Moderate-resolution imaging spectroradiometer-based vegetation indices and their fidelity in the tropics

Moderate-resolution imaging spectroradiometer (MODIS)-derived vegetation indices (VIs)—the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Land Surface Water Index (LSWI)—are evaluated in terms of their sensitivity to the seasonal greenness pattern and moisture regime of the tropical forests of Hawaii. The annual mean NDVI and EVI signals most rapidly saturated as total annual rainfall increased to a mesic condition, but LSWI was responsive to a much wetter environment. Ecoregional analyses of biweekly VI time series revealed that all three VIs followed the typical pattern of summer-low and winter-high rainfall for dry forests and shrublands. However, NDVI and EVI did not show any significant seasonality of wet forests while LSWI represented a summer-high and winter-low greenness pattern. The three VIs did not respond to the Leaf Area Index (LAI) very well as LAI reached 4, but they sensitively responded to the fraction of photosynthetically active radiation (fPAR) and leaf moisture content. Especially, LSWI responded most sensitively to fPAR and leaf water content in the wet environment, where fPAR and leaf water content were >0.6 and >40%. Greenness seasonality was more strongly represented by LSWI than by NDVI and EVI for all ecoregions considered in the study. In short, it is believed that LSWI is more appropriate for a canopy phenology study than the other two VIs in wet forests of the tropical environment.

[1]  S. Nicholson,et al.  The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. I. NDVI response to rainfall , 1994 .

[2]  Maosheng Zhao,et al.  A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production , 2004 .

[3]  M. Keller,et al.  Carbon in Amazon Forests: Unexpected Seasonal Fluxes and Disturbance-Induced Losses , 2003, Science.

[4]  J. Chambers,et al.  Regional ecosystem structure and function: ecological insights from remote sensing of tropical forests. , 2007, Trends in ecology & evolution.

[5]  David Riaño,et al.  Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona , 2008 .

[6]  S. Nilsson,et al.  Management of forests for mitigation of greenhouse gas emissions , 1996 .

[7]  T. Wilbanks,et al.  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[8]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[9]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[10]  Thomas J. Jackson,et al.  Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery , 2008 .

[11]  J. Chen,et al.  Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images , 1996 .

[12]  Jiyuan Liu,et al.  Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data , 2002 .

[13]  C. Tucker.  Remote sensing of leaf water content in the near infrared , 1980 .

[14]  G. Asner,et al.  Satellite observation of El Niño effects on Amazon Forest phenology and productivity , 2000 .

[15]  Gregory P. Asner,et al.  Satellite Monitoring of Vegetation Phenology and Fire Fuel Conditions in Hawaiian Drylands , 2005 .

[16]  S. Tarantola,et al.  Detecting vegetation leaf water content using reflectance in the optical domain , 2001 .

[17]  M. Tamura,et al.  Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data , 2004 .

[18]  G. Henebry,et al.  Spatio‐temporal change analysis to identify anomalous variation in the vegetated land surface: ENSO effects in tropical South America , 2005 .

[19]  Johannes J. Feddema,et al.  Impacts of hydrologic soil properties on drought detection with MODIS thermal data , 2004 .

[20]  Corinne Le Quéré,et al.  Regional changes in carbon dioxide fluxes of land and oceans since 1980. , 2000, Science.

[21]  A. Huete,et al.  Optical-Biophysical Relationships of Vegetation Spectra without Background Contamination , 2000 .

[22]  T. Jackson,et al.  Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands , 2005 .

[23]  S. Wofsy,et al.  Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data , 2004 .

[24]  G. Asner,et al.  Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[25]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[26]  E. Raymond Hunt,et al.  Remote sensing of vegetation water content using shortwave infrared reflectances , 2007, SPIE Optical Engineering + Applications.

[27]  E. Davidson,et al.  Satellite-based modeling of gross primary production in an evergreen needleleaf forest , 2004 .

[28]  F. Joos,et al.  The Effects of Land Use and Management on the Global Carbon Cycle , 2012 .

[29]  Sunyurp Park.  Synchronicity between satellite-measured leaf phenology and rainfall regimes in tropical forests. , 2009 .

[30]  Martha C. Anderson,et al.  Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans , 2004 .

[31]  P. S. Roy,et al.  Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product , 2010 .

[32]  Edward A. G. Schuur,et al.  PRODUCTIVITY AND GLOBAL CLIMATE REVISITED: THE SENSITIVITY OF TROPICAL FOREST GROWTH TO PRECIPITATION , 2003 .

[33]  J. Mustard,et al.  Cross-scalar satellite phenology from ground, Landsat, and MODIS data , 2007 .

[34]  F. M. Danson,et al.  High-spectral resolution data for determining leaf water content , 1992 .

[35]  M. Hardisky.  The Influence of Soil Salinity, Growth Form, and Leaf Moisture on-the Spectral Radiance of Spartina alterniflora Canopies , 2008 .

[36]  A. Huete,et al.  Amazon rainforests green‐up with sunlight in dry season , 2006 .

[37]  J. Burt,et al.  Elementary statistics for geographers , 1995 .

[38]  Alan H. Strahler,et al.  Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments , 2005 .

[39]  Roberta E. Martin,et al.  Vegetation–Climate Interactions among Native and Invasive Species in Hawaiian Rainforest , 2006, Ecosystems.

[40]  R. Dickinson,et al.  Analysis of leaf area index products from combination of MODIS Terra and Aqua data , 2006 .

[41]  G. Powell,et al.  Terrestrial Ecoregions of the World: A New Map of Life on Earth , 2001 .

[42]  Ranga B. Myneni,et al.  Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS , 2006 .

[43]  A. Strahler,et al.  Climate controls on vegetation phenological patterns in northern mid‐ and high latitudes inferred from MODIS data , 2004 .

[44]  S. Frolking,et al.  Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest , 2005 .

[45]  Karin S. Fassnacht,et al.  Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites , 1999 .

[46]  Alfredo Huete,et al.  Terrestrial Carbon Sinks for the United States Predicted from MODIS Satellite Data and Ecosystem Modeling , 2006 .

[47]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[48]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[49]  P. Vitousek,et al.  The Mauna Loa environmental matrix: foliar and soil nutrients , 1992, Oecologia.

[50]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[51]  Jiyuan Liu,et al.  Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data , 2004 .

[52]  Christopher Potter,et al.  Terrestrial Vegetation Dynamics and Global Climate Controls in North America: 2001–05 , 2008 .