Abstract：
Modeling and predicting forest landscape dynamics are crucial for forest management and policy making, especially under the context of climate change and increased severities of disturbances. As forest landscapes change rapidly due to a variety of anthropogenic and natural factors, accurately and efficiently predicting forest dynamics requires the collaboration and synthesis of domain knowledge and experience from geographically dispersed experts. Owing to advanced web techniques, such collaboration can now be achieved to a certain extent, for example, discussion about modeling methods, consultation for model use, and surveying for stakeholders\' feedback can be conducted on the web. However, a research gap remains in terms of how to facilitate online joint actions in the core task of forest landscape modeling by overcoming the challenges from decentralized and heterogeneous data, offline model computation modes, complex simulation scenarios, and exploratory modeling processes. Therefore, we propose an online collaborative strategy to enable collaborative forest landscape dynamic prediction with four core modules, namely data preparation, forest landscape model (FLM) computation, simulation scenario configuration, and process organization. These four modules are designed to support: (1) voluntary data collection and online processing, (2) online synchronous use of FLMs, (3) collaborative simulation scenario design, altering, and execution, and (4) participatory modeling process customization and coordination. We used the LANDIS-II model as a representative FLM to demonstrate the online collaborative strategy for predicting the dynamics of forest aboveground biomass. The results showed that the online collaboration strategy effectively promoted forest landscape dynamic prediction in data preparation, scenario configuration, and task arrangement, thus supporting forest-related decision making.
摘要：
模拟和预测森林景观动态对于森林管理和政策制定至关重要，特别是在气候变化和严重干扰增加的背景下。由于各种人为和自然因素，森林景观迅速变化，准确有效地预测森林动态需要来自地理上分散的专家的领域知识和经验的协作和综合。由于先进的网络技术，这种合作现在可以在一定程度上实现，例如,关于建模方法的讨论，模型使用咨询，对利益相关者的反馈调查可以在网上进行。然而,在如何通过克服分散和异构数据的挑战来促进森林景观建模核心任务中的在线联合行动方面仍然存在研究差距，离线模型计算模式，复杂的仿真场景，和探索性建模过程。因此,我们提出了一种在线协作策略，以实现具有四个核心模块的协作森林景观动态预测，即数据准备，森林景观模型(FLM)计算，模拟场景配置，和过程组织。这四个模块旨在支持:(1)自愿数据收集和在线处理，(2)在线同步使用FLM,(3)协同仿真场景设计,改变,和执行，(4)参与式建模过程的定制与协调。我们使用LANDIS-II模型作为代表性FLM来演示预测森林地上生物量动态的在线协作策略。结果表明,在线协作策略有效促进了数据准备中森林景观动态预测,场景配置，和任务安排，从而支持与森林有关的决策。
