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A dynamic relationship between the leaf phenology and rainfall regimes of Hawaiian tropical ecosystems

A remote sensing approach

Bibliographic Data

ID20153736
AuthorsSunyurp Park (Pusan National University, corresponding author)
Year2010
Volume31
Issue3
Pages371-383
Publication date2010-11-01
Open AccessYes
TypeARTICLE
VenueSingapore Journal of Tropical Geography (JOURNAL)
Journal identifiersISSN: 0129-7619 • E-ISSN: 1467-9493
PublisherWiley (PUBLISHER • GB)
DOI10.1111/j.1467-9493.2010.00408.x
OpenAlexW1583999227
LanguageEN
References cited46

Hypertemporal MODIS time series data provide a unique opportunity to investigate a dynamic relationship between leaf phenology and the climatic pattern of diverse, cloud-prone Hawaiian ecosystems. Harmonic analysis summarized the complex greenness signals of Hawaiian tropical ecosystems into two main phenological wave forms – a moisture-limited and a light-limited type. Greenness maximums occurred during the wet season in dry and mesic ecosystems, and during the dry season in wet forests. The magnitude and periodicity of greenness fluctuations were also rainfall-dependent. The annual greenness amplitude increased with increasing mean annual precipitation (MAP) in dry and mesic ecosystems. In wetter environments where MAP was greater than 3000 mm, however, annual greenness amplitude decreased with MAP. Annual greenness periodicity was stronger in drylands than in wet forests, and it weakened as annual precipitation increased. This result shows that rainfall is less important as a limiting factor in wet forests than it is in drylands. Therefore, leaf phenology is not governed by rainfall seasonality as forest wetness increases in the region

Agroforestry · Atmospheric sciences · Biology · Climatology · Dry season · Ecosystem · Forest ecology · Geography · Meteorology · Phenology · Precipitation · Seasonality · Tropical and subtropical dry broadleaf forests · Tropics · Wet season · Ecology · Environmental Science · Geology · Plant Water Relations and Carbon Dynamics · Remote Sensing in Agriculture · Species Distribution and Climate Change

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