Comparison of Airborne Laser Scanning Methods for Estimating Forest Structure Indicators Based on Lorenz Curves

Valbuena Puebla, Ruben and Vauhkonen, Jari and Packalen, Petteri and Pitkänen, Juho and Maltamo, Matti (2014). Comparison of Airborne Laser Scanning Methods for Estimating Forest Structure Indicators Based on Lorenz Curves. "ISPRS Journal of Photogrammetry and Remote Sensing", v. 95 ; pp. 23-33. ISSN 0924-2716. https://doi.org/10.1016/j.isprsjprs.2014.06.002.

Description

Title: Comparison of Airborne Laser Scanning Methods for Estimating Forest Structure Indicators Based on Lorenz Curves
Author/s:
  • Valbuena Puebla, Ruben
  • Vauhkonen, Jari
  • Packalen, Petteri
  • Pitkänen, Juho
  • Maltamo, Matti
Item Type: Article
Título de Revista/Publicación: ISPRS Journal of Photogrammetry and Remote Sensing
Date: September 2014
ISSN: 0924-2716
Volume: 95
Subjects:
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

The purpose of this study was to compare a number of state-of-the-art methods in airborne laser scan- ning (ALS) remote sensing with regards to their capacity to describe tree size inequality and other indi- cators related to forest structure. The indicators chosen were based on the analysis of the Lorenz curve: Gini coefficient ( GC ), Lorenz asymmetry ( LA ), the proportions of basal area ( BALM ) and stem density ( NSLM ) stocked above the mean quadratic diameter. Each method belonged to one of these estimation strategies: (A) estimating indicators directly; (B) estimating the whole Lorenz curve; or (C) estimating a complete tree list. Across these strategies, the most popular statistical methods for area-based approach (ABA) were used: regression, random forest (RF), and nearest neighbour imputation. The latter included distance metrics based on either RF (NN–RF) or most similar neighbour (MSN). In the case of tree list esti- mation, methods based on individual tree detection (ITD) and semi-ITD, both combined with MSN impu- tation, were also studied. The most accurate method was direct estimation by best subset regression, which obtained the lowest cross-validated coefficients of variation of their root mean squared error CV(RMSE) for most indicators: GC (16.80%), LA (8.76%), BALM (8.80%) and NSLM (14.60%). Similar figures [CV(RMSE) 16.09%, 10.49%, 10.93% and 14.07%, respectively] were obtained by MSN imputation of tree lists by ABA, a method that also showed a number of additional advantages, such as better distributing the residual variance along the predictive range. In light of our results, ITD approaches may be clearly inferior to ABA with regards to describing the structural properties related to tree size inequality in for- ested areas.

More information

Item ID: 35779
DC Identifier: http://oa.upm.es/35779/
OAI Identifier: oai:oa.upm.es:35779
DOI: 10.1016/j.isprsjprs.2014.06.002
Official URL: http://www.sciencedirect.com/science/article/pii/S0924271614001506
Deposited by: Memoria Investigacion
Deposited on: 18 Jun 2015 09:04
Last Modified: 01 Oct 2016 22:30
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