10.5061/DRYAD.QRFJ6Q5GD
Silveira, Eduarda
0000-0002-1015-4973
University of Wisconsin-Madison
Radeloff, Volker
University of Wisconsin-Madison
Martínez Pastur, Guillermo
0000-0003-2614-5403
National Scientific and Technical Research Council
Martinuzzi, Sebastián
University of Wisconsin-Madison
Politi, Natalia
National Scientific and Technical Research Council
Lizarraga, Leonidas
Administración de Parques Nacionales
Rivera, Luis
0000-0003-2960-9779
National Scientific and Technical Research Council
Gavier-Pizarro, Gregorio
National Agricultural Technology Institute
Yin, He
0000-0002-2839-1723
Kent State University
Rosas, Yamina
0000-0001-7476-399X
National Scientific and Technical Research Council
Calamari, Noelia
0000-0002-9605-2969
National Agricultural Technology Institute
Navarro, María
0000-0003-1792-1466
National Agricultural Technology Institute
Sica, Yanina
0000-0002-1720-0127
Yale University
Olah, Ashley
0000-0002-5442-5077
University of Wisconsin-Madison
Bono, Julieta
Ministerio de Ambiente y Desarrollo Sostenible de la Nación
Pidgeon, Anna
University of Wisconsin-Madison
Forest phenoclusters for Argentina based on vegetation phenology and climate
Dryad
dataset
2021
2021-10-07T00:00:00Z
2021-10-07T00:00:00Z
en
234686623 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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 does 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-non forest 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.