Methods that provide rapid assessments of changing ecosystems at multiple scales are needed to inform management to address undesirable change. We developed a remote-sensing method in partnership with, and for use by, natural resource managers to predict annually stand condition of floodplain forests along Australia's longest river, the Murray River. A measure of stand condition, which was developed in collaboration with responsible natural resource managers, is a function of plant area index, crown extent, and the percentage live basal area. We surveyed a broad range of spatial and temporal variation in condition, built predictive stand-condition models using satellite-derived variables, and validated predictions with surveys of new sites. A multi-year model using data from two drought years and a year following extensive floods provided better predictions of stand condition than did models based on data for individual years. The model provided good predictions for data collected after the build for 50 sites and for resurveys of build sites in later years (R2 ≥ 0.86). There was limited, temporary improvement in stand condition after the extensive flooding (2010–late 2010) that followed a 13-year (1997–early 2010) drought. Forest condition can be mapped accurately and annually at medium resolution (25 × 25 m) for large areas (100,000s ha) if quantitative ground surveys, satellite imagery, machine learning and future validation are combined. Regular assessments of forest condition can be related to likely causes of change by using regular, rapid assessments, and hence can provide important management information.

中文翻译：

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生态系统评估：澳大利亚最重要河流的洪泛区森林状况严格快速制图系统

需要能够在多个尺度上对不断变化的生态系统进行快速评估的方法，以便为管理层解决不良变化提供信息。我们与自然资源管理者合作开发了一种遥感方法，供自然资源管理者使用，以预测澳大利亚最长河流墨累河沿岸洪泛区森林的年度状况。与负责任的自然资源管理人员合作开发的林分状况衡量标准是植物面积指数、树冠范围和活体基面积百分比的函数。我们调查了条件的广泛空间和时间变化，使用卫星衍生变量建立了预测状态模型，并通过对新站点的调查验证了预测。使用两年干旱年和大洪水后一年数据的多年模型比基于个别年份数据的模型提供了更好的林分状况预测。该模型为 50 个站点建成后收集的数据和后期建设站点的再调查提供了良好的预测 (R2 ≥ 0.86)。在经历了 13 年（1997 年至 2010 年初）干旱之后的大面积洪水（2010 年至 2010 年末）之后，林分状况得到了有限的暂时改善。如果将定量地面调查、卫星图像、机器学习和未来验证结合起来，可以每年以中等分辨率（25 × 25 m）为大面积（100,000 公顷）准确地绘制森林状况。通过使用定期、快速的评估，对森林状况的定期评估可以与可能的变化原因相关联，