Land cover mapping in Latvia using hyperspectral airborne and simulated Sentinel-2 data

2016 ◽  
Author(s):  
Dainis Jakovels ◽  
Jevgenijs Filipovs ◽  
Agris Brauns ◽  
Juris Taskovs ◽  
Gatis Erins
Keyword(s):  
Land Cover ◽  
Land Cover Mapping ◽  
Sentinel 2

scholarly journals Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine

2019 ◽  
Vol 11 (3) ◽  
pp. 288 ◽  
Author(s):  
Luis Carrasco ◽  
Aneurin O’Neil ◽  
R. Morton ◽  
Clare Rowland
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Cloud Cover ◽  
Google Earth ◽  
Land Cover Mapping ◽  
Temporal Aggregation ◽  
Landsat 8 ◽  
Composite Approach ◽  
Google Earth Engine ◽  
Sentinel 2

Land cover mapping of large areas is challenging due to the significant volume of satellite data to acquire and process, as well as the lack of spatial continuity due to cloud cover. Temporal aggregation—the use of metrics (i.e., mean or median) derived from satellite data over a period of time—is an approach that benefits from recent increases in the frequency of free satellite data acquisition and cloud-computing power. This enables the efficient use of multi-temporal data and the exploitation of cloud-gap filling techniques for land cover mapping. Here, we provide the first formal comparison of the accuracy between land cover maps created with temporal aggregation of Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8 (L8) data from one-year and test whether this method matches the accuracy of traditional approaches. Thirty-two datasets were created for Wales by applying automated cloud-masking and temporally aggregating data over different time intervals, using Google Earth Engine. Manually processed S2 data was used for comparison using a traditional two-date composite approach. Supervised classifications were created, and their accuracy was assessed using field-based data. Temporal aggregation only matched the accuracy of the traditional two-date composite approach (77.9%) when an optimal combination of optical and radar data was used (76.5%). Combined datasets (S1, S2 or S1, S2, and L8) outperformed single-sensor datasets, while datasets based on spectral indices obtained the lowest levels of accuracy. The analysis of cloud cover showed that to ensure at least one cloud-free pixel per time interval, a maximum of two intervals per year for temporal aggregation were possible with L8, while three or four intervals could be used for S2. This study demonstrates that temporal aggregation is a promising tool for integrating large amounts of data in an efficient way and that it can compensate for the lower quality of automatic image selection and cloud masking. It also shows that combining data from different sensors can improve classification accuracy. However, this study highlights the need for identifying optimal combinations of satellite data and aggregation parameters in order to match the accuracy of manually selected and processed image composites.

scholarly journals Evaluation of Microwave-Optical-Infrared Satellite Imagery for Land Cover Mapping

2021 ◽  
Vol 2114 (1) ◽  
pp. 012090
Author(s):  
Ghufran ameer ◽  
Nawal Kh. Gazal
Keyword(s):  
Land Cover ◽  
Satellite Imagery ◽  
Point Of View ◽  
Land Cover Mapping ◽  
Land Use Land Cover ◽  
Atmospheric Scattering ◽  
Optical Wavelengths ◽  
Almost All ◽  
Fusion Feature ◽  
Sentinel 2

Abstract Satellite images are vital tool in various applications like land use, land cover mapping and geographic information system (GIS) etc. A variety of factors involved in the process of image acquisition, introduce geometric distortions, which are removed by pre-processing of the digital imagery. Geometric correction is the process of rectification of geometric errors introduced in the imagery during the process of its acquisition. From practical point of view, the Sentinel-1 images are to be depended as source of microwave satellite imagery. While, Sentinel-2 are to be used for providing the study with the required visible-infrared images. The study includes performing different digital image processing and analysis techniques, such as: geometric and radiometric corrections, spatial merge (fusion), feature extraction with using different spatial filtering techniques and spectral classification to reveal which LULC image presents better accuracy results. The microwave portion of the spectrum covers the range from approximately 1cm to 1m in wavelength. Because of their long wavelengths, compared to the visible and infrared, microwaves have special properties that are important for remote sensing. Longer wavelength microwave radiation can penetrate through cloud cover, haze, dust, and all but the heaviest rainfall as the longer wavelengths are not susceptible to atmospheric scattering which affects shorter optical wavelengths. This property allows detection of microwave energy under almost all weather and environmental conditions so that data can be collected at any time.

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