Forest phenoclusters for Argentina based on vegetation phenology and climate

SILVEIRA, EDUARDA M. O.; RADELOFF, VOLKER C.; MARTÍNEZ PASTUR, GUILLERMO J.; MARTINUZZI, SEBASTIÁN; POLITI, NATALIA; LIZARRAGA, LEONIDAS; RIVERA, LUIS O.; GAVIER-PIZARRO, GREGORIO I.; YIN, HE; ROSAS, YAMINA M.; CALAMARI, NOELIA C.; NAVARRO, MARÍA F.; SICA, YANINA; OLAH, ASHLEY M.; BONO, JULIETA; PIDGEON, ANNA M.

ECOLOGICAL APPLICATIONS

ECOLOGICAL SOC AMER

Año: 2022 vol. 32

1051-0761

Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km2), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018–2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest–nonforest in areas where the lack of detailed ecological field data precludes tree species–level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions.