Assessment of phenologic variability in Amazon tropical rainforests using hyperspectral hyperion and MODIS satellite data

Alfredo R. Huete; Youngwook Kim; Piyachat Ratana; Kamel Didan; Yosio E. Shimabukuro; Tomoaki Miura

Assessment of phenologic variability in Amazon tropical rainforests using hyperspectral hyperion and MODIS satellite data

Alfredo R. Huete, Youngwook Kim, Piyachat Ratana, Kamel Didan, Yosio E. Shimabukuro, Tomoaki Miura

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Phenology represents the seasonal timing and annual repetition of biologic life cycle events and is a characteristic property of ecosystem functioning and predictor of ecosystem processes. Shifts in phenology depict a canopy’s integrated response to environmental change and influence local biogeochemical processes, including nutrient dynamics, photosynthesis, water cycling, soil moisture depletion, and canopy physiology [1]. An understanding of vegetation phenology is prerequisite to interannual studies and predictive modeling of land surface responses to climate change [2-4].

Original language	English
Title of host publication	Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests
Publisher	CRC Press
Pages	233-259
Number of pages	27
ISBN (Electronic)	9781420053432
ISBN (Print)	9781420053418
Publication status	Published - Jan 1 2008
Externally published	Yes

ASJC Scopus subject areas

Engineering(all)
Earth and Planetary Sciences(all)
Agricultural and Biological Sciences(all)
Environmental Science(all)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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abstract = "Phenology represents the seasonal timing and annual repetition of biologic life cycle events and is a characteristic property of ecosystem functioning and predictor of ecosystem processes. Shifts in phenology depict a canopy{\textquoteright}s integrated response to environmental change and influence local biogeochemical processes, including nutrient dynamics, photosynthesis, water cycling, soil moisture depletion, and canopy physiology [1]. An understanding of vegetation phenology is prerequisite to interannual studies and predictive modeling of land surface responses to climate change [2-4].",

author = "Huete, {Alfredo R.} and Youngwook Kim and Piyachat Ratana and Kamel Didan and Shimabukuro, {Yosio E.} and Tomoaki Miura",

note = "Publisher Copyright: {\textcopyright} 2008 by Taylor & Francis Group, LLC.",

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day = "1",

language = "English",

isbn = "9781420053418",

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T1 - Assessment of phenologic variability in Amazon tropical rainforests using hyperspectral hyperion and MODIS satellite data

AU - Huete, Alfredo R.

AU - Kim, Youngwook

AU - Ratana, Piyachat

AU - Didan, Kamel

AU - Shimabukuro, Yosio E.

AU - Miura, Tomoaki

PY - 2008/1/1

Y1 - 2008/1/1

N2 - Phenology represents the seasonal timing and annual repetition of biologic life cycle events and is a characteristic property of ecosystem functioning and predictor of ecosystem processes. Shifts in phenology depict a canopy’s integrated response to environmental change and influence local biogeochemical processes, including nutrient dynamics, photosynthesis, water cycling, soil moisture depletion, and canopy physiology [1]. An understanding of vegetation phenology is prerequisite to interannual studies and predictive modeling of land surface responses to climate change [2-4].

AB - Phenology represents the seasonal timing and annual repetition of biologic life cycle events and is a characteristic property of ecosystem functioning and predictor of ecosystem processes. Shifts in phenology depict a canopy’s integrated response to environmental change and influence local biogeochemical processes, including nutrient dynamics, photosynthesis, water cycling, soil moisture depletion, and canopy physiology [1]. An understanding of vegetation phenology is prerequisite to interannual studies and predictive modeling of land surface responses to climate change [2-4].

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M3 - Chapter

AN - SCOPUS:43649097370

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BT - Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests

PB - CRC Press

ER -

Assessment of phenologic variability in Amazon tropical rainforests using hyperspectral hyperion and MODIS satellite data

Abstract

ASJC Scopus subject areas

UN SDGs

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