The Detection of Active Sinkholes by Airborne Differential LiDAR DEMs and InSAR Cloud Computing Tools

Guerrero, Jesús (Universidad de Zaragoza) ; Sevil, Jorge (Universidad de Zaragoza) ; Desir, Gloria (Universidad de Zaragoza) ; Gutiérrez, Francisco (Universidad de Zaragoza) ; García-Arnay, Ángel (Universidad de Zaragoza) ; Galve, Jorge Pedro ; Reyes-Carmona, Cristina

Resumen: InSAR (Interferometric Synthetic Aperture Radar) cloud computing and the subtraction of LiDAR (Light Detection and Ranging) DEMs (Digital Elevation Models) are innovative approaches to detect subsidence in karst areas. InSAR cloud computing allows for analyzing C-band Envisat and Sentinel S1 SAR images through web platforms to produce displacement maps of the Earth’s surface in an easy manner. The subtraction of serial LiDAR DEMs results in the same product but with a different level of accuracy and precision than InSAR maps. Here, we analyze the capability of these products to detect active sinkholes in the mantled evaporite karst of the Ebro Valley (NE Spain). We found that the capability of the displacement maps produced with open access, high-resolution airborne LiDAR DEMs was up to four times higher than InSAR displacement maps generated by the Geohazard Exploitation Platform (GEP). Differential LiDAR maps provide accurate information about the location, active sectors, maximum subsidence rate and growing trend of the most rapid and damaging sinkholes. Unfortunately, artifacts and the subsidence detection limit established at −4 cm/yr entailed important limitations in the precise mapping of the sinkhole edges and the detection of slow-moving sinkholes and small collapses. Although InSAR maps provided by GEP show a worse performance when identifying active sinkholes, in some cases they can serve as a complementary technique to overcome LiDAR limitations in urban areas.

Idioma: Inglés

DOI: 10.3390/rs13163261

Año: 2021

Publicado en: Remote Sensing 13, 16 (2021), 3261 [23 pp.]
ISSN: 2072-4292

Factor impacto JCR: 5.349 (2021)

Categ. JCR: GEOSCIENCES, MULTIDISCIPLINARY rank: 30 / 203 = 0.148 (2021) - Q1 - T1
Categ. JCR: IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY rank: 6 / 28 = 0.214 (2021) - Q1 - T1
Categ. JCR: REMOTE SENSING rank: 11 / 34 = 0.324 (2021) - Q2 - T1
Categ. JCR: ENVIRONMENTAL SCIENCES rank: 83 / 279 = 0.297 (2021) - Q2 - T1

Factor impacto CITESCORE: 7.4 - Earth and Planetary Sciences (Q1)

Factor impacto SCIMAGO: 1.283 - Earth and Planetary Sciences (miscellaneous) (Q1)

Financiación: info:eu-repo/grantAgreement/ES/MINECO/CGL2017-85045-P

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Geodinámica Externa (Dpto. Ciencias de la Tierra)