Jin-King Liu

Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.

Topographic Correction of Wind-Driven Rainfall for Landslide Analysis in Central Taiwan with Validation from Aerial and Satellite Optical Images

Rainfall intensity plays an important role in landslide prediction especially in mountain areas. However, the rainfall intensity of a location is usually interpolated from rainfall recorded at nearby gauges without considering any possible effects of topographic slopes. In order to obtain reliable rainfall intensity for disaster mitigation, this study proposes a rainfall-vector projection method for topographic-corrected rainfall. The topographic-corrected rainfall is derived from wind speed, terminal velocity of raindrops, and topographical factors from digital terrain model. In addition, scatter plot was used to present landslide distribution with two triggering factors and kernel density analysis is adopted to enhance the perception of the distribution. Numerical analysis is conducted for a historic event, typhoon Mindulle, which occurred in 2004, in a location in central Taiwan. The largest correction reaches 11%, which indicates that topographic correction is significant. The corrected rainfall distribution is then applied to the analysis of landslide triggering factors. The result with corrected rainfall distribution provides better agreement with the actual landslide occurrence than the result without correction.

Observing Land Subsidence and Revealing the Factors That Influence It Using a Multi-Sensor Approach in Yunlin County, Taiwan

Land subsidence is a worldwide problem that is typically caused by human activities, primarily the removal of groundwater. In Western Taiwan, groundwater has been pumped for industrial, residential, agricultural, and aquacultural uses for over 40 years. In this study, a multisensor monitoring system comprising GPS stations, leveling surveys, monitoring wells, and Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) was employed to monitor land subsidence in Western Taiwan. The results indicate that land subsidence in Yunlin County was mainly affected by the compaction of subsurface soils and over-pumping of groundwater from deep soils. The study area comprised western foothills, characterized by sediments containing predominantly gravel, and coastal areas, where clay was predominant. The subsidence in coastal areas was more severe than that in the western foothills, as a result of groundwater removal. An additional factor affecting subsidence was the compaction of deep layers caused by deep groundwater removal and the deep-layer compaction was difficult to recover. Based on multisensor monitoring results, severe subsidence is mainly affected by compaction of subsurface soils, over-pumping of groundwater from deep soils, and deep soil compaction.

A GEOMORPHOLOGICAL MODEL FOR LANDSLIDE DETECTION USING AIRBORNE LIDAR DATA

This study analyzes multi-temporal LiDAR data of high accuracy and high resolution by installing a geomorphometric model for extracting landslides. First, two sets of LiDAR data were acquired for before and after a heavy rainfall event. The landslides which took place from 2005 to 2009 were classified automatically by satellite images, and subsequently the landslides were interpreted and edited manually. Geomorphometric parameters including slope, curvature, OHM, OHM roughness, and topographic wetness index were then extracted using stencils of landslide polygons overlaid on respective thematic maps derived from LiDAR, DEM and DSM. The ranges of every parameter were derived from the statistics of the landslide area. Some selected non-morphometric parameters were also included in a later stage to account for all possible features of landslides, such as vegetation index and geological strength. The ranges of the parameters of landslides were optimized for the model by the statistics of the landslide area. The overall accuracy predicted by the model was 64.9%. When the buffer zones of old landslides and riverside areas were included, the overall accuracy was 64.4%, showing no improvement. When landslides smaller than 50 m^2 were filtered, the overall accuracy reached 76.6% and 72.5% for 2005 and 2009, respectively. The results show that the geomorphological model proposed in this research is effective for landslide extraction.

The Geomorphometry of Rainfall-Induced Landslides in Alishan Area Obtained by Airborne Lidar and Digital Photography

For understanding the distribution of slope angles, OHM and roughness of the rainfall-induced landslides in Alishan Area of Central Taiwan, a survey was carried out with airborne Lidar and aerial digital camera to obtain DEM and DSM of 1 m grid and color orthophotos of 50 cm grid. DEM, DSM and orthophotos are georeferenced and co-registered. The 106 landslide polygons derived from photo-interpretation are used for extracting slopes, OHM and roughness. Results show that the average slope angle of landslides is 41 degrees with a standard deviation of 14 degrees; average OHM is 4.4 m with a standard deviation of 6.3 m; average roughness is 2.05 m with a standard deviation of 2.56 m. It is also observed that scale effects are obvious for roughness but not for slope and OHM when the grid is larger than 40 m, which is the average dimension of landslides. These morphometric parameters can be further applied in the automation of landslide inventory.

Lidar DEM for characterizing the volcanic landforms of tatun volcanoes in metropolitan taipei

Tatun volcano group is a cluster of dormant volcanoes surrounding metropolitan Taipei. Rugged terrain, monotonic lithology and dense vegetation covers are adverse factors for mapping the geological structures. In this study, airborne lidar survey was conducted to obtain a bare ground DEM with 2 m grid and with an accuracy of decimeters. Shaded-relief images, pit-patterns and drainage networks are then derived from these DEMs for visual interpretation. 51 volcanoes are thus recognized. Two fissures running through the highest volcano in this area, namely Mount Seven-Stars, are extending 2000 m and 1000 m, respectively. The largest width and depth of the opening of the ruptures is located in the west flank of the volcanic cone. The slope angle of the east-wing of the volcanic cone is 36deg, whereas the angle of the west-wing is only 24.5deg. The opening of the west fissure is larger and its extension is longer than the east one. Thus, the west side can be subjected to an active extensional process of strain. The fissures could be resulted from the ongoing regional extension of the Tatun volcanic area due to the plate subduction and collision of the Eurasian plate and the Philippine sea plate.

Accuracy evaluation of ALOS DEM with airborne LiDAR data in Southern Taiwan

Recently, some global-scale DEM products, such as GTOPO30, ETOPO1, SRTM and ASTER GDEM have been published for geoscience applications. The latest product, ALOS DEM was announced to be available for a global coverage in 2016. This study examined the performance of ALOS-DEM in describing accurate morphometric and volumetric measurement of land features. A comparison was made on basis of DEM and DSM data of airborne full-waveform LiDAR data. Results showed that ALOS DEM is more approximately in reality an ALOS DSM which reveals the ground envelop surface rather than the ground bare surface. The differences between ALOS DEM and LiDAR DSM are mainly from 0 to 2.75 m with a standard deviation of 1.58 m. The differences between ALOS DEM and LiDAR DEM give a bias of as large as 20m, mostly located at the areas with abrupt change of relief and mainly in the north-facing slopes. This is probably due to ALOS sensors geometry in corresponding to its looking-direction. The stream networks derived from both ALOS DEM and LiDAR DEM are in good agreement. It is suggested that further studies on methods for assessing geomorphometric changes in landform structures should be developed and compared.

Land subsidence monitoring with a network of continuously operating GPS stations in Yunlin County, Middle Taiwan

Land subsidence in western Taiwan has been an issue since 1970s due to over extraction of ground water for aquaculture. This problem became serious in Yunlin County because high-speed rail way transects the center of land subsidence which may cause safety problem. Leveling survey is applied annually. A more efficient method with 6 continuous GPS stations is used in this study to observe the subsidence. The effectiveness and advantages are compared with leveling survey and underground monitoring wells in the period from 2007 to 2012. It is concluded that the trend of the subsidence can be fully observed by the GPS network and the observation frequency of GPS prevails the annual survey of traditional approach which is an important factor for early warning purposes.

A statistical analysis for characterizing landslide caused by heavy rainfall and severe earthquake

Heavy rainfall and earthquakes are the two major factors inducing landslides in Taiwan. The distribution of area size is the most basic quantitative parameter of landslides. Therefore, the purpose of this study is to characterize the scale and spatial difference of rainfall-induced as compared with those of earthquake-induced landslides. Two representative landslide cases, Toraji typhoon occurred in 2001 and 921 earthquake occurred in 1999, are used to analyze the causes of different kinds of landslide disasters in the paper. The test area for the 921 earthquake case is 3700 km2, the recognized number of landslide in the event is 7279, and total area for the landslides is 14766 Ha. Moreover, the maximum area of one landslide is 532 Ha, the average area for the landslides is 2 Ha, and its corresponding standard deviation is 13 Ha. In the test case of Toraji typhoon, the total study area is 8847 km2, the number of landslides is 10359, and total area for the landslide is 22305 Ha. The maximum area of a landslide is 232 Ha, the average area for the landslides is 2.2 Ha, and its corresponding standard deviation is 6.2 Ha.

Landslide detection by indices of LiDAR point-cloud density

The deliverables of an airborne LiDAR survey usually include all points, ground points, digital surface models (DSM) and digital elevation models (DEM). Indices of point clouds tested in this study include density of all points, density of ground points, density of only returns, and density of multiple returns. Shallow landslides are the most common landslides triggered by torrential rainfalls and explicit fresh scars after rainfall events. Multiple returns in forest area give the possibility of differentiating landslide scars from vegetated lands. Classification results from the indices derived from these four kinds of densities are verified by the result obtained by manual interpretation of the derived nDSM images. The experiment is carried out using the dataset obtained in I-Lan County after Typhoon Kalmaegi on 17 July 2008. The results show that a proper definition of the parameters for the indices is most critical for the detection of shallow landslides.