A new system based on digital imagers for detecting vegetation changes

Collection of time series of phenological events, at a variety of scales, appears to be of crucial importance to better understand how vegetation systems respond to climatic regimes fluctuations, and, consequently to develop effective management and adaptation strategies. Plant phenological observations have been performed collecting the date of occurrence for key events by periodic direct observations. However, some concerns are related to traditional monitoring of phenology: recording observations dates is labor intensive and costly; quality of data depends heavily upon the observational skills and effort of the observers; data can be affected to a certain degree of subjective inaccuracy; they are typically discontinuous and geographically limited. A new system to identify vegetation changes, in particular the phenological behavior of shrubland species was realized. The Automated Phenological Observation System (APOS) was developed using standard, commercially available cameras, connected to an automated robotic system, and tested in the Porto Conte (MED-IT) Italian site of the INCREASE project network. The general architecture of the APOS includes several components in order to perform the major following functions: (1) image acquisition, made through the use of a camera connected to a robot, so as to frame and pan an area in accordance with the visual coverage of the site of study; (2) image transmission, permitted by a modem-router for broadband access to Internet; (3) image processing by image stitching and elaboration made by remote computer. A study of the equipment use on the field has been made to optimize camera-plot focus distance and parameters values, and the visual coverage was calculated using specifically developed custom software (VISPO) for the MED-IT site. Phenology of shrubland was monitored during 2012 and a comparison with direct observations was performed. The application of new technologies such as digital imaging systems for detecting vegetation and plant phenology changes appeared to be promising for several reasons: new technologies can make data collection cheaper and easier reducing labor and costs of field observations, new monitoring tools will exponentially increase rates of data collection, long term data collection projects and large, long-term standardized data sets can be easier obtained because data can be systematically recorded and permanently stored.