Knowledge Resource Center for Ecological Environment in Arid Area
| Random forests applied as a soil spatial predictive model in arid Utah | |
| Stum, Alexander Knell | |
| 出版年 | 2010 |
| 学位类型 | 硕士 |
| 导师 | Boettinger, Janis L. |
| 学位授予单位 | Utah State University |
| 英文摘要 | Initial soil surveys are incomplete for large tracts of public land in the western USA. Digital soil mapping offers a quantitative approach as an alternative to traditional soil mapping. I sought to predict soil classes across an arid to semiarid watershed of western Utah by applying random forests (RF) and using environmental covariates derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and digital elevation models (DEM). Random forests are similar to classification and regression trees (CART). However, RF is doubly random. Many (e.g., 500) weak trees are grown (trained) independently because each tree is trained with a new randomly selected bootstrap sample, and a random subset of variables is used to split each node. To train and validate the RF trees, 561 soil descriptions were made in the field. An additional 111 points were added by case-based reasoning using aerial photo interpretation. As RF makes classification decisions from the mode of many independently grown trees, model uncertainty can be derived. The overall out of the bag (OOB) error was lower without weighting of classes; weighting increased the overall OOB error and the resulting output did not reflect soil-landscape relationships observed in the field. The final RF model had an OOB error of 55.2% and predicted soils on landforms consistent with soil-landscape relationships. The OOB error for individual classes typically decreased with increasing class size. In addition to the final classification, I determined the second and third most likely classification, model confidence, and the hypothetical extent of individual classes. Pixels that had high possibility of belonging to multiple soil classes were aggregated using a minimum confidence value based on limiting soil features, which is an effective and objective method of determining membership in soil map unit associations and complexes mapped at the 1:24,000 scale. Variables derived from both DEM and Landsat 7 ETM+ sources were important for predicting soil classes based on Gini and standard measures of variable importance and OOB errors from groves grown with exclusively DEM- or Landsat-derived data. Random forests was a powerful predictor of soil classes and produced outputs that facilitated further understanding of soil-landscape relationships. |
| 英文关键词 | Beaver county Landsat 7 Model confidence Random forests Soil survey |
| 语种 | 英语 |
| 国家 | United States |
| 来源学科分类 | Geographic information science; Soil sciences; Remote sensing |
| URL | https://pqdtopen.proquest.com/doc/751597932.html?FMT=AI |
| 来源机构 | Utah State University |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/244433 |
| 推荐引用方式 GB/T 7714 | Stum, Alexander Knell. Random forests applied as a soil spatial predictive model in arid Utah[D]. Utah State University,2010. |
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