TY - JOUR

T1 - Forest phenoclusters for Argentina based on vegetation phenology and climate

AU - Silveira, Eduarda M.O.

AU - Radeloff, Volker C.

AU - Martínez Pastur, Guillermo J.

AU - Martinuzzi, Sebastián

AU - Politi, Natalia

AU - Lizarraga, Leonidas

AU - Rivera, Luis O.

AU - Gavier-Pizarro, Gregorio I.

AU - Yin, He

AU - Rosas, Yamina M.

AU - Calamari, Noelia C.

AU - Navarro, María F.

AU - Sica, Yanina

AU - Olah, Ashley M.

AU - Bono, Julieta

AU - Pidgeon, Anna M.

N1 - Funding Information: We gratefully acknowledge support for this work by the National Aeronautics and Space Administration (NASA) Biodiversity and Ecological Forecasting Program, project 80NSSC19K0183. G. Gavier‐Pizarro participated through Argentine grant PICT 2014‐1481. Dr. I. Gasparri, Dr. S. Torrella, and Dr. M. Zak provided valuable suggestions for the Humid and Dry Chaco region phenoclusters labeling, and P. Aceñolaza, L. Butti, and D. Estelrich validated phenoclusters in the Espinal region. Dirección Nacional de Bosques del Ministerio de Ambiente y Desarrollo Sostenible de Argentina provided field data from the second national inventory of native forests and complementary information. Two anonymous reviewers provided valuable comments that greatly improved our manuscript. Funding Information: We gratefully acknowledge support for this work by the National Aeronautics and Space Administration (NASA) Biodiversity and Ecological Forecasting Program, project 80NSSC19K0183. G. Gavier-Pizarro participated through Argentine grant PICT 2014-1481. Dr. I. Gasparri, Dr. S. Torrella, and Dr. M. Zak provided valuable suggestions for the Humid and Dry Chaco region phenoclusters labeling, and P. Ace?olaza, L. Butti, and D. Estelrich validated phenoclusters in the Espinal region. Direcci?n Nacional de Bosques del Ministerio de Ambiente y Desarrollo Sostenible de Argentina provided field data from the second national inventory of native forests and complementary information. Two anonymous reviewers provided valuable comments that greatly improved our manuscript. Publisher Copyright: © 2022 The Ecological Society of America.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - cluster

KW - conservation

KW - enhanced vegetation index

KW - greenness

KW - land surface temperature

KW - Landsat 8

KW - precipitation

KW - Sentinel 2

U2 - 10.1002/eap.2526

DO - 10.1002/eap.2526

M3 - Journal article

C2 - 34994033

AN - SCOPUS:85126826755

VL - 32

JO - Ecological Applications

JF - Ecological Applications

SN - 1051-0761

IS - 3

M1 - e2526

ER -