ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

Mirmazloumi, S. Mohammad; Kakooei, Mohammad; Mohseni, Farzane; Ghorbanian, Arsalan; Amani, Meisam; Crosetto, Michele; Monserrat, Oriol

doi:10.3390/rs14133041

Cited by 15 publications

(6 citation statements)

References 89 publications

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“…Here, S2 satellite imagery was selected for the classification task. S2 imagery has demonstrated its utility for vegetation mapping due to its optimal radiometric resolution [ 77 ]. Typically, the most sensitive bands in vegetation mapping studies are located in the visible red, red edge, and NIR domains.…”

Section: Discussionmentioning

confidence: 99%

Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

et al. 2022

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Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To fill this gap and to facilitate and automate the usage of MLCAs, here we present a novel GUI software package that allows systematically training, validating, and applying pixel-based MLCA models to remote sensing imagery. The so-called MLCA toolbox has been integrated within ARTMO’s software framework developed in Matlab which implements most of the state-of-the-art methods in the machine learning community. To demonstrate its utility, we chose a heterogeneous case study scene, a landscape in Southwest Iran to map PTs. In this area, four main PTs were identified, consisting of shrub land, grass land, semi-shrub land, and shrub land–grass land vegetation. Having developed 21 MLCAs using the same training and validation, datasets led to varying accuracy results. Gaussian process classifier (GPC) was validated as the top-performing classifier, with an overall accuracy (OA) of 90%. GPC follows a Laplace approximation to the Gaussian likelihood under the supervised classification framework, emerging as a very competitive alternative to common MLCAs. Random forests resulted in the second-best performance with an OA of 86%. Two other types of ensemble-learning algorithms, i.e., tree-ensemble learning (bagging) and decision tree (with error-correcting output codes), yielded an OA of 83% and 82%, respectively. Following, thirteen classifiers reported OA between 70% and 80%, and the remaining four classifiers reported an OA below 70%. We conclude that GPC substantially outperformed all classifiers, and thus, provides enormous potential for the classification of a diversity of land-cover types. In addition, its probabilistic formulation provides valuable band ranking information, as well as associated predictive variance at a pixel level. Nevertheless, as these are supervised (data-driven) classifiers, performances depend on the entered training data, meaning that an assessment of all MLCAs is crucial for any application. Our analysis demonstrated the efficacy of ARTMO’s MLCA toolbox for an automated evaluation of the classifiers and subsequent thematic mapping.

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Section: Discussionmentioning

confidence: 99%

Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

et al. 2022

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“…In this study, the Simple Non-Iterative Clustering (SNIC) segmentation algorithm [60], available in GEE [61], was implemented. SLIC is a developed Simple Linear Iterative Clustering (SLIC) segmentation technique that was enhanced by removing the iterative process and enforcing the connectivity restrictions from the first step [33]. In the SNIC segmentation algorithm, a user-defined number of seed points were placed on a regular grid in image space, which were then grown using four-axis connectivity rules and a distance measure.…”

Section: Segmentationmentioning

confidence: 99%

“…GEE is a cloud-based computing platform that includes massive amounts of open access earth observation datasets, image processing, and classification algorithms [25,30]. The availability of many datasets and ready-to-use image-driven products, easy downloading and uploading of data in various formats, and access to many image processing and ML algorithms are some of the features that have contributed to the widespread use of GEE [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

et al. 2023

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The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95).

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“…To enhance environmental data use for decision-making, several groups in different areas around the world have been putting effort in building EO data cubes: spatially aligned time-series of calibrated multidimensional observations (Giuliani et al, 2017); also see Lu et al (2018); Liu et al (2021); Mirmazloumi et al (2022). Some prominent examples of EO data cubes include the Earth System Data Cube (Mahecha et al, 2020), Digital Earth Australia (Lucas et al, 2019), Digital Earth Africa (Yuan et al, 2021), and the Swiss Data Cube (Chatenoux et al, 2021).…”

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confidence: 99%

Ecodatacube.eu: Analysis-ready open environmental data cube for Europe

Witjes¹,

Parente²,

Križan

et al. 2023

Preprint

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The paper describes the production steps and accuracy assessment of an analysis-ready, open-access European data cube consisting of 2000–2020+ Landsat data, 2017–2021+ Sentinel-2 data and a 30m resolution Digital Terrain Model (DTM). The main purpose of the data cube is to make annual continental-scale spatiotemporal machine learning tasks accessible to a wider user base by providing a spatially and temporally consistent multidimensional feature space. This has required systematic spatiotemporal harmonization, efficient compression, and imputation of missing values. Sentinel-2 and Landsat reflectance values were aggregated into four quarterly averages approximating the four seasons common in Europe (winter, spring, summer and autumn), as well as the 25th and 75th percentile, in order to retain intra-seasonal variance. Remaining missing data in the Landsat time-series was imputed with a temporal moving window median (TMWM) approach. An accuracy assessment shows TMWM performs relatively better in Southern Europe and lower in mountainous regions such as the Scandinavian Mountains, the Alps, and the Pyrenees. We quantify the usability of the different component data sets for spatiotemporal machine learning tasks with a series of land cover classification experiments, which show that models utilizing the full feature space (30 m DTM, 30 m Landsat, 30 m and 10 m Sentinel-2) yield the highest land cover classification accuracy, with different data sets improving the results for different land cover classes. The data sets presented in the paper are part of the EcoDataCube platform, which also hosts open vegetation, soil, and land use / land cover (LULC) maps created. All data sets are available under CC-BY license as Cloud-Optimized GeoTIFFs (ca. 12 TB in size) through SpatioTemporal Asset Catalog (STAC) and the EcoDataCube data portal.

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ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

Cited by 15 publications

References 89 publications

Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

Introducing ARTMO’s Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

Ecodatacube.eu: Analysis-ready open environmental data cube for Europe

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