scholarly journals Sentinel-2 Remote Sensed Image Classification with Patchwise Trained ConvNets for Grassland Habitat Discrimination

2021 ◽  
Vol 13 (12) ◽  
pp. 2276
Author(s):  
Paolo Fazzini ◽  
Giuseppina De Felice Proia ◽  
Maria Adamo ◽  
Palma Blonda ◽  
Francesco Petracchini ◽  
...  
Keyword(s):  
Patch Size ◽  
Convex Sets ◽  
Reference Data ◽  
Multispectral Data ◽  
Habitat Discrimination ◽  
Fully Connected ◽  
Grassland Habitats ◽  
Single Pixel ◽  
Excellent Tool ◽  
Sentinel 2

The present study focuses on the use of Convolutional Neural Networks (CNN or ConvNet) to classify a multi-seasonal dataset of Sentinel-2 images to discriminate four grassland habitats in the “Murgia Alta” protected site. To this end, we compared two approaches differing only by the first layer machinery, which, in one case, is instantiated as a fully-connected layer and, in the other case, results in a ConvNet equipped with kernels covering the whole input (wide-kernel ConvNet). A patchwise approach, tessellating training reference data in square patches, was adopted. Besides assessing the effectiveness of ConvNets with patched multispectral data, we analyzed how the information needed for classification spreads to patterns over convex sets of pixels. Our results show that: (a) with an F1-score of around 97% (5 × 5 patch size), ConvNets provides an excellent tool for patch-based pattern recognition with multispectral input data without requiring special feature extraction; (b) the information spreads over the limit of a single pixel: the performance of the network increases until 5 × 5 patch sizes are used and then ConvNet performance starts decreasing.

scholarly journals Intra-Annual Sentinel-2 Time-Series Supporting Grassland Habitat Discrimination

2021 ◽  
Vol 13 (2) ◽  
pp. 277
Author(s):  
Cristina Tarantino ◽  
Luigi Forte ◽  
Palma Blonda ◽  
Saverio Vicario ◽  
Valeria Tomaselli ◽  
...  
Keyword(s):  
Time Series ◽  
Majority Vote ◽  
Digital Terrain Model ◽  
Natura 2000 ◽  
Support Vector ◽  
Habitat Directive ◽  
Terrain Model ◽  
Habitat Discrimination ◽  
Grassland Habitats ◽  
Sentinel 2

The present study aims to discriminate four semi-arid grassland habitats in a Mediterranean Natura 2000 site, Southern Italy, involving 6210/E1.263, 62A0/E1.55, 6220/E1.434 and X/E1.61-E1.C2-E1.C4 (according to Annex I of the European Habitat Directive/EUropean Nature Information System (EUNIS) taxonomies). For this purpose, an intra-annual time-series of 30 Sentinel-2 images, embedding phenology information, were investigated for 2018. The methodology adopted was based on a two-stage workflow employing a Support Vector Machine classifier. In the first stage only four Sentinel-2 multi-season images were analyzed, to provide an updated land cover map from where the grassland layer was extracted. The layer obtained was then used for masking the input features to the second stage. The latter stage discriminated the four grassland habitats by analyzing several input features configurations. These included multiple spectral indices selected from the time-series and the Digital Terrain Model. The results obtained from the different input configurations selected were compared to evaluate if the phenology information from time-series could improve grassland habitats discrimination. The highest F1 values (95.25% and 80.27%) were achieved for 6210/E1.263 and 6220/E1.434, respectively, whereas the results remained stable (97,33%) for 62A0/E1.55 and quite low (75,97%) for X/E1.61-E1.C2-E1.C4. However, since for all the four habitats analyzed no single configuration resulted effective, a Majority Vote algorithm was applied to achieve a reduction in classification uncertainty.

scholarly journals Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions

2019 ◽  
Vol 11 (24) ◽  
pp. 2947 ◽  
Author(s):  
Daniel Žížala ◽  
Robert Minařík ◽  
Tereza Zádorová
Keyword(s):  
Prediction Accuracy ◽  
Remote Sensing Data ◽  
Cost Effective ◽  
Hyperspectral Data ◽  
Landsat 8 ◽  
Prediction Ability ◽  
Multispectral Data ◽  
Multispectral Remote Sensing ◽  
Data Source ◽  
Sentinel 2

The image spectral data, particularly hyperspectral data, has been proven as an efficient data source for mapping of the spatial variability of soil organic carbon (SOC). Multispectral satellite data are readily available and cost-effective sources of spectral data compared to costly and technically demanding processing of hyperspectral data. Moreover, their continuous acquisition allows to develop a composite from time-series, increasing the spatial coverage of SOC maps. In this study, an evaluation of the prediction ability of models assessing SOC using real multispectral remote sensing data from different platforms was performed. The study was conducted on a study plot (1.45 km2) in the Chernozem region of South Moravia (Czechia). The adopted methods included field sampling and predictive modeling using satellite multispectral Sentinel-2, Landsat-8, and PlanetScope data, and multispectral UAS Parrot Sequoia data. Furthermore, the performance of a soil reflectance composite image from Sentinel-2 data was analyzed. Aerial hyperspectral CASI 1500 and SASI 600 data was used as a reference. Random forest, support vector machine, and the cubist regression technique were applied in the predictive modeling. The prediction accuracy of models using multispectral data, including Sentinel-2 composite, was lower (RPD range from 1.16 to 1.65; RPIQ range from 1.53 to 2.17) compared to the reference model using hyperspectral data (RPD = 2.26; RPIQ = 3.34). The obtained results show very similar prediction accuracy for all spaceborne sensors (Sentinel-2, Landsat-8, and PlanetScope). However, the spatial correlation between the reference mapping results obtained from the hyperspectral data and other maps using multispectral data was moderately strong. UAS sensors and freely available satellite multispectral data can represent an alternative cost-effective data source for remote SOC mapping on the local scale.

scholarly journals The use of Sentinel-2 MSI data in small island's nearshore benthic habitat mapping

2018 ◽  
Author(s):  
Muhammad Afif Fauzan
Keyword(s):  
Coastal Management ◽  
Satellite Remote Sensing ◽  
Reference Data ◽  
Habitat Mapping ◽  
Aerial Photographs ◽  
Benthic Habitat ◽  
Marine Habitat ◽  
Mapping Techniques ◽  
Management And Conservation ◽  
Sentinel 2

Maps of nearshore marine habitat are vital for coastal management and conservation. While traditional field mapping techniques are still commonly used, airborne and satellite remote sensing have proven to be efficient alternatives for creating benthic habitat maps. This paper evaluates the capability of new satellite data, Sentinel-2 MSI, to map nearshore benthic habitat of Derawan Island. Available aerial photographs were used as reference data. The results show that Sentinel-2 MSI data can be used to map benthic habitat with accuracy up to 75%.

scholarly journals Contribution of Sentinel-2 data for applications in vegetation monitoring

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (2) ◽  
pp. 44 ◽  
Author(s):  
Pia Addabbo ◽  
Mariano Focareta ◽  
Salvo Marcuccio ◽  
Claudio Votto ◽  
Silvia Liberata Ullo
Keyword(s):  
Added Value ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Multispectral Data ◽  
Monitoring Applications ◽  
Green Areas ◽  
Land Surfaces ◽  
Project Data ◽  
Different Sources ◽  
Sentinel 2

<p class="Abstract"><span lang="EN-US">With the entry into operation of the Sentinel-2 mission in June 2015, a new land monitoring costellation of twin satellites has been added to Copernicus project from ESA and new insights have been derived through the combination of Sentinel-2 data with other optical/multispectral data, and with other data from satellites belonging to the same Copernicus  project.  To this end, the objective of this paper has been to present new added-value tools first through the integration of different satellite platforms: data from NASA Landsat-8 and ESA Sentinel-1 have been used and combined, and furthermore through the comparison of satellite data all from the same Copernicus project: data from Sentinel-1 and Sentinel-2 have been jointly processed and compared. Although data from optical/multispectral sensors, as those of Landsat-8 and Sentinel-2, and data from SAR on board of Sentinel-1,  are very different,  their combination provides useful and interesting results. The integration and combination of these data can find useful application in many fields from oceans to waterways, from land surfaces to fossil deposits, from vegetation to forest areas. In this works authors have focused their interest in green areas and vegetation monitoring applications, by choosing as case of interest the Royal Palace of Caserta and its gardens.  The idea has started from the increasing interest in monitoring  the cultural heritage monuments and in particular  the surrounding vegetation with the green areas and the parks inside. Satellite images can put into evidence boundaries modifications, the vegetation state, their possible degradation, and other phenomena such as changes in the territories due both to natural and to anthropogenic causes. Data combination from different sources as above specified gives a good number of indexes very useful to analyze the vegetation state and its health in a very deep way. Many of these indexes have been calculated and discussed for investigation.</span></p>

scholarly journals SNOW COVER MAPPING AND ICE AVALANCHE MONITORING FROM THE SATELLITE DATA OF THE SENTINELS

2018 ◽  
Vol XLII-3 ◽  
pp. 1765-1772 ◽  
Author(s):  
S. Wang ◽  
B. Yang ◽  
Y. Zhou ◽  
F. Wang ◽  
R. Zhang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Satellite Data ◽  
Atmospheric Correction ◽  
Mapping Method ◽  
Backscattering Coefficient ◽  
Multispectral Data ◽  
Monitoring Frequency ◽  
Application Potential ◽  
Correction Result ◽  
Sentinel 2

In order to monitor ice avalanches efficiently under disaster emergency conditions, a snow cover mapping method based on the satellite data of the Sentinels is proposed, in which the coherence and backscattering coefficient image of Synthetic Aperture Radar (SAR) data (Sentinel-1) is combined with the atmospheric correction result of multispectral data (Sentinel-2). The coherence image of the Sentinel-1 data could be segmented by a certain threshold to map snow cover, with the water bodies extracted from the backscattering coefficient image and removed from the coherence segment result. A snow confidence map from Sentinel-2 was used to map the snow cover, in which the confidence values of the snow cover were relatively high. The method can make full use of the acquired SAR image and multispectral image under emergency conditions, and the application potential of Sentinel data in the field of snow cover mapping is exploited. The monitoring frequency can be ensured because the areas obscured by thick clouds are remedied in the monitoring results. The Kappa coefficient of the monitoring results is 0.946, and the data processing time is less than 2&amp;thinsp;h, which meet the requirements of disaster emergency monitoring.

scholarly journals Satellite monitoring of the environment in the area of the Iskitim coal mines in 2013–2020

2021 ◽  
Author(s):  
N.V. Rodionova
Keyword(s):  
Data Analysis ◽  
Reflection Coefficient ◽  
Coal Mines ◽  
Open Pit ◽  
Satellite Monitoring ◽  
Landsat 8 ◽  
Visualization System ◽  
Multispectral Data ◽  
Novosibirsk Region ◽  
Sentinel 2

The paper considers the use of multispectral data from the Landsat-8, Sentinel-2, Aqua and Terra satellites for monitoring pollution in the areas of open-pit coal mines in the Iskitim district of the Novosibirsk region for the period 2013–2020. The change in the values of the reflection coefficient (RC) from the surface and water bodies, the snow index NDSI during the snowmelt period, the change of NDVI in the summer, in the area of Kolyvansky and Vostochny coal mines and in the area of the Linevo village are considered. The dynamics of the aerosol optical thickness (AOT) changes, CO and CH4 concentrations in the atmosphere of the Iskitim district using the Giovanni data analysis and visualization system are shown.

scholarly journals Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1942 ◽  
Author(s):  
Sosdito Mananze ◽  
Isabel Pôças ◽  
Mario Cunha
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Indices ◽  
Cost Functions ◽  
Support Vector ◽  
Area Index ◽  
Maize Leaf ◽  
Multispectral Data ◽  
Simple Ratio ◽  
Sentinel 2

Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

scholarly journals Multispectral Data Analysis for Semantic Assessment—A SNAP Framework for Sentinel-2 Use Case Scenarios

2020 ◽  
Vol 13 ◽  
pp. 4429-4442
Author(s):  
Alexandru Cosmin Grivei ◽  
Iulia Coca Neagoe ◽  
Florin Andrei Georgescu ◽  
Andreea Griparis ◽  
Corina Vaduva ◽  
...  
Keyword(s):  
Data Analysis ◽  
Use Case ◽  
Multispectral Data ◽  
Sentinel 2
Sign in / Sign up

Export Citation Format

Share Document