Coastal sand dunes are amongst the world’s most sensitive and dynamic landforms. Unfortunately, during the last thirty years, heavy anthropogenic alterations have been observed, encompassing the greater part of the Bulgarian Black Sea coast (BBSC), which has changed the land-sea interactions significantly. As a consequence, the depositional coast has shrunk to 131 km or 25% of the aggregate Bulgarian Black Sea shoreline length. Although our research reveals that 86% of BBSC dunes are included in the Natura 2000 network of protected sites established under the Habitats Directive (Council Directive 92/43/EEC 1992), they are often heavily modified, subjected to environmental vandalism and destroyed due to mismanagement or lack of accurate information and prevention. These facts were the main reason for carrying out an inventory of the Bulgarian Black Sea coastal dune systems in 2021-2022. Our research aimed to identify all dune systems/sand dunes, update their spatial distribution and classify the observed coastal sand dunes landforms along the BBSC. The article demonstrates a successful methodology for combining unmanned aerial systems (UAS), Structure-from-Motion (SfM) photogrammetry, in situ sediment sampling, video imaging and verification and GNSS-RTK ground control points for coastal mapping. As of June 2022, over 97% of the Bulgarian shoreline has been surveyed with this technique, excluding military areas and national security sites. Based on the acquired data, as of 2021, the shoreline length was estimated to be 518.7 km at a scale 1:5000. The integrated UAS approach includes using Digital surface models (DSM), raster orthophotomosaics (OM) and 3D models, based on SfM photogrammetry to analyse the coastal topography, detect dune forms and update their spatial distribution. Throughout the inventory, 46 beach-dune systems were identified along the BBSC, which were divided into 62 dune sectors. The area of coastal dune systems was estimated at 988.21 ha (0.0089% of Bulgaria) and a total length of 73 km (14% of the shoreline). A comprehensive geomorphological analysis of the relationships between landforms morphology, aeolian and morphodynamic processes, vegetation density and type was the basis for the coastal dune landforms (CDLs) or dune systems to be classified into primary (312 ha; 32%) and secondary (676 ha; 68%). Additionally, the CDLs were classified according to Natura 2000 habitats: fixed (grey) dunes (546.27 ha; 55.28%), wooded dunes (222.61 ha; 22.53%), shifting (white) dunes (150.30 ha; 15.21%), embryonic dunes (68.3 ha; 6.91%) and humid dune slacks (0.94 ha; 0.09%). The highest positioned CDLs on the Balkan Peninsula were registered at perched Sozopol Sand Dunes (61 m a.s.l.) and cliff-top dunes at Arkutino (50.2 m a.s.l.). The multi-temporal analysis of photogrammetric DSMs and raster OMs showed the permanent loss of five dune systems in the Pomorie-Burgas-Rosenets coastal sector. The accrued UAS data approach allowed us to identify and map eight dune systems for the first time: Zlatni Pyasatsi (Panorama), Asparuhovo (Varna), Byala, Atanasovska Kosa, Central Beach (Burgas), Chernomorets, Kavatsite (partly) and Rezovo-Kastrich. A high anthropogenic footprint was registered on 50.7 ha (5.1%) of the entire dune surface. In the final stage of the study, human interventions that caused degradation and permanent loss of dunes (12 ha) over the last 15 years along the BBSC were shown. The main causes for dune degradation along BBSC have been documented, such as massive tourism development after the socialist period, road construction, recreational pressure exerted on the dunes, human trampling, lack of designated footpaths in areas with fixed and mobile dunes, off-road vehicles and parking lots (especially at camping sites), dumping of garbage and anthropogenic marine litter on the sand dunes etc.

The use of unmanned aerial vehicles (UAVs) represents an innovative remote sensing technique for surveying of large or difficult-to-access areas in less time compared to other popular methods. This paper presents a cost-effective approach to very high resolution geomorphological and landscape mapping of Bolata Cove, northern Bulgarian coast. The orthophoto imagery was acquired with a DJI Phantom 4 Pro Quadcopter. The survey accuracy was guaranteed by ground control points measured with GNSS HyTarget geodetic receivers via NRTK, as well as by seabed control points registered with a single-beam echosounder. The imagery postprocessing involved a 3D model derivation using the structure-from-motion principles. Ready-to-interpret UAV products were a digital surface model and an orthophoto mosaic with a 3 cm cell size. The aforementioned products were applied in the subsequent GIS-aided geomorphological and landscape analysis aimed at the identification of land/bedforms and corresponding ecotopes of nature conservation interest. The method discussed has demonstrated a strong potential for wider implementation in coastal geomorpological and landscape-ecological research thanks to the very high resolution of the UAV-acquired data.

Nowadays, the acquisition of very high resolution (VHR) imagery guarantees an excellent quality of coastal topographic surveys and monitoring. Alongside the rapid progress of remote sensing, digital photogrammetry and image processing of data obtained by unmanned aerial vehicles (UAVs) provides outstanding opportunities for VHR mapping of the coastal zone, including the coverage of difficult-to-access areas. Till the previous two years, the Bulgarian coast was “intact” for this modern technology and was regularly surveyed to detect and analyze the ongoing shoreline dynamics and trends of morphological changes. Both spatial planning and integrated coastal zone management, however, require highly accurate and up-to-date geodata, being among the main reasons for the wide application of drones worldwide in shoreline surveying, along with the low cost of the related mapping campaigns. This work aims to demonstrate the capabilities of UAV-based coastal landform mapping at Shabla Municipality case study site, Bulgaria. The above coastal segment was surveyed from a flight altitude of 200 m and at 5.8 cm ground sampling distance (GSD). In addition, erosion-prone areas were photographed from 50 m altitude and at 1.5 cm GSD. A digital surface model with a resolution of 20 cm for the whole coast of the Shabla Municipality was created. A finer one, with 6 cm cell size was derived after Structure for Motion data processing (SfM) for the area of Krapets Village together with the contiguous sector between Cape Shabla and Shablenska Tuzla Lagoon. A set of ground control points (GCPs) was used for georeferencing purposes. The shore was divided into erosional, erosional-depositional, and depositional segments, along with accompanying geomorphological classification of the identified landforms.

Beach-dune systems host sensitive habitats subject to protection in compliance with the EU and national legislation. Along the Bulgarian coast, the substrata for the formation of depositional landforms are of predominantly terrigenous origin. Once eroded from the source provinces, they are transported as solid runoff and accumulated in the littoral zone. Consequently, the spatial distribution of depositional landforms is primarily related to the occurrence of gullies and ravines, estuaries and lagoons. Geomorphological mapping and classification, alongside vegetation mapping, represents the backbone of habitats identification in the case of coastal environments. Depositional landforms along the Bulgarian coast correspond to a number of sensitive habitats, enlisted in the national Red Data Book, and subject to protection pursuant to the country’s Biodiversity Act transposing the EU Council Directive 92/43/EEC (Habitats Directive). Three representative coastal segments along the North Bulgarian coast, hosting several sensitive habitat types, were surveyed in 2019 with a DJI Phantom 4 Pro quadcopter. All three are parts of the Bulgarian national nature conservation network. Accordingly, three sets of high-resolution orthoimages, digital surface models (DSMs) and 3D tiled models were generated in order to facilitate the GIS interpretations. The UAS-acquired imagery, DSMs and 3D models proved to be extremely useful geospatial data, greatly facilitating the process of coastal landforms and habitats identification. Nevertheless, as monitoring requires repetitive surveys, we should emphasize that the year 2019 marked the very first season when the approach was applied by our team. Therefore, deductions regarding long-term trends in the landform/habitat dynamics would be yet inappropriate.