Mapping forest change using stacked generalization: An ensemble approach

• Published in: Remote sensing of environment Vol. 204; pp. 717 - 728
• Main Authors: Healey, Sean P., Cohen, Warren B., Yang, Zhiqiang, Kenneth Brewer, C., Brooks, Evan B., Gorelick, Noel, Hernandez, Alexander J., Huang, Chengquan, Joseph Hughes, M., Kennedy, Robert E., Loveland, Thomas R., Moisen, Gretchen G., Schroeder, Todd A., Stehman, Stephen V., Vogelmann, James E., Woodcock, Curtis E., Yang, Limin, Zhu, Zhe
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.01.2018; Elsevier BV
• Subjects: Accessibility; Algorithms; Change detection; Data processing; Empirical models; Ensemble; Error detection; forest damage; Forest management; Forests; Image detection; Landsat; Landsat satellites; Mapping; Multiple classifier systems; prediction; Remote sensing; Satellite imagery; spatial data; Stacking; time series analysis; Multiple classifier systems; Change detection; Landsat; Ensemble
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2017.09.029

The ever-increasing volume and accessibility of remote sensing data has spawned many alternative approaches for mapping important environmental features and processes. For example, there are several viable but highly varied strategies for using time series of Landsat imagery to detect changes in forest cover. Performance among algorithms varies across complex natural systems, and it is reasonable to ask if aggregating the strengths of an ensemble of classifiers might result in increased overall accuracy. Relatively simple rules have been used in the past to aggregate classifications among remotely sensed maps (e.g. using majority predictions), and in other fields, empirical models have been used to create situationally specific algorithm weights. The latter process, called “stacked generalization” (or “stacking”), typically uses a parametric model for the fusion of algorithm outputs. We tested the performance of several leading forest disturbance detection algorithms against ensembles of the outputs of those same algorithms based upon stacking using both parametric and Random Forests-based fusion rules. Stacking using a Random Forests model cut omission and commission error rates in half in many cases in relation to individual change detection algorithms, and cut error rates by one quarter compared to more conventional parametric stacking. Stacking also offers two auxiliary benefits: alignment of outputs to the precise definitions built into a particular set of empirical calibration data; and, outputs which may be adjusted such that map class totals match independent estimates of change in each year. In general, ensemble predictions improve when new inputs are added that are both informative and uncorrelated with existing ensemble components. As increased use of cloud-based computing makes ensemble mapping methods more accessible, the most useful new algorithms may be those that specialize in providing spectral, temporal, or thematic information not already available through members of existing ensembles. [Display omitted] •Stacking can be used to leverage an ensemble of maps to improve accuracy.•Stacking can align a mapping process with project-specific class definitions.•Random Forests was better than logistic regression as an ensemble fusion rule.•Cloud computing lowers barriers to stacking.•Future algorithm development may focus on specialization to fill ensemble gaps.