Nowadays, the employment of high‐resolution Digital Surface Models (DSMs) and RGB orthophotos has become fundamental in coastal system studies. This work aims to explore the potentiality of low‐cost Unmanned Aerial Vehicle (UAV) surveys to monitor the geomorphic and vegetation state of coastal sand dunes by means of high‐resolution (2–4 cm) RGB orthophotos and DSMs. The area of study (Punta Marina, Ravenna, Italy), in the North Adriatic Sea, was considered very suitable for these purposes because it involves a residual coastal dune system, damaged by decades of erosion, fragmentation and human intervention. Recently, part of the dune system has been involved in a restoration project aimed at limiting its deterioration. RGB orthophotos have been used to calculate the spectral information of vegetation and bare sand and therefore, to monitor changes in their relative cover area extension over time, through the using of semi‐automatic classification algorithms in a GIS environment. Elevation data from high‐resolution DSMs were used to identify the principal morphological features: (i) Dune Foot Line (DFL); (ii) Dune Crest Line (DCL); Dune seaward Crest Line (DsCL); Stable Vegetation line (SVL). The USGS tool DSAS was used to monitor dune dynamics, considering every source of error: a stable pattern was observed for the two crest lines (DCL and DsCL), and an advancing one for the others two features (DFL and SVL). Geomorphological data, as well as RGB data, confirmed the effectiveness of planting operations, since a constant and progressive increase of the vegetated cover area and consolidation of the dune system was observed, in a period with no energetic storms. The proposed methodology is rapid, low‐cost and easily replicable by coastal managers to quantify the effectiveness of restoration projects.
Stefano Fabbri, E.G. (2021). Using high‐spatial resolution uav‐derived data to evaluate vegetation and geomorphological changes on a dune field involved in a restoration endeavour. REMOTE SENSING, 13(10), 1-25 [10.3390/rs13101987].
Using high‐spatial resolution uav‐derived data to evaluate vegetation and geomorphological changes on a dune field involved in a restoration endeavour
Stefano Fabbri
;Clara Armaroli;
2021
Abstract
Nowadays, the employment of high‐resolution Digital Surface Models (DSMs) and RGB orthophotos has become fundamental in coastal system studies. This work aims to explore the potentiality of low‐cost Unmanned Aerial Vehicle (UAV) surveys to monitor the geomorphic and vegetation state of coastal sand dunes by means of high‐resolution (2–4 cm) RGB orthophotos and DSMs. The area of study (Punta Marina, Ravenna, Italy), in the North Adriatic Sea, was considered very suitable for these purposes because it involves a residual coastal dune system, damaged by decades of erosion, fragmentation and human intervention. Recently, part of the dune system has been involved in a restoration project aimed at limiting its deterioration. RGB orthophotos have been used to calculate the spectral information of vegetation and bare sand and therefore, to monitor changes in their relative cover area extension over time, through the using of semi‐automatic classification algorithms in a GIS environment. Elevation data from high‐resolution DSMs were used to identify the principal morphological features: (i) Dune Foot Line (DFL); (ii) Dune Crest Line (DCL); Dune seaward Crest Line (DsCL); Stable Vegetation line (SVL). The USGS tool DSAS was used to monitor dune dynamics, considering every source of error: a stable pattern was observed for the two crest lines (DCL and DsCL), and an advancing one for the others two features (DFL and SVL). Geomorphological data, as well as RGB data, confirmed the effectiveness of planting operations, since a constant and progressive increase of the vegetated cover area and consolidation of the dune system was observed, in a period with no energetic storms. The proposed methodology is rapid, low‐cost and easily replicable by coastal managers to quantify the effectiveness of restoration projects.File | Dimensione | Formato | |
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