Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning
The tree species composition (TSC) reflects a forest's tree species diversity and is relevant for forest planning, biodiversity conservation, and forest resources management. Yet, accurate information on tree species composition at landscape scale is largely missing, especially for mixed forest...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222003429 |
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| author | Vahid Nasiri Mirela Beloiu Ali Asghar Darvishsefat Verena C. Griess Carmen Maftei Lars T. Waser |
| author_facet | Vahid Nasiri Mirela Beloiu Ali Asghar Darvishsefat Verena C. Griess Carmen Maftei Lars T. Waser |
| author_sort | Vahid Nasiri |
| collection | DOAJ |
| description | The tree species composition (TSC) reflects a forest's tree species diversity and is relevant for forest planning, biodiversity conservation, and forest resources management. Yet, accurate information on tree species composition at landscape scale is largely missing, especially for mixed forests and remote areas. One reason being that mapping tree species is time-consuming, and costly, especially in mixed forests and remote areas. Here we develop a robust method for mapping TSC in a mixed temperate forest. Based on forest inventory plots and considering the frequency of dominant tree species in the inventory dataset, five species groups were defined: pure oriental beech, mixed oriental beech, pure common hornbeam, mixed common hornbeam, and mixed deciduous. The classification is based on three-year time series data of Landsat-8 (L8) and Sentinel-2 (S2) derived spectral-temporal features (STMs) and vegetation indices within the long-term, seasonal, and monthly time scales. Model performances of three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) were compared and revealed different classification accuracies (overall accuracies (OAs) between ∼ 70 % and 86 %). Highest OA was obtained using SVM regardless of the classification dataset (STMs and satellite time series). The comparisons between different time scales indicated that with both L8 and S2 time series the seasonal STMs produced higher accuracies than monthly and long-term STMs with S2 outperforming L8 across all time scales and with all tested ML algorithms. We conclude that the freely available satellite time series, spectral-temporal features, and ML algorithms are favourable for accurate TSC mapping. |
| first_indexed | 2024-04-10T22:20:52Z |
| format | Article |
| id | doaj.art-603b788883b9416b9c9186ad6d170911 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-10T22:20:52Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-603b788883b9416b9c9186ad6d1709112023-01-18T04:30:06ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-02-01116103154Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learningVahid Nasiri0Mirela Beloiu1Ali Asghar Darvishsefat2Verena C. Griess3Carmen Maftei4Lars T. Waser5Faculty of Civil Engineering, Transilvania University of Brasov, 900152 Brasov, Romania; Corresponding author.Department of Environmental System Sciences, Institute of Terrestrial Ecosystems, ETH Zürich, 8092 Zurich, SwitzerlandDepartment of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj 1417643184, IranDepartment of Environmental System Sciences, Institute of Terrestrial Ecosystems, ETH Zürich, 8092 Zurich, SwitzerlandFaculty of Civil Engineering, Transilvania University of Brasov, 900152 Brasov, RomaniaLand Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, 8903 Birmensdorf, SwitzerlandThe tree species composition (TSC) reflects a forest's tree species diversity and is relevant for forest planning, biodiversity conservation, and forest resources management. Yet, accurate information on tree species composition at landscape scale is largely missing, especially for mixed forests and remote areas. One reason being that mapping tree species is time-consuming, and costly, especially in mixed forests and remote areas. Here we develop a robust method for mapping TSC in a mixed temperate forest. Based on forest inventory plots and considering the frequency of dominant tree species in the inventory dataset, five species groups were defined: pure oriental beech, mixed oriental beech, pure common hornbeam, mixed common hornbeam, and mixed deciduous. The classification is based on three-year time series data of Landsat-8 (L8) and Sentinel-2 (S2) derived spectral-temporal features (STMs) and vegetation indices within the long-term, seasonal, and monthly time scales. Model performances of three Machine Learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) were compared and revealed different classification accuracies (overall accuracies (OAs) between ∼ 70 % and 86 %). Highest OA was obtained using SVM regardless of the classification dataset (STMs and satellite time series). The comparisons between different time scales indicated that with both L8 and S2 time series the seasonal STMs produced higher accuracies than monthly and long-term STMs with S2 outperforming L8 across all time scales and with all tested ML algorithms. We conclude that the freely available satellite time series, spectral-temporal features, and ML algorithms are favourable for accurate TSC mapping.http://www.sciencedirect.com/science/article/pii/S1569843222003429Caspian temperate forestLandsat-8Sentinel-2Google Earth EngineTime seriesMachine learning |
| spellingShingle | Vahid Nasiri Mirela Beloiu Ali Asghar Darvishsefat Verena C. Griess Carmen Maftei Lars T. Waser Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning International Journal of Applied Earth Observations and Geoinformation Caspian temperate forest Landsat-8 Sentinel-2 Google Earth Engine Time series Machine learning |
| title | Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning |
| title_full | Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning |
| title_fullStr | Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning |
| title_full_unstemmed | Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning |
| title_short | Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning |
| title_sort | mapping tree species composition in a caspian temperate mixed forest based on spectral temporal metrics and machine learning |
| topic | Caspian temperate forest Landsat-8 Sentinel-2 Google Earth Engine Time series Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222003429 |
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