URKIO EŽERO TYRIMAS NAUDOJANT SENTINEL-2 VAIZDUS

2020 ◽  
Author(s):  
Akvilė Giedraitytė ◽  
Arminas Stanionis
Keyword(s):  
Global Nature ◽  
Sentinel 2

Didėjantis Lietuvos gyventojų susidomėjimas gamtos būkle ir kylantis aplinkosaugos poreikis natūraliai siejamas su didžiausiu Lietuvoje esančiu parku – Labanoro regioniniu parku. Šis parkas 2004 m. gegužės 7 dieną buvo įtrauktas į Pasaulio gamtos fondą (angl. Global Nature Fund), siekiantį išsaugoti gyvūnų pasaulį, jų buveines ir migracijos maršrutus. Labanoro regioninį parką sudaro daugiau nei 70 ežerų, 80 proc. parko ploto užima miškų masyvai. Tyrimui pasirinktas mažiausiai ištyrinėtas, telmologinio draustinio teritorijoje esantis Urkio ežeras. Jame draudžiama plaukioti ir laisvai vaikščioti po jį supančią pelkę. Naudotos dviejų laikotarpių (2015 ir 2019 m.) Sentinel-2 palydovinės nuotraukos. Vaizdai apdoroti ArcGIS Pro programine įranga, įvertintas augalų vegetacinis indeksas (NDVI), atliktas rastrų klasifikavimas, palygintas rankiniu ir automatiniu būdu sukurtas ežero kontūras, nustatyta aktuali Urkio ežero vandens linija, atlikta Urkio pelkės analizė ir apskaičiuotas Urkio ežero kaitos greitis.

Book Reviews

2015 ◽  
Vol 12 (2) ◽  
pp. 172-179
Author(s):  
Deianira Ganga ◽  
B. Dilara Seker ◽  
Wadim Strielkowski ◽  
Tuncay Bilecen
Keyword(s):  
Social Integration ◽  
Irregular Migration ◽  
Ethnic Economy ◽  
University Of Texas ◽  
Global Nature

Ambrosini, Maurizio. Irregular Migration and Invisible Welfare. Palgrave Macmillan, 2013. 256 pages (ISBN: 9781137314321).Cohen, Jeffrey H., and Ibrahim Sirkeci. Cultures of Migration: The Global Nature of Contemporary Mobility. Austin, TX: University of Texas Press, 2011. xiv + 165 pages. (ISBN: 9780292726857). Dedeoğlu, Saniye. Migrants, Work and Social Integration: Women's Labour in the Turkish Ethnic Economy, Palgrave Macmillan, 2014. 216 pages. (ISBN: 9781137371119)

Soil organic carbon prediction using phenological parameters and remote sensing variables generated from Sentinel-2 images

CATENA ◽  
2021 ◽  
Vol 205 ◽  
pp. 105442
Author(s):  
Xianglin He ◽  
Lin Yang ◽  
Anqi Li ◽  
Lei Zhang ◽  
Feixue Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Sentinel 2

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions

2021 ◽  
Vol 10 (4) ◽  
pp. 251
Author(s):  
Christina Ludwig ◽  
Robert Hecht ◽  
Sven Lautenbach ◽  
Martin Schorcht ◽  
Alexander Zipf
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Green Spaces ◽  
Data Sets ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Urban Quality Of Life ◽  
Shafer Theory ◽  
Sentinel 2

Public urban green spaces are important for the urban quality of life. Still, comprehensive open data sets on urban green spaces are not available for most cities. As open and globally available data sets, the potential of Sentinel-2 satellite imagery and OpenStreetMap (OSM) data for urban green space mapping is high but limited due to their respective uncertainties. Sentinel-2 imagery cannot distinguish public from private green spaces and its spatial resolution of 10 m fails to capture fine-grained urban structures, while in OSM green spaces are not mapped consistently and with the same level of completeness everywhere. To address these limitations, we propose to fuse these data sets under explicit consideration of their uncertainties. The Sentinel-2 derived Normalized Difference Vegetation Index was fused with OSM data using the Dempster–Shafer theory to enhance the detection of small vegetated areas. The distinction between public and private green spaces was achieved using a Bayesian hierarchical model and OSM data. The analysis was performed based on land use parcels derived from OSM data and tested for the city of Dresden, Germany. The overall accuracy of the final map of public urban green spaces was 95% and was mainly influenced by the uncertainty of the public accessibility model.

scholarly journals Reply to Comment on “Comparison of Cloud Cover Detection Algorithms on Sentinel–2 Images of the Amazon Tropical Forest”

2021 ◽  
Vol 13 (5) ◽  
pp. 1028
Author(s):  
Alber Hamersson Sanchez ◽  
Michelle Cristina A. Picoli ◽  
Gilberto Camara ◽  
Pedro R. Andrade ◽  
Michel Eustaquio D. Chaves ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Cloud Cover ◽  
Optimal Performance ◽  
Detection Algorithms ◽  
Sentinel 2

In their comments about our paper, the authors remark on two issues regarding our results relating to the MACCS-ATCOR Joint Algorithm (MAJA). The first relates to the sub-optimal performance of this algorithm under the conditions of our tests, while the second corresponds to an error in our interpretation of MAJA’s bit mask. To answer the first issue, we acknowledge MAJA’s capacity to improve its performance as the number of images increases with time. However, in our paper, we used the images we had available at the time we wrote our paper. Regarding the second issue, we misread the MAJA’s bit mask and mistakenly labelled shadows as clouds. We regret our error and here we present the updated tables and images. We corrected our estimation and, consequently, there is an increment in MAJA’s accuracy in the detection of clouds and cloud shadows. However, these increments are not enough to change the conclusion of our original paper.

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