Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-12T21:16:34.221Z Has data issue: false hasContentIssue false
  • English
  • Français

Capability of the CANAPI algorithm to derive shrub structural parameters from satellite imagery in the Alaskan Arctic

Published online by Cambridge University Press:  05 October 2015

Rocio R. Duchesne ,
Mark J. Chopping  and
Ken D. Tape
Rocio R. Duchesne
Affiliation:
Department of Geography and Geology, University of Wisconsin – Whitewater, 800 W. Main Street, Whitewater, Wisconsin 53190, USA ([email protected])
Mark J. Chopping
Affiliation:
Department of Earth and Environmental Studies, Montclair State University, 1 Normal Avenue, Montclair, New Jersey 07043, USA
Ken D. Tape
Affiliation:
Institute of Northern Engineering, University of Alaska, Fairbanks, PO Box 755910, Fairbanks, Alaska 99775, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

The observed greening of Arctic vegetation and the expansion of shrubs in the last few decades probably have profound implications for the tundra ecosystem, including feedbacks to climate. Uncertainty surrounding this vegetation shift and its implications calls for monitoring of vegetation structural parameters, such as fractional cover of shrubs. In this study, CANAPI, a semi-automated image interpretation algorithm that identifies and traces crowns by locating its crescent-shaped sunlit portion, was evaluated for its ability to derive structural data for tall (> 0.5 m) shrubs in the Arctic. CANAPI estimates of shrub canopy parameters were obtained from high-resolution imagery at 26 sites (250 m x 250 m each) by adjusting the algorithm's parameters and filter settings for each site, such that the number of crowns delineated by CANAPI roughly matched those observed in the high-resolution imagery. The CANAPI estimates were then compared with field measurements to evaluate the algorithm's performance. CANAPI successfully retrieved fractional cover (R2 = 0.83, P < 0.001), mean crown radius (R2 = 0.81, P < 0.001), and total number of shrubs (R2 = 0.54, P < 0.001). CANAPI performed best in sparse vegetation where shrub canopies were distinct, while it tended to underestimate shrub cover where shrubs were clustered. The CANAPI algorithm and the regression equations presented here can be used in Arctic tundra environments to derive vegetation parameters from any sub-meter panchromatic imagery.

Type
Research Article
Information
Polar Record , Volume 52 , Issue 2 , March 2016 , pp. 124 - 133
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Asner, G.P. and Heidebrecht, K.B.. 2002. Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations. International Journal of Remote Sensing 23 (19): 3939–3958.CrossRefGoogle Scholar
Anderson, P. M., Bartlein, P.J. and Brubaker, L.B.. 1994. Late quaternary history of tundra vegetation in northwestern Alaska. Quaternary Research 41 (3): 306–315.CrossRefGoogle Scholar
Beck, P.S.A., Horning, A., Goetz, S.J.and others. 2011. Shrub cover on the North Slope of Alaska: a circa 2000 baseline map. Arctic, Antarctic, and Alpine Research 43 (3): 355–363.CrossRefGoogle Scholar
Blok, D., Schaepman–Strub, G., Bartholomeus, H.and others. 2011. The response of Arctic vegetation to the summer climate: relation between shrub cover, NDVI, surface albedo, and temperature. Environmental Research Letters 6 (3): 035502.CrossRefGoogle Scholar
Boelman, N.T., Gough, L., McLaren, J.R.and other. 2011. Does NDVI reflect variation in the structural attributes associated with increasing shrub dominance in arctic tundra? Environmental Research Letters 6 (3): 035501.CrossRefGoogle Scholar
CAVM Team. 2003. Circumpolar Arctic vegetation map. (1:7,500,000 scale), Conservation of Arctic Flora and Fauna (CAFF) Map No. 1. Anchorage: U.S. Fish and Wildlife Service. ISBN: 0-9767525-0-6, ISBN-13: 978-0-9767525-0-9Google Scholar
Chapin, F.S., Shaver, G.R., Giblin, A.E.and others. 1995. Responses of Arctic tundra to experimental and observed changes in climate. Ecology 76 (3): 694.CrossRefGoogle Scholar
Chapin, F.S., Sturm, M., Serreze, M.C.and others. 2005. Role of land–surface changes in Arctic summer warming. Science 310 (5748): 657–660.CrossRefGoogle ScholarPubMed
Chapman, W.L. and Walsh, J.E.. 1993. Recent variations of sea ice and air temperatures in high latitudes. Bulletin of the American Meteorological Society 74: 33–47.2.0.CO;2>CrossRefGoogle Scholar
Chopping, M. 2011. CANAPI: canopy analysis with panchromatic imagery. Remote Sensing Letters 2 (1): 21–29.CrossRefGoogle Scholar
Chopping, M., North, M., Chen, J.and others. 2012. Forest canopy cover and height from MISR in topographically complex southwestern US landscapes assessed with high quality reference data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5 (1): 44–58.CrossRefGoogle Scholar
Duchesne, R.R., Chopping, M.J. and Tape, K.D.. 2015. NACP woody vegetation characteristics of 1,039 sites across the North Slope, Alaska. Data set. Oak Ridge TN: Oak Ridge National Laboratory. URL: http://daac/ornl.gov/. URL: http://dx.doi.org/10.3334/ORNLDAAC/1270 (accessed 17 April 2015).CrossRefGoogle Scholar
Elmendorf, S.C., Henry, G.H.R., Hollister, R.D.and others. 2012. Plot–scale evidence of tundra vegetation change and links to recent summer warming. Nature Climate Change 2 (6): 453–457.CrossRefGoogle Scholar
Elmhagen, B., Kindberg, J. and Hellstrom, P.. 2015. A boreal invasion in response to climate change? Range shifts and community effects in the borderland between forest and tundra. AMBIO 44 (1): 39–50.CrossRefGoogle ScholarPubMed
Elzinga, C.L., Salzer, D.W., and Willoughby, J.W.. 1998. Measuring and monitoring plant populations. BLM Technical Reference 1730–1731.Google Scholar
Epstein, H.E., Beringer, J., Gould, W.A.and others. 2004. The nature of spatial transitions in the Arctic. Journal of Biogeography 31 (12): 1917–1933.CrossRefGoogle Scholar
Euskirchen, E.S., McGuire, A.D., Chapin, F.S.and others. 2009. Changes in vegetation in northern Alaska under scenarios of climate change, 2003–2100: implications for climate feedbacks. Ecological Applications 19 (4): 1022–1043.CrossRefGoogle ScholarPubMed
Forbes, B.C., Fauria, M.M. and Zetterberg, P.. 2010. Russian Arctic warming and ‘greening’ are closely tracked by tundra shrub willows. Global Change Biology 16 (5): 1542–1554.CrossRefGoogle Scholar
Hill, D., Fasham, M., Tucker, G., Shewry, M. and Shaw, P. (editors). 2005. Handbook of biodiversity methods: survey, evaluation, and monitoring. New York, NY: Cambridge University Press.CrossRefGoogle Scholar
Higuera, P.E., Brubaker, L.B., Anderson, P.M.and others. 2008. Frequent fires in ancient shrub tundra: implications of paleorecords for arctic environmental change. PloS one 3 (3): e1744.CrossRefGoogle ScholarPubMed
Hinzman, L.D., Bettez, N.D., Bolton, W.R.and others. 2005. Evidence and implications of recent climate change in northern Alaska and other Arctic regions. Climatic Change 72 (3): 251–298.CrossRefGoogle Scholar
Hopkinson, C., Chasmer, L.E., Sass, G.and others. 2005. Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment. Canadian Journal of Remote Sensing, 31 (2), 191–206.CrossRefGoogle Scholar
Hudson, J. M. G., and Henry, G.H.R. 2009. Increased plant biomass in a high Arctic heath community from 1981 to 2008. Ecology 90 (10): 2657–2663.CrossRefGoogle Scholar
Huemmrich, K., Gamon, J.A., Tweedie, C.E.and others. 2010. Remote sensing of tundra gross ecosystem productivity and light use efficiency under varying temperature and moisture conditions. Remote Sensing of Environment 114 (3): 481–489.CrossRefGoogle Scholar
IPCC (Intergovernmental Panel on Climate Change). 2014. Climate change 2013: the physical science basis: working group I contribution. Cambridge: Cambridge University Press (IPCC fifth assessment report).Google Scholar
Jia, G.J. and Howard, E.E.. 2003. Greening of Arctic Alaska, 1981–2001. Geophysical Research Letters 30 (20).CrossRefGoogle Scholar
Kasischke, E.S., Goetz, S.J., Kimball, J.S. and Mack, M.M.. 2010. The Arctic–Boreal vulnerability experiment (ABoVE): a concise plan for a NASA–sponsored field campaign. (Final report on the VuRSAL/ABoVE scoping Study).Google Scholar
Liston, G.E., Mcfadden, J.P., Sturm, M.and other. 2002. Modeled changes in Arctic tundra snow, energy and moisture fluxes due to increased shrubs. Global Change Biology 8 (1): 17–32.CrossRefGoogle Scholar
Meyer, T., and Okin, G.S.. 2015. Evaluation of spectral unmixing techiniques using MODIS in a structural complex savanna environment for retrieval of green vegetation, non photosynthetic vegetation, and soil fractional cover. Remote Sensing of Environment 161: 122–130.CrossRefGoogle Scholar
Myers–Smith, I.H., Forbes, B., Wilmking, M.and others. 2011. Shrub expansion in tundra ecosystems: dynamics, impacts, and research priorities. Environmental Research Letters 6 (4): 045509.CrossRefGoogle Scholar
Myneni, R.B., Keeling, C.D., Tucker, C.J.and others. 1997. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386 (6626): 698–702.CrossRefGoogle Scholar
Popescu, S.C., Zhao, K., Neuenschwander, A.and others. 2011. Satellite lidar vs. small footprint airborne lidar: comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sensing of Environment 115, 2786–2797.CrossRefGoogle Scholar
Rosette, J.A.B., North, P.R.J. and Suarez, J.C.. 2008. Vegetation height estimates for a mixed temperate forest using satellite laser altimetry. International Journal of Remote Sensing, 29 (5): 1475–1493.CrossRefGoogle Scholar
Selkowitz, D.J. 2010. A comparison of multi–spectral, multi–angular, and multi–temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska. Remote Sensing of Environment 114 (7): 1338–1352.CrossRefGoogle Scholar
Stow, D.A., Hope, A., McGuire, D.and others. 2004. Remote sensing of vegetation and land–cover change in Arctic Tundra Ecosystems. Remote Sensing of Environment 89 (3): 281–308.CrossRefGoogle Scholar
Streutker, D. and Glenn, N.F.. 2006. LiDAR measurement of sagebrush steppe vegetation height. Remote Sensing of Environment 102 (1–2): 135–145.CrossRefGoogle Scholar
Sturm, M., Holmgren, J., Mcfadden, J.P.and others. 2001a. Snow–shrub interactions in Arctic tundra: a hypothesis with climatic implications. Journal of Climate 14: 336–344.2.0.CO;2>CrossRefGoogle Scholar
Sturm, M., Racine, C. and Tape, K.. 2001b. Climate change: increasing shrub abundance in the Arctic. Nature 411 (6837): 546–547.CrossRefGoogle ScholarPubMed
Tape, K., Sturm, M. and Racine, C.. 2006. The evidence for shrub expansion in northern Alaska and the pan–Arctic. Global Change Biology 12 (4): 686–702.CrossRefGoogle Scholar
Tape, K.D., Hallinger, M., Welker, J.M.and other. 2012. Landscape heterogeneity of shrub expansion in Arctic Alaska. Ecosystems 15 (5): 711–724CrossRefGoogle Scholar
Tazik, D., Warren, S., Diersing, V.and others. 1992. U.S. Army land condition–trend analysis (LCTA). Plot inventory field methods. Champaign, IL. US Army Corps of Engineers: Construction Engineering Research Laboratory (Technical report N–92/03).Google Scholar
Wofsy, S.C. and Harriss, R.C.. 2002. The North American carbon program (NACP). Washington D.C.: US Global Change Research Program (Report of the NACP Committee of the US Interagency Carbon Cycle Science Program 59).Google Scholar