Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves

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.

How to integrate remotely sensed data and biodiversity for ecosystem assessments at landscape scale

“…Luoto et al 2004). However, only spectral information is observed with imaging sensors, although the 3D structure of the ecosystem is often a more relevant measure (Valbuena et al 2014). Therefore, satellite image-based predictions of ecosystem structure parameters are usually substantially less accurate than ALS-based predictions.…”

“…Tree species proportions and dominant tree species in terms of volume were computed for plots. Following Valbuena et al (2014), the forest structure attributes selected were: the Gini coefficient (GC; Glasser 1962), Lorenz asymmetry (LA; Damgaard and Weiner 2000), and the proportions of number of stems (NSLM) and basal area (BALM) larger than mean (Gove 2004). In the context of a forest ecosystem, the GC assesses the degree of tree size inequality, whereas LA evaluates the balance between overstory and understory, and hence the development of natural regeneration (Valbuena et al 2013a).…”

“…Furthermore, Valbuena et al (2013b) detected the importance of considering the components of LA: NSLM and BALM, describing separately the shares of number of trees and basal area that are stocked above the DGM. Gove (2004) described the importance of BALM with regards to analysing diameter distributions of differing shape, so that BALM can be employed to distinguish a Gaussian from an exponential distribution, which may be otherwise be similar in light or other attributes (Valbuena et al 2014).…”

Analysis of Land Use Structure in Liaoning Province Based on Lorenz Curves and Gini Coefficient

“…Location entropy, also known as a specialized rate, refers to the ratio between the areas of land use of a certain area accounting for the total areas of the regional land use and the total land areas of a certain area accounting for the total land areas of the study region [14].…”

“…Contrary to these complicated models, Lorenz curve as a relatively simple mathematical statistic method has been adopted from economy science to analyze land use structure, which was put forward by the United States economic statistician M. Lorenz [14][15][16][17]. In the field of economics, Lorenz curve analyzes income gap or wealth inequality of different areas, to describe inequalities (centralized or decentralized) degree by plotted curve of cumulative frequency.…”