Classification of Danube Delta lakes based on aquatic vegetation and turbidity

Author(s):  
Hugo Coops ◽  
Jenica Hanganu ◽  
Marian Tudor ◽  
Willem Oosterberg
2020 ◽  
Vol 12 (14) ◽  
pp. 2188 ◽  
Author(s):  
Simona Niculescu ◽  
Jean-Baptiste Boissonnat ◽  
Cédric Lardeux ◽  
Dar Roberts ◽  
Jenica Hanganu ◽  
...  

In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work was to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophyte communities with all sensor combinations. In all combinations, the model-based producer’s accuracy (PA) and user’s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.


1992 ◽  
Vol 21 (3) ◽  
pp. 598-603 ◽  
Author(s):  
E. Rejmankova ◽  
H. M. Savage ◽  
M. H. Rodriguez ◽  
D. R. Roberts ◽  
M. Rejmanek

2013 ◽  
Vol 5 (4) ◽  
pp. 1856-1874 ◽  
Author(s):  
Fernanda Watanabe ◽  
Nilton Imai ◽  
Enner Alcântara ◽  
Luiz da Silva Rotta ◽  
Alex Utsumi

2015 ◽  
Vol 26 (4) ◽  
pp. 791-803 ◽  
Author(s):  
Flavia Landucci ◽  
Lubomír Tichý ◽  
Kateřina Šumberová ◽  
Milan Chytrý
Keyword(s):  

Author(s):  
S. Niculescu ◽  
D. Ienco ◽  
J. Hanganu

Land cover is a fundamental variable for regional planning, as well as for the study and understanding of the environment. This work propose a multi-temporal approach relying on a fusion of radar multi-sensor data and information collected by the latest sensor (Sentinel-1) with a view to obtaining better results than traditional image processing techniques. The Danube Delta is the site for this work. The spatial approach relies on new spatial analysis technologies and methodologies: Deep Learning of multi-temporal Sentinel-1. We propose a deep learning network for image classification which exploits the multi-temporal characteristic of Sentinel-1 data. The model we employ is a Gated Recurrent Unit (GRU) Network, a recurrent neural network that explicitly takes into account the time dimension via a gated mechanism to perform the final prediction. The main quality of the GRU network is its ability to consider only the important part of the information coming from the temporal data discarding the irrelevant information via a forgetting mechanism. We propose to use such network structure to classify a series of images Sentinel-1 (20 Sentinel-1 images acquired between 9.10.2014 and 01.04.2016). The results are compared with results of the classification of Random Forest.


2009 ◽  
Vol 49 (1) ◽  
Author(s):  
L. Alberotanza ◽  
R. M. Cavalli ◽  
S. Pignatti ◽  
A. Zandonella

2015 ◽  
Vol 21 (1) ◽  
pp. 1
Author(s):  
Zulkarnaen Fahmi ◽  
Husnah Husnah

Identification and classification of benthic habitats in Lake of Laut Tawar, Aceh by using hydro acoustic method can provide data and information on types of substrate and aquatic vegetation in a short time and wide spatial coverage, as done in the present work. Data acoustic collection was performed in 2013 using quantitative echosounder with split beam frequency of 120 kHz, and through a visual observation. The later is destined to look at the bottom types and macrophytes that lie on the line transect of acoustic survey. Analysis of data is to extract the value of bottom volume backscattering for each transect of 0.5-1 km. Classification of the bottom type was done based on the value of Sv using geospatial models. Results show the interval value of Sv for soft bottom ranged between -24.00 dB and -32.00 dB, the type of hard bottom (e.g. rocks, rocky sand substrate) ranged between -14.00 dB and -22.00 dB, whereas the Sv value of macrophyte ranged between- 45.00 dB and -54.00 dB. The percent covers were about 42.90%, 44.71% and 12.93% for hard bottom type, soft bottom and macrophytes, respectively. The types of aquatic vegetation commonly found in the lake were two genera belonging Hydrocharitaceaea and Gramineae. The current work is still lack of information on the classification of organisms into genera scales. Therefore, more signal verification and algorithms verification would be needed in order to estimate macrophytes biomass by comparing with other visual observation.


2021 ◽  
Vol 38 ◽  
pp. 00057
Author(s):  
Laura Kipryanova ◽  
Victor Chepinoga ◽  
Sergey Rosbakh

In a frame of the project “Vegetation classification of Russia”, we have compiled the prodromus of two classes of aquatic vegetation, i.e. Lemnetea O. de Bolòs et Masclans 1955 and Potamogetonetea Klika in Klika et Novák. The diversity of Lemnetea in the Russian Federation is consists of 15 associations belonging to 3 alliances and one order. Diversity of Potamogetonetea is presented by 56 associations from 5 alliances and two orders. The presented version of the prodromus is a preliminary. We count all valid sytaxa revealed from the territory of the Russian Fedeeeration. The final prodromus will be obtained after the numerical data processing and considering the plurality of syntaxonomic decisions.


2019 ◽  
Vol 49 (2) ◽  
pp. 107-133 ◽  
Author(s):  
Richard Hrivnák ◽  
Kateřina Bubíková ◽  
Helena Oťaheľová ◽  
Kateřina Šumberová
Keyword(s):  

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