High-resolution information of the marine environment is very important for the protection and the management of marine species. In recent years, the ability of the unmanned aerial systems (UAS) to acquire remotely, high-resolution imagery has made them very popular in the marine remote sensing field. Observations in the marine environment are very challenging in many ways as they are affected by weather and oceanographic conditions that interact with each other. The UAS aerial surveys deal with many limitations affecting the quality and reliability of the acquired data, directly dependent on the environmental conditions prevailing in the survey area. For example, high wind speed affects the sea surface state and the safety of a UAS survey, resulting in unsatisfying observations of the marine environment.
This study presents the validation of a UAS toolbox designed to calculate the optimal flight times on a day for UAS surveys. The ruleset of the toolbox is based on a theoretical UAS data acquisition protocol which summarizes all the parameters that affect the quality and reliability of the UAS acquired data in the marine environment. For the validation of the toolbox, flights were conducted in different conditions, according to the toolbox predictions, while underwater targets were placed and compared as to their characteristics in different conditions. The aerial images of each flight were processed for the creation of high-resolution orthophoto-maps that showed significant differences between the optimal flight times and the non-optimal flights. The present work emphasizes the importance of the environmental conditions during an aerial survey and evidence that data quality is superior during the toolbox suggested flight times.
Seagrass meadows play a vital role in coastal ecosystems health as constitute an important pillar of the coastal environment. So far, regional scale habitat mapping was implemented with the use of freely available medium scale satellite images (Sentinel-2 or Landsat-8). The Unmanned Aerial Systems (UAS) have increase the spatial resolution of the observation from meter to sub-decimeter. Using sub-decimeter imagery, seagrass can be mapped in great detail revealing significant habitat species and detect new habitat patterns. In the present study, we suggest a multi-scale image analysis methodology consisting of georeferencing, atmospheric and water column correction and Object- Based Image Analysis (OBIA). OBIA process is performed using nearest neighborhood and fuzzy rules as classifiers in three major classes, a) seagrass, b) shallow areas with soft bottom and c) shallow areas with hard bottom (reefs). UAS very high-resolution data treated as in situ observations and used for training the classifiers and for accuracy assessment. The methodology applied in two satellite images Sentinel-2 and Landsat-8 with 10m and 30m spatial resolution respectively, at Livadi beach, Folegandros Island, Greece. The results show better classification accuracies in Sentinel-2 data than in Landsat-8. There was a great difficulty in the detection of the reef habitat in satellite images because it covered a small area. Reef habitat was clearly detected only in the UAS data. In conclusion, the present study highlights the necessity of new high precision geospatial data for examining the habitat detection accuracies on satellite images of different resolutions.
KEYWORDS: 3D modeling, Data acquisition, 3D acquisition, Earthquakes, Unmanned aerial vehicles, 3D image processing, 3D visualizations, 3D image reconstruction, UAV imaging systems, Onboard cameras, Data visualization, Large dataset visualization
Last years the role of Unmanned Aerial Systems is increasing in a wide variety of scientific aspects that need fast and reliable geodata. Nowadays, the effectiveness of the quality and the resolution that the UAS provide in spatial data acquisition are fulfilling scientific standards. Thus, UAS have a prominent role in post-earthquake damage assessment as they are capable of collecting high in resolution data for mapping spatiotemporal phenomena. The implementation of very detailed 3D Geovisualization requires oblique photos of the building faces. Thus, the UAS’s data acquisition of nadir photos solely, is limited as it lacks crucial information for buildings facades. In this work, a UAS multi-camera rig installation is presented for the collection and simultaneous acquisition of nadir and in three different directions oblique photos. The acquired data were used for the creation of post-earthquake building facade 3D geovisualisation of Vrisa village in Lesvos island after the Mw6.3 earthquake on June 12, 2017 at two different spatial scales. The results showed that the use of a multi-camera rig attached to UAS can produce 3D visualizations capable of depicting in detail the diversity and the small size of cracks in roofs or facades of the post-earthquake buildings. Thus, the produced geovisualizations are a valuable tool for measurements of area and volume of house debris. Moreover, the results proved that the installation of a multi-camera rig in a UAS for data acquisition and the creation of accurate 3D visualizations using these data could be a valuable and useful tool for post-earthquake damage assessment.
The collection of detailed and accurate information about marine habitats and flora species is crucial for mapping, monitoring and management of marine and coastal environments. Remote sensing is widely used to collect information at marine environments, while in recent years the potential use of UAS for mapping is examined. The aim of this paper is the creation of a prediction model for the optimal flight windows of UAS, using the programming language R. The methodology examines several limitations of UAS data acquisition over coastal areas, related to environmental conditions, mainly due to weather and sea state. A theoretical protocol that summarizes the parameters that affect the quality of aerial data acquisition, was created. These parameters are related to the weather conditions (wind, temperature, clouds etc.) and oceanographic phenomena (waves, turbidity, sun glint etc.), prevailing in the study area during the UAV flight. The protocol for the collection of accurate and reliable geospatial information in coastal and marine areas using UAS will be a useful mapping tool for the coastal zone mapping. The produced prediction model will act as a versatile computation approach to different input variables and therefore can be used widely. The input variables of this model refer to weather conditions prevailing in the area of interest and measurements of oceanographic parameters. The result of the prediction model is the optimal flight windows for the collection of accurate and qualitative marine information, in a region of interest.
Coastline change and marine litter concentration in shoreline zones are two different emerging problems indicating the vulnerability as well as the quality of a coastal environment. Both problems present spatiotemporal changes due to weather and anthropogenic factors. Traditionally spatiotemporal changes in coastal environments are monitored using high-resolution satellite images and manned surveys. The last years, Unmanned Aerial Systems (UAS) are used as additional tool for monitoring environmental phenomena in sensitive coastal areas. In this study, two different case studies for mapping emerging coastal phenomena i.e. coastline changes and marine litter in Lesvos island, are presented. Both phenomena have increasing interest among scientists monitoring sensitive coastal areas. This paper outlines the integration of UAS for data acquisition and Structure from Motion (SfM) pipeline for the visualization of selected coastal areas in the Aegean Sea. The followed UAS-SfM methodology produces very detailed orthophoto maps. This high resolution spatial information is used for mapping and detecting primarily, marine litter on coastal and underwater zones and secondly, coastline changes and coastal erosion. More specific the produced orthophoto maps analyzed through GIS and with the use of the appropriate cartographic techniques the objective environmental parameters were mapped. Results showed that UAS-SfM pipeline produces geoinformation with high accuracy and spatial resolution that helps scientists to map with confidence environmental changes that take place in shoreline zones.
The significance of coastal habitat mapping lies in the need to prevent from anthropogenic interventions and other factors. Until 2015, Landsat-8 (30m) imagery were used as medium spatial resolution satellite imagery. So far, Sentinel-2 satellite imagery is very useful for more detailed regional scale mapping. However, the use of high resolution orthophoto maps, which are determined from UAV data, is expected to improve the mapping accuracy. This is due to small spatial resolution of the orthophoto maps (30 cm). This paper outlines the integration of UAS for data acquisition and Structure from Motion (SfM) pipeline for the visualization of selected coastal areas in the Aegean Sea. Additionally, the produced orthophoto maps analyzed through an object-based image analysis (OBIA) and nearest-neighbor classification for mapping the coastal habitats. Classification classes included the main general habitat types, i.e. seagrass, soft bottom, and hard bottom The developed methodology applied at the Koumbara beach (Ios Island - Greece). Results showed that UAS’s data revealed the sub-bottom complexity in large shallow areas since they provide such information in the spatial resolution that permits the mapping of seagrass meadows with extreme detail. The produced habitat vectors are ideal as reference data for studies with satellite data of lower spatial resolution.
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