scholarly journals Spatial and Temporal Changes of Tidal Inlet Using Object-Based Image Analysis of Multibeam Echosounder Measurements: A Case from the Lagoon of Venice, Italy

2020 ◽  
Vol 12 (13) ◽  
pp. 2117 ◽  
Author(s):  
Lukasz Janowski ◽  
Fantina Madricardo ◽  
Stefano Fogarin ◽  
Aleksandra Kruss ◽  
Emanuela Molinaroli ◽  
...  
Keyword(s):  
Image Analysis ◽  
Methodological Approach ◽  
Venice Lagoon ◽  
Tidal Inlet ◽  
Lagoon Of Venice ◽  
Seabed Sediment ◽  
Multibeam Echosounder ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Muddy Sediments

Scientific exploration of seabed substrata has significantly progressed in the last few years. Hydroacoustic methods of seafloor investigation, including multibeam echosounder measurements, allow us to map large areas of the seabed with unprecedented precision. Through time-series of hydroacoustic measurements, it was possible to determine areas with distinct characteristics in the inlets of the Lagoon of Venice, Italy. Their temporal variability was investigated. Monitoring the changes was particularly relevant, considering the presence at the channel inlets of mobile barriers of the Experimental Electromechanical Module (MoSE) project installed to protect the historical city of Venice from flooding. The detection of temporal and spatial changes was performed by comparing seafloor maps created using object-based image analysis and supervised classifiers. The analysis included extraction of 25 multibeam echosounder bathymetry and backscatter features. Their importance was estimated using an objective approach with two feature selection methods. Moreover, the study investigated how the accuracy of classification could be affected by the scale of object-based segmentation. The application of the classification method at the proper scale allowed us to observe habitat changes in the tidal inlet of the Venice Lagoon, showing that the sediment substrates located in the Chioggia inlet were subjected to very dynamic changes. In general, during the study period, the area was enriched in mixed and muddy sediments and was depleted in sandy deposits. This study presents a unique methodological approach to predictive seabed sediment composition mapping and change detection in a very shallow marine environment. A consistent, repeatable, logical site-specific workflow was designed, whose main assumptions could be applied to other seabed mapping case studies in both shallow and deep marine environments, all over the world.

Seafloor mapping based on multibeam echosounder bathymetry and backscatter data using Object-Based Image Analysis: a case study from the Rewal site, the Southern Baltic

2018 ◽  
Vol 47 (3) ◽  
pp. 248-259 ◽  
Author(s):  
Łukasz Janowski ◽  
Jarosław Tęgowski ◽  
Jarosław Nowak
Keyword(s):  
Image Analysis ◽  
Large Scale ◽  
Fine Sand ◽  
Empirical Method ◽  
Angular Variable ◽  
Seafloor Mapping ◽  
Multibeam Echosounder ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Southern Baltic

Abstract Seafloor mapping is a fast developing multidisciplinary branch of oceanology that combines geophysics, geostatistics, sedimentology and ecology. One of its objectives is to isolate distinct seabed features in a repeatable, fast and objective way, taking into consideration multibeam echosounder (MBES) bathymetry and backscatter data. A large-scale acoustic survey was conducted by the Maritime Institute in Gdańsk in 2010 using Reson 8125 MBES. The dataset covered over 20 km2 of a shallow seabed area (depth of up to 22 m) in the Polish Exclusive Economic Zone within the Southern Baltic. Determination of sediments was possible based on ground-truth grab samples acquired during the MBES survey. Four classes of sediments were recognized as muddy sand, very fine sand, fine sand and clay. The backscatter mosaic created using the Angular Variable Gain (AVG) empirical method was the primary contribution to the image processing method used in this study. The use of the Object-Based Image Analysis (OBIA) and the Classification and Regression Trees (CART) classifier makes it possible to isolate the backscatter image with 87.5% overall and 81.0% Kappa accuracy. The obtained results confirm the possibility of creating reliable maps of the seafloor based on MBES measurements. Once developed, the OBIA workflow can be applied to other spatial and temporal scenes.

scholarly journals A Scalable, Supervised Classification of Seabed Sediment Waves Using an Object-Based Image Analysis Approach

2021 ◽  
Vol 13 (12) ◽  
pp. 2317
Author(s):  
Gerard Summers ◽  
Aaron Lim ◽  
Andrew J. Wheeler
Keyword(s):  
Image Analysis ◽  
Spatial Resolution ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Seabed Sediment ◽  
Object Based Image Analysis ◽  
Bathymetric Data ◽  
Sediment Waves ◽  
Object Based

National mapping programs (e.g., INFOMAR and MAREANO) and global efforts (Seabed 2030) acquire large volumes of multibeam echosounder data to map large areas of the seafloor. Developing an objective, automated and repeatable approach to extract meaningful information from such vast quantities of data is now essential. Many automated or semi-automated approaches have been defined to achieve this goal. However, such efforts have resulted in classification schemes that are isolated or bespoke, and therefore it is necessary to form a standardised classification method. Sediment wave fields are the ideal platform for this as they maintain consistent morphologies across various spatial scales and influence the distribution of biological assemblages. Here, we apply an object-based image analysis (OBIA) workflow to multibeam bathymetry to compare the accuracy of four classifiers (two multilayer perceptrons, support vector machine, and voting ensemble) in identifying seabed sediment waves across three separate study sites. The classifiers are trained on high-spatial-resolution (0.5 m) multibeam bathymetric data from Cork Harbour, Ireland and are then applied to lower-spatial-resolution EMODnet data (25 m) from the Hemptons Turbot Bank SAC and offshore of County Wexford, Ireland. A stratified 10-fold cross-validation was enacted to assess overfitting to the sample data. Samples were taken from the lower-resolution sites and examined separately to determine the efficacy of classification. Results showed that the voting ensemble classifier achieved the most consistent accuracy scores across the high-resolution and low-resolution sites. This is the first object-based image analysis classification of bathymetric data able to cope with significant disparity in spatial resolution. Applications for this approach include benthic current speed assessments, a geomorphological classification framework for benthic biota, and a baseline for monitoring of marine protected areas.

scholarly journals New tools for old problems — comparing drone- and field-based assessments of a problematic plant species

2021 ◽  
Vol 193 (2) ◽  
Author(s):  
Jens Oldeland ◽  
Rasmus Revermann ◽  
Jona Luther-Mosebach ◽  
Tillmann Buttschardt ◽  
Jan R. K. Lehmann
Keyword(s):  
Image Analysis ◽  
Plant Species ◽  
Just In Time ◽  
Post Processing ◽  
Tall Grass ◽  
Field Surveys ◽  
Distribution Maps ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Before And After

AbstractPlant species that negatively affect their environment by encroachment require constant management and monitoring through field surveys. Drones have been suggested to support field surveyors allowing more accurate mapping with just-in-time aerial imagery. Furthermore, object-based image analysis tools could increase the accuracy of species maps. However, only few studies compare species distribution maps resulting from traditional field surveys and object-based image analysis using drone imagery. We acquired drone imagery for a saltmarsh area (18 ha) on the Hallig Nordstrandischmoor (Germany) with patches of Elymus athericus, a tall grass which encroaches higher parts of saltmarshes. A field survey was conducted afterwards using the drone orthoimagery as a baseline. We used object-based image analysis (OBIA) to segment CIR imagery into polygons which were classified into eight land cover classes. Finally, we compared polygons of the field-based and OBIA-based maps visually and for location, area, and overlap before and after post-processing. OBIA-based classification yielded good results (kappa = 0.937) and agreed in general with the field-based maps (field = 6.29 ha, drone = 6.22 ha with E. athericus dominance). Post-processing revealed 0.31 ha of misclassified polygons, which were often related to water runnels or shadows, leaving 5.91 ha of E. athericus cover. Overlap of both polygon maps was only 70% resulting from many small patches identified where E. athericus was absent. In sum, drones can greatly support field surveys in monitoring of plant species by allowing for accurate species maps and just-in-time captured very-high-resolution imagery.

scholarly journals Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

2021 ◽  
Vol 13 (4) ◽  
pp. 830
Author(s):  
Adam R. Benjamin ◽  
Amr Abd-Elrahman ◽  
Lyn A. Gettys ◽  
Hartwig H. Hochmair ◽  
Kyle Thayer
Keyword(s):  
Image Analysis ◽  
Aquatic Vegetation ◽  
Accuracy Assessment ◽  
Unmanned Aerial Systems ◽  
Temporal Area ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Significant Difference ◽  
Additional Processing ◽  
Multispectral Sensors

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

scholarly journals GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

2019 ◽  
Vol 11 (10) ◽  
pp. 1181 ◽  
Author(s):  
Norman Kerle ◽  
Markus Gerke ◽  
Sébastien Lefèvre
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
The Netherlands ◽  
Object Based Image Analysis ◽  
Biennial Conference ◽  
Object Based ◽  
The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

scholarly journals Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

2020 ◽  
Vol 12 (11) ◽  
pp. 1772
Author(s):  
Brian Alan Johnson ◽  
Lei Ma
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Online Survey ◽  
Accuracy Assessment ◽  
Learning Approaches ◽  
Special Issue ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Wide Range

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out.

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