Canadian Forest Service Publications

Evaluating the impact of leaf-on and leaf-off airborne laser scanning data on the estimation of forest inventory attributes with the area-based approach. 2015. White, J.C., Arnett, J.T.T.R., Wulder, M.A., Tompalski, P., Coops, N.C. Canadian Journal of Forest Research. Vol. 45, pp. 1498-1513.

Year: 2015

Available from: Pacific Forestry Centre

Catalog ID: 36446

Language: English

CFS Availability: PDF (download), PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.1139/cjfr-2015-0192

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Abstract

In this study, we explored the consequences of using leaf-on and leaf-off airborne laser scanning (ALS) data on area-based model outcomes in a lodgepole pine (Pinus contorta var. latifolia Engelm.) dominated forest in the foothills of the Rocky Mountains in Alberta, Canada. We considered eight forest attributes: top height, mean height, Lorey's mean height, basal area, quadratic mean diameter, merchantable volume, total volume, and total aboveground biomass. We used 787 ground plots for model development, stratified by ALS acquisition conditions (leaf-on or leaf-off) and dominant forest type (coniferous or deciduous).Wealso generated pooled models that combined leaf-on and leaf-off ALS data and generic models that combined plot data for all forest types. We evaluated differences in ALS metrics and leaf-on and leaf-off model outcomes, as well as the impacts of pooling leaf-on and leaf-off ALS data, creating generic models, and of applying leaf-on models to leaf-off data (and vice versa). In general, leaf-off and leaf-on ALS metrics were not significantly different (p < 0.05), except for the 5th percentile of height (coniferous) and canopy density metrics (deciduous). Overall, coniferous leaf-on and leaf-off models were comparable, with differences in relative root mean square error (RMSE) and bias of <2% for all attributes except volume, which differed by <4%. RMSE and bias for deciduous leaf-on and leaf-off models for height attributes and quadratic mean diameter differed by <2%, whereas models for volume and biomass differed by <7%. These results affirm that leaf-off data can be used in an area-based approach to estimate forest attributes for both coniferous and deciduous forest types. Relative RMSE and bias for pooled models (combining leaf-on and leaf-off ALS data) differed by <2% relative to leaf-on and leaf-off models, suggesting that in the forests studied herein, combining leaf-on and leaf-off data in an area-based approach does not adversely impact model outcomes. Generic models that did not account for forest type had large errors for volume and biomass (e.g., the relative RMSE for merchantable volume was twice as large as forest type specific models). Likewise, the mixing of leaf-on models with leaf-off data and vice versa resulted in large RMSE and bias for both forest types, and therefore mixing of models and data types should be avoided.

Plain Language Summary

ALS data is increasingly being used to enhance forest inventories across Canada. Since these data are expensive to acquire, opportunistic use of existing ALS data has been common. As many of these data were acquired under leaf-off canopy conditions to support terrain mapping applications, an outstanding question has been the suitability of these data for modelling forest inventory attributes. A growing body of evidence in the scientific literature suggests that leaf-off data can be used to generate accurate models of inventory attributes, however many of these studies have been conducted in managed boreal forest environments. Moreover, whilst the results have been compelling for coniferous forest types, existing studies have been less conclusive for mixed or deciduous forest types. These results affirm that leaf-off data can be used effectively in an area-based approach to estimate forest attributes for both coniferous and deciduous forest types, and that the mixing of leaf-on models with leaf-off data and vice versa can result in large model error and bias for both forest types, and therefore mixing of models and data types should be avoided.

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