Scaling wood volume estimates from inventory plots to landscapes with airborne LiDAR in temperate deciduous forest.

Shaun R Levick,Dominik Hessenmöller,E-Detlef Schulze

doi:10.1186/s13021-016-0048-7

Shaun R Levick, Dominik Hessenmöller + Show 1 more

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https://doi.org/10.1186/s13021-016-0048-7

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Abstract

BackgroundMonitoring and managing carbon stocks in forested ecosystems requires accurate and repeatable quantification of the spatial distribution of wood volume at landscape to regional scales. Grid-based forest inventory networks have provided valuable records of forest structure and dynamics at individual plot scales, but in isolation they may not represent the carbon dynamics of heterogeneous landscapes encompassing diverse land-management strategies and site conditions. Airborne LiDAR has greatly enhanced forest structural characterisation and, in conjunction with field-based inventories, it provides avenues for monitoring carbon over broader spatial scales. Here we aim to enhance the integration of airborne LiDAR surveying with field-based inventories by exploring the effect of inventory plot size and number on the relationship between field-estimated and LiDAR-predicted wood volume in deciduous broad-leafed forest in central Germany.ResultsEstimation of wood volume from airborne LiDAR was most robust (R2 = 0.92, RMSE = 50.57 m3 ha−1 ~14.13 Mg C ha−1) when trained and tested with 1 ha experimental plot data (n = 50). Predictions based on a more extensive (n = 1100) plot network with considerably smaller (0.05 ha) plots were inferior (R2 = 0.68, RMSE = 101.01 ~28.09 Mg C ha−1). Differences between the 1 and 0.05 ha volume models from LiDAR were negligible however at the scale of individual land-management units. Sample size permutation tests showed that increasing the number of inventory plots above 350 for the 0.05 ha plots returned no improvement in R2 and RMSE variability of the LiDAR-predicted wood volume model.ConclusionsOur results from this study confirm the utility of LiDAR for estimating wood volume in deciduous broad-leafed forest, but highlight the challenges associated with field plot size and number in establishing robust relationships between airborne LiDAR and field derived wood volume. We are moving into a forest management era where field-inventory and airborne LiDAR are inextricably linked, and we encourage field inventory campaigns to strive for increased plot size and give greater attention to precise stem geolocation for better integration with remote sensing strategies.

Highlights

Monitoring and managing carbon stocks in forested ecosystems requires accurate and repeatable quantification of the spatial distribution of wood volume at landscape to regional scales
As the number of studies comparing field-based estimates of above ground biomass (AGB) with light-detection and ranging (LiDAR) derived metrics has increased over the past decade, it has become increasingly apparent that performance is dependent upon the forest type and both the size and number of the field plots used for model development and evaluation [14,15,16]
Step-wise multiple linear regression analysis showed that a combination of LiDAR derived height metrics could account for 92 % of the variation in field-measured wood volume at the 1 ha plot scale (R2 = 0.92, root mean square error (RMSE) = 50.79, Fig. 3a)

Summary

Introduction

Monitoring and managing carbon stocks in forested ecosystems requires accurate and repeatable quantification of the spatial distribution of wood volume at landscape to regional scales. AGB mapping with airborne LiDAR is most commonly conducted by deriving empirical models between a suite of LiDAR metrics and field-measured AGB values obtained from georeferenced field sample plots. This relationship is applied across the broader area of LiDAR data coverage at the same spatial resolution as the field sample plots from which the relationship was derived. Irrespective of forest type calibration errors between field measured and LiDAR predicted AGB tend to increase with decreasing plot size [15, 16] This pattern partly arises from increasing edge effects as plots get smaller. Temporal differences between field and LiDAR data acquisitions can strain the relationship between field and LiDAR measured AGB—due to natural growth/mortality, harvest, land-use and land-use change [20]

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