Global land use / land cover with Sentinel 2 and deep learning

Land Cover monitoring is an essential task for a better understanding of the ecosystem’s dynamicity and complexity. The availability of Remote Sensing data improved the Land Use Land Cover mapping as it is routine work in ecosystem management. The complexity of the Mediterranean ecosystems involves a complexity of the surrounding environmental factors. An attempt to quantitatively investigate the interdependencies between land covers and affected environmental factors was conducted in Nisos Elafonisos to represent diverse and fragile coastal Mediterranean ecosystems. Sentinel-2 (MSI) sensor and ASTER Digital Elevation Model (DEM) data were used to classify the LULC as well as to draw different vegetation conditions over the designated study area. DEM derivatives were conducted and incorporated. The developed methodology is intended to assess the land use land cover for different practices under the present environmental condition of Nisos Elafonisos. Supervised classification resulted in six different land cover clusters and was tested against three different environmental clusters. The findings of the current research pointed out that the environmental variables are independent and there is a vertical distribution of the vegetation according to altitude.

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A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery

Neural Computing and Applications ◽

10.1007/s00521-019-04349-9 ◽

2019 ◽

Vol 32 (12) ◽

pp. 8529-8544

Author(s):

Victor Alhassan ◽

Christopher Henry ◽

Sheela Ramanna ◽

Christopher Storie

Keyword(s):

Land Use ◽

Deep Learning ◽

Land Cover ◽

Satellite Imagery ◽

Land Cover Mapping ◽

Land Use Land Cover ◽

Learning Framework ◽

Multispectral Satellite Imagery

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Performance evaluation of MLE, RF and SVM classification algorithms for watershed scale land use/land cover mapping using sentinel 2 bands

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2020.100351 ◽

2020 ◽

Vol 19 ◽

pp. 100351 ◽

Cited By ~ 1

Author(s):

Vikas Kumar Rana ◽

Tallavajhala Maruthi Venkata Suryanarayana

Keyword(s):

Land Use ◽

Performance Evaluation ◽

Land Cover ◽

Land Cover Mapping ◽

Classification Algorithms ◽

Land Use Land Cover ◽

Watershed Scale ◽

Svm Classification ◽

Sentinel 2

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PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w6-91-2017 ◽

2017 ◽

Vol XLII-4/W6 ◽

pp. 91-93 ◽

Cited By ~ 11

Author(s):

A. Sekertekin ◽

A. M. Marangoz ◽

H. Akcin

Keyword(s):

Land Use ◽

Land Cover ◽

Supervised Classification ◽

Accuracy Assessment ◽

Kappa Statistics ◽

Landsat 8 ◽

Land Use Land Cover ◽

Landsat 8 Oli ◽

Sentinel 2

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2.

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