Land-Use and Land-Cover Mapping Using a Combination of Radar and Optical Sensors in Roraima – Brazil

Valero, Miguel A. Maffei; Araújo, Wellington Farias; Melo, Valdinar Ferreira; Augusti, Maurício Lourenzoni; Filho, Elpı́dio Inácio Fernandes

doi:10.1590/1809-4430-eng.agric.v42n2e20210142/2022

Eng. Agríc.

2022

DOI: 10.1590/1809-4430-eng.agric.v42n2e20210142/2022

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Land-Use and Land-Cover Mapping Using a Combination of Radar and Optical Sensors in Roraima – Brazil

Miguel A. Maffei Valero

Wellington Farias Araújo

Valdinar Ferreira Melo

et al.

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Google Earth Engine Üzerinde Sentinel-2 Görüntülerinin Arazi Örtüsü Haritalama Doğruluğunun İyileştirilmesi

Yilmaz

2022

Turk J Remote Sens GIS

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Accurate land cover information is necessary for successful monitoring, planning and management of the land cover features. Thanks to free-access satellite images, studies have focused on the creation of more accurate thematic maps, which have been used as a base data in many applications. The cloud-based Google Earth Engine (GEE) service makes it easier to access, store and process these satellite images. This study aims to improve the accuracy of a land cover map produced with the Sentinel-2 satellite image. For this purpose, as the very first step, study site was classified using only traditional bands of the Sentinel-2 data. To improve the classification accuracy, Sentinel-1 Synthetic Aperture Radar (SAR) data, Advanced Land Observing Satellite (ALOS) World 3D data, various spectral indices and gray-level co-occurrence matrix (GLCM) features were added to the traditional bands of the Sentinel-2 data, leading to a multi-source classification process. In this study, where the city center of Trabzon was selected as the study area, the accuracy of the land cover map produced using the Random Forest (RF) classification algorithm was increased from 83.51% to 92.78% with the auxiliary data used.

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Google Earth Engine Üzerinde Sentinel-2 Görüntülerinin Arazi Örtüsü Haritalama Doğruluğunun İyileştirilmesi

Yilmaz

2022

Turk J Remote Sens GIS

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Land-Use and Land-Cover Mapping Using a Combination of Radar and Optical Sensors in Roraima – Brazil

Cited by 1 publication

References 32 publications

Google Earth Engine Üzerinde Sentinel-2 Görüntülerinin Arazi Örtüsü Haritalama Doğruluğunun İyileştirilmesi

Google Earth Engine Üzerinde Sentinel-2 Görüntülerinin Arazi Örtüsü Haritalama Doğruluğunun İyileştirilmesi

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