scholarly journals Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1140 ◽  
Author(s):  
Paulo Tavares ◽  
Norma Beltrão ◽  
Ulisses Guimarães ◽  
Ana Teodoro
Keyword(s):  
Optical Sensors ◽  
Brazilian Amazon ◽  
Synthetic Aperture ◽  
Tropical Regions ◽  
Cloud Coverage ◽  
Optical Images ◽  
Vegetation Indexes ◽  
Tropical Areas ◽  
The City ◽  
Sentinel 2

In tropical regions, such as in the Amazon, the use of optical sensors is limited by high cloud coverage throughout the year. As an alternative, Synthetic Aperture Radar (SAR) products could be used, alone or in combination with optical images, to monitor tropical areas. In this sense, we aimed to select the best Land Use and Land Cover (LULC) classification approach for tropical regions using Sentinel family products. We choose the city of Belém, Brazil, as the study area. Images of close dates from Sentinel-1 (S-1) and Sentinel-2 (S-2) were selected, preprocessed, segmented, and integrated to develop a machine learning LULC classification through a Random Forest (RF) classifier. We also combined textural image analysis (S-1) and vegetation indexes (S-2). A total of six LULC classifications were made. Results showed that the best overall accuracy (OA) was found for the integration of S-1 and S-2 (91.07%) data, followed by S-2 only (89.53%), and S-2 with radiometric indexes (89.45%). The worse result was for S-1 data only (56.01). For our analysis the integration of optical products in the stacking increased de OA in all classifications. However, we suggest the development of more investigations with S-1 products due to its importance for tropical regions.

scholarly journals Analysis of snow cover pollution in Arctic cities based on satellite albedo measurements

2021 ◽  
Vol 27 (1) ◽  
pp. 394-408
Author(s):  
Polina Mikhaylyukova ◽  
Dmitry Petrakov ◽  
Olga Tutubalina ◽  
Mikhail Zimin ◽  
Marina Vikulina
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Coal Dust ◽  
Urban Pollution ◽  
Spatiotemporal Analysis ◽  
Industrial Enterprises ◽  
Optical Images ◽  
The City ◽  
Sentinel 2

The article presents the results of the work devoted to the analysis of the Sentinel-2/MSI optical images applicability for monitoring the snow cover pollution in industrial Arctic cities. Initially, the authors evaluate the accuracy of calculating the albedo values from satellite images based on the albedo ground-based measurements with a pyranometer in Moscow and Kirovsk. Statistical analysis has shown a high correlation between ground-based and satellite albedo measurements, which makes it possible to use quantitative albedo values in the spatiotemporal analysis of snow cover contamination. For three cities (Murmansk, Vorkuta, Norilsk) that differ in physical and geographical conditions and the type of industrial enterprises, the analysis of snow cover contamination for the period 2016–2020 was carried out. For Murmansk, the main pollutant is coal dust from the seaport, where coal is handled in an open way. In early 2020. the city authorities have completed the construction of a dust screen around the port terminals to reduce urban pollution. The analysis carried out in the work showed that the installed screen significantly reduced the area of pollution in the city of Murmansk. For terrain height more than 120 m, the albedo values correspond to the maximum values for the selected date, which indicates that coal dust spreads for territories located at altitudes of less than 100 m. It was not possible to identify long-term dynamics of albedo values for Vorkuta and Norilsk. Polluted snow cover is observed at a distance of up to 10 km from polluting enterprises.

scholarly journals Efficient Identification of Corn Cultivation Area with Multitemporal Synthetic Aperture Radar and Optical Images in the Google Earth Engine Cloud Platform

2019 ◽  
Vol 11 (6) ◽  
pp. 629 ◽  
Author(s):  
Fuyou Tian ◽  
Bingfang Wu ◽  
Hongwei Zeng ◽  
Xin Zhang ◽  
Jiaming Xu
Keyword(s):  
Synthetic Aperture Radar ◽  
Large Scale ◽  
Census Data ◽  
Google Earth ◽  
Synthetic Aperture ◽  
Cloud Platform ◽  
Optical Images ◽  
Google Earth Engine ◽  
Sentinel 2 ◽  
Aperture Radar

The distribution of corn cultivation areas is crucial for ensuring food security, eradicating hunger, adjusting crop structures, and managing water resources. The emergence of high-resolution images, such as Sentinel-1 and Sentinel-2, enables the identification of corn at the field scale, and these images can be applied on a large scale with the support of cloud computing technology. Hebei Province is the major production area of corn in China, and faces serious groundwater overexploitation due to irrigation. Corn was mapped using multitemporal synthetic aperture radar (SAR) and optical images in the Google Earth Engine (GEE) cloud platform. A total of 1712 scenes of Sentinel-2 data and 206 scenes of Sentinel-1 data acquired from June to October 2017 were processed to composite image metrics as input to a random forest (RF) classifier. To avoid speckle noise in the classification results, the pixel-based classification result was integrated with the object segmentation boundary completed in eCognition software to generate an object-based corn map according to crop intensity. The results indicated that the approach using multitemporal SAR and optical images in the GEE cloud platform is reliable for corn mapping. The corn map had a high F1-Score of 90.08% and overall accuracy of 89.89% according to the test dataset, which was not involved in model training. The corn area estimated from optical and SAR images was well correlated with the census data, with an R2 = 0.91 and a root mean square error (RMSE) of 470.90 km2. The results of the corn map are expected to provide detailed information for optimizing crop structure and water management, which are critical issues in this region.

scholarly journals Selecting the Best Image Pairs to Measure Slope Deformation

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4721
Author(s):  
Wentao Yang
Keyword(s):  
Optical Sensors ◽  
Low Cost ◽  
Slope Deformation ◽  
Optical Remote Sensing ◽  
Optical Images ◽  
Large Numbers ◽  
Image Pairs ◽  
Slope Displacement ◽  
Selection Of ◽  
Sentinel 2

Optical remote sensing images can be used to monitor slope deformation in mountain regions. Abundant optical sensors onboard various platforms were designed to provide increasingly high spatial–temporal resolution images at low cost; however, finding the best image pairs to derive slope deformation remains difficult. By selecting a location in the east Tibetan Plateau, this work used the co-registration of optically sensed images and correlation (COSI-Corr) method to analyze 402 Sentinel-2 images from August 2015 to February 2020, to quantify temporal patterns of uncertainty in deriving slope deformation. By excluding 66% of the Sentinel-2 images that were contaminated by unfavorable weather, uncertainties were found to fluctuate annually, with the least uncertainty achieved in image pairs of similar dates in different years. Six image pairs with the least uncertainties were selected to derive ground displacement for a moving slope in the study area. Cross-checks among these image pairs showed consistent results, with uncertainties less than 1/10 pixels in length. The findings from this work could help in the selection of the best image pairs to derive reliable slope displacement from large numbers of optical images.

scholarly journals Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 956
Author(s):  
Florian Mouret ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Denis Kouamé ◽  
Guillaume Rieu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Experimental Validation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Images ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Different Types ◽  
Selection Of ◽  
Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

scholarly journals The Quality of Remote Sensing Optical Images from Acquisition to Users

2021 ◽  
Vol 13 (7) ◽  
pp. 1295
Author(s):  
Massimo Selva
Keyword(s):  
Remote Sensing ◽  
Optical Sensors ◽  
Constant Increase ◽  
Optical Images

The need to observe and characterize the environment leads to a constant increase of the spatial, spectral, and radiometric resolution of new optical sensors [...]

scholarly journals Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images

2018 ◽  
Vol 10 (11) ◽  
pp. 1705 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Hossein Rizeei ◽  
Abdinur Abdulle
Keyword(s):  
Noise Removal ◽  
Synthetic Aperture ◽  
Support Vector ◽  
Spectral Difference ◽  
Sar Images ◽  
Thematic Map ◽  
Tropical Regions ◽  
Passive Sensor ◽  
Multiresolution Segmentation ◽  
Salt And Pepper

This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds.

scholarly journals SAR AND OBLIQUE AERIAL OPTICAL IMAGE FUSION FOR URBAN AREA IMAGE SEGMENTATION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 639-642 ◽  
Author(s):  
J. Fagir ◽  
A. Schubert ◽  
M. Frioud ◽  
D. Henke
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Single Channel ◽  
Research Area ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Surface Model ◽  
Digital Surface Model ◽  
Oblique View ◽  
Optical Images

The fusion of synthetic aperture radar (SAR) and optical data is a dynamic research area, but image segmentation is rarely treated. While a few studies use low-resolution nadir-view optical images, we approached the segmentation of SAR and optical images acquired from the same airborne platform – leading to an oblique view with high resolution and thus increased complexity. To overcome the geometric differences, we generated a digital surface model (DSM) from adjacent optical images and used it to project both the DSM and SAR data into the optical camera frame, followed by segmentation with each channel. The fused segmentation algorithm was found to out-perform the single-channel version.

scholarly journals An era of Sentinels in flood management: Potential of Sentinel-1, -2, and -3 satellites for effective flood management

2021 ◽  
Vol 13 (1) ◽  
pp. 1616-1642
Author(s):  
Sai Kiran Kuntla
Keyword(s):  
Optical Sensors ◽  
Management Strategies ◽  
Flood Management ◽  
Flood Frequency ◽  
Economic Damage ◽  
Earth Observations ◽  
The Earth ◽  
The People ◽  
Technical Specifications ◽  
Sentinel 2

Abstract The repetitive and destructive nature of floods across the globe causes significant economic damage, loss of human lives, and leaves the people living in flood-prone areas with fear and insecurity. With enough literature projecting an increase in flood frequency, severity, and magnitude in the future, there is a clear need for effective flood management strategies and timely implementation. The earth observatory satellites of the European Space Agency’s Sentinel series, Sentinel-1, Sentinel-2, and Sentinel-3, have a great potential to combat these disastrous floods by their peerless surveillance capabilities that could assist in various phases of flood management. In this article, the technical specifications and operations of the microwave synthetic aperture radar (SAR) onboard Sentinel-1, optical sensors onboard Sentinel-2 (Multispectral Instrument) and Sentinel-3 (Ocean and Land Color Instrument), and SAR altimeter onboard Sentinel-3 are described. Moreover, the observational capabilities of these three satellites and how these observations can meet the needs of researchers and flood disaster managers are discussed in detail. Furthermore, we reviewed how these satellites carrying a range of technologies that provide a broad spectrum of earth observations stand out among their predecessors and have bought a step-change in flood monitoring, understanding, and management to mitigate their adverse effects. Finally, the study is concluded by highlighting the revolution this fleet of Sentinel satellites has brought in the flood management studies and applications.

Mapping land use on Irish raised bogs using Sentinel-2 imagery and Google Earth Engine

2021 ◽  
Author(s):  
Wahaj Habib ◽  
John Connolly ◽  
Kevin McGuiness
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Use Change ◽  
Optical Sensors ◽  
Ghg Emissions ◽  
Google Earth ◽  
Good Prospect ◽  
Raised Bogs ◽  
Google Earth Engine ◽  
Sentinel 2

<p>Peatlands are one of the most space-efficient terrestrial carbon stores. They cover approximately 3 % of the terrestrial land surface and account for about one-third of the total soil organic carbon stock. Peatlands have been under severe strain for centuries all over the world due to management related activities. In Ireland, peatlands span over approximately 14600 km<sup>2</sup>, and 85 % of that has already been degraded to some extent. To achieve temperature goals agreed in the Paris agreement and fulfil the EU’s commitment to quantifying the Carbon/Green House Gases (C/GHG) emissions from land use, land use change forestry, accurate mapping and identification of management related activities (land use) on peatlands is important.</p><p>High-resolution multispectral satellite imagery by European Space Agency (ESA) i.e., Sentinel-2 provides a good prospect for mapping peatland land use in Ireland. However, due to persistent cloud cover over Ireland, and the inability of optical sensors to penetrate the clouds makes the acquisition of clear sky imagery a challenge and hence hampers the analysis of the landscape. Google Earth Engine (a cloud-based planetary-scale satellite image platform) was used to create a cloud-free image mosaic from sentinel-2 data was created for raised bogs in Ireland (images collected for the time period between 2017-2020). A preliminary analysis was conducted to identify peatland land use classes, i.e., grassland/pasture, crop/tillage, built-up, cutover, cutaway and coniferous, broadleaf forests using this mosaicked image. The land-use classification results may be used as a baseline dataset since currently, no high-resolution peatland land use dataset exists for Ireland. It can also be used for quantification of land-use change on peatlands. Moreover, since Ireland will now be voluntarily accounting the GHG emissions from managed wetlands (including bogs), this data could also be useful for such type of assessment.</p>

Surviving the City: Urban Home Gardens and the Economy of Affection in the Brazilian Amazon

2005 ◽  
Vol 4 (1) ◽  
pp. 107-126 ◽  
Author(s):  
Antoinette M. G. A. WinklerPrins ◽  
Perpetuo S. de Souza
Keyword(s):  
Brazilian Amazon ◽  
Home Gardens ◽  
The City
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