期刊
REMOTE SENSING OF ENVIRONMENT
卷 264, 期 -, 页码 -出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2021.112600
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
资金
- Norwegian Agency for Development Cooperation (Norad) [QZA-016/0110, 1500551]
- International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) [15_III_075]
- CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA)
- CGIAR
- European Commission H2020 REDD Copernicus project [821880]
- European Commission H2020 LANDSENSE project [689812]
- H2020 Societal Challenges Programme [821880] Funding Source: H2020 Societal Challenges Programme
This study assessed land-use classification methods following deforestation, finding that spatio-temporal models outperformed spatial or temporal models. Spatial patterns of land-use within a continent had more commonalities than temporal patterns across continents.
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.
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