Spatial Random Forest (S-RF): A random forest approach for spatially interpolating missing land-cover data with multiple classes

• Published in: International journal of remote sensing Vol. 42; no. 10; pp. 3756 - 3776
• Main Authors: Holloway-Brown, Jacinta, Helmstedt, Kate J., Mengersen, Kerrie L.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 19.05.2021; Taylor & Francis Ltd
• Subjects: Case studies; Cloud cover; Computation; Data; Environmental changes; Environmental monitoring; Imagery; Land cover; Latitude; Longitude; Missing data; Model accuracy; Performance enhancement; Satellite imagery; Satellites; Sensitivity analysis; Spaceborne remote sensing; Training

Land-cover maps are important tools for monitoring large-scale environmental change and can be regularly updated using free satellite imagery data. A key challenge with constructing these maps is missing data in the satellite images on which they are based. To address this challenge, we created a Sp...