Exploring Spring Onset at Continental Scales: Mapping Phenoregions and Correlating Temperature and Satellite-Based Phenometrics

Abstract

Each spring many plants put on new leaves and/or open their flowers creating a "green-wave" that can be tracked using phenological data. Various phenological datasets can be used to study spring onset at continental to global scales. Here we present a novel exploratory analysis where we link two multi-decadal and high-spatial resolution datasets: temperature-based phenological indices and land surface phenological metrics derived from satellite images. Our exploratory analysis, illustrated with data for the conterminous US, focuses on identifying regions with similar spring onset, and on mapping the coherence between these phenological products. Our results show that the spring onset patterns captured by the satellite are more complex than the ones identified using temperature-based phenological indices. They also highlight areas with stable and unstable spring onsets (i.e., areas that tend to remain or change of phenoregion from year to year). Finally, our results reveal that temperature-based indices are both positively and negatively correlated with the phenological information that can be derived from satellites. This opens the door to the definition of rules to integrate multi-source phenological data. To cope with the computational challenges of analyzing big geospatial rasters, we executed our analysis on a cloud platform running Apache Spark and various of its extensions (e.g., Geotrellis, SparkMLlib). This platform performed well and allowed the execution of user-tailored analyses. Hence, we believe that our computational platform paves the path towards the efficient analysis of global vegetation phenology at very high spatial resolution and, more generally, to the analysis of the ever-increasing collections of geospatial data about our planet.

Springing forward: Migrating songbirds catch up with the start of spring in North America

“…Temperature influences arthropod emergence and activity both directly, because their development is linked to temperature, and indirectly, by promoting the development of leaves upon which caterpillars and other arthropods feed (van Asch & Visser, 2007). However, in general, dates of spring onset derived from temperature‐based models and remotely sensed vegetation indices are broadly correlated (Zurita‐Milla et al, 2020). Accordingly, we found similar patterns in migration rate and timing regardless of whether we characterized spring using temperature‐based first leaf models or satellite‐derived NDVI models, despite absolute differences in their estimates of spring onset day.…”

“…thropod emergence and activity both directly, because their development is linked to temperature, and indirectly, by promoting the development of leaves upon which caterpillars and other arthropods feed(van Asch & Visser, 2007). However, in general, dates of spring onset derived from temperature-based models and remotely sensed vegetation indices are broadly correlated(Zurita-Milla et al, 2020).…”

A Spark-Based Platform to Extract Phenological Information from Satellite Images

“…In our previous work we used cloud computing to generate temperature-based phenological indices [1], [2], and to relate one phenological metric, namely the Start-of-Season (SOS), with those indices [3], [4]. Here we present an extension of our work where we use a Spark-based platform to efficiently extract phenological metrics from time series of NDVI and EVI.…”

“…This platform allows obtaining and analyzing high spatial resolution metrics (in this case 1km) from 10-day composites. The platform uses the same architecture as in [3], i.e., it is organized into three layers: a storage layer, a processing layer, and JupyterHub services for user-interaction. It is designed to store the data in well-known file formats like GeoTiffs and Hierarchical Data Format (HDF).…”