Regression Tree Modeling of Spatial Pattern and Process Interactions
| Autor: | Nelson, Trisalyn A., Nijland, Wiebe, Bourbonnais, Mathieu L., Wulder, Michael A., Landscape functioning, Geocomputation and Hydrology |
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| Přispěvatelé: | Landscape functioning, Geocomputation and Hydrology |
| Rok vydání: | 2017 |
| Předmět: |
0106 biological sciences
010504 meteorology & atmospheric sciences Computer science Decision tree Wildfire Machine learning computer.software_genre 010603 evolutionary biology 01 natural sciences Environmental Science(all) Spatial pattern Covariate Data mining Spatial analysis Engineering(all) 0105 earth and related environmental sciences Agricultural and Biological Sciences(all) Classification and regression tree business.industry Regression Random forest Spatial ecology Spatial process Common spatial pattern Artificial intelligence business computer |
| Zdroj: | Mapping Forest Landscape Patterns ISBN: 9781493973293 Mapping Forest Landscape Patterns, 187. Springer New York LLC STARTPAGE=187;TITLE=Mapping Forest Landscape Patterns |
| DOI: | 10.1007/978-1-4939-7331-6_5 |
| Popis: | In forestry, many fundamental spatial processes cannot be measured directly and data on spatial patterns are used as a surrogate for studying processes. To characterize the outcomes of a dynamic process in terms of a spatial pattern, we often consider the probability of certain outcomes over a large area rather than on the scale of the particular process. In this chapter we demonstrate data mining approaches that leverage the growing availability of forestry-related spatial data sets for understanding spatial processes. We present classification and regression trees (CART) and associated methods, including boosted regression trees (BRT) and random forests (RT). We demonstrate how data mining or machine learning approaches are useful for relating spatial patterns and processes. Methods are applied to a wildfire data and covariate data are used to contextualize the quantified patterns. Results indicate that fire patterns are mostly related to processes influenced by people. Given the growing number of multi-temporal and large area datasets on forests and ecology machine learning and data mining approaches should be leveraged to quantify dynamic space-time relationships. |
| Databáze: | OpenAIRE |
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