Rice is one of the major corps of Taiwan. Accordingly, in the past, governments put great efforts on estimating the size of paddy rice. The objective of this study is to classify the area of paddy through satellite images by our spatial information system.In the present study, the material adopted is QuickBird satellite images. Owing to the low resolution of QuickBird satellite images, the texture information and NDVI were used as auxiliary material to enhance the quality of the images. On the other hands, leeve is one of the most important component to extract information on evaluating the area of paddy rice. Thus, it is decided to use two different cases to study the outcomes of extracting the leeves: Case (a) using BPN to classify the image into three category(paddy rice, leave, non-paddy rice) and Case (b)using Tree-Neural Network concept to step-by-step output the paddy rice, leave, non-paddy rice. The results are drawn and rational discussions are made.

In Taiwan, more than half of area is national forest land, if there was forest fire which often causes damage to the ecological environment. Therefore, rapid mapping of forest fires have become a very important job. We studied the forest fires of Dapu working cycle which was occurred in 2009, and we used Z/I DMC airborne multispectral image as material. In order to investigate the feasibility of automated forest fire mapping. We use three kinds of change detection techniques that include NDVI differencing (dNDVI), spectral change vector analysis (SCVA), principal components analysis (PCA), and we combined the three of change detection techniques and iterative self-organizing data analysis technique (ISODATA) to classify the fire affected area. We focused on the characteristics of high-resolution image to remove the noise of change detection, and to explore the differences between the three change detection techniques that combination ISODATA unsupervised classification. In order to establish the appropriate processes of automatic forest fires mapping for airborne multispectral image. The results showed that each change detection technique can enhance the characteristics of the fire area effective. However, those change detection techniques with ISODATA to distinguish between burned and unburned area, which will be affected by the distribution of enhanced image values. The numerical distribution of images to a single peak skewed distribution seems to be more suitable for two categories of classification using ISODATA. Image processing of shadow removal or morphology can reduce classification error and improve the overall classification accuracy. Overall, the dNDVI method with image processing of shadow removal and morphology showed the best overall accuracy of 91.93%, and with highly automated processes. The forest fire automatic mapping technology can provide the coherent units to be applied.

Synthetic Aperture Radars (SARs) take the advantages of all-weather and day-and-night capabilities to detect object information in microwave bands. In order to enhance the applications of SAR data, the geometric correction is a must. Thus, the generated ortho-image can be registered with other GIS data layers. The traditional methods for the rectification of SAR imagery adjust orbit polynomials according to ground control points. From direct georeferencing point of view, this paper investigates the feasibility of Radar back projection and analyzes the accuracy of proposed method to simplify the processing for SAR imagery rectification at first. Owing to the implementation and standardization of Rational Function Model (RFM), it is widely used in the optical sensor adjusting. This research also investigates the feasibility of RFM for radar images positioning. The major works includeradar back projection and RFM. The proposed scheme for radar back projection includes (1) establishing the Radar back projection procedure, (2) linear transformation, and (3) least squares collocation. In addition, the proposed scheme for RFM includes (1) establishment of RFM (2) refining, and (3) least squares collocation. The experiments test the images for different satellites and, then, compare the proposed method with the PCI Geomatica Toutin’s model. The proposed method can reach similar or ever better result than PCI Geomatica Toutin’s model.

This study aims at applying remote sensing techniques to monitor the area of north Taiwan, that attempted to interpret the potential illegal waste dumping fields in an efficiently way. The research integrates spatial data mining analysis and remote sensing analysis. The former used the extraordinary cases of illegal waste dumping by the environmental protection unit over the years to generalize the factors of waste dumping, and using CART algorithm for data mining to produce the spatial rules of waste dumping that used for drawing the area of potential occurred event. The latter implements images classification and NDVI analysis focus on the potential dumping areas, and implementing integrated analysis of spectrum to interpret the changing of images. Finally, it obtains the results of monitoring areas for the checking and verifying on-the-spot. These research results as follows. (1)For the part of data mining, it comes out 13 rules, and acquires 6 rules of it of potential area. Then, these rules are implemented as buffer analysis through ArcGIS software to draw the spatial maps of potential waste dumping areas. After the integrated analysis of spectrum and spatial, 12 places has been verified and interpreted which were dumping fields. The results of successful rates could reach approximately 18.750%. (2) According to the research result indicated that remote sensing is an effective and useful tool for strengthening the monitoring mechanism, saving costs and enhancing the working efficiency for the environmental protection unit. Further, that could complete and improve the original system of environmental monitor in a way of comprehensiveness.

The 2009 Typhoon Morakot caused a massive debris flow that buried Hsiaolin village and resulted in the death of hundreds of people. Satellite image interpretation reveals that the Hsiaolin debris flow originated in a single location and then split into two parts. The northern debris flow, the smaller of the two parts, flowed within a curve ravine. The southern part of the debris flow was four times larger than the northern part and responsible for the burial of Hsiaolin village. The movement of the debris flow can be divided into three processes. First a slope failure and subsequent debris flow occurred within a curve ravine. Second, the debris flow eroded the bank of the ravine laterally, causing translational failure of the ravine walls. A massive debris flow, made up of a combination of materials from both the original debris flow and the ravine walls, jammed within the ravine. Finally, as a result of the jam, the debris flow was redirected straightly towards the Hsiaolin village. Overlaying the zonation of the post Hsiaolin debris flow landforms on top of pre-failure satellite images reveals that characteristics of the post failure landforms match perfectly that observed in the pre-failure satellite images. This finding may support the thought that large scale geologic disasters are reoccurring. Satellite images can assist in identifying ancient debris flows and deep seated failures and if paired with hazard zoning and evacuation planning, disasters such as the Hsiaolin Village incident can be avoided.