Seasonal and Inter-annual Phenological Variability is Greatest in Low-Arctic and Wet Sites Across the North Slope of Alaska as Observed from Multiple Remote Sensing Platforms

Sergio A. Vargas Zesati, University of Texas at El Paso, savargas@utep.edu (Presenter)
Gesuri A. Ramirez, University of Texas at El Paso, gramirez12@utep.edu
Geovany Ramirez, New Mexico State University, geoabi@gmail.com
Christian G. Andresen, Los Alamos National Laboratory, candresen@lanl.gov
Nathan Healey, NASA Jet Propulsion Laboratory, natehealey@hotmail.com
Jeremy L. May, Florida International University, jmay010@fiu.edu
Steve F. Oberbauer, Florida International University, oberbaue@fiu.edu
Bob Hollister, Grand Valley State University, hollistr@gvsu.edu
Craig E Tweedie, University of Texas at El Paso, ctweedie@utep.edu

Rates of climate change have been documented to be highest in the Arctic when compared to other ecosystems across the globe, however the magnitude and variability of change is not well known. Plant phenological trends can shift in response to climate change and has the potential to elucidate seasonal and inter-annual shifts in ecosystem properties and processes. Traditionally, ecosystem phenology has been quantified using satellite-based systems and ground-based observations but each approach has limitations especially in high latitude regions. Mid-scale sensing platforms that measure plot to landscape scale optical properties (e.g. robotic tram systems, unmanned aerial vehicles (UAVs), pheno-cams) have shown to provide alternative, and in most cases, low-cost solutions with comparable results to those acquired traditionally. This study contributes to the US Arctic Observing Network (AON) and assesses the effectiveness of using plot-level images (PLI), imagery acquired from pheno-cams, and kite aerial photography (KAP) for deriving measures of phenological variability (e.g. start of season (SOS), greening and end of season (EOS)) for dominant vegetation communities near Utqiaġvik (formerly Barrow) and Atqasuk, Alaska. Using five growing seasons of digital imagery acquired from these platforms, the Green Chromatic Coordinate (GCC)) was derived from RGB digital numbers (DN) and compared to the normalized difference vegetation index (NDVI) calculated from ground-based reflectance measurements. NDVI has been shown to be an effective proxy of primary productivity across multiple ecosystems including the Arctic. The three low-cost sensor platforms showed trends that tracked the traditionally preferred NDVI but showed an improved capacity to document fine-scale species-level phenological changes at high temporal frequencies. Seasonal and inter-annual variability in GCC and NDVI were greatest in low arctic and wet sites while high arctic and dry sites showed less variability. Preliminary results suggest that the strong seasonal and inter-annual variability in arctic landscapes, similar to those sampled for this study, is driven by moist to wet land cover types. Future studies will extend cross-scale analysis to a variety of satellite platforms (i.e. WorldView, Landsat, MODIS) to understand how such patterns transcend sampling at different spatial scales and sensor platforms.

Associated Project(s): 

• Huemmrich-01

Poster Location ID: 103

Session Assigned: Vegetation Dynamics and Distribution