An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

Rebecca Spriggs,Trevor Jones,John Caspersen,David Coomes,Mark Vanderwel

doi:10.3390/rs9090944

Rebecca Spriggs, Trevor Jones + Show 3 more

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https://doi.org/10.3390/rs9090944

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Abstract

We apply a spatially-implicit, allometry-based modelling approach to predict stem diameter distributions (SDDs) from low density airborne LiDAR data in a heterogeneous, temperate forest in Ontario, Canada. Using a recently published algorithm that relates the density, size, and species of individual trees to the height distribution of first returns, we estimated parameters that succinctly describe SDDs that are most consistent with each 0.25-ha LiDAR tile across a 30,000 ha forest landscape. Tests with independent validation plots showed that the diameter distribution of stems was predicted with reasonable accuracy in most cases (half of validation plots had R2 ≥ 0.75, and another 23% had 0.5 ≤ R2 < 0.75). The predicted frequency of larger stems was much better than that of small stems (8 ≤ x < 11 cm diameter), particularly small conifers. We used the predicted SDDs to calculate aboveground carbon density (ACD; RMSE = 21.4 Mg C/ha), quadratic mean diameter (RMSE = 3.64 cm), basal area (RMSE = 6.99 m2/ha) and stem number (RMSE = 272 stems/ha). The accuracy of our predictions compared favorably with previous studies that have generally been undertaken in simpler conifer-dominated forest types. We demonstrate the utility of our results to spatial forest management planning by mapping SDDs, the proportion of broadleaves, and ACD at a 0.25 ha resolution.

Highlights

Remote sensing data provide landscape-level information on forest resources, and can be used to inform spatial forest management planning
We evaluate the performance of our model by comparing SDDPREDICT and related stand attributes to observations from the validation plots
We have developed an area-based approach to translating low density airborne

Summary

Introduction

Remote sensing data provide landscape-level information on forest resources, and can be used to inform spatial forest management planning. A number of statistical approaches have been used for estimating stand attributes from airborne LiDAR data in particular [1], and these have subsequently been used to produce detailed maps showing how forest characteristics vary across a landscape. Stem density and stem volume are often estimated as well for forest management planning [6,7,8,9]. SDDs represent the fundamental recordings from which other characteristics can be calculated, including basal area, stem density, quadratic mean diameter and (when coupled with wood density information) aboveground carbon density. SDDs reflect the cumulative effects of growth, disturbance, and regeneration through time, and represent an important indicator of past (and future) stand dynamics and are critical for developing management strategies [10]

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