Abstract
Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resources.
Highlights
Other than the meadow (Figure 5b), patch formation of the seagrass on rocks (Figure 5c) and sand (Figure 5d), dead leaves of Posidonia oceanica lying on the sea bottom (Figure 5e), impacted areas from boat anchoring with exposed root-rhizomes, and the accumulation areas of beached organic debris, were clearly distinguishable
Unmanned aerial vehicle -based remote sensing provides new advanced tool for the monitoring of key coastal areas, including sensitive shallow habitats, where often species are threatened by human activities
The results of this study demonstrate how the combination of two new remote sensing technologies in the form of unmanned aerial vehicles (UAVs) and object-based image analysis (OBIA) methods can be successfully combined to reach very fine mapping and classification of coastal habitats
Summary
A typical OBIA workflow involves firstly image segmentation (sequence of processes that are executed in a defined order including segmentation parameters that create meaningful objects made up of multiple neighbouring pixels sharing similar spectral values) and secondly classification of the segmented data. The application of such methodology aimed at classifying underwater cover classes may become an important tool along shallow coastal environments because it could detect in a rapid, accurate and cost-effective way the health status as well as the impacts acting on these habitats. The degradation of such habitats could adversely affect the whole coastal biota, but it could have strong socio-economic implications
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