# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 1 Introduction  
Human activities induce changes in forest disturbance regimes that can convert once vast carbon sinks, like those of Canada's forests, into carbon sources [1; 2]. In Canada, an average of 27 Mt of C year\({}^{-1}\) was released into the atmosphere due to fires (corresponding to the burning of 2 M ha of forests) from 1959 to 1999 [3]. Then, in 2023, the Canadian forest fire season was record-breaking, burning more than 18 M ha of forests (about 6.7 times the prior 10-year average). Similarly, insect disturbance affects vast areas of Canadian forests not only reducing forest productivity but also making forests more prone to fire [4]. Furthermore, droughs not only kill trees outright but also cause tree stress, which impacts productivity negatively, reducing forest ecosystem services [5; 6; 7]. Importantly, droughs can make trees more susceptible to insect damage [8] and show multi-year temporal lag effects [6; 9]. All these forest disturbances are (and will continue to be) exacerbated by climate change [10; 11], with important impacts on the forests of northern latitudes [12; 13; 14]. Given the changes in the disturbance regimes, forest ecosystem services need to be \"monitored for action\" as is being advocated by the biodiversity monitoring community [15]. That is, the information gained through monitoring needs to be translated into actions at the level of policy and on the ground [16]. There is no single monitoring approach that can provide all the information needed for effective action, but approaches that adopt principles of adaptive monitoring [17] and change detection-attribution frameworks [18; 19] are likely to be more effective. Moreover, monitoring approaches that leverage open data are more likely to succeed and scale up globally [20]. Furthermore, it is increasingly important to monitor not only the ecological supply of forest ecosystem services but also other dimensions of ecosystem services [21; 22] as well as the drivers affecting these services. In the absence of accessible forest aboveground biomass high-resolution time series, remote sensing-derived vegetation indices (VIs) can be useful proxies of forest carbon services and other forest ecosystem services. Commonly used VIs are readily and openly available online and have become important variables for ecological analyses [23; 24; 25]. These VIs, which are available at relatively fine spatiotemporal resolutions for periods of a decade or more, are good indicators of forest health status as they reflect the effects of environmental and ecological factors [26; 27]. Indices such as NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index), which measure vegetation greenness, are common inputs for the estimation or validation of forest greenness, biomass, and carbon [28; 29; 30; 31]. Decision-makers are often in need of simple and easy-to-interpret values or thresholds of ecological change to reach consensus and prompt action across government levels. Easy-to-implement monitoring approaches that leverage easy-to-interpret VIs can help translate knowledge to action, which is key for achieving biodiversity conservation goals [15; 16] as well as managing ecosystem services. The analysis of EVI time series of forested ecosystems can provide important insights about EVI dynamics and forest health. Negative abrupt changes in the EVI time series (known as trend breakpoints or simply breaks) can be due to pulse disturbances (e.g., intense forest fires or windthrows [32]) while positive abrupt changes can be due to boosts of natural regeneration or reforestation (e.g., plantation). Then, gradual change (i.e., trends) in a time series can be due to press disturbances [33] like extended droughts (negative trend) or sustained good growing conditions (positive trend). Thus, breaks and trends can be either positive or negative (Figure 11) depending on the disturbing or enhancing factor at play.