Geology topics

D.R. Cutler

Publications and source records attributed to D.R. Cutler.

3 recordsLinked to original sources

USGS2007

Random forests for classification in ecology

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

Ecology↗

USGS2006

Effects of sample survey design on the accuracy of classification tree models in species distribution models

We evaluated the effects of probabilistic (hereafter DESIGN) and non-probabilistic (PURPOSIVE) sample surveys on resultant classification tree models for predicting the presence of four lichen species in the Pacific Northwest, USA. Models derived from both survey forms were assessed using an independent data set (EVALUATION). Measures of accuracy as gauged by resubstitution rates were similar for each lichen species irrespective of the underlying sample survey form. Cross-validation estimates of prediction accuracies were lower than resubstitution accuracies for all species and both design types, and in all cases were closer to the true prediction accuracies based on the EVALUATION data set. We argue that greater emphasis should be placed on calculating and reporting cross-validation accuracy rates rather than simple resubstitution accuracy rates. Evaluation of the DESIGN and PURPOSIVE tree models on the EVALUATION data set shows significantly lower prediction accuracy for the PURPOSIVE tree models relative to the DESIGN models, indicating that non-probabilistic sample surveys may generate models with limited predictive capability. These differences were consistent across all four lichen species, with 11 of the 12 possible species and sample survey type comparisons having significantly lower accuracy rates. Some differences in accuracy were as large as 50%. The classification tree structures also differed considerably both among and within the modelled species, depending on the sample survey form. Overlap in the predictor variables selected by the DESIGN and PURPOSIVE tree models ranged from only 20% to 38%, indicating the classification trees fit the two evaluated survey forms on different sets of predictor variables. The magnitude of these differences in predictor variables throws doubt on ecological interpretation derived from prediction models based on non-probabilistic sample surveys. ?? 2006 Elsevier B.V. All rights reserved.

Ecological Modelling↗

USGS1998

Assessing map accuracy in a remotely sensed, ecoregion-scale cover map

Landscape- and ecoregion-based conservation efforts increasingly use a spatial component to organize data for analysis and interpretation. A challenge particular to remotely sensed cover maps generated from these efforts is how best to assess the accuracy of the cover maps, especially when they can exceed 1000 s/km2 in size. Here we develop and describe a methodological approach for assessing the accuracy of large-area cover maps, using as a test case the 21.9 million ha cover map developed for Utah Gap Analysis. As part of our design process, we first reviewed the effect of intracluster correlation and a simple cost function on the relative efficiency of cluster sample designs to simple random designs. Our design ultimately combined clustered and subsampled field data stratified by ecological modeling unit and accessibility (hereafter a mixed design). We next outline estimation formulas for simple map accuracy measures under our mixed design and report results for eight major cover types and the three ecoregions mapped as part of the Utah Gap Analysis. Overall accuracy of the map was 83.2% (SE=1.4). Within ecoregions, accuracy ranged from 78.9% to 85.0%. Accuracy by cover type varied, ranging from a low of 50.4% for barren to a high of 90.6% for man modified. In addition, we examined gains in efficiency of our mixed design compared with a simple random sample approach. In regard to precision, our mixed design was more precise than a simple random design, given fixed sample costs. We close with a discussion of the logistical constraints facing attempts to assess the accuracy of large-area, remotely sensed cover maps.

Remote Sensing of Environment↗