UAV-based doline mapping in Brazilian karst: A cave heritage protection reconnaissance

• Published in: Open Geosciences Vol. 15; no. 1; pp. 1 - 15
• Main Authors: Ferreira, Cristiano Fernandes, Hussain, Yawar, Uagoda, Rogério, Silva, Tiago Castro, Cicerelli, Rejane Ennes
• Format: Journal Article
• Language: English
• Published: Warsaw De Gruyter 16.10.2023; De Gruyter Poland
• Subjects: Carbonates; DEM; doline typology; Environmental management; Flora; Geologic depressions; Geology; Hydrogeology; Inspection; Karst; Land use planning; photogrammetry; Sediments; Unmanned aerial vehicles; vulnerability-spots
• ISSN: 2391-5447; 2391-5447
• DOI: 10.1515/geo-2022-0535

Dolines are depressions in the soluble ground that indicates the degree of karstification. They may also act as connection points (vulnerability spots) between the surface and underground for the transmission of runoff, sediments, and pollutants. The delineation of these spots (dolines) is a crucial step in environmental management through land use planning to protect the karst underground, which is rich in flora and fauna. This requirement can benefit from a cost-effective, accessible, and non-invasion high-resolution investigation generating digital elevation models (DEMs) from unmanned aerial vehicle (UAV) imagery and automated object detection techniques. This study examines the capabilities of UAV-based DEM in detecting dolines across 50 km in the environmentally protected area of river Vermelho (APANRV – Área de Proteção Ambiental das Nascentes do Rio Vermelho). Initially, an automatic objects (doline and no-doline) detection algorithm was applied to the DEM, followed by a visual inspection to differentiate doline from possible dolines in orthomosaic photos, topographic profiles, and shaded UAV-based relief (digital terrain model; DTM and DSM). For the redundancy checking, a cluster analysis with four tests was conducted. The objects generated from the best clusters and morphological analysis were gathered in the same base for visual inspection. Out of a total of 933 objects identified, 41% were obtained from the DSM base, 25% from the perimeter-to-area ratio, and 34% through convergence between the two-analyses. Subsequently, the resulting doline typologies are discussed in reference to their proximity to hydrogeological features and their impacts on underground vulnerability. The findings aligned with the previous research as dolines were highly concentrated near sites where carbonates come in contact with siliciclastic sediments.