GIS model for riparian forest management in Ontario emulating post-fire residual forest patterns**

安大略省河岸森林管理 GIS 模型模拟火灾后残留森林模式**

• 批准号: 536369-2018
• 负责人: Mallik, Azim
• 金额: $ 1.7万
• 依托单位: Lakehead University
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=658593
• 关键词: GIS; model; riparian; forest; management

Forest industries are faced with the challenge of obtaining necessary saw logs and wood fiber from decreasing landbase at a reasonable cost and complying with the general forest management policy of maintaining the ecosystem integrity. It is believed that careful harvesting of trees in shoreline areas is possible through emulating natural disturbance pattern (ENDP), that will maximize wood fiber procurement while conserving biodiversity and ecosystem functions. To be successful there is a critical need for a reliable method of locating and mapping the natural disturbance patterns, for example post-fire residual forests. In the proposed study a GIS-based model will be developed to predict shoreline residual forest patterns that would emulate natural disturbance patterns in the shorelines which can be used for the dual purpose of protecting ecologically sensitive riparian areas as well as locating opportunity for selectively harvesting. Our predictive GIS model, will enable Resolute Woodland manager develop new shoreline forest management strategies based on topography, hydrology, vegetation, and other landscape characteristics, which better emulate natural disturbance patterns and help delineate the ecologically sensitive riparian areas. Application of the model will achieve a balanced approach of habitat protection and economic trade-off unlike the current practice of maintaining fixed-width buffers that do not consider the ecological complexity of the land-water interface. The outcome of this research will directly benefit the forest industries across the country by contributing to hydrology-based shoreline forest management strategies that will facilitate ecologically based harvesting as well as conservation of riparian habitat and biotic integrity.

林业面临的挑战是以合理的成本从不断减少的土地基地获得必要的锯材原木和木材纤维，并遵守维持生态系统完整性的一般森林管理政策。人们认为，通过模仿自然干扰模式（ENDP），可以在海岸线地区仔细采伐树木，这将最大限度地增加木材纤维采购，同时保护生物多样性和生态系统功能。为了取得成功，迫切需要一种可靠的方法来确定和绘制自然干扰模式，例如火灾后残留的森林。在拟议的研究中，将开发一个基于地理信息系统的模型来预测海岸线的剩余森林模式，将模仿自然干扰模式的海岸线，可用于保护生态敏感的河岸地区，以及定位有选择地收获的机会的双重目的。我们的预测性GIS模型将使Resolute Woodland管理者能够根据地形、水文、植被和其他景观特征制定新的海岸线森林管理策略，这些策略可以更好地模拟自然干扰模式，并有助于划定生态敏感的河岸区。该模型的应用将实现栖息地保护和经济权衡的平衡方法，不像目前的做法，保持固定宽度的缓冲区，不考虑生态复杂性的土地-水界面。这项研究的结果将直接有利于全国各地的林业，有助于水文为基础的海岸线森林管理战略，将促进生态为基础的收获以及保护河岸栖息地和生物的完整性。