Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library
Permanent grasslands play a very important role in the landscape. The loss of permanent grasslands and their subsequent conversion into arable land create erosion-prone agricultural areas in the landscape and have a negative impact on the biodiversity. From this point of view, there is a need for th...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2023.2294127 |
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| author | Jiří Šandera Přemysl Štych |
| author_facet | Jiří Šandera Přemysl Štych |
| author_sort | Jiří Šandera |
| collection | DOAJ |
| description | Permanent grasslands play a very important role in the landscape. The loss of permanent grasslands and their subsequent conversion into arable land create erosion-prone agricultural areas in the landscape and have a negative impact on the biodiversity. From this point of view, there is a need for the accurate and effective monitoring of changes in the agricultural landscape along with an assessment of the influence of the agricultural policies on the landscape. Sentinel-2 from the Copernicus programme has improved options for the implementation of remote sensing data into the monitoring of agricultural land. The aim of this study was to evaluate the potential of H2O library and within implemented Automachine learning function (AutoML) and its stacked ensembles for mapping changes from grasslands to arable lands. All results show high overall accuracy from 93.5% to 96.6% and high values of area under the ROC curve (0.94–0.98). Stacked ensembles appear to be the most accurate machine learning models for mapping changes from grasslands to arable lands. The importance of several biological predictors has been tested (FAPAR, FCOVER, LAI, NDVI, etc.) with the help of a heatmap that is part of AutoML function of H2O library. |
| format | Article |
| id | doaj-art-ad2dfb4e8dda4a7bb271cc8356cc529a |
| institution | OA Journals |
| issn | 2279-7254 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj-art-ad2dfb4e8dda4a7bb271cc8356cc529a2025-08-20T01:58:55ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542024-12-0157110.1080/22797254.2023.2294127Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O libraryJiří Šandera0Přemysl Štych1Department of Applied Geoinformatics and Cartography, Charles University, Prague, Czech RepublicDepartment of Applied Geoinformatics and Cartography, Charles University, Prague, Czech RepublicPermanent grasslands play a very important role in the landscape. The loss of permanent grasslands and their subsequent conversion into arable land create erosion-prone agricultural areas in the landscape and have a negative impact on the biodiversity. From this point of view, there is a need for the accurate and effective monitoring of changes in the agricultural landscape along with an assessment of the influence of the agricultural policies on the landscape. Sentinel-2 from the Copernicus programme has improved options for the implementation of remote sensing data into the monitoring of agricultural land. The aim of this study was to evaluate the potential of H2O library and within implemented Automachine learning function (AutoML) and its stacked ensembles for mapping changes from grasslands to arable lands. All results show high overall accuracy from 93.5% to 96.6% and high values of area under the ROC curve (0.94–0.98). Stacked ensembles appear to be the most accurate machine learning models for mapping changes from grasslands to arable lands. The importance of several biological predictors has been tested (FAPAR, FCOVER, LAI, NDVI, etc.) with the help of a heatmap that is part of AutoML function of H2O library.https://www.tandfonline.com/doi/10.1080/22797254.2023.2294127Stacked ensemblesmachine learningH2O librarygrassland mappingchange detection |
| spellingShingle | Jiří Šandera Přemysl Štych Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library European Journal of Remote Sensing Stacked ensembles machine learning H2O library grassland mapping change detection |
| title | Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library |
| title_full | Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library |
| title_fullStr | Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library |
| title_full_unstemmed | Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library |
| title_short | Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library |
| title_sort | mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and h2o library |
| topic | Stacked ensembles machine learning H2O library grassland mapping change detection |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2023.2294127 |
| work_keys_str_mv | AT jirisandera mappingchangesofgrasslandtoarablelandusingautomaticmachinelearningofstackedensemblesandh2olibrary AT premyslstych mappingchangesofgrasslandtoarablelandusingautomaticmachinelearningofstackedensemblesandh2olibrary |