PLANT PHENOLOGICAL MONITORING BASED ON AUTOMATED RECORDING OF HIGH RESOLUTION DIGITAL IMAGES

The importance of phenological research for understanding the consequences of global environmental change on vegetation is highlighted in the most recent IPCC reports. Collecting time series of phenological events appears to be crucial to better understand how vegetation systems respond to climatic regime fluctuations, and, consequently, to develop effective management and adaptation strategies. However, traditional monitoring of phenology is labor intensive and costly and affected to a certain degree of subjective inaccuracy. Other methods used to quantify the seasonal patterns of vegetation development are based on satellite remote sensing (land surface phenology) but they operate at coarse spatial and temporal resolution. To overcome the issues related to the application of these methodologies, different approaches for vegetation monitoring based on “near-surface” remote sensing have been proposed in recent researches (Sonnentag et al., 2010). In particular, the use of digital cameras has become more common for phenological monitoring. Digital images provide spectral information in the red, green, and blue (RGB) wavelengths. Inflection points in seasonal variations of intensities of each color channel can be used to identify phenological events. Canopy green-up phenology can be quantified from the greenness indices. Species-specific dates of leaf emergence can be estimated by RGB image analyses (Richardson et al. 2009). In this research, an Automated Phenological Observation System (APOS), based on digital image sensors was developed for monitoring the phenological behavior of shrubland species in a Mediterranean site. The APOS system was developed under the INCREASE (an Integrated Network on Climate Change Research) EU-funded research infrastructure project, which is based upon large scale field experiments with non-intrusive climatic manipulations. The experimental site is located in North-West Sardinia, within the Nature Reserve Porto Conte – Capo Caccia. Vegetation mainly consists of Mediterranean shrubland species. Vegetation monitoring was conducted from October 2012 to November 2013. The APOS system was set to acquire one panorama per day at noon (36 shots x panorama - 3 rows x 12 columns) with a 30% of image overlapping. On each panorama ROIs (Regions of Interest) focusing major species of the shrubland ecosystem were fixed. An image analysis was performed to obtain information on vegetation status (i.e. color signals and phenology). From the visual analysis of the high resolution images, dates of the key phenological stages (i.e. leafing and flowering) were identified. The color channel information (digital numbers) was extracted using a Mathlab script (R2014b, The MathWorks). Chromatic coordinates and several indices were calculated over a 2-years period from May 2012 to end of 2013. Mean daily values of the green chromatic coordinates (gcc) were calculated for the most representative species of the site (such as Cistus monspeliensis L. and Pistacia lentiscus L.). The green colors signals clearly followed the pattern of vegetative development, with evident peaks of gcc when leafing stage was observed in the studied species. For some species (i.e. Cistus), values of Red Excess Index were related to the changes of vegetation status during the drought periods, when leaf fall occur to avoid water stress by reducing transpiration surface. The use of near-surface remote sensing methods based on digital images appeared to be promising (e.g. increasing rates of data collection and standardized data sets). Preliminary results indicate that the use of digital images is well-suited to identify phenological behavior of shrubland Mediterranean species. Results of digital images analysis provide information useful to interpret phenological responses of plants to climate change, to validate satellite-based phenology data, and to provide input to adaption strategies and action plans to climate change.