Studying the Orientations of Luxor Ancient Egyptian Temples Using QuickBird Images

Ancient Egyptians change the orientation of their temples from True East （during the old kingdom） to the Winter Solstice （during the new kingdom）, because of the cultic change from a rather pure solar to a mixed solar religion （R^a to Amfin-R^a）. The traditional methods for measuring the orientations and alignment, the axis of ancient Egyptian constructions like field survey are usually time consuming and costly. Remote sensing techniques are becoming powerful tools for archaeologists in their discovery and exploration ancient sites. Many researchers have considered very high resolution sensors, especially IKONOS with 1.0 m resolution, QuickBird with 0.61 m resolution and Geoeye-1 with 0.50 m resolutions as possible substitutes of the classical aerial photos used for multi purposes such as cartographic at large scales. Research objectives is to establish a practical methodology for calculation the orientation of the main axis of the ancient Egyptian temple based on very high resolution QuickBird images as alternative for traditional methods. Five sites have been selected to perform our research lies in Luxor city, Egypt： Luxor, Karnak, Hatshepsut, Ramesseum temples and Madinat Habu. Analysis of the obtained results has been done to explain the type of orientation of Luxor temples. Previous archaeologist＇s studies classified the orientation of ancient Egyptians constructions into astronomical orientation （solar, moon and stars） or topographical orientation （Nile direction and hills） or both. A comparative study has been done between the results obtained from traditional methods （field survey and Google Earth images） and from very high resolution QuickBird satellite images. From the obtained results, error in the main axis azimuth of Luxor, Karnak, Madinat Habu, Hatshepsut and Ramesseum temples range about minutes of are. One can concluded that using VHR （Very High Resolution） QuickBird satellite images in calculation the azimuth of ancient sites is the accurate method.

Impacts of Park Landscape Structure on Thermal Environment Using QuickBird and Landsat Images

Urban parks composed mostly of vegetation and water bodies can effectively mitigate the urban heat island effect. Many studies have investigated the cooling effects of urban parks; however, little attention has been given to park landscape structure. Based on landscape metrics, this study has explored the influences of the park landscape structure on its inner thermal environment, taking heavily urbanized Beijing Municipality in China as the study area. Three indices, including the percentage of landscape （PLAND）, landscape shape index （LSI） and aggregation index （AI）, were used to measure the composition and configuration characteristics of the landscape components inside the parks. The indices were calculated for five landscape types being interpreted from Quickbird images. Urban thermal conditions were measured using the land surface temperature （LST） derived from Landsat TM images. The results showed that the park LST had a negative relationship with the park size, but no significant relationship was found with park shape. For the park＇s interior landscape, however, the configuration and composition characteristics of the landscape components inside the park explained 70% of the park LST variance. The area percentage of water bodies and the aggregation index of woodland were identified as the key influencing characteristics. In addition, when the composition and configuration characteristics of the park landscape components were separately considered, the configuration characteristics （LSI and A1） explained approximately 54% of the variance in park LST, which was comparable with that explained by the composition characteristics （PLAND）. Thus, this study suggested that an effective and practical way for urban cooling park design is the optimization of spatial configuration of landscape components inside the park.

Estimating Stem Volume Using QuickBird Imagery and Allometric Relationships for Open Populus xiaohei Plantations

There has been a great deal of interest in studying the crown of trees using remote sensing data. In this study, crown width was extracted from high-resolution QuickBird images for open Populus xiaohei plantations. Regression models for predicting the individual stem volumes of Populus xiaohei were established using extracted crown width, as well as estimated tree parameters （i.e. diameter at breast height [DBH] and tree height） as predictors. Our results indicated that crown width could be accurately extracted from QuickBird images using a multi-scale segmentation approach with a mean relative error of 5.74%, especially for wide-spacing stands. Using either extracted crown width alone or with estimated DBH and tree height can successfully estimate individual stem volume of Populus xiaohei with the R2 value ranging from 0.87 to 0.92 depending on different model forms. In particular, the two second-order polynomial models （model 2 and model 6）, based on QuickBird image-derived crown widths and estimated DBH and tree heights, respectively, were the best at describing the relationship between stem volume and tree characteristics.

WETLAND RESOURCES INVESTIGATION IN URBAN PARKS BASED ON QUICKBIRD REMOTE SENSING IMAGE IN JINAN,CHINA

Wetlands in urban area are an important part of environment. The status of wetland is tightly related with the security and sustainable development of urban ecosystem and society. Jinan is the capital of Shandong province, China. It is the centre of culture and economy of this province and famous for its springs. So it has important meaning to survey the wetland resources of the urban parks. In this study, the quantity and location of various vegetation types and water areas were obtained using manual classification on QUICKBIRD images. Study results showed that arbor vegetation was domain green land type in the urban parks, so the vegetation structure in parks was reasonable; water quality of the wetlands in the parks was poor, and eutrophication phenomena was obvious in some water areas; the quantity of the water areas has been declined. Through the surveying, we should think that it is a big challenge for the government to protect and restore wetland resources in the parks from 2 respects of quantity and quality in urban area.

Segmentation of High Spatial Resolution Remote Sensing Images of Mountainous Areas Based on the Improved Mean Shift Algorithm

Using conventional Mean Shift Algorithm to segment high spatial resolution Remote sensing images of mountainous areas usually leads to an unsatisfactory result, due to its rich texture information. In this paper, we propose an improved Mean Shift Algorithm in consideration of the characteristics of these images. First, images were classified into several homogeneous color regions and texture regions by conducting variance detection on the color space. Next, each homogeneous color region was directly segmented to generate the preliminary results by applying the Mean Shift Algorithm. For each texture region, we conduct a high-dimensional feature space by extracting information such as color, texture and shape comprehensively, and work out a proper bandwidth according to the normalized distribution density. Then the bandwidth variable Mean Shift Algorithm was applied to obtain segmentation results by conducting the pattern classification in feature space. Last, the final results were obtained by merging these regions by means of the constructed cost functions and removing the oversegmented regions from the merged regions. It has been experimentally segmented on the high spatial resolution remote sensing images collected by Quickbird and Unmanned Aerial Vehicle(UAV). We put forward an approach to evaluate the segmentation results by using the segmentation matching index(SMI). This takes into consideration both the area and the spectrum. The experimental results suggest that the improved Mean Shift Algorithm outperforms the conventional one in terms of accuracy of segmentation.