TY  - JOUR
AU  - Witjes, Martijn
AU  - Parente, Leandro
AU  - J van Diemen, Chris
AU  - Hengl, Tomislav
AU  - Landa, Martin
AU  - Brodský, Lukáš
AU  - Halounova, Lena
AU  - Križan, Josip
AU  - Antonić, Luka
AU  - Ilie, Codrina Maria
AU  - Craciunescu, Vasile
AU  - Kilibarda, Milan
AU  - Antonijević, Ognjen
AU  - Glušica, Luka
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3343
AB  - A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python.
T2  - PeerJ
T1  - A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat
IS  - 10
SP  - e13573
DO  - doi.org/10.7717/peerj.13573
ER  - 

@article{
author = "Witjes, Martijn and Parente, Leandro and J van Diemen, Chris and Hengl, Tomislav and Landa, Martin and Brodský, Lukáš and Halounova, Lena and Križan, Josip and Antonić, Luka and Ilie, Codrina Maria and Craciunescu, Vasile and Kilibarda, Milan and Antonijević, Ognjen and Glušica, Luka",
year = "2022",
abstract = "A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python.",
journal = "PeerJ",
title = "A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat",
number = "10",
pages = "e13573",
doi = "doi.org/10.7717/peerj.13573"
}

Witjes, M., Parente, L., J van Diemen, C., Hengl, T., Landa, M., Brodský, L., Halounova, L., Križan, J., Antonić, L., Ilie, C. M., Craciunescu, V., Kilibarda, M., Antonijević, O.,& Glušica, L.. (2022). A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat. in PeerJ(10), e13573.
https://doi.org/doi.org/10.7717/peerj.13573

Witjes M, Parente L, J van Diemen C, Hengl T, Landa M, Brodský L, Halounova L, Križan J, Antonić L, Ilie CM, Craciunescu V, Kilibarda M, Antonijević O, Glušica L. A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat. in PeerJ. 2022;(10):e13573.
doi:doi.org/10.7717/peerj.13573 .

Witjes, Martijn, Parente, Leandro, J van Diemen, Chris, Hengl, Tomislav, Landa, Martin, Brodský, Lukáš, Halounova, Lena, Križan, Josip, Antonić, Luka, Ilie, Codrina Maria, Craciunescu, Vasile, Kilibarda, Milan, Antonijević, Ognjen, Glušica, Luka, "A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat" in PeerJ, no. 10 (2022):e13573,
https://doi.org/doi.org/10.7717/peerj.13573 . .

TY  - JOUR
AU  - Hengl, Tomislav
AU  - Miller, Matthew A. E.
AU  - Križan, Josip
AU  - Shepherd, Keith D.
AU  - Sila, Andrew
AU  - Kilibarda, Milan
AU  - Antonijević, Ognjen
AU  - Glušica, Luka
AU  - Dobermann, Achim
AU  - Haefele, Stephan M.
AU  - McGrath, Steve P.
AU  - Acquah, Gifty E.
AU  - Collinson, Jamie
AU  - Parente, Leandro
AU  - Sheykhmousa, Mohammadreza
AU  - Saito, Kazuki
AU  - Johnson, Jean‑Martial
AU  - Chamberlin, Jordan
AU  - Silatsa, Francis B. T.
AU  - Yemefack, Martin
AU  - Wendt, John
AU  - MacMillan, Robert A.
AU  - Wheeler, Ichsani
AU  - Crouch, Jonathan
PY  - 2021
UR  - https://www.nature.com/articles/s41598-021-85639-y
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2362
AB  - Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N≈150,000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.
T2  - Scientific Reports
T1  - African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning
IS  - 11
SP  - 6130 (2021)
DO  - https://doi.org/10.1038/s41598-021-85639-y
ER  - 

@article{
author = "Hengl, Tomislav and Miller, Matthew A. E. and Križan, Josip and Shepherd, Keith D. and Sila, Andrew and Kilibarda, Milan and Antonijević, Ognjen and Glušica, Luka and Dobermann, Achim and Haefele, Stephan M. and McGrath, Steve P. and Acquah, Gifty E. and Collinson, Jamie and Parente, Leandro and Sheykhmousa, Mohammadreza and Saito, Kazuki and Johnson, Jean‑Martial and Chamberlin, Jordan and Silatsa, Francis B. T. and Yemefack, Martin and Wendt, John and MacMillan, Robert A. and Wheeler, Ichsani and Crouch, Jonathan",
year = "2021",
abstract = "Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N≈150,000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.",
journal = "Scientific Reports",
title = "African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning",
number = "11",
pages = "6130 (2021)",
doi = "https://doi.org/10.1038/s41598-021-85639-y"
}

Hengl, T., Miller, M. A. E., Križan, J., Shepherd, K. D., Sila, A., Kilibarda, M., Antonijević, O., Glušica, L., Dobermann, A., Haefele, S. M., McGrath, S. P., Acquah, G. E., Collinson, J., Parente, L., Sheykhmousa, M., Saito, K., Johnson, J., Chamberlin, J., Silatsa, F. B. T., Yemefack, M., Wendt, J., MacMillan, R. A., Wheeler, I.,& Crouch, J.. (2021). African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning. in Scientific Reports(11), 6130 (2021).
https://doi.org/https://doi.org/10.1038/s41598-021-85639-y

Hengl T, Miller MAE, Križan J, Shepherd KD, Sila A, Kilibarda M, Antonijević O, Glušica L, Dobermann A, Haefele SM, McGrath SP, Acquah GE, Collinson J, Parente L, Sheykhmousa M, Saito K, Johnson J, Chamberlin J, Silatsa FBT, Yemefack M, Wendt J, MacMillan RA, Wheeler I, Crouch J. African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning. in Scientific Reports. 2021;(11):6130 (2021).
doi:https://doi.org/10.1038/s41598-021-85639-y .

Hengl, Tomislav, Miller, Matthew A. E., Križan, Josip, Shepherd, Keith D., Sila, Andrew, Kilibarda, Milan, Antonijević, Ognjen, Glušica, Luka, Dobermann, Achim, Haefele, Stephan M., McGrath, Steve P., Acquah, Gifty E., Collinson, Jamie, Parente, Leandro, Sheykhmousa, Mohammadreza, Saito, Kazuki, Johnson, Jean‑Martial, Chamberlin, Jordan, Silatsa, Francis B. T., Yemefack, Martin, Wendt, John, MacMillan, Robert A., Wheeler, Ichsani, Crouch, Jonathan, "African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning" in Scientific Reports, no. 11 (2021):6130 (2021),
https://doi.org/https://doi.org/10.1038/s41598-021-85639-y . .