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

卷:264

Article

Masolele, Robert N.¹  De Sy, Veronique¹  Herold, Martin¹  Marcos, Diego¹  Verbesselt, Jan¹  Gieseke, Fabian²  Mullissa, Adugna G.¹  Martius, Christopher³ 

[1] Wageningen Univ & Res, Lab Geoinformat Sci & Remote Sensing, Droevendaalsesteeg 3, NL-6708 PB Wageningen, Netherlands

[2] Univ Munster, Dept Informat Syst, Leonardo Campus 3, D-48149 Munster, Germany

[3] Germany GmbH, Ctr Int Forestry Res CIFOR, D-53113 Bonn, Germany

关键词: Spatio-temporal;    Deep learning methods;    Large-scale land-use classification;    Satellite imagery time series;    Landsat imagery;    Pan-tropical model;    Continental models;    Land-use following deforestation;   

DOI  :  10.1016/j.rse.2021.112600

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

Free