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

Abstract

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.

Spatiotemporal Evolution of Land Subsidence in the Beijing Plain 2003–2015 Using Persistent Scatterer Interferometry (PSI) with Multi-Source SAR Data

“…As the locations of the PS pixels generally cannot be exactly the same between ENVISAT ASAR and RADARSAT-2 results, we use Kriging to interpolate all of the discrete PS points into raster images [44]. As the ENVISAT ASAR and RADARSAT-2 SAR images are limited, there is a small time gap (about several months) between the two data sets.…”

“…Generally an ADI value of 0.4 or higher is chosen, as a higher threshold is better. In this study, we adopted D A with a value of 0.5 to generate the largest set of PS candidate pixels and guarantee the quality of them [44]. Then PS pixels were selected from the PS candidate pixels based on the noise levels.…”

On the Use of Repeat Leveling for the Determination of Vertical Land Motion: Artifacts, Aliasing, and Extrapolation Errors

“…Demoulin & Collignon, 2000;Vaníček & Krakiwsky, 1986, p. 622). However, the influence of systematic errors may be more significant in other applications of repeat leveling in which the observation conditions are not as well-controlled (e.g., Bilham, 2001;Hsu et al, 2015;Kall et al, 2014;Kostoglodov et al, 2001;Verdonck, 2006;Vigny et al, 2007;cf. section 4).…”

“…Repeat leveling provides a time series of heights from which vertical land motion (VLM) can be derived and subsequently interpreted with respect to geodynamic, geophysical, and anthropogenic processes. Prominent applications include crustal deformation monitoring (e.g., Amoruso et al, 2005;D'Anastasio et al, 2006;Kostoglodov et al, 2001;Schlatter et al, 2005), measuring glacial isostatic adjustment (e.g., Kall et al, 2014;Koohzare et al, 2008;Mäkinen & Saaranen, 1998), the estimation of land subsidence due to the withdrawal of subsurface fluids or gasses (e.g., Chi & Reilinger, 1984;Hsu et al, 2015;Liu & Huang, 2013), volcanology (e.g., Dzurisin et al, 2002;Lanari et al, 2004;Poland et al, 2017), and natural disaster monitoring (e.g., Albattah, 2003;Dobrovolsky, 2006;Rikitake, 1972). Although continuous Global Positioning System (GPS) measurements provide a higher temporal sampling (e.g., daily solutions), and interferometric synthetic aperture radar (InSAR) provides a greater spatial coverage and resolution, leveling remains the most precise method for measuring height differences (e.g., Fuhrmann et al, 2015;Guglielmino et al, 2011;Kall et al, 2016) and usually provides the longest temporal coverage due to the availability of historical measurements that can date back more than 100 years in some countries (e.g., Bilham, 2001;Giménez et al, 2000;Kooi et al, 1998).…”

The Political Ecology of Land Subsidence: A Case Study of the Solar Energy-Farming Scheme, Pingtung County, Taiwan

“…Figure 7.4 shows the distribution of land subsidence as of 2005 highlighting that three counties are of particular concern, namely Changhua, Yunlin, and Pingtung; the situation remains broadly representative of the contemporary pattern. Hsu et al (2014) highlighted how the coastal areas are especially impacted, notably Yunlin County, which witnessed the most severe subsidence over the last two decades as a consequence of groundwater extraction for aquaculture and agriculture. Over the past three decades, subsidence of almost 3 m has been recorded for several coastal counties .…”