# Monitoring Forest Phenology in a Changing World
## 2 Monitoring Phenology
### Trade-Offs among Phenological Monitoring Techniques
#### 2.3.1 Variation in the Spatial Resolution of Phenological Data  
Spatial resolution describes the area that is monitored per unit, where the unit for most sensor-based methods is an individual pixel. For non-sensor-based methods, spatial resolution describes the area covered within an individual observation or by an individual device (e.g., accelerometer or collection trap). Satellite mounted sensors form the backbone of the remote sensing approach envisaged by [PERSON] et al. [87], but until recently they provided relatively low spatial pixel resolution (0.31 to 1000 m) compared to drones or fixed digital cameras (0.02 m) (Table 1). When monitoring at the landscape level, such as studiesmonitoring the temperate whole-forest seasonal change, satellites can be highly effective for monitoring phenology as they can cover large areas in a few pixels. For example, the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Terra satellite possesses seven spectral bands that are specifically designed to monitor landscape changes, including phenology [21].  
Studies in temperate regions using satellites like Landsat (30 m resolution) and MODIS (500 m resolution) have been effective in modelling leaf flush and senescence dates [21; 40; 96; 122; 122]. A multisite study of temperate and boreal forest phenology using Landsat's phenology algorithm detected two-thirds of start of season and end of season dates over the 32 years included in the analysis [123]. Furthermore, [PERSON] et al., [75] successfully used Advanced Very High Resolution Radiometer (AVHRR) and MODIS to observe changes in land surface phenology from 1984-2014 that were caused by land-use change.  
In the tropics, by contrast, there is a disparity between the scale of plant phenology--often aseasonal and asynchronous--and that of satellite pixel resolution [25]. As phenological changes often occur at the scale of individual tree crowns, most phenology is undetectable by satellite mounted sensors (although see: [124]), and ground-based or UAV mounted cameras can offer a better alternative [125] (Table 1).  
A growing number of studies have used an approach based on fixed digital phenological cameras, in a mix of environments, to monitor phenology at high spatial resolution. Once the effective use of digital webcams to monitor spring green-up (leaf flushing) dates was demonstrated in a deciduous forest in North-Central USA [24], the approach has been extended to a wide set of ecosystems: savanna [126]; deciduous broadleaf forest [127; 128; 129; 105; 126; 127; 129]; Evergreen Broadleaf Forest [130]; Evergreen Needleleaf Forest [131; 132; 133]; and a mix of several habitats [134; 101; 106; 135; 42]. Similarly, recent studies have utilised UAV mounted cameras to monitor phenology in a variety of biomes, ranging from polar vegetation [136] to tropical forests [137], with the majority of studies to date focused on temperate regions [138; 139; 140; 125; 126; 127; 128; 129; 130; 131; 125; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143]. For example, [PERSON] and [PERSON] [140] found that in an oak-dominated deciduous forest, the Gcc derived from individual-tree pixels within drone imagery increases in spring following budburst and leaf flush, and decreases in autumn following leaf senescence and loss, with the opposite trend occurring for the Rcc. A similar approach adequately recorded phenological change in a multi-species, deciduous evergreen forest [142], and Berra, Gaulton and Barr [125] consistently detected individual tree level leaf flush dates using chromatic coordinates calculated from drone imagery.