Ming-Der Yang

Assessing debris-flow hazard in a watershed in Taiwan

This paper presents the results of a pilot study for assessing debris-flow hazards using geographic information system (GIS) techniques. The watershed of the Chen-Yu-Lan River is investigated in this pilot study. Factors that are believed to be critical to the occurrence of debris flow are identified and considered in the assessment of debris-flow hazards. Using the spatial analysis feature of GIS, the impact of these factors, expressed in terms of debris-flow hazard (DH) index, is calculated. By taking a simple summation of all DH indexes according to each factor, the overall debris-flow hazard at a particular watershed may be assessed. The applicability of the proposed approach for analyzing the watershed of the Chen-Yu-Lan River has been confirmed with the field observations in a recent typhoon event.

Eutrophication modelling of reservoirs in Taiwan

Two reservoirs in Taiwan were modeled to simulate the hydrodynamics and water quality in the water column. The modelling effort was supported with data collected in the field for a 2-year period for both reservoirs. Spatial and temporal distributions of temperature in the water column of the two reservoirs were well reproduced by the hydrodynamic model. Model calculated concentrations of key water quality constituents such as nutrients, dissolved oxygen, and algal biomass matched the measured values closely in both reservoirs. Most importantly, vertical stratification of temperature and dissolved oxygen in the Tseng-Wen Reservoir was mimicked by the model throughout this 2-year period. The calibrated model was then used to simulate water quality response to various nutrient reduction scenarios. Model results reveal that a 30-55% reduction of the phosphorus loads will upgrade the existing eutrophic/mesotrophic to oligotrophic conditions in the Te-Chi Reservoir.

Estimation of algal biological parameters using water quality modeling and SPOT satellite data

Discovering biological parameters is the essential step to understand and even control an ecosystem. Traditional in situ sampling is time-consuming, expensive, and can only be taken for small areas with limited samples. It also needs a long-term observation program to acquire biological growth records. In this paper, a novel approach is proposed to estimate algal biological parameters, which are important factors of eutrophication control, by using water quality modeling and remote sensing techniques. Algal growth rate and respiration rate were estimated using a one-dimensional water quality model (QUAL2E) and two-dimension spatially distributed water quality data derived from SPOT satellite imagery for the Te-Chi Reservoir in Taiwan. A nonlinear calibration model was developed by minimizing the average difference between observed and simulated values using a least squares method. The calibration model using a mathematical approach provides an alternative method to estimate biological parameters of algae besides in situ sampling and experiment.

Segmenting ideal morphologies of sewer pipe defects on CCTV images for automated diagnosis

Several literatures presented automated systems for detecting or classifying sewer pipe defects based on morphological features of pipe defects. In those automated systems, however, the morphologies of the darker center or some uncertain objects on CCTV images are also segmented and become noises while morphology-based pipe defect segmentation is implemented. In this paper, the morphology-based pipe defect segmentation is proposed and discussed to be an improved approach for automated diagnosis of pipe defects on CCTV images. The segmentation of pipe defect morphologies is first to implement an opening operation for gray-level CCTV images to distinguish pipe defects. Then, Otsus technique is used to segment pipe defects by determining the optimal thresholds for gray-level CCTV images of opening operation. Based on the segmentation results of CCTV images, the ideal morphologies of four typical pipe defects are defined. If the segmented CCTV images match the definition of those ideal morphologies, the pipe defects on those CCTV images can be successfully identified by a radial basis network (RBN) based diagnostic system. As for the rest CCTV images failing to match the ideal morphologies, the failure causes was discussed so to suggest a regulation for imaging conditions, such as camera pose and light source, in order to obtain CCTV images for successful segmentation.

Morphological segmentation based on edge detection for sewer pipe defects on CCTV images

The essential work of sewer rehabilitation is a sewer inspection through diagnoses of sewer pipe defects. At present, image processing and artificial intelligence techniques have been used to develop diagnostic systems to assist engineers in interpreting sewer pipe defects on CCTV images to overcome humans fatigue and subjectivity, and time-consumption. Based on the segmented morphologies on images, the diagnostic systems were proposed to diagnose sewer pipe defects. However, the environmental influence and image noise hamper the efficiency of automatic diagnosis. For example, the central area of a CCTV image, where is always darker than the surrounding due to the vanishing light and slight reflectance, causes a difficulty to segment correct morphologies. In this paper, a novel approach of morphological segmentation based on edge detection (MSED) is presented and applied to identify the morphology representatives for the sewer pipe defects on CCTV images. Compared with the performances of the opening top-hat operation, which is a popular morphological segmentation approach, MSED can generate better segmentation results. As long as the proper morphologies of sewer pipe defects on CCTV images can be segmented, the morphological features, including area, ratio of major axis length to minor axis length, and eccentricity, can be measured to effectively diagnose sewer pipe defects.

A genetic algorithm (GA) based automated classifier for remote sensing imagery

Conventional unsupervised classification divides all pixels within an image into corresponding classes based on the distance between pixels and the cluster centres. The number of classes must be selected a priori but is seldom ascertainable with little information. To analyze a large dataset, such as a remote sensing dataset, requires an automatic unsupervised classifier which needs no human effort during the process of image clustering. A genetic algorithm (GA) is adopted to search the cluster centres and choose a suitable cluster number for digital images to overcome the disadvantages of the conventional unsupervised classifier. The GA-based automated classifier was executed on several test images for validity and SPOT satellite imagery for practical application. The satellite images classified by the GA-based classifier and iterative self-organizing data analysis technique (ISODATA) were compared with a classified result through a supervised classification. According to the estimation of classification accuracy by error matrices and K statistic, the GA-based classifier performed better than the unsupervised ISODATA and as good as a supervised classifier, even without manipulation by an analyst. A modified GA-based classifier using maximum likelihood (represented by the z score) as a clustering criterion was also proposed and proven to be capable of performing automatically as well as a supervised classifier.

Application of remotely sensed data to the assessment of terrain factors affecting the Tsao-Ling landslide

The Chi-Chi earthquake, the most severe natural disaster of the last century in Taiwan, struck the west-central part of the island in the very early morning on 21 September 1999. This serious earthquake changed the landscape of central Taiwan. The strong natural motion triggered thousands of landslides and debris flows in the hills and mountains. Tsao-Ling Lake was formed by massive landslides that blocked Chinshui Creek. Besides geological conditions, terrain factors such as slope, aspect, and vegetation greatly affect the stability of the slopes. To monitor and evaluate the locations and variations of the landslide scars in the Chinshui watershed, remote sensing techniques were applied to provide temporal and spatial information. Airborne and satellite imagery was adopted to help in the collection of terrain information. Aerial photographs and SPOT satellite images were processed to assess the damage level of slide areas and the potential risk of landslide reoccurrence in the watershed of the Tsao-Ling area. The overall affecting scores combining three terrain factors were processed by an aggregation function to produce a synthetic probability map of landslide reoccurrence. The probability of landslide reoccurrence in the Tsao-Ling area can be a reference for decision-makers to efficiently locate areas with high probability of landslide occurrence and create treatment and rehabilitation plans.

Pavement performance prediction through fuzzy regression

Accurate predictions of future pavement conditions are essential for determining the most cost-effective maintenance strategy. The current methods for assessing pavement conditions involve either equipment measures or visual inspections. Equipment measures are not extensively implemented because of high cost; thus, subjective evaluations by road inspectors are often used as a replacement. Nevertheless, visual inspections could draw in errors and variations due to subjectivity and uncertainty. The present serviceability index (PSI), one of the most common indicators used to evaluate pavement performance, is incapable of transforming ones imprecise judgment into an exact number between 0 (the worst) and 5 (the best). Conventional regression cannot deal with visual inspection data that are linguistic or non-crisp. In contrast, fuzzy regression is capable of handling such fuzzy data. In this paper, pavement conditions are exemplified by five membership functions and estimated by using fuzzy regression to better account the uncertainties of the traditional method. Also, a similarity indicator is applied to measure the goodness of fit. A case study using pavement inspection data is presented to establish estimated fuzzy regression equations. The results demonstrate the capability of the model, which is able to assist road administration units to determine desirable repair actions regarding the predicted pavement conditions.

Systematic image quality assessment for sewer inspection

Closed circuit television (CCTV) has been applied in many developing or developed counties for sewer inspection due to its low setup cost and technical requirement. Several automated diagnosis systems of sewer pipe defects had been developed to assist the technicians in interpreting or classifying sewer pipe defects. However, many researchers pointed out that good image quality is the prerequisite for accurate interpretation and diagnosis of CCTV inspection but has not a proper evaluation approach. In this paper, a CCTV image quality index considering both of the luminance distortion and the contrast distortion of a CCTV image compared by reference images is proposed and was applied to assess the image quality of the CCTV images shot for a sewer house-connection project. The experimental result indicates that rather than luminance contrast plays a more important role in the CCTV image quality that can be effectively improved by contrast enhancement. Since CCTV image quality can hardly distinguished by human eyes, the proposed image quality index can provide helpful information to efficiently assist the on-site technicians in precisely shooting better CCTV images for the pipe defection. Additionally, a sensitivity analysis of contrast stretch was implemented to quantify the CCTV image quality improvement. CCTV imaging conditions, such as pipe materials and imager status, are found as the factors affecting the CCTV image quality. In the future, a real-time CCTV image quality assessment will be developed by modifying the CCTV image quality index as an instantaneous reference for imaging adjustment that can be expected to be practicable for the on-site sewer inspection because of the extremely short computation time.

Mapping of the 26 December 2004 tsunami disaster by using FORMOSAT-2 images

The Sumatra earthquake struck South Asia on 26 December 2004 and triggered monstrous waves that turned into a tsunami hitting the ocean regions and caused the most severe natural disaster of recent decades. The devastating earthquake and tsunami changed the landscape of coastal areas in many countries around the whole Indian Ocean region. To provide real‐time information for rescue and rehabilitation plans, satellite images were applied to monitor and evaluate the damage over several devastated spots. The FORMOSAT‐2 satellite, which was launched on 21 May 2004 and operated by the National Space Organization, Taiwan, is uniquely designed to take timely and low‐cost black and white images daily with a resolution of 2 m and colour images of 8‐m resolution. FORMOSAT‐2 is expected to have many useful applications, such as natural‐disaster evaluation, land‐usage analysis, environmental monitoring, and coastal search and rescue. FORMOSAT‐2 successfully acquired several post‐tsunami images of the hazardous areas, both Puhket, Thailand and Banda Aceh, Indonesia on 28 December. A series of FORMOSAT‐2 satellite images were processed by geometric and radiometric correction, haze reduction, image enhancement, feature extraction, image classification, and image fusion to assess the damage over those devastated areas. FORMOSAT‐2 satellite images with a high‐temporal resolution and high‐spatial resolution were proved to be an efficient and useful information source for decision‐makers to make rescue and recovery plans, especially for some isolated islands hard to reach in time.