Detection of sinkhole occurrence, experiences from South Africa

Abstract

Sinkholes are alarming and dangerous events, they have a worldwide occurrence, and are imposing a potential risk to urban communities and the widely developed built environment. Losses due to catastrophic sinkhole collapse, foundation, pavement and structural repairs, occur more often, due to the increased pressure to develop even on sinkhole prone land, and the aging of existing water supply infrastructure in the majority of cities. Remote sensing earth observation methods have proved to be valuable tools during the last two decades in long-term sinkhole hazard assessment. Satellite air borne and ground earth observation methods have primarily facilitated the wide detection of continuous displacement on the earth's crust. National sinkholes catalogues are necessary for town planers decision makers, and government authorities. In many instances the ground collapse is the result of water ingress from old poorly maintained leaking pipelines, or extensive dewatering activities. In the current study a comprehensive review of the current literature is presented in order to show experiences from South Africa and present recent mapping using PSInSAR methodology in Centurion South Africa.

Sinkhole risk mapping and early warning: the case of Camaiore (Italy)

“…In most cases, satellite interferometry has proved capable of measuring precursory deformation anticipating the occurrence of sinkholes in areas with a high density of sinkholes, like along the Dead Sea in Israel (Baer et al, 2018;Nof et al, 2019), in the Ebro Valley in Spain (Guerrero et al, 2021) or in underground mining districts (Malinowska et al, 2019;Ferentinou et al, 2020). Guerrero et al (2021), using C-band Sentinel-1 satellite data, managed to detect up to 6.5% of the sinkhole population in a 135 km 2 sector of the Ebro Valley (23 out of 349), the main bottleneck being the limited spatial resolution of the C-band; one of the algorithms used enabled them to process interferometric in order to obtain a 40 m pixel resolution, which made it impossible to individuate sinkholes with a diameter smaller than 55 m (171 out of 349; 48%).…”

Can Machine Learning and PS-InSAR Reliably Stand in for Road Profilometric Surveys?

“…The technique can be efficiently employed for detecting surface motion patterns in the context of slow or relatively slow movements due to human-related [ 18 , 19 ] or environmental-related activities, e.g., subsidence [ 20 , 21 , 22 ], sinkholes [ 23 , 24 ], and landslides [ 25 , 26 , 27 , 28 , 29 , 30 ]. Readers may find additional valuable sources in Reference [ 8 ].…”

Surface Motion Prediction and Mapping for Road Infrastructures Management by PS-InSAR Measurements and Machine Learning Algorithms

“…The technique has been used broadly for monitoring slow or relatively slow movements caused by human-related factors, such as mining activities [10,11], tunneling [12,13], and groundwater extraction [14]. Moreover, PS-InSAR can detect efficiently phenomena in which the environment plays also a key role, e.g., subsidence phenomena [15][16][17][18][19][20], landslides [21][22][23][24][25][26][27], sinkholes [20,[28][29][30][31], active rock glacier movements [32,33], and displacement patterns driven by permafrost processes [34]. Nonetheless, there are some practice barriers to the use of PS-InSAR; the most significant flaw of this technique is the lack of PS in non-urbanized areas (e.g., agricultural and forested regions), where irregularities in geometry among SAR acquisitions generate phase decorrelation and cause a problematic PS identification [35].…”