Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data
| dc.contributor.author | Holloway-Brown, J. | |
| dc.contributor.author | Helmstedt, K.J. | |
| dc.contributor.author | Mengersen, K.L. | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Forests are a global environmental priority that need to be monitored frequently and at large scales. Satellite images are a proven useful, free data source for regular global forest monitoring but these images often have missing data in tropical regions due to climate driven persistent cloud cover. Remote sensing and statistical approaches to filling these missing data gaps exist and these can be highly accurate, but any interpolation method results are uncertain and these methods do not provide measures of this uncertainty. We present a new two-step spatial stochastic random forest (SS-RF) method that uses random forest algorithms to construct Beta distributions for interpolating missing data. This method has comparable performance with the traditional remote sensing compositing method, and additionally provides a probability for each interpolated data point. Our results show that the SS-RF method can accurately interpolate missing data and quantify uncertainty and its applicability to the challenge of monitoring forest using free and incomplete satellite imagery data. We propose that there is scope for our SS-RF method to be applied to other big data problems where a measurement of uncertainty is needed in addition to estimates. | |
| dc.description.statementofresponsibility | Jacinta Holloway, Brown, Kate J Helmstedt, and Kerrie L Mengersen | |
| dc.identifier.citation | Journal of Big Data, 2020; 7(1) | |
| dc.identifier.doi | 10.1186/s40537-020-00331-8 | |
| dc.identifier.issn | 2196-1115 | |
| dc.identifier.orcid | Holloway-Brown, J. [0000-0003-4608-5313] | |
| dc.identifier.uri | https://hdl.handle.net/2440/137272 | |
| dc.language.iso | en | |
| dc.publisher | Springer Science and Business Media LLC | |
| dc.relation.grant | http://purl.org/au-research/grants/arc/CE140100049 | |
| dc.relation.grant | http://purl.org/au-research/grants/arc/DE200101791 | |
| dc.rights | © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. | |
| dc.source.uri | https://doi.org/10.1186/s40537-020-00331-8 | |
| dc.subject | Random forest; Uncertainty; Stochastic; Machine learning; Spatial interpolation; Remote sensing; Land cover; Probability; Bayesian | |
| dc.title | Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data | |
| dc.type | Journal article | |
| pubs.publication-status | Published |
Files
Original bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- hdl_137272.pdf
- Size:
- 1.98 MB
- Format:
- Adobe Portable Document Format
- Description:
- Published version