# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 4 Discussion
### Attribution of Change  
Once EVI breaks have been detected, linking these breaks to specific drivers becomes crucial but challenging. Stand-replacing disturbances such as intense forest fires and clear-cut harvesting are usually easier to identify with remote sensing data due to their abrupt change signature [33]. Non-stand replacing disturbances such as tree thinning, low to moderate insect or pathogen damage, and low to moderate drought are likely to show more gradual changes and are relatively harder to detect [33]. The former type of disturbance leaves a noticeable change (e.g., negative EVI breaks of large magnitude) in the forest while the latter may leave a more subtle change (e.g., negative EVI breaks of smaller magnitude). Furthermore, some forest breaks may not be caused by a single intense short-lived disturbance event that occurred abruptly (pulse event, [32]) but rather from the cumulative effects of several interacting disturbance factors of varying intensities that have occurred over extended periods of time. Although timber harvest is an ongoing activity in the non-protected forests of the AGPE, only 16% of abrupt negative changes matched harvest pixels (unmatched harvest pixels 82%). Similarly, only about 1.2% of break pixels matched forest fires (unmatched fire pixels 77%). The low match between negative breaks and harvest pixels could be related to the type of harvesting taking place. Clearcut systems are likely to produce more forest EVI breaks than selection and shelterwood systems, which are commonly used in the study area [37]. Perhaps, the latter two harvesting systems are harder to detect using MODIS EVI data at 250 m resolution, as done here. Very few fire events are likely to occur in the AGPE due to fire management activities. Yet, the relatively low number of EVI break pixels overlapping fire pixels was not expected given that fire disturbance usually produces a distinct abrupt change on the ground. Less than 1% of EVI negative breaks matched insect damage (only 7.7% of insect damage polygons were intersected). This little overlap with insect damage data is harder to explain. [PERSON] et al. [9] found that insects can produce high-severity disturbance in temperate forests as noted by on-the-ground local experts. Here, there were very few matches between negative breaks and insect damage. It is possible that the severity of insect damage is diluted at the pixel resolution used (250 m), producing more gradual changes rather than abrupt ones. As such, these gradual changes may not be detected as trend breaks but rather as browning or reversal trends (greening to browning). When using CanLaD (which captures mostly fire and harvest disturbance pixels) and GFC disturbance data (not labeled), about 28.1% and 60.4% of EVI negative breaks overlapped with these disturbance pixels. Notably, only 13.7% of CanLaD and 10.3% of GFC disturbance pixels overlapped with EVI negative breaks. This means that most disturbance pixels in these two data sources were not linked to negative breaks. (A match among different disturbance datasets (Table 19) in Appendix A.5 shows important mismatches too). The relatively poor match between EVI negative breaks and the different disturbance data used may reflect the fact that EVI is a one-dimensional metric that may not capture the full complexity of disturbance patterns. Yet, our approach likely detected low-intensity disturbances (e.g., hail, short-lived climate stress) and/or forest degradation [69]. XGBoost models revealed predictors causing variation in the magnitude of breaks as well as strong non-linearities and interactions among predictors. Forest age, winter DD5, and summer CMI were among the top five predictors for negative and positive breaks but with different rankings. Notably, four out of the top five climate predictors ranged in temporal lags from 1 to 3 years. These results are in line with other forest studies that have shown that 2- and 3-year climate lag effects characterize low and moderate disturbance activity clusters in temperate forests [9].