An analysis of the impact of historical spatial patterns of forest harvesting practices for carbon management.

该项目研究了历史上森林管理模式对当前碳管理行动的影响，以及这些限制对更广泛的碳管理意味着什么。林业越来越多地寻求碳管理和市场，以创造收入并可持续地管理其土地。再生历史收成是碳管理的主要机会，类似于向其他行业出售碳抵消。然而，由一套不同的目标管理的历史管理模式的遗产可能会限制以碳为重点的未来管理的发生。历史目标与当前和未来目标之间这种不匹配的影响目前尚不清楚。该项目将卫星图像和地面数据与一个综合生态管理模型相结合，该模型量化了历史管理、环境变化和自然干扰如何影响碳的空间变化、脆弱性和估值。研究生将与当地的私营行业合作伙伴合作，获得碳市场的商业层面的知识，并在STEM研究中得到指导。科学家对森林管理和干扰的历史空间格局在限制或加强未来碳管理方面的作用的理解是不够的。这种缺乏了解的情况严重限制了私营部门和公共部门规划森林管理的能力，因为受自然和人为干扰影响的空间差异很大。该项目使用高分辨率计算机模型来量化历史管理，环境变化和干扰如何限制和影响碳的空间变化，脆弱性和估值。研究人员将构建一个大规模的干扰模式量化，并将其纳入开源的LANDIS-II框架。从这项工作中，科学家们将开发出未来碳封存和评估的方案，这些方案或多或少受到过去管理足迹的限制。该项目的总体目标是确定过去行动的空间遗产在多大程度上限制或改善了碳管理能力。了解管理的历史模式如何限制这些机会对于评估未来的碳潜力至关重要。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project examines the influence of patterns of historical management of forests on current carbon-management actions, and what these limits mean for carbon management more broadly. The forestry industry is increasingly looking towards carbon management and markets to both create income, and to sustainably manage its lands. Regenerating historical harvests are prime opportunities for carbon management, similar to selling carbon offsets to other industries. However, the legacy of historical management patterns that were governed by a different set of objectives can limit where carbon-focused future management occurs. The impact of this mismatch between historical objectives and current and future goals is presently unknown. This project couples satellite imagery and ground data with an integrated ecological management model that quantifies how historical management, environmental variability, and natural disturbance influences the spatial variation, vulnerabilities, and valuation of carbon. Graduate students will work with local, private industry partners to receive business-level knowledge of carbon markets as well as be mentored in STEM research. The PIs will also develop teaching modules for K-12 education.Scientist’s understanding of the role of historical spatial patterns of forest management and disturbance in limiting or enhancing future carbon management is inadequate. This lack of understanding severely constrains the ability to plan forestry management at both the private and public sectors as spatial variability in exposure to both natural and human-caused disturbances is significant. This project uses a high-resolution computer model to quantify how historical management, environmental variability, and disturbance constrains and influences the spatial variation, vulnerabilities, and valuation of carbon. The researchers will construct a broad-scale quantification of disturbance patterning and incorporate that into the open-source, LANDIS-II framework. From this work scientists will develop scenarios of future carbon sequestration and valuation that are more or less constrained by the footprint of past management. The overarching goal of this project is to determine to what extent the ability to manage for carbon is limited, or improved, by the spatial legacy of past actions. Understanding how historical patterns of management constrain those opportunities is critical to assessing future carbon potential.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.