Uncertainty analysis in the creation of a fine-resolution leaf area index (LAI) reference map for validation of moderate resolution LAI products

Remote Sensing

Volumn 7, Issue 2, 2015, Pages 1397-1421

  Iiames, John S ^a   Congalton, Russell G ^b   Lewis, Timothy E ^c   Pilant, Andrew N ^a  

^a U S ENVIRONMENTAL PROTECTION AGENCY   (United States)

^b UNIVERSITY OF NEW HAMPSHIRE   (United States)

^c US ARMY ENGINEER RESEARCH AND DEVELOPMENT CENTER   (United States)

Author keywords

Digital hemispherical photography; In situ; LAI; Land cover; Landsat; Loblolly pine; MODIS; PAI; TRAC; Uncertainty; Validation

Indexed keywords

FORESTRY; NEEDLES; OPTICAL DATA PROCESSING; POLYAMIDEIMIDES; RADIOMETERS; SATELLITE IMAGERY;

HEMISPHERICAL PHOTOGRAPHY; LAI; LAND COVER; LANDSAT; LOBLOLLY PINE; MODIS; TRAC; UNCERTAINTY; VALIDATION;

UNCERTAINTY ANALYSIS;

EID: 84928796823     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70201397     Document Type: Article

References (65)

1. Chen, J.M. Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agr. Forest Meteorol. 2006, 80, 135-163.
2. Morisette, J.T.; Baret, F.; Privette, J.L.; Myneni, R.B.; Nickeson, J.; Garrigues, S.; Shabanov, N.; Weiss, M.; Fernandes, R.; Leblanc, S.; et al. Validation of global moderate-resolution LAI products: A framework proposed within the CEOS land product validation subgroup. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1804-1817.
3. Foody, G.M. Estimation of sub-pixel land cover composition in the presence of untrained classes. Comput. Geosci. 2000, 26, 469-478.
4. Huang, D.; Yang, W.; Tan, B.; Rautiainen, M.; Zhang, P.; Hu, J.; Shabanov, N.; Linder, S.; Knyazikhin, Y.; Myneni, R.B. The importance of measurement error for deriving accurate reference leaf area index maps and validation of the MODIS LAI product. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1866-1871.
5. Abbaspour, R.A.; Delavar, M.R.; Batouli, R. The issue of uncertainty propagation in spatial decision making. In Proceedings of the 9th Scandinavian Research Conference on Geographical Information Science, Espoo, Finland, 4-6 June 2003; pp. 57-65.
6. Geographic Information Technologies in Society, NCGIA Core Curriculum GIScience. Available online: http://www.ncgia.ucsb.edu/giscc/units/u098/u098.html (accessed on 27 August 2014).
7. Hunter, G.J.; Goodchild, M.F. Modelling the uncertainty of slope and aspect estimates derived from spatial databases. Geogr. Anal. 1997, 29, 35-49.
8. Sampson, D.A.; Albaugh, T.J.; Johnsen, K.H.; Allen, H.L.; Zarnoch, S.J. Monthly leaf area index estimates from point-in-time measurements and needle phenology for Pinus taeda. Can. J. Forest Res. 2003, 33, 2477-2490.
9. Aerts, J.C.J.H.; Goodchild, M.F.; Huevelink, G.B.M. Accounting for spatial uncertainty in optimization with spatial decision support systems. Trans. GIS 2003, 7, 211-230.
10. Maier, C.A.; Johnsen, K.H.; Butnor, J.; Kress, L.W.; Anderson, P.H. Branch growth and gas exchange in 13-year-old loblolly pine (Pinus taeda) trees in response to elevated carbon dioxide concentration and fertilization. Tree Physiol. 2002, 22, 1093-1106.
11. Dougherty, P.M.; Hennessey, T.C.; Zarnoch, S.J.; Stenberg, P.T.; Holeman, R.T.; Wittwer, R.F. Effects of stand development and weather on monthly leaf biomass dynamics of a loblolly pine (Pinus taeda L.) stand. Forest Ecol. Manag. 1995, 72, 213-227.
12. Herbert, M.T.; Jack, S.B. Leaf area index and site water balance of loblolly pine (Pinus taeda L.) across a precipitation gradient in East Texas. Forest Ecol. Manag. 1998, 105, 273-282.
13. Vose, J.M. Patterns of leaf area distribution within crowns of nitrogen- and phosphorus-fertilized loblolly pine trees. Forest Sci. 1988, 34, 564-573.
14. Sword, M.A.; Chambers, J.L.; Tang, Z.; Dean, T.J.; Goelz, J.C. Long-term Trends in Loblolly Pine Productivity and Stand Characteristics in Response to Stand Density and Fertilization in the Western Gulf Region. Available online: http://www.treesearch.fs.fed.us/pubs/viewpub.jsp?index=3111 (accessed on 4 September 2014).
15. Teskey, R.O.; Bongarten, B.C.; Cregg, B.M.; Douherty, P.M.; Hennessey, T.C. Physiology and genetics of tree growth response to moisture and temperature stress: An examination of the characteristics of Loblolly pine (Pinus taeda L.). Tree Physiol. 1987, 3, 41-61.
16. Vose, J.; Allen, H.L. Leaf area, stemwood growth, and nutrition relationships in loblolly pine. Forest Sci. 1988, 34, 547-563.
17. Colbert, S.R.; Jokela, E.J.; Neary, D.G. Effects of annual fertilization and sustained weed control on dry matter partitioning, leaf area and growth efficiency of juvenile loblolly and slash pine. Forest Sci. 1990, 36, 995-1014.
18. Hennessey, T.C.; Dougherty, P.M.; Cregg, B.M.; Wittwer, R.F. Annual variation in needlefall of a loblolly pine stand in relation to climate and stand density. For. Ecol. Manag. 1992, 51, 329-338.
19. Stow, T.K.; Allen, H.L.; Kress, L.W. Ozone impacts on seasonal foliage dynamics of young loblolly pine. Forest. Sci. 1992, 38, 102-119.
20. Albaugh, T.J.; Allen, H.L.; Dougherty, P.M.; Kress, L.W.; King, J.S. Leaf area and above- and belowground growth responses of loblolly pine to nutrient and water additions. Forest Sci. 1998, 44, 317-328.
21. Yu, S.; Chambers, J.L.; Zhenmin, T.; Barnett, J.P. Crown characteristics of juvenile loblolly pine 6 years after application of thinning and fertilization. Forest Ecol. Manag. 2003, 180, 345-352.
22. Beer, A. Einleitung in Die Hohere Optik; Vieweg und Sohn: Braunschweig, Germany, 1853.
23. Jonckheere, I.; Muys, B.; Coppin, P. Allometry and evaluation of in situ optical LAI determination in Scots pine: A case study in Belgium. Tree Physiol. 2005, 25, 723-732.
24. Stenberg, P.; DeLucia, E.H.; Schoettle, A.W.; Smolander, H. Photosynthetic light capture and processing from cell to canopy. In Resource Physiology of Conifers; Smith, W.K., Hinckley, T.M., Eds.; Academic Press: New York, NY, USA, 1995; pp. 3-38.
25. Chen, J.M.; Rich, P.M.; Gower, S.T.; Norman, J.M.; Plummer, S. Leaf area index of boreal forests: Theory, techniques, and measurements. J. Geophys. Res. 1997, 102, 429-443.
26. Chen, J.M. Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agr. Forest Meteorol. 1996, 80, 135-163.
27. Deblonde, G.; Penner, M.; Royer, A. Measuring leaf area index with the LI-COR LAI-2000 in pine stands. Ecology 1994, 75, 1507-1511.
28. Leblanc, S.G.; Chen, J.M.; Kwong, M. Tracing Radiation and Architecture of Canopies; Natural Resources Canada: Ottawa, ON, Canada, 2002.
29. Iiames, J.S.; Congalton, R.G.; Lunetta, R.S. Analyst variation associated with landcover image classification of Landsat ETM+ data for the assessment of coarse spatial resolution regional/global landcover products. GISci. Remote Sens. 2013, 50, 604-622.
30. Jones, K.; Neale, A.; Wade, T.; Wickham, J.; Cross, C.; Edmonds, C.; Loveland, T.; Nash, M.; Riitters, K.; Smith, E. The consequences of landscape change on ecological resources: An assessment of the US Mid-Atlantic region, 1973-1993. Ecosyst. Health 2001, 7, 229-242.
31. Riitters, K.; Wickham, J.; O'Neill, R.; Jones, K.; Smith, E.; Coulston, J.; Wade, T.; Smith, J. Fragmentation of continental United States forests. Ecosystem 2002, 5, 815-822.
32. Smith, J.; Wickham, J.; Norton, D.; Wade, T.; Jones, K. Utilization of landscape indicators to model pathogen impaired waters. J. Am. Water Resour. Assoc. 2001, 37, 1-10.
33. Wickham, J.; O'Neill, R.; Riitters, K.; Smith, E.; Wade, T.; Jones, K. Geographic targeting of increases in nutrient export due to future urbanization. Ecol. Appl. 2002, 12, 93-106.
34. Powell, R.L.; Matzke, N.; de Souza, C., Jr; Clark, M.; Numata, I.; Hess, L.L.; Roberts, D.A. Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon. Remote Sens. Environ. 2004, 90, 221-234.
35. Gopal, S.; Woodcock, C. Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Photogramm. Eng. Remote Sens. 1994, 60, 181-188.
36. Tran, L.T.; Jarnagin, S.T.; Knight, C.G.; Baskaran, L. Mapping Spatial Accuracy and Estimating Landscape Indicators From Thematic Land Cover Maps Using Fuzzy Set Theory. Available online: http://www.crcnetbase.com/doi/abs/10.1201/9780203497586.ch13 (accessed on 4 September 2014).
37. Foody, G.M.; McCulloch, M.B.; Yates, W.B. Classification of remotely sensed data by an artificial neural network: Issues related to training data characteristics. Photogramm. Eng. Remote Sens. 1995, 61, 391-401.
38. Lunetta, R.S.; Congalton, R.G.; Fenstermaker, L.K.; Jensen, J.R.; McGuire, K.C.; Tinney, L.R. Remote sensing and geographical information system data integration: Error sources and research issues. Photogramm. Eng. Remote Sens. 1991, 57, 677-687.
39. Stehman, S.V. Statistical rigor and practical utility in thematic map accuracy assessment. Photogramm. Eng. Remote Sens. 2001, 67, 727-734.
40. Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2nd ed.; CRC Press: Boca Raton, FL, USA, 2009.
41. Iiames, J.S.; Congalton, R.G.; Pilant, A.N.; Lewis, T.E. Validation of an integrated estimation of Loblolly pine (Pinus taeda L.) leaf area index (LAI) utilizing two indirect optical methods in the southeastern United States. South. J. Appl. Forest 2008, 32, 101-110.
42. Ringold, P.L.; Sickle, J.V.; Rasar, K.; Schacher, J. Use of hemispherical imagery for estimating stream solar exposure. J. Am. Water Resour. Assoc. 2003, 39, 1373-1384.
43. Chen, J.M.; Black, T.A. Foliage area and architecture of plant canopies from sunfleck size distributions. Agric. Forest Meteorol. 1992, 60, 249-266.
44. Chen, J.M.; Black, T.A. Defining leaf area index for non-flat leaves. Plant Cell Environ. 1992, 15, 42l-429.
45. Fassnacht, K.S.; Gower, S.T.; Norman, J.M.; McMurtrie, R.E. A comparison of optical and direct methods for estimating foliage surface area index in forests. Agric. Forest Meteorol. 1994, 71, 183-207.
46. Tou, J.T.; Gonzalez, R.C. Pattern Recognition Principles; Addison-Wesley Pub. Co.: Reading, MA, USA, 1974.
47. Baret, F.; Guyot, G. Potentials and limits of vegetation indices for LAI and APAR assessment (absorbed photosynthetically active radiation). Remote Sens. Environ. 1991, 35, 161-173.
48. Williams, D.L. A comparison of spectral reflectance properties at the needle, branch, and canopy level for selected conifer species. Remote Sens. Environ. 1991, 35, 79-93.
49. Bouman, B.A. Accuracy of estimating the leaf area index from vegetative indices derived from crop reflectance characteristics, a simulation study. Int. J. Remote Sens. 1992, 13, 3069-3084.
50. Yoder, B.J.; Waring, R.H. The normalized difference vegetation index of small Douglas-fir canopies with varying chlorophyll concentrations. Remote Sens. Environ. 1994, 49, 81-91.
51. Huemmrich, K.F.; Goward, S.N. Vegetation canopy PAR absorbance and NDVI: An assessment for ten tree species with the SAIL model. Remote Sens. Environ. 1997, 61, 254-269.
52. Cohen, W.B.; Spies, T.A.; Bradshaw, G.A. Semivariograms of digital imagery for analysis of conifer canopy structure. Remote Sens. Environ. 1990, 34, 167-178.
53. Cohen, W.B.; Spies, T.A. Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery. Remote Sens. Environ. 1992, 4, 1-17.
54. Leblon, B.; Gallant, L.; Grandberg, H. Effects of shadowing types on ground-measured visible and nearinfrared shadow reflectances. Remote Sens. Environ. 1996, 58, 322-328.
55. Gitelson, A.A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 2004, 161, 165-173.
56. Flores, F.J.; Allen, H.L.; Cheshire, H.M.; Davis, J.G.; Fuentes, M.; Kelting, D.L. Using multispectral satellite imagery to estimate leaf area and response to silvicultural treatments in loblolly pine stands. Can. J. Forest Res. 2006, 36, 1587-1596.
57. USGS Earth Explorer. Available online: http://earthexplorer.usgs.gov/ (accessed on 27 August 2014).
58. Boryan, C.; Yang, Z.; Mueller, R.; Craig, M. Monitoring US agriculture: The US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto. Int. 2011, 26, 341-358.
59. Mitchell, R.B.; Moser, L.E.; Moore, K.J.; Redfearn, D.D. Tiller demographics and leaf area index of four perennial pasture grasses. Agron. J. 1998, 90, 47-53.
60. Chen, J.M.; Cihlar, J. Quantifying the effect of canopy architecture on optical measurements of leaf area index using two gap size analysis methods. IEEE Trans. Geosci. Remote Sens. 1995, 33, 777-787.
61. Chen, J.M.; Cihlar, J. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sens. Environ. 1996, 55, 153-162.
62. Gower, S.T.; Kucharik, C.J.; Norman, J.M. Direct and indirect estimation of leaf area index, fAPAR, and net primary production of terrestrial ecosystems. Remote Sens. Environ. 1999, 70, 29-51.
63. Smith, J.H.; Wickham, J.D.; Stehman, S.V.; Yang, L. Impacts of patch size and land-cover heterogeneity on thematic image classification accuracy. Photogramm. Eng. Remote Sens. 2002, 68, 65-70.
64. Clawges, R.; Vierling, L.; Calhoon, M.; Toomey, M. Use of a ground-based scanning LIDAR for estimation of biophysical properties of western larch (Larix occidentalis). Int. J. Remote Sens. 2007, 28, 4331-4344.
65. Zhao, F.; Yang, X.; Schull, M.A.; Roman-Colon, M.O.; Yao, T.; Wang, Z.; Zhang, Q.; Jupp, D.L.B.; Lovell, J.L.; Culvenor, D.S.; et al. Measuring effective leaf area index, foliage profile, and stand height in New England forest stands using a full-waveform ground-based LiDAR. Remote Sens. Environ. 2011, 11, 2954-2964.