scholarly journals Object-Based Flood Analysis Using a Graph-Based Representation

2020 ◽  
Author(s):  
Bos Debusscher ◽  
Frieke Van Coillie
Keyword(s):  
Temporal Dynamics ◽  
Ground Truth ◽  
Small Region ◽  
Water Area ◽  
Small Scale ◽  
Mapping Unit ◽  
Object Based ◽  
Flood Analysis ◽  
Spatio Temporal ◽  
Sar Imagery

<p>Describing spatio-temporal dynamics of a flood using an object-based approach with a graph-based representation proved useful for analysis of small-scale flood dynamics in Belgium (about 100 km²) (Debusscher, et al., 2019).  Starting from pre-processed Sentinel-1 SAR imagery, the water bodies are delineated in each timestep (using a thresholding algorithm), after which all water-polygons are grouped into graphs according to their spatial overlap on consecutive timesteps.  Change in (water)area and backscatter are used to quantify the amount of variation.  Products of this tool are a global variation map covering the whole study are, and a temporal profile for each waterbody, visually describing the evolution of the backscatter and number of polygons that make up the waterbody.  <br>After establishing this proof of concept in a small region (flood of June 2016 in Schulensbroek, a nature reserve in north-eastern Belgium), this approach is applied on floods covering larger areas (about 10000 km²).  Two cases are studied, the Mozambique flood of March 2019 (near Beira) and the India flood of September 2019 (near Patna).  The process of upscaling leads to solving issues regarding the minimal mapping unit, adding extra pre-processing in order to simplify polygons (morphological operators), increasing code efficiency (mainly regarding for-loops).<br>In the absence of ground truth, produced flood maps are compared to existing flood extent maps (from Disaster Charter (unitary) and Hasard (LIST)) in order to estimate accuracy.</p><p>References<br><strong>Debusscher Bos and Van Coillie Frieke</strong> Object-Based Flood Analysis Using a Graph-Based Representation, Remote Sensing. - 2019. - p. p. 1883.</p><p> </p>

scholarly journals A Spatial-Temporal Feature Extraction and Unsupervised Recognition of Video Anomalies in Real Time

2021 ◽  
pp. 66-72
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Prediction Models ◽  
Temporal Dynamics ◽  
Ground Truth ◽  
Reconstruction Error ◽  
Method Performance ◽  
Novel Approach ◽  
Spatio Temporal ◽  
Time Accuracy

Anomaly detection is an area of video analysis has a great importance in automated surveillance. Although it has been extensively studied, there has been little work started using CNN networks. Hence, in this thesis we presented a novel approach for learning motion features and modeling normal Spatio-temporal dynamics for anomaly detection. In our technique, we capture variations in scale of the patterns of motion in an image object by using optical flow dense estimation technique and train our auto encoder model using convolution long short term memories (ConvLSTM2D) as we are processing video frames and we predict the anomaly in real time using Euclidean distance between the generated and the ground truth frame and we achieved a real time accuracy of nearly 98% for the youtube videos which are not used for either testing or training. Error between the network’s output and the target output is used to classify a video volume as normal or abnormal. In addition to the use of reconstruction error, we also use prediction error for anomaly detection. The prediction models show comparable performance with state of the art methods. In comparison with the proposed method, performance is improved in one dataset. Moreover, running time is significantly faster.

scholarly journals A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach

2020 ◽  
Vol 12 (13) ◽  
pp. 2118
Author(s):  
Bos Debusscher ◽  
Lisa Landuyt ◽  
Frieke Van Coillie
Keyword(s):  
Water Body ◽  
Satellite Image ◽  
Mapping Unit ◽  
Emergency Relief ◽  
Temporal Behaviour ◽  
Global Variation ◽  
Object Based ◽  
Spatio Temporal ◽  
Radar Satellite ◽  
Flood Dynamics

Insights into flood dynamics, rather than solely flood extent, are critical for effective flood disaster management, in particular in the context of emergency relief and damage assessment. Although flood dynamics provide insight in the spatio-temporal behaviour of a flood event, to date operational visualization tools are scarce or even non-existent. In this letter, we distil a flood dynamics map from a radar satellite image time series (SITS). For this, we have upscaled and refined an existing design that was originally developed on a small area, describing flood dynamics using an object-based approach and a graph-based representation. Two case studies are used to demonstrate the operational value of this method by visualizing flood dynamics which are not visible on regular flood extent maps. Delineated water bodies are grouped into graphs according to their spatial overlap on consecutive timesteps. Differences in area and backscatter are used to quantify the amount of variation, resulting in a global variation map and a temporal profile for each water body, visually describing the evolution of the backscatter and number of polygons that make up the water body. The process of upscaling led us to applying a different water delineation approach, a different way of ensuring the minimal mapping unit and an increased code efficiency. The framework delivers a new way of visualizing floods, which is straightforward and efficient. Produced global variation maps can be applied in a context of data assimilation and disaster impact management.

On the potential and challenges of using machine-learning for automated quality control of environmental sensor data

2020 ◽  
Author(s):  
Lennart Schmidt ◽  
Hannes Mollenhauer ◽  
Corinna Rebmann ◽  
David Schäfer ◽  
Antje Claussnitzer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Ground Truth ◽  
Sensor Data ◽  
Small Scale ◽  
Ground Truth Data ◽  
Starting Point ◽  
Environmental Sensor ◽  
Spatio Temporal ◽  
Automated Quality Control

<p>With more and more data being gathered from environmental sensor networks, the importance of automated quality-control (QC) routines to provide usable data in near-real time is becoming increasingly apparent. Machine-learning (ML) algorithms exhibit a high potential to this respect as they are able to exploit the spatio-temporal relation of multiple sensors to identify anomalies while allowing for non-linear functional relations in the data. In this study, we evaluate the potential of ML for automated QC on two spatio-temporal datasets at different spatial scales: One is a dataset of atmospheric variables at 53 stations across Northern Germany. The second dataset contains timeseries of soil moisture and temperature at 40 sensors at a small-scale measurement plot.</p><p>Furthermore, we investigate strategies to tackle three challenges that are commonly present when applying ML for QC: 1) As sensors might drop out, the ML models have to be designed to be robust against missing values in the input data. We address this by comparing different data imputation methods, coupled with a binary representation of whether a value is missing or not. 2) Quality flags that mark erroneous data points to serve as ground truth for model training might not be available. And 3) There is no guarantee that the system under study is stationary, which might render the outputs of a trained model useless in the future. To address 2) and 3), we frame the problem both as a supervised and unsupervised learning problem. Here, the use of unsupervised ML-models can be beneficial as they do not require ground truth data and can thus be retrained more easily should the system be subject to significant changes. In this presentation, we discuss the performance, advantages and drawbacks of the proposed strategies to tackle the aforementioned challenges. Thus, we provide a starting point for researchers in the largely untouched field of ML application for automated quality control of environmental sensor data.</p>

scholarly journals The bluefish Pomatomus saltatrix (Pisces: Pomatomidae) in the Adriatic and Tyrrhenian Seas, can we call it climate invader?

2020 ◽  
Author(s):  
Jacopo Cerri ◽  
Ernesto Azzurro
Keyword(s):  
Invasive Species ◽  
Climate Warming ◽  
Temporal Dynamics ◽  
Small Scale ◽  
Ecological Knowledge ◽  
Pomatomus Saltatrix ◽  
Warm Water Species ◽  
Northward Expansion ◽  
Spatio Temporal ◽  
Water Species

Here, we investigate the recent spatio-temporal dynamics of the bluefish Pomatomus saltatrix, a warm water species, which is considered to have conquered the northern Mediterranean coasts due to climate warming. Capitalizing two independent surveys carried out through online questionnaires and vis-à-vis interviews, we accessed the ecological knowledge of 640 recreational fishers, and 206 small-scale fishers, respectively. Respondents provided coherent evidence of a rapid northward expansion of the bluefish along the Tyrrhenian and Adriatic Seas at an estimated speed of 0.8-1.4 degrees of latitude per year. Most fishers in the two seas believed the bluefish to negatively affect both fishing activities and the environment, just as if it was an invasive species and this negative perception was positively correlated with increasing bluefish abundances. The phenomenological effects of this widespread outbreak can be assimilated to a large invasion across various sectors of the Mediterranean Sea, posing the urgency of manage this issue and better understanding its linkage to climate drivers.

scholarly journals The shifting distribution of Mediterranean fishes: a spatio-temporal assessment based on Local Ecological Knowledge .

2019 ◽  
Author(s):  
Ernesto Azzurro ◽  
Valerio Sbragaglia ◽  
Jacopo Cerri ◽  
Michel Bariche ◽  
Luca Bolognini ◽  
...  
Keyword(s):  
Mediterranean Sea ◽  
Temporal Dynamics ◽  
Local Ecological Knowledge ◽  
Rapid Change ◽  
Species Abundance ◽  
Global Ocean ◽  
Small Scale ◽  
Ecological Knowledge ◽  
The Mediterranean ◽  
Spatio Temporal

A major problem worldwide is the rapid change in species abundance and distribution, which is rapidly restructuring the biological communities of many ecosystems under changing climates. Tracking these transformations in the marine environment is crucial but our understanding is often hampered by the absence of historical data and by the practical challenge of survey large geographical areas. Here we focus on the Mediterranean Sea, a region which is warming faster than the rest of the global ocean, tracing back the spatio-temporal dynamic of species, which are emerging the most in terms of increasing abundances and expanding distributions. To this aim, we accessed the Local Ecological Knowledge (LEK) of small-scale and recreational fishers reconstructing the dynamics of fish perceived as ‘new’ or increasing in different fishing area. Over 500 fishers across 95 locations and 9 different countries were interviewed and semi-quantitative information on yearly changes in species abundance was collected. Overall, 75 species were mentioned by the respondents, being the most frequent citations related to warm-adapted species of both, native and exotic origin. Respondents belonging to the same biogeographic sectors described coherent spatio-temporal dynamics, and gradients along latitudinal and longitudinal axes were revealed. This information provides a more complete understanding of recent bio-geographical changes in the Mediterranean Sea and it also demonstrates that adequately structured LEK methodology might be applied successfully beyond the local scale, across national borders and jurisdictions. Acknowledging this potential through macro-regional coordination, could pave the ground for future large-scale aggregations of individual observations, increasing our potential for integrated monitoring and conservation planning at the regional or even global level.

scholarly journals WHEAT LODGING ASSESSMENT USING MULTISPECTRAL UAV DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 235-240 ◽  
Author(s):  
S. Chauhan ◽  
R. Darvishzadeh ◽  
Y. Lu ◽  
D. Stroppiana ◽  
M. Boschetti ◽  
...  
Keyword(s):  
High Resolution ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Small Scale ◽  
Spectral Variability ◽  
Promising Tool ◽  
Object Based ◽  
Spatio Temporal ◽  
Uav Images ◽  
Sentinel 2

<p><strong>Abstract.</strong> Lodging is a major yield-reducing factors in wheat, causing reductions up to 80%. Timely detection of lodging can reduce its impacts and support proper decisions regarding expected yield, crop price or its insurance. Since the incidence of lodging is heterogeneous within a field, very high-resolution remote sensing data can be viable for accurate and prompt spatio-temporal assessment of lodging severity. As such unmanned aerial vehicles (UAVs) provide a versatile and cost-effective solution to monitor crops on a small scale with sub-centimetre spatial resolution. In this study, we analysed the spectral variability between different grades of lodging severity (non-lodged (NL), moderate (ML), severe (SL) and very severe (VSL)) and classified them using high-resolution UAV data. Multispectral orthomosaic UAV images with 5cm resolution and nine bands (covering the VIS-NIR spectrum with Sentinel-2 filters) were acquired in May 2018 for two wheat fields in Bonifiche Ferraresi farm, Jolanda di Savoia, Italy. Concurrent to the time of image acquisition, a field campaign was carried out in which crop characteristics and lodging related parameters were collected. The results showed that reflectance magnitude increased with lodging severity and demonstrated that the red-edge and NIR bands can be used to clearly discriminate between NL and lodged (all grades) wheat and to some extent between different lodging classes (ML, SL and VSL). The nearest neighbourhood classification performed using an object-based segmentation yielded optimal results with an overall accuracy of 90%, thus demonstrating the use of multispectral UAV data as a promising tool for wheat lodging assessment.</p>

scholarly journals Structure and spatio-temporal dynamics of the artisanal small-scale fisheries at the future MPA of “Taza” (Algerian coast, SW Mediterranean)

2018 ◽  
Vol 19 (3) ◽  
pp. 555 ◽  
Author(s):  
IBRAHIM BOUBEKRI ◽  
ALEX JAMES CAVEEN ◽  
ABDALLAH BORHANE DJEBAR ◽  
RACHID AMARA ◽  
HUBERT MAZUREK
Keyword(s):  
Temporal Dynamics ◽  
Field Work ◽  
Management Plan ◽  
Small Scale ◽  
Target Species ◽  
Small Scale Fisheries ◽  
The Future ◽  
Mullus Surmuletus ◽  
Spatio Temporal ◽  
Management Approaches

Artisanal Small-Scale Fisheries (SSFs) are a primordial and very diverse activity in the Mediterranean, also within Marine Protected Areas (MPAs). This diversity is explained in terms of target species, gears, and fishing strategies. The main objective of this work was to investigate the spatio-temporal dynamics of artisanal SSFs of the future MPA of “Taza” (Algeria, SW Mediterranean). Data were collected through direct assessment of daily landings and using questionnaires. They were the subject of multivariate analyses that allowed us to identify the métiers practiced by artisanal fishers. During the one year (May 2013 to April 2014) field work period, 1330 fishing trips and 1613 fishing operations in 16 fishing grounds were recorded in the Ziama fishing harbor, where 15.2 tons of total catch was assessed. Our results show that, in the study area, the boats are predominantly gillnetters and that among the five métiers characterized by target species, gear type, fishing grounds, and fishing seasons, two métiers (“Mullus surmuletus trammel net” and “Sparids monofilament gillnet”) are practiced throughout the year, while the remaining three (“Sarda sarda driftnet”, “Merluccius merluccius set gillnet”, and “Pagellus set gillnet”) are specific to a determined period of the year. The ‘Mullus surmuletus trammel net’ métier represents 40% of the total fishing operations, of which 57.5% are carried out in the coastal sector at - 25 m. This study could contribute to defining the appropriate management approaches for SSFs in the future MPA of “Taza” by providing baseline information to build a sound management plan. In Algeria, it will certainly serve as a scientific reference in terms of zoning, protection of biodiversity, and specific monitoring at particular locations and periods of the year for the sustainable management of MPAs.

scholarly journals Small-scale spatio-temporal variability in ichthyoplankton and zooplankton distribution in relation to a tidal-mixing front in the Irish Sea

2005 ◽  
Vol 62 (6) ◽  
pp. 1021-1036 ◽  
Author(s):  
O. Lee ◽  
R.D.M. Nash ◽  
B.S. Danilowicz
Keyword(s):  
Temporal Dynamics ◽  
Fish Larvae ◽  
Sampling Period ◽  
Tidal Mixing ◽  
Irish Sea ◽  
Small Scale ◽  
Surface Salinity ◽  
Average Value ◽  
Tidal Mixing Front ◽  
Spatio Temporal

Abstract This study examines the spatio-temporal dynamics of fish larvae and their prey at a tidal-mixing front in the central Irish Sea. The distribution of ichthyoplankton and zooplankton was analysed in relation to environmental variables (depth, surface temperature, surface salinity, and water column stratification) using Redundancy Analysis (RDA). Significant interannual variability in the formation and position of the tidal-mixing front coincided with large differences in the species abundances of both ichthyoplankton and zooplankton. During the summer, ichthyoplankton and zooplankton communities were structured by a combination of depth and hydrography, and the variability in species composition was directly related to the average value of the stratification parameter. Several ichthyoplankton species were consistently associated with frontal waters, while fewer species were concentrated in mixed water masses throughout the sampling period. The distribution of individual zooplankton species was also examined, and water mass affinities were shown to vary with developmental stage.

Participatory research to understand spatio-temporal dynamics of small-scale fleets: The C. bellicosus fishery in Magdalena Bay, Baja California Sur, Mexico

2020 ◽  
Vol 198 ◽  
pp. 105369
Author(s):  
Dayana Méndez-Espinoza ◽  
Miguel Ángel Ojeda-Ruiz ◽  
Elvia Aida Marín-Monroy ◽  
Victoria Jiménez-Esquivel ◽  
Juan José Cota-Nieto
Keyword(s):  
Participatory Research ◽  
Baja California ◽  
Temporal Dynamics ◽  
Small Scale ◽  
Baja California Sur ◽  
Spatio Temporal ◽  
Magdalena Bay
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