Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty

Abstract Current remote sensing studies of phenology have been limited to coarse spatial or temporal resolution and often lack a direct link to field measurements. To address this gap, we compared remote sensing methodologies using Landsat Thematic Mapper (TM) imagery to extensive field measurements in a mixed northern hardwood forest. Five vegetation indices, five mathematical fits to model a continuous temporal response, and a suite of threshold estimates for “start of spring/season” (SOS) assessments were compared to field measurements of bud burst stage and hemispherical photo derived canopy structural metrics (transparency, leaf area index, greenness). Results indicated that a four-parameter logistic model based on at least five spring coverages of the Enhanced Vegetation Index (EVI) and a SOS threshold of 0.3 was most closely related to field metrics and most accurate in predicting the date of full leaf out. Plot level SOS was predicted with a mean absolute error of 11 days for all species and elevation combinations, but improved to 9 days for hardwood dominated plots and 7 days for sugar maple dominated plots. Mean absolute error was improved to 8 days when forest type (mixed, conifer hardwood) was used to refine predictions. The consistency of prediction errors across forest types indicates that while overall accuracy across pixels may be low, inter-annual comparisons of changes in phenology on a pixel basis may provide accurate assessments of changes in phenology over time. This was confirmed by application to seven years of independent phenology data predicted with 12 days of mean absolute error. However, image availability will be a limiting factor in areas of frequent cloud cover.

[1]  Giles M. Foody,et al.  An assessment of radiance in Landsat TM middle and thermal infrared wavebands for the detection of tropical forest regeneration , 1996 .

[2]  T. Akiyama,et al.  Field experiments to test the use of the normalized-difference vegetation index for phenology detection. , 2010 .

[3]  Annette Menzel,et al.  Phenology: Its Importance to the Global Change Community , 2002 .

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

[5]  A. Richardson,et al.  Landscape controls on the timing of spring, autumn, and growing season length in mid‐Atlantic forests , 2012 .

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

[7]  J. Schönherr,et al.  Development of plant cuticles: fine structure and cutin composition of Clivia miniata Reg. leaves , 1988, Planta.

[8]  J. Mustard,et al.  Green leaf phenology at Landsat resolution: Scaling from the field to the satellite , 2006 .

[9]  K. Soudani,et al.  Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands , 2006 .

[10]  H. Mooney,et al.  Shifting plant phenology in response to global change. , 2007, Trends in ecology & evolution.

[11]  Erwin Ulrich,et al.  Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data , 2008 .

[12]  Jessica Gurevitch,et al.  Responses of insect pests, pathogens, and invasive plant species to climate change in the forests of northeastern North America: what can we predict? , 2009 .

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

[14]  P. Chavez.  An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data , 1988 .

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

[16]  S. Bassow,et al.  HOW ENVIRONMENTAL CONDITIONS AFFECT CANOPY LEAF‐LEVEL PHOTOSYNTHESIS IN FOUR DECIDUOUS TREE SPECIES , 1998 .

[17]  S. Running,et al.  The impact of growing-season length variability on carbon assimilation and evapotranspiration over 88 years in the eastern US deciduous forest , 1999, International journal of biometeorology.

[18]  M. Lechowicz,et al.  Why Do Temperate Deciduous Trees Leaf Out at Different Times? Adaptation and Ecology of Forest Communities , 1984, The American Naturalist.

[19]  M. Friedl,et al.  Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data , 2013 .

[20]  N. Breda.  Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[21]  N. H. Brogea,et al.  Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density , 2022 .

[22]  Liang Liang,et al.  Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest , 2011 .

[23]  Teodora V Minkova,et al.  Comparing spherical densiometry and hemispherical photography for estimating canopy closure , 2014 .

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

[25]  Lars Eklundh,et al.  Annual changes in MODIS vegetation indices of Swedish coniferous forests in relation to snow dynamics and tree phenology , 2010 .

[26]  Michael Nobis,et al.  Automatic thresholding for hemispherical canopy-photographs based on edge detection , 2005 .

[27]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[28]  Stuart E. Marsh,et al.  Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data , 2010, Remote. Sens..

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

[30]  R. Hall,et al.  A comparison of digital and film fisheye photography for analysis of forest canopy structure and gap light transmission , 2001 .

[31]  Mark A. Friedl,et al.  Digital repeat photography for phenological research in forest ecosystems , 2012 .

[32]  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 .

[33]  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.

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

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

[36]  Heikki Hänninen,et al.  The Annual Cycle of Development of Trees and Process-Based Modelling of Growth to Scale Up From the Tree To the Stand , 2009 .

[37]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[38]  N. Delbart,et al.  Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982-2004) , 2006 .

[39]  J. Schaber,et al.  Responses of spring phenology to climate change , 2004 .

[40]  A. Richardson,et al.  Coordinating a Northeast Regional Phenology Network , 2008 .

[41]  K. E. Moore,et al.  Climatic Consequences of Leaf Presence in the Eastern United States , 2001 .

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

[43]  Mark A. Friedl,et al.  Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology , 2012 .

[44]  C. Tucker,et al.  Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999 , 2001, International journal of biometeorology.

[45]  N. Dengler,et al.  Cell enlargement and tissue differentiation during leaf expansion in beech, Fagus grandifolia , 1975 .

[46]  Ü. Niinemets,et al.  Development of leaf photosynthetic parameters in Betula pendula Roth leaves: correlations with photosystem I density. , 2004, Plant biology.

[47]  D. Hollinger,et al.  Use of digital webcam images to track spring green-up in a deciduous broadleaf forest , 2007, Oecologia.

[48]  Sylvain G. Leblanc,et al.  Methodology comparison for canopy structure parameters extraction from digital hemispherical photography in boreal forests , 2005 .

[49]  Serge Rambal,et al.  Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements , 2013 .

[50]  Elizabeth R. Ellwood,et al.  Forecasting phenology under global warming , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[51]  J. O'keefe,et al.  Phenology of a northern hardwood forest canopy , 2006 .

[52]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[53]  F. Baret,et al.  Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling , 2004 .

[54]  Andrew D. Richardson,et al.  Phenological Differences Between Understory and Overstory: A Case Study Using the Long-Term Harvard Forest Records , 2009 .