Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series
Assessing land-use following deforestation is vital for reducing emissions from deforestation and forest degradation. In this paper, for the first time, we assess the potential of spatial, temporal and spatio-temporal deep learning methods for large-scale classification of land-use following tropica...
| Autores principales: | , , , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2021
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/115561 |
| _version_ | 1855524684280365056 |
|---|---|
| author | Masolele, R.N. Sy, Veronique de Herold, M. Gonzalez, D.M. Verbesselt, Jan Gieseke, F. Mullissa, A.G. Martius, C. |
| author_browse | Gieseke, F. Gonzalez, D.M. Herold, M. Martius, C. Masolele, R.N. Mullissa, A.G. Sy, Veronique de Verbesselt, Jan |
| author_facet | Masolele, R.N. Sy, Veronique de Herold, M. Gonzalez, D.M. Verbesselt, Jan Gieseke, F. Mullissa, A.G. Martius, C. |
| author_sort | Masolele, R.N. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Assessing land-use following deforestation is vital for reducing emissions from deforestation and forest degradation. In this paper, for the first time, we assess the potential of spatial, temporal and spatio-temporal deep learning methods for large-scale classification of land-use following tropical deforestation using dense satellite time series over six years on the pan-tropical scale (incl. Latin America, Africa, and Asia). Based on an extensive reference database of six forest to land-use conversion types, we find that the spatio-temporal models achieved a substantially higher F1-score accuracies than models that account only for spatial or temporal patterns. Although all models performed better when the scope of the problem was limited to a single continent, the spatial models were more competitive than the temporal ones in this setting. These results suggest that the spatial patterns of land-use within a continent share more commonalities than the temporal patterns and the spatial patterns across continents. This work explores the feasibility of extending and complementing previous efforts for characterizing follow-up land-use after deforestation at a small-scale via human visual interpretation of high resolution RGB imagery. It supports the usage of fast and automated large-scale land-use classification and showcases the value of deep learning methods combined with spatio-temporal satellite data to effectively address the complex tasks of identifying land-use following deforestation in a scalable and cost effective manner. |
| format | Journal Article |
| id | CGSpace115561 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1155612024-01-17T12:58:34Z Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series Masolele, R.N. Sy, Veronique de Herold, M. Gonzalez, D.M. Verbesselt, Jan Gieseke, F. Mullissa, A.G. Martius, C. deforestation land use satellite imagery Assessing land-use following deforestation is vital for reducing emissions from deforestation and forest degradation. In this paper, for the first time, we assess the potential of spatial, temporal and spatio-temporal deep learning methods for large-scale classification of land-use following tropical deforestation using dense satellite time series over six years on the pan-tropical scale (incl. Latin America, Africa, and Asia). Based on an extensive reference database of six forest to land-use conversion types, we find that the spatio-temporal models achieved a substantially higher F1-score accuracies than models that account only for spatial or temporal patterns. Although all models performed better when the scope of the problem was limited to a single continent, the spatial models were more competitive than the temporal ones in this setting. These results suggest that the spatial patterns of land-use within a continent share more commonalities than the temporal patterns and the spatial patterns across continents. This work explores the feasibility of extending and complementing previous efforts for characterizing follow-up land-use after deforestation at a small-scale via human visual interpretation of high resolution RGB imagery. It supports the usage of fast and automated large-scale land-use classification and showcases the value of deep learning methods combined with spatio-temporal satellite data to effectively address the complex tasks of identifying land-use following deforestation in a scalable and cost effective manner. 2021-10 2021-10-21T02:35:53Z 2021-10-21T02:35:53Z Journal Article https://hdl.handle.net/10568/115561 en Open Access Elsevier Masolele, R.N., De Sy, V., Herold, M., Gonzalez, D.M., Verbesselt, J., Gieseke, F., Mullissa, A.G. and Martius, C., 2021. Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series. Remote Sensing of Environment, 264, 112600. https://doi.org/10.1016/j.rse.2021.112600 |
| spellingShingle | deforestation land use satellite imagery Masolele, R.N. Sy, Veronique de Herold, M. Gonzalez, D.M. Verbesselt, Jan Gieseke, F. Mullissa, A.G. Martius, C. Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series |
| title | Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series |
| title_full | Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series |
| title_fullStr | Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series |
| title_full_unstemmed | Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series |
| title_short | Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series |
| title_sort | spatial and temporal deep learning methods for deriving land use following deforestation a pan tropical case study using landsat time series |
| topic | deforestation land use satellite imagery |
| url | https://hdl.handle.net/10568/115561 |
| work_keys_str_mv | AT masolelern spatialandtemporaldeeplearningmethodsforderivinglandusefollowingdeforestationapantropicalcasestudyusinglandsattimeseries AT syveroniquede spatialandtemporaldeeplearningmethodsforderivinglandusefollowingdeforestationapantropicalcasestudyusinglandsattimeseries AT heroldm spatialandtemporaldeeplearningmethodsforderivinglandusefollowingdeforestationapantropicalcasestudyusinglandsattimeseries AT gonzalezdm spatialandtemporaldeeplearningmethodsforderivinglandusefollowingdeforestationapantropicalcasestudyusinglandsattimeseries AT verbesseltjan spatialandtemporaldeeplearningmethodsforderivinglandusefollowingdeforestationapantropicalcasestudyusinglandsattimeseries AT giesekef spatialandtemporaldeeplearningmethodsforderivinglandusefollowingdeforestationapantropicalcasestudyusinglandsattimeseries AT mullissaag spatialandtemporaldeeplearningmethodsforderivinglandusefollowingdeforestationapantropicalcasestudyusinglandsattimeseries AT martiusc spatialandtemporaldeeplearningmethodsforderivinglandusefollowingdeforestationapantropicalcasestudyusinglandsattimeseries |