Next Article in Journal
Assimilation of Remotely-Sensed LAI into WOFOST Model with the SUBPLEX Algorithm for Improving the Field-Scale Jujube Yield Forecasts
Previous Article in Journal
Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran
Open AccessArticle

Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data

Departamento de Topografía y Geomática, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Agresta Soc. Coop., 28012 Madrid, Spain
Northern Research Station, U.S. Forest Service, St. Paul, MN 55108, USA
Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1944;
Received: 26 June 2019 / Revised: 9 August 2019 / Accepted: 12 August 2019 / Published: 20 August 2019
(This article belongs to the Section Forest Remote Sensing)
Despite the popularity of random forests (RF) as a prediction algorithm, methods for constructing confidence intervals for population means using this technique are still only sparsely reported. For two regional study areas (Spain and Norway) RF was used to predict forest volume or aboveground biomass using remotely sensed auxiliary data obtained from multiple sensors. Additionally, the changes per unit area of these forest attributes were estimated using indirect and direct methods. Multiple inferential frameworks have attracted increased recent attention for estimating the variances required for confidence intervals. For this study, three different statistical frameworks, design-based expansion, model-assisted and model-based estimators, were used for estimating population parameters and their variances. Pairs and wild bootstrapping approaches at different levels were compared for estimating the variances of the model-based estimates of the population means, as well as for mapping the uncertainty of the change predictions. The RF models accurately represented the relationship between the response and remotely sensed predictor variables, resulting in increased precision for estimates of the population means relative to design-based expansion estimates. Standard errors based on pairs bootstrapping within or internal to RF were considerably larger than standard errors based on both pairs and wild external bootstrapping of the entire RF algorithm. Pairs and wild external bootstrapping produced similar standard errors, but wild bootstrapping better mimicked the original structure of the sample data and better preserved the ranges of the predictor variables. View Full-Text
Keywords: bootstrapping; model-assisted; model-based; population parameters bootstrapping; model-assisted; model-based; population parameters
Show Figures

Graphical abstract

MDPI and ACS Style

Esteban, J.; McRoberts, R.E.; Fernández-Landa, A.; Tomé, J.L.; Nӕsset, E. Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data. Remote Sens. 2019, 11, 1944.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop