Post-processing tools for land cover classification of Sentinel-2

2019 ◽  
Author(s):  
Ewa Gromny ◽  
Stanisław Lewiński ◽  
Marcin Rybicki ◽  
Radosław Malinowski ◽  
Michał Krupiński ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Post Processing ◽  
Sentinel 2

scholarly journals Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Proceedings ◽  
2018 ◽  
Vol 2 (20) ◽  
pp. 1280 ◽  
Author(s):  
Laura Fragoso-Campón ◽  
Elia Quirós ◽  
Julián Mora ◽  
José Antonio Gutiérrez ◽  
Pablo Durán-Barroso
Keyword(s):  
Land Cover ◽  
Vegetation Cover ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Accuracy Enhancement ◽  
Multispectral Data ◽  
Sentinel 2

Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.

scholarly journals SYNTHETIC APERTURE RADAR (SAR) BASED CLASSIFIERS FOR LAND APPLICATIONS IN GERMANY

2016 ◽  
Vol XLI-B1 ◽  
pp. 1187-1193 ◽  
Author(s):  
G. Suresh ◽  
R. Gehrke ◽  
T. Wiatr ◽  
M. Hovenbitzer
Keyword(s):  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Sar Images ◽  
Sentinel 2 ◽  
Aperture Radar

Land cover information is essential for urban planning and for land cover change monitoring. This paper presents an overview of the work conducted at the Federal Agency for Cartography and Geodesy (BKG) with respect to Synthetic Aperture Radar (SAR) based land cover classification. Two land cover classification approaches using SAR images are reported in this paper. The first method involves a rule-based classification using only SAR backscatter intensity while the other method involves supervised classification of a polarimetric composite of the same SAR image. The LBM-DE has been used for training and validation of the SAR classification results. Images acquired from the Sentinel-1a satellite are used for classification and the results have been reported and discussed. The availability of Sentinel-1a images that are weather and daylight independent allows for the creation of a land cover classification system that can be updated and validated periodically, and hence, be used to assist other land cover classification systems that use optical data. With the availability of Sentinel-2 data, land cover classification combining Sentinel-1a and Sentinel-2 images present a path for the future.

scholarly journals SYNTHETIC APERTURE RADAR (SAR) BASED CLASSIFIERS FOR LAND APPLICATIONS IN GERMANY

2016 ◽  
Vol XLI-B1 ◽  
pp. 1187-1193 ◽  
Author(s):  
G. Suresh ◽  
R. Gehrke ◽  
T. Wiatr ◽  
M. Hovenbitzer
Keyword(s):  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Sar Images ◽  
Sentinel 2 ◽  
Aperture Radar

Land cover information is essential for urban planning and for land cover change monitoring. This paper presents an overview of the work conducted at the Federal Agency for Cartography and Geodesy (BKG) with respect to Synthetic Aperture Radar (SAR) based land cover classification. Two land cover classification approaches using SAR images are reported in this paper. The first method involves a rule-based classification using only SAR backscatter intensity while the other method involves supervised classification of a polarimetric composite of the same SAR image. The LBM-DE has been used for training and validation of the SAR classification results. Images acquired from the Sentinel-1a satellite are used for classification and the results have been reported and discussed. The availability of Sentinel-1a images that are weather and daylight independent allows for the creation of a land cover classification system that can be updated and validated periodically, and hence, be used to assist other land cover classification systems that use optical data. With the availability of Sentinel-2 data, land cover classification combining Sentinel-1a and Sentinel-2 images present a path for the future.

Data post processing

2018 ◽  
Author(s):  
Serge A. Wich ◽  
Lian Pin Koh
Keyword(s):  
Land Cover ◽  
Structure From Motion ◽  
Land Cover Classification ◽  
Post Processing ◽  
Surface Models

This chapter discusses how data that have been collected with drones can be used to derive orthomosaics and digital surface models through structure-from-motion software and how these can be processed further for land-cover classification or into vegetation metrics. Some examples of the various programs are provided as well. The chapter ends with a discussion on the approaches that have been used to automate counts of animals in drone images.

scholarly journals Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4483
Author(s):  
W. Gareth Rees ◽  
Jack Tomaney ◽  
Olga Tutubalina ◽  
Vasily Zharko ◽  
Sergey Bartalev
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Boreal Forest ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Fundamental Parameter ◽  
Sensing Data ◽  
Growing Stock ◽  
Stock Volume ◽  
Sentinel 2

Growing stock volume (GSV) is a fundamental parameter of forests, closely related to the above-ground biomass and hence to carbon storage. Estimation of GSV at regional to global scales depends on the use of satellite remote sensing data, although accuracies are generally lower over the sparse boreal forest. This is especially true of boreal forest in Russia, for which knowledge of GSV is currently poor despite its global importance. Here we develop a new empirical method in which the primary remote sensing data source is a single summer Sentinel-2 MSI image, augmented by land-cover classification based on the same MSI image trained using MODIS-derived data. In our work the method is calibrated and validated using an extensive set of field measurements from two contrasting regions of the Russian arctic. Results show that GSV can be estimated with an RMS uncertainty of approximately 35–55%, comparable to other spaceborne estimates of low-GSV forest areas, with 70% spatial correspondence between our GSV maps and existing products derived from MODIS data. Our empirical approach requires somewhat laborious data collection when used for upscaling from field data, but could also be used to downscale global data.

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