Using Ordinary Digital Cameras in Place of Near-Infrared Sensors to Derive Vegetation Indices for Phenology Studies of High Arctic Vegetation

To remotely monitor vegetation at temporal and spatial resolutions unobtainable with satellite-based systems, near remote sensing systems must be employed. To this extent we used Normalized Difference Vegetation Index NDVI sensors and normal digital cameras to monitor the greenness of six different but common and widespread High Arctic plant species/groups (graminoid/Salix polaris; Cassiope tetragona; Luzula spp.; Dryas octopetala/S. polaris; C. tetragona/D. octopetala; graminoid/bryophyte) during an entire growing season in central Svalbard. Of the three greenness indices (2G_RBi, Channel G% and GRVI) derived from digital camera images, only GRVI showed significant correlations with NDVI in all vegetation types. The GRVI (Green-Red Vegetation Index) is calculated as (GDN − RDN)/(GDN + RDN) where GDN is Green digital number and RDN is Red digital number. Both NDVI and GRVI successfully recorded timings of the green-up and plant growth periods and senescence in all six plant species/groups. Some differences in phenology between plant species/groups occurred: the mid-season growing period reached a sharp peak in NDVI and GRVI values where graminoids were present, but a prolonged period of higher values occurred with the other plant species/groups. In particular, plots containing C. tetragona experienced increased NDVI and GRVI values towards the end of the season. NDVI measured with active and passive sensors were strongly correlated (r > 0.70) for the same plant species/groups. Although NDVI recorded by the active sensor was consistently lower than that of the passive sensor for the same plant species/groups, differences were small and likely due to the differing light sources used. Thus, it is evident that GRVI and NDVI measured with active and passive sensors captured similar vegetation attributes of High Arctic plants. Hence, inexpensive digital cameras can be used with passive and active NDVI devices to establish a near remote sensing network for monitoring changing vegetation dynamics in the High Arctic.

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

[2]  J. Elster,et al.  Cyanobacterial community composition in Arctic soil crusts at different stages of development , 2015, FEMS microbiology ecology.

[3]  D. Westfall,et al.  Active remote sensing and grain yield in irrigated maize , 2007, Precision Agriculture.

[4]  G. Carter Ratios of leaf reflectances in narrow wavebands as indicators of plant stress , 1994 .

[5]  D. A. Stow,et al.  Shortwave Reflectance Properties of Arctic Tundra Landscapes , 1996 .

[6]  P. Pilewskie,et al.  Characteristics, sources, and transport of aerosols measured in spring 2008 during the aerosol, radiation, and cloud processes affecting Arctic Climate (ARCPAC) Project , 2010 .

[7]  Birger Ulf Hansen,et al.  Detection of spatial, temporal, and spectral surface changes in the Ny-Ålesund area 79° N, Svalbard, using a low cost multispectral camera in combination with spectroradiometer measurements , 2003 .

[8]  B. Carlsson,et al.  Historical Records of Climate-Related Growth in Cassiope Tetragona from the Arctic , 1989 .

[9]  Jun Ni,et al.  Comparison and Intercalibration of Vegetation Indices from Different Sensors for Monitoring Above-Ground Plant Nitrogen Uptake in Winter Wheat , 2013, Sensors.

[10]  G. Yohe,et al.  A globally coherent fingerprint of climate change impacts across natural systems , 2003, Nature.

[11]  Rik Leemans,et al.  Faculty Opinions recommendation of European phenological response to climate change matches the warming pattern. , 2006 .

[12]  B. Elberling,et al.  High Arctic plant phenology is determined by snowmelt patterns but duration of phenological periods is fixed: an example of periodicity , 2016 .

[13]  R. Myneni,et al.  The interpretation of spectral vegetation indexes , 1995 .

[14]  S. Schneider,et al.  Fingerprints of global warming on wild animals and plants , 2003, Nature.

[15]  Oliver Sonnentag,et al.  Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis. , 2015, Ecological applications : a publication of the Ecological Society of America.

[16]  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.

[17]  A. Huete,et al.  A review of vegetation indices , 1995 .

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

[19]  J. Overpeck,et al.  Recent Warming Reverses Long-Term Arctic Cooling , 2009, Science.

[20]  Kuo-Wei Chang,et al.  A Simple Spectral Index Using Reflectance of 735 nm to Assess Nitrogen Status of Rice Canopy , 2008 .

[21]  Jean-Michel Friedt,et al.  Monitoring seasonal snow dynamics using ground based high resolution photography ( Austre Lovenbreen , Svalbard , 79 N ) , 2012 .

[22]  T. Sparks,et al.  The Responses of Species to Climate Over Two Centuries: An Analysis of the Marsham Phenological Record, 1736-1947 , 1995 .

[23]  Craig E. Tweedie,et al.  Multi-Decadal Changes in Tundra Environments and Ecosystems: Synthesis of the International Polar Year-Back to the Future Project (IPY-BTF) , 2011, AMBIO.

[24]  K. Moffett,et al.  Remote Sens , 2015 .

[25]  A. Westergaard‐Nielsen,et al.  Camera derived vegetation greenness index as proxy for gross primary production in a low Arctic wetland area , 2013 .

[26]  Markus Reichstein,et al.  Similarities in ground- and satellite-based NDVI time series and their relationship to physiological activity of a Scots pine forest in Finland , 2004 .

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

[28]  Capturing Migration Phenology of Terrestrial Wildlife Using Camera Traps , 2014 .

[29]  B. Elberling,et al.  Snow cover and extreme winter warming events control flower abundance of some, but not all species in high arctic Svalbard , 2013, Ecology and evolution.

[30]  M. Fladeland,et al.  Remote sensing for biodiversity science and conservation , 2003 .

[31]  N. Pettorelli,et al.  Using the satellite-derived NDVI to assess ecological responses to environmental change. , 2005, Trends in ecology & evolution.

[32]  James F. Reynolds,et al.  Landscape Function and Disturbance in Arctic Tundra , 1996, Ecological Studies.

[33]  Ranga B. Myneni,et al.  Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems , 2004 .

[34]  J. Welker,et al.  Phenological response of tundra plants to background climate variation tested using the International Tundra Experiment , 2013, Philosophical Transactions of the Royal Society B: Biological Sciences.

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

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

[37]  John A. Gamon,et al.  Monitoring seasonal and diurnal changes in photosynthetic pigments with automated PRI and NDVI sensors , 2015 .

[38]  Olaf I. Rønning,et al.  The flora of Svalbard , 1996 .

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

[40]  S. Rumpf,et al.  Idiosyncratic Responses of High Arctic Plants to Changing Snow Regimes , 2014, PloS one.

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

[42]  B. Johansen,et al.  Monitoring cultural heritage environments in Svalbard – Smeerenburg, a whaling station on Amsterdam Island , 2015 .

[43]  Frédéric Baret,et al.  Intercalibration of vegetation indices from different sensor systems , 2003 .

[44]  Stein Rune Karlsen,et al.  Vegetation mapping of Svalbard utilising Landsat TM/ETM+ data , 2011, Polar Record.

[45]  S. Dullinger,et al.  Late snowmelt delays plant development and results in lower reproductive success in the High Arctic. , 2011, Plant science : an international journal of experimental plant biology.

[46]  T. Eiken,et al.  Photogrammetric methods applied to Svalbard glaciers: accuracies and challenges , 2012 .

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

[48]  G. Fitzgerald Characterizing vegetation indices derived from active and passive sensors , 2010 .

[49]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[50]  Maurizio Mencuccini,et al.  The relationship between carbon dioxide uptake and canopy colour from two camera systems in a deciduous forest in southern England , 2012, Functional Ecology.

[51]  Anne D. Bjorkman,et al.  Greater temperature sensitivity of plant phenology at colder sites: implications for convergence across northern latitudes , 2017, Global change biology.

[52]  Birger Ulf Hansen,et al.  Automatic snow cover monitoring at high temporal and spatial resolution, using images taken by a standard digital camera , 2002 .

[53]  Anne D. Bjorkman,et al.  Contrasting effects of warming and increased snowfall on Arctic tundra plant phenology over the past two decades , 2015, Global change biology.

[54]  W. Peltier,et al.  Polar Climate Instability and Climate Teleconnections from the Arctic to the Midlatitudes and Tropics , 2009 .

[55]  J. Kerr,et al.  From space to species: ecological applications for remote sensing , 2003 .

[56]  D. O. Hessen,et al.  Analogous aquatic and terrestrial food webs in the high Arctic: The structuring force of a harsh climate , 2009 .

[57]  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.

[58]  Birger Ulf Hansen,et al.  Snow-vegetation relations in a High Arctic ecosystem : Inter-annual variability inferred from new monitoring and modeling concepts , 2006 .

[59]  Nathalie Pettorelli,et al.  The Normalized Difference Vegetation Index , 2014 .

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

[61]  Andreas Burkart,et al.  Deploying four optical UAV-based sensors over grassland: challenges and limitations , 2015 .

[62]  René van der Wal,et al.  High-arctic plants like it hot: a long-term investigation of between-year variability in plant biomass , 2014 .

[63]  Stephen G. Warren,et al.  Arctic Cloud Changes from Surface and Satellite Observations , 2010 .

[64]  Birger Ulf Hansen,et al.  Seasonal Variation in Gross Ecosystem Production, Plant Biomass, and Carbon and Nitrogen Pools in Five High Arctic Vegetation Types , 2009 .