; 24] and a series of global climate-induced changes [25; 26; 27]. In particular, examining the quantitatively relationship between LST and urban spatial structure features has been also suggested as the one of the major applications in urban environment study. Nevertheless, many researchers have examined the relationships between ISA and LST only on a certain zoning scale [28; 29], leaving whether these zoning areas are appropriate for understanding landscape dynamics rarely discussed. Few studies have explored the quantitative relationships between ISA and landscape cover features at the object level in an automatic or semiautomatic way [30; 31]. Subjective delineation of spatial units also makes it difficult to consider multiple dimensions of landscape arrangement [32; 33]. Therefore, object-based image segmentation were proposed to scale the landscape information in proper way, in order to exploit the flexibility of object-based spatial units in measuring the relationships between impervious surface change and land cover features [8; 34; 35].