Restoration Ecology · 2015 · 27 citations · 28 references
Natural ResourcesEcologyForestryForest ManagementForest RestorationRestoration EcologyForest Change DetectionForest Co-managementForest PlanningForest Resource ManagementRestoration EvaluationVegetation Change DetectionCanopy Cover AnalysisCanopy Cover EstimationCanopy Loss AssessmentComplexityForest Canopy Cover EstimationForest Cover Change MappingForest MonitoringStakeholder Communication Mapping
Forest management today often seeks to restore ecological integrity and enhance human well‐being by increasing forest complexity, resilience, and functionality. However, effective and financially expedient monitoring of forest complexity is challenging. In this study, we developed a practical and inexpensive technique to measure horizontal forest complexity. This monitoring method uses intuitively understandable data (imagery) and facilitates stakeholder participation in the adaptive management process within collaborative projects. We used this technique to determine if current restoration projects are successfully achieving their spatial restoration goals. We focused on the Colorado Front Range Landscape Restoration Initiative ( CFRLRI ) as a representative of the typical collaborative restoration projects underway in formerly fire‐dependent dry conifer forests. The developed monitoring method is practical and cost‐effective by using free aerial imagery to map, quantify, and analyze the distribution of canopy cover pre‐ and post‐treatment. We found the CFRLRI has successfully reduced canopy cover (from 44 to 26% on average) and increased some aspects of horizontal forest complexity. The application of these monitoring techniques has allowed the CFRLRI collaborative group to objectively quantify changes to horizontal forest complexity, and has facilitated stakeholder communication about forest spatial patterns. These methods could be adapted for use by other similar forest restoration projects around the world by utilizing increasingly available satellite or aerial imagery.
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The Measurement of Observer Agreement for Categorical Data
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