Mapping recent timber harvest activity in a temperate forest using single date airborne LiDAR surveys and machine learning: lessons for conservation planning

Abstract

ABSTRACT Effective planning for natural resource management and wildlife conservation requires detailed information on vegetation structure at landscape scales and how structure is influenced by land-use practices. In many forested landscapes, the largest impacts of land use on forest structure are driven by forest management activities, which can include invasive species control, prescribed fire, partial harvests (e.g. shelterwood harvests) or overstory removals and clearcuts. Active timber management is often used to achieve forest conservation objectives, but to be used effectively, managers require knowledge of harvest frequency and extent in adjacent forests and at landscape scales. In this paper, we develop a timber harvest mapping workflow using machine learning (XGBoost algorithm) and single campaign airborne light detection and ranging (LiDAR) surveys for the state of Pennsylvania, USA. We show that harvest type (shelterwood and overstory removals) can be mapped at high accuracy (overall accuracy = 94.9%), including broad age classes defined by the number of years since harvest. Errors of omission (false negatives) were lowest for recent (<10 yr old) overstory removal harvests (1.5%) and highest for older (10–18 yr old) shelterwood harvests (34.9%), which is consistent with the expectation that older, partial timber harvests are more difficult to distinguish from untreated forests than are recent harvests. Errors of commission (false positives) were low (<6.0%) for all timber harvest types and ages. Analysis of model results across both public and private lands in three highly forested conservation regions of Pennsylvania (the Poconos, PA Wilds, and Laurel Highlands) revealed a propensity for young overstory removals along forest edges, suggesting edge effects from inaccuracies in the underlying forest mask and mixed pixels contribute to errors of commission. Acknowledging this, overstory removal and shelterwood harvests were roughly equally common across public and private lands when expressed as a fraction of all interior forests (forests >30 m from an edge). The expectation that these harvest treatments would be rarer in private forests was not supported by the model results, which is likely due to the model’s inability to distinguish between alternative natural processes (weather damage, wildfire, pathogens, etc.) and forest treatment types (high-grading and firewood collection) that result in similar forest structures to the trained classes in the XGBoost model. This study provides a framework and validation for combining approachable machine-learning techniques with large-campaign LiDAR to accurately predict forest structure with application to a host of forestry, natural resource, and conservation-related problems. Future efforts that refine the model’s ability to better distinguish between more complex harvest classes and natural processes would be valuable.