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Abstract Many recent studies have explored remote sensing approaches to facilitate non‐destructive sampling of aboveground biomass (AGB). Lidar platforms (e.g., iPhone and iPad PRO models) have recently made remote sensing technologies widely available and present an alternative to traditional approaches for estimating AGB. Lidar approaches can be completed within a fraction of the time required by many analog methods. However, it is unknown if handheld sensors are capable of accurately predicting AGB or how different modeling techniques affect prediction accuracy. Here, we collected AGB from 0.25‐m2plots (N = 45) from three sites along an elevational gradient within rangelands surrounding Flagstaff, Arizona, USA. Each plot was scanned with a mobile laser scanner (MLS) and iPad before plants were clipped, dried, and weighed. We compared the capability of iPad and MLS sensors to estimate AGB via minimization of model normalized root mean square error (NRMSE). This process was performed on predictor subsets describing structural, spectral, and field‐based characteristics across a suite of modeling approaches including simple linear, stepwise, lasso, and random forest regression. We found that models developed from MLS and iPad data were equally capable of predicting AGB (NRMSE 26.6% and 29.3%, respectively) regardless of the variable subsets considered. We also found that stepwise regression regularly resulted in the lowest NRMSE. Structural variables were consistently selected during each modeling approach, while spectral variables were rarely included. Field‐based variables were important in linear regression models but were not included after variable selection within random forest models. These findings support the notion that remote sensing techniques offer a valid alternative to analog field‐based data collection methods. Together, our results demonstrate that data collected using a more widely available platform will perform similarly to a more costly option and outline a workflow for modeling AGB using remote sensing systems alone.
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Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning
In 2001, Leo Breiman wrote of a divide between "data modeling" and "algorithmic modeling" cultures. Twenty years later this division feels far more ephemeral, both in terms of assigning individuals to camps, and in terms of intellectual boundaries. We argue that this is largely due to the "data modelers" incorporating algorithmic methods into their toolbox, particularly driven by recent developments in the statistical understanding of Breiman's own Random Forest methods. While this can be simplistically described as "Breiman won", these same developments also expose the limitations of the prediction-first philosophy that he espoused, making careful statistical analysis all the more important. This paper outlines these exciting recent developments in the random forest literature which, in our view, occurred as a result of a necessary blending of the two ways of thinking Breiman originally described. We also ask what areas statistics and statisticians might currently overlook.
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- Award ID(s):
- 1712554
- PAR ID:
- 10298553
- Date Published:
- Journal Name:
- Observational studies
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2767-3324
- Page Range / eLocation ID:
- 107-125
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1353/obs.2021.0027.
