Apostolos Papakonstantinou

Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

Spatial data acquisition is a critical process for the identification of the coastline and coastal zones for scientists involved in the study of coastal morphology. The availability of very high-resolution digital surface models (DSMs) and orthophoto maps is of increasing interest to all scientists, especially those monitoring small variations in the earth’s surface, such as coastline morphology. In this article, we present a methodology to acquire and process high resolution data for coastal zones acquired by a vertical take off and landing (VTOL) unmanned aerial vehicle (UAV) attached to a small commercial camera. The proposed methodology integrated computer vision algorithms for 3D representation with image processing techniques for analysis. The computer vision algorithms used the structure from motion (SfM) approach while the image processing techniques used the geographic object-based image analysis (GEOBIA) with fuzzy classification. The SfM pipeline was used to construct the DSMs and orthophotos with a measurement precision in the order of centimeters. Consequently, GEOBIA was used to create objects by grouping pixels that had the same spectral characteristics together and extracting statistical features from them. The objects produced were classified by fuzzy classification using the statistical features as input. The classification output classes included beach composition (sand, rubble, and rocks) and sub-surface classes (seagrass, sand, algae, and rocks). The methodology was applied to two case studies of coastal areas with different compositions: a sandy beach with a large face and a rubble beach with a small face. Both are threatened by beach erosion and have been degraded by the action of sea storms. Results show that the coastline, which is the low limit of the swash zone, was detected successfully by both the 3D representations and the image classifications. Furthermore, several traces representing previous sea states were successfully recognized in the case of the sandy beach, while the erosion and beach crests were detected in the case of the rubble beach. The achieved level of detail of the 3D representations revealed new beach characteristics, including erosion crests, berm zones, and sand dunes. In conclusion, the UAV SfM workflow provides information in a spatial resolution that permits the study of coastal changes with confidence and provides accurate 3D visualizations of the beach zones, even for areas with complex topography. The overall results show that the presented methodology is a robust tool for the classification, 3D visualization, and mapping of coastal morphology.

Inset Mapper: A Software Tool In Island Cartography

Island cartography deals with special cartographic problems confronted in the portrayal of island regions and demands the use of specially developed software tools. One of the most commonly faced problems is the need of inset map creation for very small islands, and sometimes isolated ones, that must be displayed in the main map. This paper presents the methodology followed for the development of the Inset Mapper (IM) Software toolbox, describes the toolbox, and showcases its ability to create inset maps in Island regions. The IM software tool provides a useful cartographic tool for assisting the selection of the most appropriate position and scale of the inset map in an Island region.

Seagrass mapping in Greek territorial waters using Landsat-8 satellite images

Abstract Seagrass meadows are among the most valuable coastal ecosystems on earth due to their structural and functional roles in the coastal environment. This study demonstrates remote sensing’s capacity to produce seagrass distribution maps on a regional scale. The seagrass coverage maps provided here describe and quantify for the first time the extent and the spatial distribution of seagrass meadows in Greek waters. This information is needed for identifying priority conservation sites and to help coastal ecosystem managers and stakeholders to develop conservation strategies and design a resilient network of protected marine areas. The results were based on an object-based image analysis of 50 Landsat-8 satellite images. The time window of image acquisition was between June 2013 and July 2015. In total, the seagrass coverage in Greek waters was estimated at 2619 km2. The largest coverages of individual seagrass meadows were found around Lemnos Island (124 km2), Corfu Island (46 km2), and East Peloponnese (47 km2). The accuracy assessment of the detected areas was based on 62 Natura 2000 sites, for which habitat maps were available. The mean total accuracy for all 62 sites was estimated at 76.3%.

The use of Sentinel-2 imagery for seagrass mapping: Kalloni Gulf (Lesvos Island, Greece) case study

Seagrass meadows play a significant role in ecosystems by stabilizing sediment and improving water clarity, which enhances seagrass growing conditions. It is high on the priority of EU legislation to map and protect them. The traditional use of medium spatial resolution satellite imagery e.g. Landsat-8 (30m) is very useful for mapping seagrass meadows on a regional scale. However, the availability of Sentinel-2 data, the recent ESA’s satellite with its payload Multi-Spectral Instrument (MSI) is expected to improve the mapping accuracy. MSI designed to improve coastline studies due to its enhanced spatial and spectral capabilities e.g. optical bands with 10m spatial resolution. The present work examines the quality of Sentinel-2 images for seagrass mapping, the ability of each band in detection and discrimination of different habitats and estimates the accuracy of seagrass mapping. After pre-processing steps, e.g. radiometric calibration and atmospheric correction, image classified into four classes. Classification classes included sub-bottom composition e.g. seagrass, soft bottom, and hard bottom. Concrete vectors describing the areas covered by seagrass extracted from the high-resolution satellite image and used as in situ measurements. The developed methodology applied in the Gulf of Kalloni, (Lesvos Island - Greece). Results showed that Sentinel-2 images can be robustly used for seagrass mapping due to their spatial resolution, band availability and radiometric accuracy.

Preview of a protocol for UAV data collection in coastal areas

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.

Multi-scale seagrass mapping in satellite data and the use of UAS in accuracy assessment

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.

UAS multi-camera rig for post-earthquake damage 3D geovisualization of Vrisa village

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.

Coastal habitat mapping in the Aegean Sea using high resolution orthophoto maps

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.