Key Structural Features of Boreal Forests May Be Detected Directly Using L-Moments from Airborne Lidar Data

Valbuena Puebla, Ruben and Maltamo, Matti and Mehtätalo, Lauri and Packalen, Petteri (2017). Key Structural Features of Boreal Forests May Be Detected Directly Using L-Moments from Airborne Lidar Data. "Remote Sensing of Environment", v. 194 ; pp. 437-446. ISSN 0034-4257. https://doi.org/10.1016/j.rse.2016.10.024.

Description

Title: Key Structural Features of Boreal Forests May Be Detected Directly Using L-Moments from Airborne Lidar Data
Author/s:
  • Valbuena Puebla, Ruben
  • Maltamo, Matti
  • Mehtätalo, Lauri
  • Packalen, Petteri
Item Type: Article
Título de Revista/Publicación: Remote Sensing of Environment
Date: June 2017
Volume: 194
Subjects:
Freetext Keywords: Airborne laser scanning; L-moments; Gini coefficient;L-coefficient of variation;Forest structure; Tree size inequality; Shade-tolerance.
Faculty: E.T.S.I. Montes (UPM)
Department: Economía y Gestión Forestal [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

This article introduces a novel methodology for automated classification of forest areas from airborne laser scanning (ALS) datasets based on two direct and simple rules: L-coefficient of variation Lcv = 0.5 and L-skewness Lskew = 0, thresholds based on descriptors of the mathematical properties of ALS height distributions. We observed that, while Lcv > 0.5 may represent forests with large tree size inequality, Lskew > 0 can be an indicator for areas lacking a closed dominant canopy. Lcv = 0.5 discriminated forests with trees of approximately equal sizes (even tree size classes) from those with large tree size inequality (uneven tree size classes) with kappa κ = 0.48 and overall accuracy OA = 92.4%, while Lskew = 0 segregated oligophotic and euphotic zones with κ = 0.56 and OA = 84.6%. We showed that a supervised classification could only marginally improve some of these accuracy results. The rule-based approach presents a simple method for detecting structural properties key to tree competition and potential for natural regeneration. The study was carried out with low-density datasets from the national program on ALS surveying of Finland, which shows potential for replication with the ALS datasets typically acquired at nation-wide scales. Since the presented method was based on deductive mathematical rules for describing distributions, it stands out from inductive supervised and unsupervised classification methods which are more commonly used in remote sensing. Therefore, it presents an opportunity for deducing physical relations which could partly eliminate the need for supporting ALS applications with field plot data for training and modelling, at least in Boreal forest ecosystems.

More information

Item ID: 49935
DC Identifier: http://oa.upm.es/49935/
OAI Identifier: oai:oa.upm.es:49935
DOI: 10.1016/j.rse.2016.10.024
Official URL: https://www.sciencedirect.com/science/article/pii/S0034425716303960
Deposited by: Memoria Investigacion
Deposited on: 09 Apr 2018 09:08
Last Modified: 09 Apr 2018 09:08
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