Determining the drivers of aboveground net primary production (ANPP), a key ecosystem process, is an important goal of ecosystem ecology. However, accurate estimation of ANPP across larger areas remains challenging, especially for savanna ecosystems that are characterized by large spatiotemporal heterogeneity in ANPP. Satellite remote sensing methods are frequently used to estimate productivity at the landscape scale but generally lack the spatial and temporal resolution to capture the determinants of productivity variation. Here, we developed and tested methods for estimating herbaceous productivity as an alternative to labour-intensive repeated biomass clipping and caging of small plots. We compared measures of three spectral greenness indices, normalized difference vegetation index derived from Sentinel-2 (NDVIs) and a handheld radiometer (NDVIg), and green chromatic coordinate derived from digital repeat cameras (GCC) and tested their relationship to biweekly field-measured herbaceous ANPP using movable exclosures. We found that a satellite-based model including average NDVIs and its rate of change (ΔNDVIs) over the biweekly productivity measurement interval predicted herbaceous ANPP reasonably well (Jackknife R² = 0.26). However, the predictive accuracy doubled (Jackknife R² = 0.52) when including the sum of day to day increases in camera trap-derived vegetation greenness (tGCC). This result can be considered promising, given the current lack of productivity estimation methods at comparable spatiotemporal resolution. We furthermore found that the fine (daily) temporal resolution of GCC time series captured fast vegetation responses to rainfall events that were missed when using a coarser temporal resolution (>2 days). These findings demonstrate the importance of measuring at a fine temporal resolution for predicting herbaceous ANPP in savanna ecosystems. We conclude that camera traps are promising in offering a reliable and cost-effective method to estimate productivity in savannas and contribute to a better understanding of ecosystem functioning and its drivers.

中文翻译：

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相机陷阱能够以高时间分辨率估计非洲稀树草原中草本植物的地上净初级生产量 (ANPP)

确定地上净初级生产（ANPP）的驱动因素，这是一个关键的生态系统过程，是生态系统生态学的一个重要目标。然而，在更大范围内准确估计 ANPP 仍然具有挑战性，特别是对于以 ANPP 具有大时空异质性为特征的稀树草原生态系统而言。卫星遥感方法经常用于估计景观尺度的生产力，但通常缺乏空间和时间分辨率来捕捉生产力变化的决定因素。在这里，我们开发并测试了估算草本植物生产力的方法，以替代劳动密集型的重复生物质修剪和小块地的笼养。我们比较了三个光谱绿度指数的测量值，来自 Sentinel-2 (NDVIs) 和手持辐射计 (NDVIg) 的归一化差异植被指数，以及来自数字重复相机 (GCC) 的绿色色度坐标，并使用可移动的外壳测试了它们与每两周实地测量的草本 ANPP 的关系。我们发现基于卫星的模型包括平均 NDVIs 及其在双周生产力测量间隔内的变化率 (ΔNDVIs) 可以相当好地预测草本 ANPP (JackknifeR ²  = 0.26)。然而，预测准确度翻了一番（Jackknife R ² = 0.52）当包括相机陷阱衍生的植被绿度（tGCC）的每日增加总和时。鉴于目前缺乏具有可比时空分辨率的生产力估计方法，这一结果被认为是有希望的。我们还发现，GCC 时间序列的精细（每日）时间分辨率捕获了植被对降雨事件的快速响应，而使用较粗的时间分辨率（>2 天）时，这些响应会被遗漏。这些发现证明了以精细的时间分辨率进行测量对于预测稀树草原生态系统中草本 ANPP 的重要性。我们得出结论，相机陷阱有望提供一种可靠且具有成本效益的方法来估计稀树草原的生产力，并有助于更好地了解生态系统功能及其驱动因素。