This study focuses on applying remote sensing techniques to monitor Taipei’s land cover change. The research processes include generating 1993 and 2007 land cover maps through image classification; assessing Taipei’s land cover change during the period of 15 years based on GIS, landscape indices, and Shannon diversity t-test ; and investigating the effect of administrative districts on Taipei’s landscape. The research result indicated that Taipei’s land cover change did have significant change from 1993 to 2007 according to land cover maps, landscape indices and Shannon diversity t-test. As for the effect of administrative districts on Taipei’s land cover change, the result showed that four districts (i.e. Zhongshan District, Songshan District, Neihu District, and Wanhua District) have strong significant effect on Taipei’s land cover change. From the above results, it is concluded as follows. (1) Remote sensing is an effective and useful tool for monitoring urban land cover change via landscape indices and Shannon diversity t-test. (2) According to the land cover change from 1997 to 2007, Taipei landscape is strongly affected by urban sprawl. Therefore, a detailed urban development model is needed to achieve Taipei’s sustainable development.

Many image enhancement algorithms have been developed to improve the appearance of optical satellite images. However, it is usually difficult to enhance all land cover classes appearing in the images, because local contrast information and details may be lost in the dark and bright areas. In order to compensate the local brightness lost in the dark and bright areas, a fuzzy-based image enhancement method is developed to enhance the source image according to its fuzzified class information. The algorithm contains three stages: First, the satellite image is transformed from gray-level space to membership space by Fuzzy C-Means clustering. Second, appropriate stretch model of each cluster is constructed based on corresponding memberships. Third, the image is transformed back to the gray-level space by merging stretched gray values of each cluster. Various satellite images are used to test the proposed algorithm. Since the gray values of some classes are extremely dark or bright, apparently the global enhancement will result in poor contrast quality. After using the proposed enhancement method, the results are evaluated and compared with other conventional methods. The test results indicate that the proposed enhancement method can provide superior appearance and quality than other conventional methods.

Typhoons, frequently visit Taiwan in average of 4 to 5 per year, often cause direct or indirectly damage to lives and properties, and the agricultural related damage is most severe due to the extremely strong wind and heavy rain. In Taiwan, the oyster culture occupies about 44 thousands of hectares, and the culturing raft is vulnerable to the attack of typhoon. Conventionally, the estimation of the typhoon caused damage of oyster culture requires considerable human effort for on-site investigation, and the whole process may last for more than 6 months. FORMOSAT-2, a satellite that passes Taiwan daily, is potentially a powerful tool to assist the estimation of typhoon caused damages. In the present study, through the image processing, classification and GIS spatial analysis of the FORMOSAT-2 images the typhoon cause damage to the oyster culture has been estimated. A 98.4 % of accuracy was obtained as compared with the result of on-site calculation. Therefore, we concluded that the analysis of FORMOSAT-2 images could provide a time saving but accurate method for damage estimation of coastal oyster farming. The completion of the analysis is within a few hours once received the images, and thus the oyster farm culture could be reconstructed with no time lagging.

In this paper, the camera calibration for a mobile mapping system (MMS) is used to determine the interior orientation parameters, field of systematic bias vectors of image point positions, and the relative orientation of arbitrary two cameras on the MMS. Image coordinates of image points and diverse object space conditions are utilized to evaluate the aforementioned parameters without the need of moving the MMS into a calibration field. Test results show that the proposed calibration method using diverse object space conditions is applicable for calibrating cameras on a MMS. It integrates different kinds of object conditions (such as known lengths, angles, coordinate differences, coordinates, object heights as well as the perpendicular and parallel conditions) to estimate the camera parameters by using the images taken with the cameras mounted on a MMS. The object space conditions adopted in all tests include known distances, coordinate differences, the parallel and perpendicular conditions, and are used to determine the interior and exterior orientation parameters of all cameras on a MMS. The precision of the interior and exterior orientation parameters are about 0.008mm~0.010mm, 1cm~13cm, 0.052° ~0.211°, respectively.

Digital scanning devices such as LiDAR have recently become affordable and available. They are capable of acquiring high-accuracy and high-resolution point clouds. Thus, the techniques for point cloud modeling have received increasingly attentions in the last decade. As the approaches reconstruct the point clouds, they face a common problem: how to handle point clouds with inherent noises. Moreover, it will be especially challenge in handing point clouds that contains sharp features, e.g., city buildings. In the paper, a novel template-based modeling approach for 3D point clouds sampled from unknown city buildings is introduced. A hierarchy algebraic template, comprising of three types of primitive geometries (that is, plane, sphere, and cylinder), is used to fit point clouds. The algebraic template is organized in a hierarchical manner. The first-level, i.e., the lowest-level, consists of the primitive geometries which are represented in algebra form. These primitive geometries are merged into 3D objects with simple shapes in the next level. These 3D objects are further joined to form the final template model in the last level. After the point clouds are partitioned into several geometric sets, the constructed template model is used to fit them. The point cloud fitting is archived by solving a least-square linear system instead of solving a non-linear one, making the approach efficient and robust in the modeling. The experimental results show that the approach is better, in terms of sharp feature fitting and noise withstanding, than the approaches based on implicit surfaces. In addition, comparing to the general least-square fitting approaches, the template-based fitting with geometry constraints improves modeling quality with respect to human visual system