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 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.

Bibliographic Details

Main Authors:	Masolele, R.N., Sy, Veronique de, Herold, M., Gonzalez, D.M., Verbesselt, J., Gieseke, F., Mullissa, A.G., Martius, C.
Format:	Journal Article biblioteca
Language:	English
Published:	Elsevier 2021-10
Subjects:	deforestation, land use, satellite imagery,
Online Access:	https://hdl.handle.net/10568/115561 https://doi.org/10.1016/j.rse.2021.112600
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