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Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series

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Title Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series
 
Creator Masolele, R.N.
De Sy, V.
Herold, M.
Gonzalez, D.M.
Verbesselt, J.
Gieseke, F.
Mullissa, A.G.
Martius, C.
 
Subject deforestation
land use
satellite imagery
 
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.
 
Date 2021-10-01
2021-10-21T02:35:53Z
2021-10-21T02:35:53Z
 
Type Journal Article
 
Identifier 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
0034-4257
https://hdl.handle.net/10568/115561
https://doi.org/10.1016/j.rse.2021.112600
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format 112600
 
Source Remote Sensing of Environment