scholarly journals SENTINEL-1 AND SENTINEL-2 DATA FUSION FOR WETLANDS MAPPING: BALIKDAMI, TURKEY

2018 ◽  
Vol XLII-3 ◽  
pp. 729-734 ◽  
Author(s):  
G. Kaplan ◽  
U. Avdan
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Economic Benefits ◽  
Future Research ◽  
Wetland Classification ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Sentinel 2

Wetlands provide a number of environmental and socio-economic benefits such as their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Remote sensing technology has proven to be a useful and frequent application in monitoring and mapping wetlands. Combining optical and microwave satellite data can help with mapping and monitoring the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing radar and optical remote sensing data can increase the wetland classification accuracy.<br> In this paper, data from the fine spatial resolution optical satellite, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, were fused for mapping wetlands. Both Sentinel-1 and Sentinel-2 images were pre-processed. After the pre-processing, vegetation indices were calculated using the Sentinel-2 bands and the results were included in the fusion data set. For the classification of the fused data, three different classification approaches were used and compared.<br> The results showed significant improvement in the wetland classification using both multispectral and microwave data. Also, the presence of the red edge bands and the vegetation indices used in the data set showed significant improvement in the discrimination between wetlands and other vegetated areas. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, showing an overall classification accuracy of approximately 90&amp;thinsp;% in the object-based classification method. For future research, we recommend multi-temporal image use, terrain data collection, as well as a comparison of the used method with the traditional image fusion techniques.

scholarly journals Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

2018 ◽  
Author(s):  
Gordana Kaplan ◽  
Ugur Avdan
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Geographical Information ◽  
Wetland Classification ◽  
Significant Information ◽  
Data Set ◽  
Object Based ◽  
Sentinel 2

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

scholarly journals Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2313
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Spectral Reflectance ◽  
Remote Sensing Data ◽  
Soil Surface ◽  
Optical Remote Sensing ◽  
Test Field ◽  
Sensing Data ◽  
Sentinel 2

Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance.

Wetland Classification Using Multi-Source and Multi-Temporal Optical Remote Sensing Data in Newfoundland and Labrador, Canada

2017 ◽  
Vol 43 (4) ◽  
pp. 360-373 ◽  
Author(s):  
Meisam Amani ◽  
Bahram Salehi ◽  
Sahel Mahdavi ◽  
Jean Elizabeth Granger ◽  
Brian Brisco ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Newfoundland And Labrador ◽  
Remote Sensing Data ◽  
Wetland Classification ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Multi Temporal

Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data

2019 ◽  
Vol 41 (8) ◽  
pp. 2861-2876 ◽  
Author(s):  
Marildo Guerini Filho ◽  
Tatiana Mora Kuplich ◽  
Fernando L. F. De Quadros
Keyword(s):  
Remote Sensing ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Natural Grassland ◽  
Sentinel 2

Modelling of soil organic matter of arable soils with optical remote sensing data: the impact of soil surface state

2020 ◽  
Author(s):  
Elena Prudnikova ◽  
Igor Savin
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Surface State ◽  
Organic Matter Content ◽  
Remote Sensing Data ◽  
Matter Content ◽  
Optical Remote Sensing ◽  
Arable Soils ◽  
Sensing Data ◽  
Sentinel 2

&lt;p&gt;The study presents the analysis of effect of changes of the open surface of arable soils occuring due to the influence of agricultural practices or natural factors (mainly, precipitation) on the possibility of assessment of organic matter content in the arable layer with optical remote sensing data.&lt;/p&gt;&lt;p&gt;The object of the research was gray forest arable soil of a test field located in the Yasnogorsky district of the Tula region. In 2019, the field was complete fallow.&lt;/p&gt;&lt;p&gt;During field work conducted on the test field on 15.08.2019, the spectral reflectance of the surface of arable soils and a wetter subsurface horizon was measured at 30 points. At the same points, 30 mixed samples of the arable horizon were collected for laboratory estimation of organic matter content.&lt;/p&gt;&lt;p&gt;Spectral reflectance was measured using a HandHeld-2 field spectroradiometer, which operates in the range 325&amp;#8211;1050 nm with a step of 1 nm.&lt;/p&gt;&lt;p&gt;Proximal sensing data were smoothed with Savitzky-Golley function and recalculated into Sentinel-2 bands using Gaussian function.&lt;/p&gt;&lt;p&gt;We also chose seven Sentinel-2 scenes for 2019 for the studied region: 2.04.2019, 17.04.2019, 20.04.2019, 5.05.2019; 6.06.2019, 19.06.2019, 28.08.2019. Atmospheric correction for chosen scenes was performed with Sen2Cor model in SNAP. Aftewords we extracted reflectance values at points, where we collected spectral data and soil samples in the field.&lt;/p&gt;&lt;p&gt;Then we calculated a number of spectral indices and ratios for both proximal and Sentinel-2 data which were further used in regression modelling. Models were cross-validated by bootstrapping.&lt;/p&gt;&lt;p&gt;At field scale, difference in moisture content did not significantly affect the accuracy and quality of the models. R&lt;sup&gt;2&lt;/sup&gt;adjcv of model for dry surface layer was a bit higher than in case of model for wet subsurface layer (0.77 vs. 0.72). RMSEPcv and RPIQ for both cases were very close (0.71 and 0.71; 2.09 and 2.12).&lt;/p&gt;&lt;p&gt;When we used models developed based on proximal sensing data to calculate OM content with Sentinel-2 data at different acquisition dates, we found that the accuracy of OM prediction varied. In some cases RMSE was higher than 7 % and predicted OM content was two times higher than actual.&lt;/p&gt;&lt;p&gt;Models developed based only on Sentinel-2 data for different acquisition dates, varied in accuracy, quality and informative bands. R&lt;sup&gt;2&lt;/sup&gt;adjcv of most models was about 0.72-0.83, RPIQ was 2.09-2.07, and RMSEPcv was in the range of 0.56-0.77 %.&lt;/p&gt;&lt;p&gt;Therefore changes in surface state of arable soils result in a situation when for each state we have different model. That imposes restrictions on further use of such models for remote evaluation and monitoring of organic matter content in arable soils. To deal with this problem, it is necessary to account for soil surface state when developing models for properties of arable soils based on optical remote sensing data.&lt;/p&gt;&lt;p&gt;The research was funded by the Ministry of Science and Higher Education of Russia (contract &amp;#8470; 05.607.21.0302).&amp;#160;&lt;/p&gt;

Delineation of erosion classes in semi-arid southern African grasslands using vegetation indices from optical remote sensing data

2003 ◽  
Vol 17 (5) ◽  
pp. 917-928 ◽  
Author(s):  
Volker Hochschild ◽  
Michael Märker ◽  
Giuliano Rodolfi ◽  
Helmut Staudenrausch
Keyword(s):  
Remote Sensing ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Sensing Data ◽  
Semi Arid

scholarly journals Aerial Biomass and Nitrogen Estimation of Emerged and Floating Macrophytes using Optical Remote Sensing: A Non-Destructive Method

2021 ◽  
Author(s):  
Jessica Cristina Carvalho Medeiros ◽  
Maurício Perine ◽  
Marcelo Pompêo ◽  
Marisa Bitencourt
Keyword(s):  
Remote Sensing ◽  
Aquatic Plants ◽  
Spectral Response ◽  
Nitrogen Concentration ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Pistia Stratiotes ◽  
Optical Remote Sensing ◽  
Eichhornia Azurea ◽  
Sentinel 2

Abstract Freshwater resources faces threats with aquatic plants invasion, considered biological pollution with deep effects on water quality and nutrients cycling due to their rapid growth. Orbital remote sensing has been an effective instrument of monitoring large water bodies. Thus, the aim of this study was to analyze the relation between reflectance and field measurements (biomass and nitrogen concentration) of aquatic plants to develop estimation equations and to test vegetation indices to use in orbital remote sensing. The most common tropical infesting species (Salvinia auriculata, Pistia stratiotes, Eichhornia crassipes and Eichhornia azurea) were collected during a year, measured their spectral response to simulate satellite bands, and the biomass and nitrogen concentration measurements. The bands intervals of Sentinel-2 satellite were choosing to the simulation due to their narrow bands and the RedEdge new band. The obtained field data were correlated with the reflectance obtained from spectroradiometry of each species and the equations showed R² = 0.64 to estimate biomass and R² = 0.60 to estimate nitrogen using the entire spectrum. Several indices described in the literature were tested with different Sentinel-2 bands but with no significant results. The NDVI index showed a separation among species using RedEdge band and can be used to identify the species, but not to estimate their biomass.

scholarly journals Novel Vegetation Indices for Cotton Boll Opening Status Estimation Using Sentinel-2 Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1712 ◽  
Author(s):  
Yu Ren ◽  
Yanhua Meng ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
Yuxing Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Great Influence ◽  
Remote Sensing Data ◽  
Area Ratio ◽  
Wide Field ◽  
Entire Area ◽  
Sentinel 2

The application of chemical harvest aids to defoliate leaves and ripen bolls plays a significant role in the once-over machine harvest of cotton (Gossypium hirsutum L.) fields. The boll opening rate (BOR) is a key indicator for the determination of harvest aid spraying times. However, the most commonly used method to determine BOR is manual investigation, which is subjective and cannot have a holistic judgment of the entire area. Remote sensing can be employed to overcome these limitations, due to a wide field of vision, acceptably spatial and temporal resolution, and rich spectral information beyond the perception of the human eye. The reflectance of open cotton bolls is relatively high in the visible and near-infrared bands. High reflectance of open bolls has a great influence on the reflectance of the mixed pixels on remote sensing imagery. Therefore, it is an effective method to detect boll opening status by constructing vegetation indices with the sensitive spectral bands of imagery. In this study, we proposed two new vegetation indices based on Sentinel-2 remote sensing data, namely, the boll area ratio index (BARI) and the boll opening rate index (BORI), in order to estimate the boll opening status on a regional scale. The proposed indices were strongly correlated with the boll area ratio (BAR) and BOR. In particular, BARI exhibited the most accurate and robust performance with BAR in the prediction (R2 = 0.754, RMSE = 2.56%) and validation (R2 = 0.706, RMSE = 5.00%) among all the indices, including published indices we chose. Furthermore, when comparing to all other indices, BORI demonstrated the best and satisfactory estimation with BOR in the prediction (R2 = 0.675, RMSE = 7.96%) and validation (R2 = 0.616, RMSE = 2.79%). Meanwhile, an exponential growth relationship between BOR and BAR was identified, and the underlying mechanisms behind this phenomenon were discussed. Overall, through our study, we provided convenient and accurate vegetation indices for the investigation of boll opening status in a cotton-producing area by accessible and free Sentinel-2 imagery.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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