Usability of time-lapse digital camera images to detect characteristics of tree phenology in a tropical rainforest

Abstract We evaluated the usability of the red (R), green (G), and blue (B) digital numbers (DNRGB) extracted from daily phenological images of a tropical rainforest in Malaysian Borneo. We examined temporal patterns in the proportions of DNR, DNG, and DNB as percentages of total DN (denoted as %R, %G and %B), in the hue, saturation, and lightness values in the HSL color model, and in a green excess index (GEI) of the whole canopy and of individual trees for 2 years. We also examined temporal patterns in the proportions of the red, green, and blue reflectance of the whole canopy surface as percentages of total reflectance (denoted as %ref_R, %ref_G, and %ref_B), and vegetation indices (the normalized-difference vegetation index, enhanced vegetation index, and green–red vegetation index) of the whole canopy by using daily measurements from quantum sensors. The temporal patterns of %RGB and saturation of individual trees revealed the characteristics of tree phenology caused by flowering, coloring, and leaf flushing. In contrast, those of the whole canopy did not, nor did those of %ref_R, %ref_G, or %ref_B, or the vegetation indices. The temporal patterns of GEI, however, could detect differences among individual trees caused by leaf flushing and coloring. Our results show the importance of installing multiple time-lapse digital cameras in tropical rainforests to accurately evaluate the sensitivity of tree phenology to meteorological and climatic changes. However, more work needs to be done to adequately describe whole-canopy changes.

[1]  Tomoaki Ichie,et al.  Short-term drought causes synchronous leaf shedding and flushing in a lowland mixed dipterocarp forest, Sarawak, Malaysia , 2004, Journal of Tropical Ecology.

[2]  Kenlo Nishida Nasahara,et al.  Detection of Bio-Meteorological Year-to-Year Variation by Using Digital Canopy Surface Images of a Deciduous Broad-Leaved Forest , 2013 .

[3]  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.

[4]  Kenlo Nishida Nasahara,et al.  Year-to-year blooming phenology observation using time-lapse digital camera images , 2014 .

[5]  Kenlo Nishida Nasahara,et al.  Using digital camera images to detect canopy condition of deciduous broad-leaved trees , 2011 .

[6]  Kazuhito Ichii,et al.  Interannual variations in vegetation activities and climate variability caused by ENSO in tropical rainforests , 2007 .

[7]  Hideki Kobayashi,et al.  Assessing the use of camera-based indices for characterizing canopy phenology in relation to gross primary production in a deciduous broad-leaved and an evergreen coniferous forest in Japan , 2012, Ecol. Informatics.

[8]  Tomoaki Ichie,et al.  Seasonality in light‐attracted chrysomelid populations in a Bornean rainforest , 2010 .

[9]  Kenlo Nishida Nasahara,et al.  Detection of the different characteristics of year-to-year variation in foliage phenology among deciduous broad-leaved tree species by using daily continuous canopy surface images , 2014, Ecol. Informatics.

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

[11]  Christopher Uhl,et al.  Leaf demography and phenology in Amazonian rain forest : a census of 40,000 leaves of 23 tree species , 2004 .

[12]  Lei Yu,et al.  Using digital cameras for comparative phenological monitoring in an evergreen broad-leaved forest and a seasonal rain forest , 2012, Ecol. Informatics.

[13]  Andrew E. Suyker,et al.  An alternative method using digital cameras for continuous monitoring of crop status , 2012 .

[14]  T. Kume,et al.  Mass flowering of the tropical tree Shorea beccariana was preceded by expression changes in flowering and drought-responsive genes , 2013, Molecular ecology.

[15]  Reiko Ide,et al.  Use of digital cameras for phenological observations , 2010, Ecol. Informatics.

[16]  Maurizio Mencuccini,et al.  Sensitivity of colour indices for discriminating leaf colours from digital photographs , 2014 .

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

[18]  Yoshinobu Sato,et al.  Water cycling in a Bornean tropical rain forest under current and projected precipitation scenarios , 2004 .

[19]  D. Sims,et al.  Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages , 2002 .

[20]  Thomas Hilker,et al.  Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment , 2011, Environmental monitoring and assessment.

[21]  Robert J. Scholes,et al.  The Carbon Cycle and Atmospheric Carbon Dioxide , 2001 .

[22]  Mark D. Schwartz,et al.  Comparing carbon flux and high-resolution spring phenological measurements in a northern mixed forest , 2013 .

[23]  Jurandy Almeida,et al.  Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees , 2014, Ecol. Informatics.

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

[25]  P. Ciais,et al.  Influence of spring and autumn phenological transitions on forest ecosystem productivity , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[26]  T. Itioka,et al.  Severe drought, leafing phenology, leaf damage and lepidopteran abundance in the canopy of a Bornean aseasonal tropical rain forest , 2004, Journal of Tropical Ecology.

[27]  Yoshiko Kosugi,et al.  Estimation of light-use efficiency through a combinational use of the photochemical reflectance index and vapor pressure deficit in an evergreen tropical rainforest at Pasoh, Peninsular Malaysia , 2014 .

[28]  R. D. Harrison,et al.  Drought and the consequences of El Niño in Borneo: a case study of figs , 2001, Population Ecology.

[29]  G. Meyer,et al.  Verification of color vegetation indices for automated crop imaging applications , 2008 .

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

[31]  M. Rossini,et al.  Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake , 2011 .

[32]  Tomo'omi Kumagai,et al.  Influences of diurnal rainfall cycle on CO2 exchange over Bornean tropical rainforests , 2012 .

[33]  Martin Kappas,et al.  Spatial Patterns of NDVI Variation over Indonesia and Their Relationship to ENSO Warm Events during the Period 1982-2006 , 2009 .

[34]  O. Sonnentag,et al.  Climate change, phenology, and phenological control of vegetation feedbacks to the climate system , 2013 .

[35]  Maurizio Mencuccini,et al.  The relationship between carbon dioxide uptake and canopy colour from two camera systems in a deciduous forest in southern England , 2013 .

[36]  Reiko Ide,et al.  A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems , 2013, Ecol. Informatics.

[37]  Hiroyuki Oguma,et al.  Ground-based monitoring of the leaf phenology of deciduous broad-leaved trees using high resolution NDVI camera images (Special issue: Remote sensing and GIS research group) , 2011 .

[38]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[39]  Tetsuzo Yasunari,et al.  Irregular droughts trigger mass flowering in aseasonal tropical forests in asia. , 2006, American journal of botany.

[40]  T. A. Black,et al.  Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: a synthesis of FLUXNET data , 2005, International journal of biometeorology.

[41]  Lei Zhou,et al.  Modeling winter wheat phenology and carbon dioxide fluxes at the ecosystem scale based on digital photography and eddy covariance data , 2013, Ecol. Informatics.

[42]  Kenlo Nishida Nasahara,et al.  The comparison of several colour indices for the photographic recording of canopy phenology of Fagus crenata Blume in eastern Japan , 2011 .

[43]  N. Saigusa,et al.  Spectral vegetation indices as the indicator of canopy photosynthetic productivity in a deciduous broadleaf forest , 2013 .

[44]  Rikie Suzuki,et al.  Seasonal changes in camera-based indices from an open canopy black spruce forest in Alaska, and comparison with indices from a closed canopy evergreen coniferous forest in Japan , 2013 .

[45]  I. Ninomiya,et al.  Photosynthetic Activity in Seed Wings of Dipterocarpaceae in a Masting Year: Does Wing Photosynthesis Contribute to Reproduction? , 2003, Photosynthetica.

[46]  Hideki Kobayashi,et al.  Utilization of ground-based digital photography for the evaluation of seasonal changes in the aboveground green biomass and foliage phenology in a grassland ecosystem , 2015, Ecol. Informatics.

[47]  Roberta E. Martin,et al.  Spectroscopy of canopy chemicals in humid tropical forests , 2011 .

[48]  J. Mustard,et al.  Beyond leaf color: Comparing camera‐based phenological metrics with leaf biochemical, biophysical, and spectral properties throughout the growing season of a temperate deciduous forest , 2014 .

[49]  Mizue Ohashi,et al.  Modeling CO2 exchange over a Bornean tropical rain forest using measured vertical and horizontal variations in leaf-level physiological parameters and leaf area densities , 2006 .

[50]  Hideki Kobayashi,et al.  Usability of noise-free daily satellite-observed green–red vegetation index values for monitoring ecosystem changes in Borneo , 2014 .

[51]  Minoru Gamo,et al.  Multiple site tower flux and remote sensing comparisons of tropical forest dynamics in Monsoon Asia , 2008 .

[52]  Richard B Primack,et al.  Leaf-out phenology of temperate woody plants: from trees to ecosystems. , 2011, The New phytologist.

[53]  Tetsuzo Yasunari,et al.  Deforestation‐induced reduction in rainfall , 2013 .

[54]  Hideki Kobayashi,et al.  Relationship between spatio-temporal characteristics of leaf-fall phenology and seasonal variations in near surface- and satellite-observed vegetation indices in a cool-temperate deciduous broad-leaved forest in Japan , 2014 .

[55]  Josep Peñuelas,et al.  Phenology Feedbacks on Climate Change , 2009, Science.

[56]  Yoshinobu Sato,et al.  Annual water balance and seasonality of evapotranspiration in a Bornean tropical rainforest , 2005 .

[57]  Takeshi Motohka,et al.  Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology , 2010, Remote. Sens..

[58]  Amilcare Porporato,et al.  Drought-induced mortality of a Bornean tropical rain forest amplified by climate change , 2012 .

[59]  S. Nagai,et al.  Review: Development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN) , 2015, Ecological Research.

[60]  Jurandy Almeida,et al.  Using phenological cameras to track the green up in a cerrado savanna and its on-the-ground validation , 2014, Ecol. Informatics.

[61]  Hiroyuki Muraoka,et al.  Photosynthetic and structural characteristics of canopy and shrub trees in a cool-temperate deciduous broadleaved forest: Implication to the ecosystem carbon gain , 2005 .

[62]  Stuart J. Davies,et al.  The 52-Hectare Forest Research Plot at Lambir Hills, Sarawak, Malaysia: tree distribution maps, diameter tables and species documentation. , 2002 .

[63]  Deborah Estrin,et al.  Public Internet‐connected cameras used as a cross‐continental ground‐based plant phenology monitoring system , 2010 .