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Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2020-06-05 , DOI: 10.1016/j.isprsjprs.2020.05.017
Jasper A. Slingsby , Glenn R. Moncrieff , Adam M. Wilson

Managing fire, water, biodiversity and carbon stocks can greatly benefit from early warning of changes in the state of vegetation. While near-real time tools to detect forest change based on satellite remote sensing exist, these ecosystems have relatively stable natural vegetation dynamics. Open (i.e. non-forest) ecosystems like grasslands, savannas and shrublands are more challenging as they show complex natural dynamics due to factors such as fire, postfire recovery, greater contribution of bare soil to observed vegetation indices, as well as high sensitivity to rainfall and strong seasonality. Tools to aid the management of open ecosystems are desperately required as they dominate much of the globe and harbour substantial biodiversity and carbon. We present an innovative approach that overcomes the difficulties posed by open ecosystems by using a spatio-temporal hierarchical Bayesian model that uses data on climate, topography, soils and fire history to generate ecological forecasts of the expected land surface signal under natural conditions. This allows us to monitor and detect abrupt or gradual changes in the state of an ecosystem in near-real time by identifying areas where the observed vegetation signal has deviated from the expected natural variation. We apply our approach to a case study from the hyperdiverse fire-dependent African shrubland, the fynbos of the Cape Floristic Region, a Global Biodiversity Hotspot and UNESCO World Heritage Site that faces a number of threats to vegetation health and ecosystem function. The case study demonstrates that our approach is useful for identifying a range of change agents such as fire, alien plant species invasions, drought, pathogen outbreaks and clearing of vegetation. We describe and provide our full workflow, including an interactive web application. Our approach is highly versatile, allowing us to collect data on the impacts of change agents for research in ecology and earth system science, and to predict aspects of ecosystem structure and function such as biomass, fire return interval and the influence of vegetation on hydrology.



中文翻译:

具有复杂自然动力学的开放生态系统的近实时预测和变化检测

植被状况变化的预警可以极大地帮助管理火,水,生物多样性和碳储量。尽管存在基于卫星遥感的近实时检测森林变化的工具,但这些生态系统具有相对稳定的自然植被动态。诸如草原,热带稀树草原和灌木丛等开放(即非森林)生态系统更具挑战性,因为由于诸如火灾,火灾后恢复,裸土对观测植被指数的贡献更大以及对降雨的敏感性高等因素,它们表现出复杂的自然动态季节性强。迫切需要用于管理开放生态系统的工具,因为它们在全球大部分地区都占据着主导地位,并拥有大量的生物多样性和碳。我们提出了一种创新方法,该方法通过使用时空分层贝叶斯模型来克服开放生态系统带来的困难,该模型使用有关气候,地形,土壤和火灾历史的数据来生成自然条件下预期土地表面信号的生态预测。这使我们能够通过识别观察到的植被信号已偏离预期自然变化的区域来近实时地监测和检测生态系统状态的突然或逐渐变化。我们将我们的方法应用于以下案例研究中:依赖火种的非洲多样化灌木丛,开普植物区的fynbos,全球生物多样性热点和联合国教科文组织世界遗产,这些遗产面临着对植被健康和生态系统功能的多种威胁。案例研究表明,我们的方法可用于识别各种变化因素,例如火灾,外来植物物种入侵,干旱,病原体暴发和植被清除。我们描述并提供完整的工作流程,包括交互式Web应用程序。我们的方法用途广泛,可以收集有关变革推动者影响的数据,用于生态学和地球系统科学研究,并预测生态系统结构和功能的各个方面,例如生物量,回火间隔和植被对水文学的影响。

更新日期:2020-06-05
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