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 OPEN ACCESSRemote Sens. 2015, 7 8203 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.

The geomorphology of Taiwan is characterized by marked changes in terrain, geological fractures, and frequent natural disasters. Because of sustained economic growth, urbanization and land development, the land cover in Taiwan has undergone frequent use changes. Among the various technologies for monitoring changes in land cover, remote sensing technologies, such as LiDAR, are efficient tools for collecting a broad range of spectral and spatial data. Two types of airborne LiDAR systems exist; full-waveform (FW) LiDAR and traditional discrete-echo LiDAR. Because reflected waveforms are affected by the land object material type and properties, the waveform features can be applied to analyze the characteristics specifically associated with land-cover classification (LCC). Five types of land cover that characterize the volcanic Guishan Island were investigated. The automatic LCC method was used to elucidate the spectral, geomorphometric and textural characteristics. Interpretation keys accompanied by additional information were extracted from the FW LiDAR data for subsequent statistical and separation analyses. The results show that the Gabor texture and geomorphometric features, such as the normalized digital surface model (nDSM) and slopes can enhance the overall LCC accuracy to higher than 90%. Moreover, both the producer and user accuracy can be higher than 92% for forest and built-up types using amplitude and pulse width. Although the waveform characteristics did not perform as well as anticipated due to the waveform data sampling rate, the data provides suitable training samples for testing the waveform feature effects.

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This study proposed a Hough Transform algorithm based on the probabilistic scheme termed as the Orientation Constrained Probabilistic Hough Transform. The orientation constraints for segmentation are applied to form compact and reliable sampling subsets. This process is subsequently followed by constrained searching. Numerical experiments are performed with both synthetic and real datasets indicate that the proposed method performs better than other algorithms in terms of correctness and omission rate. Moreover, computational time deemed necessary is much less than that for other algorithms.