Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

Chaves, Michel Eustáquio Dantas; Picoli, Michelle Cristina Araújo; Sanches, I. D.

doi:10.3390/rs12183062

Cited by 194 publications

(88 citation statements)

References 221 publications

(498 reference statements)

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“…According to Chaves et al [18], we used Sentinel-2 imagery, because its bands are considered more suitable for vegetation analysis, thanks to its finer spatial resolution compared to other satellite images and its wavelength sensitivity to chlorophyll content and phenological states [72], making the VIs more accurate for LU/LC discrimination [73]. The payload carried on Sentinel-2 is a multispectral sensor with 13 bands at different ground resolutions (Table 1).…”

Section: Image Pre-processing 221 Satellite Data Selectionmentioning

confidence: 99%

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

et al. 2021

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The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.

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Section: Image Pre-processing 221 Satellite Data Selectionmentioning

confidence: 99%

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

et al. 2021

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“…For example, Santos et al [30] proposes a method for the detection and selection of representative samples, excluding outliers or samples from different classes with similar time series profiles from the data set. Moreover, Chaves et al [47] suggests that the use of SWIR and Redge-Edge bands produces better land use and cover classification results. This indicates that there is room to improve further the results of Sentinel-2 and Landsat-8 data cubes.…”

Section: Resultsmentioning

confidence: 99%

Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products

et al. 2020

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Recently, remote sensing image time series analysis has being widely used to investigate the dynamics of environments over time. Many studies have combined image time series analysis with machine learning methods to improve land use and cover change mapping. In order to support image time series analysis, analysis-ready data (ARD) image collections have been modeled and organized as multidimensional data cubes. Data cubes can be defined as sets of time series associated with spatially aligned pixels. Based on lessons learned in the research project e-Sensing, related to national demands for land use and cover monitoring and related to state-of-the-art studies on relevant topics, we define the requirements to build Earth observation data cubes for Brazil. This paper presents the methodology to generate ARD and multidimensional data cubes from remote sensing images for Brazil. We describe the computational infrastructure that we are developing in the Brazil Data Cube project, composed of software applications and Web services to create, integrate, discover, access, and process the data sets. We also present how we are producing land use and cover maps from data cubes using image time series analysis and machine learning techniques.

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“…The combination of Landsat-8 and Sentinel-2A/2B has been widely used to monitor landcover use and for landcover mapping [42]. The BRDF effect has been demonstrated in both Landsat-8 and Sentinel-2 reflectance data [10].…”

Section: Discussionmentioning

confidence: 99%

Optimal Solar Zenith Angle Definition for Combined Landsat-8 and Sentinel-2A/2B Data Angular Normalization Using Machine Learning Methods

Chen

2021

Remote Sensing

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Data from Landsat-8 and Sentinel-2A/2B are often combined for terrestrial monitoring because of their similar spectral bands. The bidirectional reflectance distribution function (BRDF) effect has been observed in both Landsat-8 and Sentinel-2A/2B reflectance data. However, there is currently no definition of solar zenith angle (θsz) that is suitable for the normalization of the BRDF-adjusted reflectance from the three sensors’ combined data. This paper describes the use of four machine learning (ML) models to predict a global θsz that is suitable for the normalization of bidirectional reflectance from the combined data in 2018. The observed θsz collected globally, and the three locations in the Democratic Republic of Congo (26.622°E, 0.356°N), Texas in the USA (99.406°W 30.751°N), and Finland (25.194°E, 61.653°N), are chosen to compare the performance of the ML models. At a global scale, the ML models of Support Vector Regression (SVR), Multi-Layer Perception (MLP), and Gaussian Process Regression (GPR) exhibit comparably good performance to that of polynomial regression, considering center latitude as the input to predict the global θsz. GPR achieves the best overall performance considering the center latitude and acquisition time as inputs, with a root mean square error (RMSE) of 1.390°, a mean absolute error (MAE) of 0.689°, and a coefficient of determination (R2) of 0.994. SVR shows an RMSE of 1.396°, an MAE of 0.638°, and an R2 of 0.994, following GPR. For a specific location, the SVR and GPR models have higher accuracy than the polynomial regression, with GPR exhibiting the best performance, when center latitude and acquisition time are considered as inputs. GPR is recommended for predicting the global θsz using the three sensors’ combined data.

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Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review

Cited by 194 publications

References 221 publications

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products

Optimal Solar Zenith Angle Definition for Combined Landsat-8 and Sentinel-2A/2B Data Angular Normalization Using Machine Learning Methods

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