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Conservation easements are voluntary legal agreements designed to constrain land-use activities on private land to achieve conservation goals. Extensive public and private funding has been used to establish 'working forest' conservation easements (WFCE) that aim to protect conservation values while maintaining commercial timber production. We use variation in the timing and location of easements to estimate the impacts of WFCEs in Maine from a 33-year time-series of forest loss and harvesting. We find that WFCEs had negligible impacts on an already low rate of forest loss. Compared to matched control areas, easements decreased forest loss by 0.0004% yr−1 (95% CI: −0.0008, to −0.00003%) the equivalent of 3.17 ha yr−1 (95% C.I.: 1.6, to 6.7 ha yr−1) when scaled to the 839 142 ha of total conserved area. In contrast, WFCEs increased the rate of harvesting by 0.37% yr−1 (95% CI: 0.11%–0.63%), or 3,105 ha yr−1 (95% C.I.: 923–5,287 ha yr−1) when scaled to the conserved area. However, more recently established easements contained stricter restrictions on harvest practices and stricter easements reduced harvest by 0.66% yr−1 (95% CI: −1.03, −0.29). Our results suggest that future easements could be more effective if they were targeted to higher risk of loss areas and included additional provisions for harvest restrictions and monitoring. 

Ensemble-based change detection can improve map accuracies by combining information from multiple datasets. There is a growing literature investigating ensemble inputs and applications for forest disturbance detection and mapping. However, few studies have evaluated ensemble methods other than Random Forest classifiers, which rely on uninterpretable “black box” algorithms with hundreds of parameters. Additionally, most ensemble-based disturbance maps do not utilize independently and systematically collected field-based forest inventory measurements. Here, we compared three approaches for combining change detection results generated from multi-spectral Landsat time series with forest inventory measurements to map forest harvest events at an annual time step. We found that seven-parameter degenerate decision tree ensembles performed at least as well as 500-tree Random Forest ensembles trained and tested on the same LandTrendr segmentation results and both supervised decision tree methods consistently outperformed the top-performing voting approach (majority). Comparisons with an existing national forest disturbance dataset indicated notable improvements in accuracy that demonstrate the value of developing locally calibrated, process-specific disturbance datasets like the harvest event maps developed in this study. Furthermore, by using multi-date forest inventory measurements, we are able to establish a lower bound of 30% basal area removal on detectable harvests, providing biophysical context for our harvest event maps. Our results suggest that simple interpretable decision trees applied to multi-spectral temporal segmentation outputs can be as effective as more complex machine learning approaches for characterizing forest harvest events ranging from partial clearing to clear cuts, with important implications for locally accurate mapping of forest harvests and other types of disturbances.