|Title||Watching grass grow: Successes and limitations of image-based methods for monitoring grassland phenology|
|Publication Type||Conference Paper|
|Year of Publication||2017|
|Conference Name||12th International Congress of Ecology INTECOL 2017|
|Publisher||International Congress of Ecology INTECOL|
|Conference Location||Beijing, China|
|ARIS Log Number||345976|
Seasonal changes in aboveground primary production (i.e. phenology) are influenced by environmental conditions with implications for land-atmosphere interactions, carbon cycling, and agricultural production. Monitoring phenology and quantifying seasonal patterns across spatially extensive grasslands and savannas require cost-effective, consistent, and accurate approaches for detecting changes in the growing season. Remotely sensed imagery offers capabilities to quantify growing season metrics via vegetation indices that have been linked to biomass and forage and net primary production. However, in many water-limited ecosystems contributions of exposed soil due to modest vegetation cover and susceptibility of vegetation to drought challenge these biophysical retrievals using moderate- and coarse-resolution satellite imagery. This challenge emphasizes the importance of verifying seasonal metrics derived from remotely sensed imagery (e.g., start, end, and length of growing season and timing of peak greenness) to identify limitations and uncertainty of said metrics used to forecast plant responses to climate and estimate biomass accrual and crop yields. We collected and evaluated data at multiple spatial and temporal scales from 2012 through 2016 to quantify the degree of correspondence between field and remotely sensed growing season metrics. This study was conducted at an ungrazed desert grassland site in southern New Mexico. Weekly field observations of plant phenology and canopy greenness were compared with metrics derived from MODIS satellite imagery and near-surface digital cameras (hereafter “phenocams”). MODIS 250-m NDVI vegetation index data are acquired every 16 days while color images are acquired daily from phenocams mounted at a height of ca 6 meters providing data at finer spatial and temporal scales that those from satellite. In addition, phenocams offer the opportunity to identify individual plants in the image to extract species-specific patterns in greenness. Growing season metrics from both types of imagery were derived using Timesat software (v3.2).