# Monitoring Forest Phenology in a Changing World
## 2 Monitoring Phenology
### Trade-Offs among Phenological Monitoring Techniques
#### 2.3.4 Data Processing and Interpretation  
The processing and interpretation of data can be a major factor in determining the effectiveness of a method for monitoring (Table 1). Visually observed data from binoculars written down on paper require very little processing and interpretation; just the transcription from paper to computer databases for analysis. However, the introduction of cloud-based data collection applications such as EpiCollect [155] remove the need for even this form of processing, allowing mobile phone-based data recording in the field that is automatically stored and backed up online in a data table format. Data collected via traps, accelerometers, dendrometers, and microcores is more time consuming to process as it requires the initial collection of phenological samples or records stored in the traps, followed by the processing of those samples and recording in either paper or digital form. Overall, however, the process, apart from microcore samples, requires little expertise and is straightforward to conduct. By contrast, eddy covariance and remote sensing data--both satellite and near ground (drones and fixed cameras)--typically requires more data processing and expertise to interpret. Studies on phenology that utilise far or near ground remotely sensed data have slightly different methods for data processing and interpretation (See Section 2.2. for more details), but the general workflow includes turning image features into proxies for phenological change. For example, for fixed cameras and UAV imagery this includes identifying a region of interest in the image (often a tree crown), extracting the red-green-blue pixel values from this region of interest, transforming the values into a spectral feature that may indicate the high presence of a certain colour band, and then comparing the spectral features in a time series to examine if there is a change in a specific colour band which may indicate the presence of phenological change. For example, a leaf flush event is characterized by an increase in greenness within the region of interest. The remote sensing methods arguably require the skill to code in a programming language such as Python, MATLAB or R, and expertise in coding, photogrammetry and image processing as they mostly produce data in the form of images which require processing to extract quantitative information from their pixel contents. Some applications make these data more easily accessible, such as Google Earth Engine [156], where the remotely sensed satellite data has be pre- collected and processed. Similarly, some software, such as TIMESAT [157], reduce the programming requirements by helping with the analysis of remotely sensed data for the user but does still require pre-processing and a thorough understanding of the data to set parameters. Determining the optimal metric(s) to use for quantifying phenology often requires pre-existing knowledge of the environment. However, for near-ground remote sensing such as fixed cameras and UAVs, most studies follow the standard set by [PERSON] et al., [24] and [PERSON] et al., [105] of using chromatic coordinates to suppress influences of scene illumination [105]. Similarly, for satellite-based sensing, most studies use the nadir BRDF-adjusted reflectance (NBAR) bands to calculate different Vegetation Indices which correlate with phenological change [21; 30; 121; 144]. Both methods rely on changing spectral characteristics to indicate a phenological event. Both methods are more quantitative than a visual assessment from the ground and have the potential to pick up more subtle changes in spectral characteristics that may be missed by the eye. However, in new environments this method still requires validation using ground observed data, to ensure the correct phenology is associated with the correct spectral change.