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

Esteban Cava, Jessica and McRoberts, Ronald E. and Fernández Landa, Alfredo and Tomé, José Luis and Naesset, Erik (2019). Estimating Forest Volume and Biomass and their Changes Using Random Forests and Remotely Sensed Data. "Remote Sensing", v. 11 (n. 16); p. 1944. ISSN 2072-4292. https://doi.org/10.3390/rs11161944.

Description

Title: Estimating Forest Volume and Biomass and their Changes Using Random Forests and Remotely Sensed Data
Author/s:
  • Esteban Cava, Jessica
  • McRoberts, Ronald E.
  • Fernández Landa, Alfredo
  • Tomé, José Luis
  • Naesset, Erik
Item Type: Article
Título de Revista/Publicación: Remote Sensing
Date: 20 August 2019
ISSN: 2072-4292
Volume: 11
Subjects:
Freetext Keywords: Bootstrapping, Model-Assisted, Model-Based, Population Parameters
Faculty: E.T.S.I. Caminos, Canales y Puertos (UPM)
Department: Ingeniería y Morfología del Terreno
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 67795
DC Identifier: https://oa.upm.es/67795/
OAI Identifier: oai:oa.upm.es:67795
DOI: 10.3390/rs11161944
Official URL: https://www.mdpi.com/2072-4292/11/16/1944
Deposited by: Memoria Investigacion
Deposited on: 23 Jul 2021 14:36
Last Modified: 23 Jul 2021 14:36
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