Monitoring fall foliage coloration dynamics using time-series satellite data

Fall foliage coloration is a phenomenon that occurs in many deciduous trees and shrubs worldwide. Measuring the phenology of fall foliage development is of great interest for climate change, the carbon cycle, ecology, and the tourist industry; but little effort has been devoted to monitoring the regional fall foliage status using remotely-sensed data. This study developed an innovative approach to monitoring fall foliage status by means of temporally-normalized brownness derived from MODIS (Moderate Resolution Imaging Spectroradiometer) data. Specifically, the time series of the MODIS Normalized Difference Vegetation Index (NDVI) was smoothed and functionalized using a sigmoidal model to depict the continuous dynamics of vegetation growth. The modeled temporal NDVI trajectory during the senescent phase was further combined with the mixture modeling to deduce the temporally-normalized brownness index which was independent of the surface background, vegetation abundance, and species composition. This brownness index was quantitatively linked with the fraction of colored and fallen leaves in order to model the fall foliage coloration status. This algorithm was tested by monitoring the fall foliage coloration phase using MODIS data in northeastern North America from 2001 to 2004. The MODIS-derived timing of foliage coloration phases was compared with in-situ measurements, which showed an overall absolute mean difference of less than 5 days for all foliage coloration phases and about 3 days for near peak coloration and peak coloration. This suggested that the fall foliage coloration phase retrieved from the temporally-normalized brownness index was qualitatively realistic and repeatable.

[1]  H. Kerdiles,et al.  NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa , 1995 .

[2]  Dan Tarpley,et al.  Diverse responses of vegetation phenology to a warming climate , 2007 .

[3]  Zhao-Liang Li,et al.  Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data , 2002 .

[4]  Mark D. Schwartz,et al.  Intercomparing multiple measures of the onset of spring in eastern North America , 2010 .

[5]  K. Wittich,et al.  Area-averaged vegetative cover fraction estimated from satellite data , 1995 .

[6]  M. Schaepman,et al.  Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006 , 2009 .

[7]  D. Lobell,et al.  Cropland distributions from temporal unmixing of MODIS data , 2004 .

[8]  George Alan Blackburn,et al.  Seasonal variations in the spectral reflectance of deciduous tree canopies , 1995 .

[9]  Hella Ellen Ahrends,et al.  Quantitative phenological observations of a mixed beech forest in northern Switzerland with digital photography , 2008 .

[10]  David W. Lee,et al.  Why leaves turn red in autumn. The role of anthocyanins in senescing leaves of red-osier dogwood. , 2001, Plant physiology.

[11]  J. B. Thornes,et al.  The Ecology of Erosion , 1985, Geography.

[12]  Manfred Owe,et al.  Vegetation spatial variability and its effect on vegetation indices , 1987 .

[13]  D. Barrett,et al.  Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors. , 2009 .

[14]  Bradley C. Reed,et al.  Remote Sensing Phenology , 2009 .

[15]  Mark A. Friedl,et al.  Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements , 2006 .

[16]  Aaron Moody,et al.  Land-Surface Phenologies from AVHRR Using the Discrete Fourier Transform , 2001 .

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

[18]  P. Beck,et al.  Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI , 2006 .

[19]  Paul E. Johnson,et al.  Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander 1 Site , 1986 .

[20]  N. Drake,et al.  Scaling land surface parameters for global‐scale soil erosion estimation , 2002 .

[21]  Paul E. Johnson,et al.  Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis , 1985 .

[22]  M. S. Moran,et al.  Spatial and temporal dynamics of vegetation in the San Pedro River basin area , 2000 .

[23]  Martha C. Anderson,et al.  A Two-Source Time-Integrated Model for Estimating Surface Fluxes Using Thermal Infrared Remote Sensing , 1997 .

[24]  John F. Mustard,et al.  A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data , 2007 .

[25]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[26]  William P. Kustas,et al.  An intercomparison of the Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) modeling schemes , 2007 .

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

[28]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[29]  John F. Mustard,et al.  Abundance and distribution of ultramafic microbreccia in Moses Rock dike - Quantitative application of mapping spectroscopy , 1987 .

[30]  Graeme L. Hammer,et al.  Improved methods for predicting individual leaf area and leaf senescence in maize (Zea mays) , 1998 .

[31]  P. Schaberg,et al.  Factors influencing red expression in autumn foliage of sugar maple trees. , 2003, Tree physiology.

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

[33]  R. DeFries,et al.  Derivation and Evaluation of Global 1-km Fractional Vegetation Cover Data for Land Modeling , 2000 .

[34]  N. C. Strugnell,et al.  First operational BRDF, albedo nadir reflectance products from MODIS , 2002 .

[35]  Asko Noormets,et al.  Phenology of ecosystem processes : applications in global change research , 2009 .

[36]  A. Holtslag,et al.  Water vapour and carbon dioxide fluxes over bog vegetation , 2003 .

[37]  David A. Ratkowsky,et al.  Nonlinear regression modeling : a unified practical approach , 1984 .

[38]  M. Friedl,et al.  Land Surface Phenology from MODIS: Characterization of the Collection 5 Global Land Cover Dynamics Product , 2010 .

[39]  Mark A. Friedl,et al.  Sensitivity of vegetation phenology detection to the temporal resolution of satellite data , 2009 .

[40]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.

[41]  H. Nam,et al.  Molecular genetics of leaf senescence in Arabidopsis. , 2003, Trends in plant science.

[42]  R. Tateishi,et al.  Relationships between percent vegetation cover and vegetation indices , 1998 .

[43]  Andrew D. Richardson,et al.  Phenology of a northern hardwood forest canopy , 2006 .

[44]  S. Tompkins,et al.  Optimization of endmembers for spectral mixture analysis , 1997 .

[45]  A. Hopkins,et al.  Bioclimatics: A Science of Life and Climate Relations , 1938 .

[46]  J. Settle,et al.  Linear mixing and the estimation of ground cover proportions , 1993 .

[47]  Liang Liang,et al.  Landscape phenology: an integrative approach to seasonal vegetation dynamics , 2009, Landscape Ecology.

[48]  Alan R. Gillespie,et al.  Vegetation in deserts. I - A regional measure of abundance from multispectral images. II - Environmental influences on regional abundance , 1990 .

[49]  J. Abatzoglou,et al.  Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. , 2009 .

[50]  K. Davis,et al.  The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes , 2009 .

[51]  J. Nicolau,et al.  Effect of vegetation cover on the hydrology of reclaimed mining soils under Mediterranean-Continental climate , 2009 .

[52]  Bruno Andrieu,et al.  Candidate high spectral resolution infrared indices for crop cover , 1993 .

[53]  Andrew D Richardson,et al.  Near-surface remote sensing of spatial and temporal variation in canopy phenology. , 2009, Ecological applications : a publication of the Ecological Society of America.

[54]  S. Running,et al.  A continental phenology model for monitoring vegetation responses to interannual climatic variability , 1997 .

[55]  F. J. García-Haro,et al.  Linear spectral mixture modelling to estimate vegetation amount from optical spectral data , 1996 .

[56]  F. J. Richards.  A Flexible Growth Function for Empirical Use , 1959 .

[57]  Paul E. Johnson,et al.  Quantitative analysis of planetary reflectance spectra with principal components analysis , 1985 .

[58]  G. Gutman,et al.  The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models , 1998 .

[59]  Michele Meroni,et al.  Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series , 2008 .

[60]  G. Henebry,et al.  Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan , 2004 .

[61]  Geoffrey M. Henebry,et al.  Spatio-Temporal Statistical Methods for Modelling Land Surface Phenology , 2010 .

[62]  A. Huete,et al.  Leaf area index and normalized difference vegetation index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River , 2004 .

[63]  Jingfeng Xiao,et al.  A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA , 2005 .

[64]  Fabio Maselli,et al.  Definition of Spatially Variable Spectral Endmembers by Locally Calibrated Multivariate Regression Analyses , 2001 .

[65]  Mark D. Schwartz,et al.  Changes in North American spring , 2000 .

[66]  Ranga B. Myneni,et al.  The impact of gridding artifacts on the local spatial properties of MODIS data : Implications for validation, compositing, and band-to-band registration across resolutions , 2006 .

[67]  E. Harrison,et al.  The molecular analysis of leaf senescence--a genomics approach. , 2002, Plant biotechnology journal.

[68]  J. Settle,et al.  Mapping Vegetation, Soils, and Geology in Semiarid Shrublands Using Spectral Matching and Mixture Modeling of SWIR AVIRIS Imagery , 1999 .