An operational UAV-based approach for stand-level assessment of soil disturbance after forest harvesting

Bruce Talbot,Johannes Rahlf,Rasmus Astrup

doi:10.1080/02827581.2017.1418421

Bruce Talbot, Johannes Rahlf + Show 1 more

Open Access

https://doi.org/10.1080/02827581.2017.1418421

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Abstract

ABSTRACTThe effectiveness of generating virtual transects on unmanned aerial vehicle-derived orthomosaics was evaluated in estimating the extent of soil disturbance by severity class. Combinations of 4 transect lengths (5–50 m) and five sampling intensities (1–20 transects per ha) were used in assessing traffic intensity and the severity of soil disturbance on six post-harvest, cut-to-length (CTL) clearfell sites. In total, 15% of the 33 ha studied showed some trace of vehicle traffic. Of this, 63% of was categorized as light (no visible surface disturbance). Traffic intensity varied from 787 to 1256 m ha−1, with a weighted mean of 956 m ha−1, approximately twice the geometrical minimum achievable with CTL technology under perfect conditions. An overall weighted mean of 4.7% of the total site area was compromised by severe rutting. A high sampling intensity, increasing with decreasing incidence of soil disturbance, is required if mean estimation error is to be kept below 20%. The paper presents a methodology that can be generally applied in forest management or in similar land-use evaluations.

Highlights

Soil disturbance is an unavoidable consequence of timber harvesting but the severity of its impact is variable, and can be managed through good planning and operations (Ares et al 2005)
To estimate the amount of soil disturbance after harvesting, we propose the utilization of unmanned aerial vehicle (UAV)derived orthomosaics in conjunction with desktop-based line interception sampling
The aim of this work was to evaluate the utility of a method that could be adopted into everyday forest management routines

Summary

Introduction

Soil disturbance is an unavoidable consequence of timber harvesting but the severity of its impact is variable, and can be managed through good planning and operations (Ares et al 2005). Soil compaction and wheel rutting can be detrimental to forest ecosystems for reasons including the physical, physiological and pathogenic consequences to residual trees (Wästerlund 1994; Quesnel and Curran 2000; Sirén et al 2013) caused by damage to the roots, and the effects of reduction in hydraulic conductivity and gaseous exchange capacity (Startsev and McNabb 2009). Soil disturbance can lead to channelling where erosional energy increases with the length of the rut (Startsev and McNabb 2000). The advent of large scale LiDAR surveys has provided a basis for detailed decision support to machine operators in the form of soil moisture prediction such as the topographic wetness index and depth to water mapping, effectively identifying areas to avoid or take special regard of (Seibert et al 2007; Murphy et al 2011; Campbell et al 2013; Ågren et al 2014), potentially at a dayto-day level (Jones and Arp 2017)

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