000108357 001__ 108357
000108357 005__ 20230519145416.0
000108357 0247_ $$2doi$$a10.3390/rs13163261
000108357 0248_ $$2sideral$$a125008
000108357 037__ $$aART-2021-125008
000108357 041__ $$aeng
000108357 100__ $$0(orcid)0000-0003-2181-732X$$aGuerrero, Jesús$$uUniversidad de Zaragoza
000108357 245__ $$aThe Detection of Active Sinkholes by Airborne Differential LiDAR DEMs and InSAR Cloud Computing Tools
000108357 260__ $$c2021
000108357 5060_ $$aAccess copy available to the general public$$fUnrestricted
000108357 5203_ $$aInSAR (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.
000108357 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/CGL2017-85045-P
000108357 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000108357 590__ $$a5.349$$b2021
000108357 592__ $$a1.283$$b2021
000108357 594__ $$a7.4$$b2021
000108357 591__ $$aGEOSCIENCES, MULTIDISCIPLINARY$$b30 / 203 = 0.148$$c2021$$dQ1$$eT1
000108357 593__ $$aEarth and Planetary Sciences (miscellaneous)$$c2021$$dQ1
000108357 591__ $$aIMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY$$b6 / 28 = 0.214$$c2021$$dQ1$$eT1
000108357 591__ $$aREMOTE SENSING$$b11 / 34 = 0.324$$c2021$$dQ2$$eT1
000108357 591__ $$aENVIRONMENTAL SCIENCES$$b83 / 279 = 0.297$$c2021$$dQ2$$eT1
000108357 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000108357 700__ $$0(orcid)0000-0002-0068-4532$$aSevil, Jorge$$uUniversidad de Zaragoza
000108357 700__ $$0(orcid)0000-0001-8949-5676$$aDesir, Gloria$$uUniversidad de Zaragoza
000108357 700__ $$0(orcid)0000-0002-5407-940X$$aGutiérrez, Francisco$$uUniversidad de Zaragoza
000108357 700__ $$0(orcid)0000-0002-4899-680X$$aGarcía-Arnay, Ángel$$uUniversidad de Zaragoza
000108357 700__ $$aGalve, Jorge Pedro
000108357 700__ $$aReyes-Carmona, Cristina
000108357 7102_ $$12000$$2427$$aUniversidad de Zaragoza$$bDpto. Ciencias de la Tierra$$cÁrea Geodinámica Externa
000108357 773__ $$g13, 16 (2021), 3261 [23 pp.]$$pRemote sens. (Basel)$$tRemote Sensing$$x2072-4292
000108357 8564_ $$s13711456$$uhttps://zaguan.unizar.es/record/108357/files/texto_completo.pdf$$yVersión publicada
000108357 8564_ $$s2859486$$uhttps://zaguan.unizar.es/record/108357/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000108357 909CO $$ooai:zaguan.unizar.es:108357$$particulos$$pdriver
000108357 951__ $$a2023-05-18-14:00:33
000108357 980__ $$aARTICLE