Advertisement

Modeling Forest Net Primary Productivity with Reduced Uncertainty by Remote Sensing of Cover Type and Leaf Area Index

  • Steven E. Franklin

Abstract

Process-based ecosystem models have emerged as a powerful new tool in forest management with applications at multiple scales (Landsberg and Gower 1997; Waring and Running 1998; see also Running et al. 1989; Running 1990; Peterson and Waring 1994; Ruimy et al. 1994; Green et al. 1996; Hunt et al. 1996; Milner et al. 1996; McNulty et al. 1997; Coops 1999; Landsberg and Coops 1999). Resource managers can use ecosystem models to describe the state of a forest at a point in time relative to a range of potential management treatments, and to generate projections of future growth and stand development. As commercial forestry approaches the sustainable limit of resource use in a wide range of ecological settings, the value of these process models as new tools and an information source for managers in a wide variety of applications, including wildlife habitat mapping, biodiversity monitoring, and forest growth assessment, is increasingly clear. For example, the models can be used to estimate stand or site net primary production (NPP) when the necessary information on species, soils, topography, and climate are available. Improved ecosystem process models in the future may replace empirical stand growth and yield models (Landsberg and Coops 1999).

Keywords

Remote Sensing Cover Type Leaf Area Index Forest Inventory Photogrammetric Engineer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahern, F., T. Erdle, D.A. MacLean, and I.D. Kneppeck. 1991. A quantitative relationship between Landsat TM spectral response and forest growth rates. International Journal of Remote Sensing 12:387–400.CrossRefGoogle Scholar
  2. Beaubien, J. 1994. Landsat TM images of forests: from enhancements to classification. Canadian Journal of Remote Sensing 20:17–26.Google Scholar
  3. Bonan, G. 1993. Importance of LAI and forest type when estimating photosynthesis in boreal forests. Remote Sensing of Environment 43:303–314.CrossRefGoogle Scholar
  4. Brockhaus, J.A., S. Khorram, R. Bruck, and M.V. Campbell. 1993. Characterization of defoliation conditions within a boreal montane forest ecosystem. Geocarto International 8:35–42.CrossRefGoogle Scholar
  5. Chalifoux, S., F. Cavayas, and J.T. Gray. 1998. Map-guided approach for the automatic detection on Landsat TM images of forest stands damaged by the spruce budworm. Photogrammetric Engineering and Remote Sensing 64:629–635.Google Scholar
  6. Chen, J., and J. Cihlar. 1996. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment 55:153–162.CrossRefGoogle Scholar
  7. Cohen, W., and T. Spies, 1992. Estimating structural attributes of Douglas-fir/ western hemlock forest stands from Landsat and SPOT imagery. Remote Sensing of Environment 41:1–17.CrossRefGoogle Scholar
  8. Cohen, W., T. Spies, and M. Fiorella. 1995. Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, USA. International Journal of Remote Sensing 16:72M6.Google Scholar
  9. Cohen, W., J.D. Kushla, W.J. Ripple, and S.L. Garman. 1996. An introduction to digital methods in remote sensing of forested ecosystems: focus on the Pacific northwest, USA. Environmental Management 20:421–435.PubMedCrossRefGoogle Scholar
  10. Coops, N.C. 1999. Linking multiresolution satellite derived estimates of canopy photosynthetic capacity and metereological data to assess forest productivity in a Pinus radiata (D. Don) stand. Photogrammetric Engineering and Remote Sensing 65:1149–1165.Google Scholar
  11. Coughlan, J.C., and J.L. Dungan. 1997. Combining remote sensing and forest ecosystem modeling: an example using the Regional HydroEcological Simulation System (RHESSys). Pages 135–158 in H.L. Gholz, K. Nakane, and H. Shimoda, eds. The use of remote sensing in the modeling of forest productivity. Kluwer Academic Publishers, Boston.CrossRefGoogle Scholar
  12. Curran, P. 1980. Multispectral remote sensing of vegetation amount. Progress in Physical Geography 4:315–341.CrossRefGoogle Scholar
  13. Curran, P., J.A. Kupiec, and G.M. Smith. 1997. Remote sensing the biochemical composition of a slash pine canopy. IEEE Transactions on Geoscience and Remote Sensing 35:415–420.CrossRefGoogle Scholar
  14. Ekstrand, S. 1996. Landsat TM based forest damage assessment: correction for topographic effects. Photogrammetric Engineering and Remote Sensing 62:151–161.Google Scholar
  15. Eldridge, N.R, and G. Edwards. 1993. Acquiring localized forest inventory information: extraction from high resolution airborne digital images. Pages 443–448 in Proceedings, Thirteenth Canadian Symposium on Remote Sensing. Canadian Aeronautics and Space Institute, Ottawa, Canada.Google Scholar
  16. Fassnacht, K.S., S.T. Gower, M.D. MacKenzie, E. Nordheim, and T.M. Lillesand. 1997. Estimating the leaf area index of north central Wisconsin forests using the Landsat Thematic Mapper. Remote Sensing of Environment 61:229–245.CrossRefGoogle Scholar
  17. Foody, G.M. 1996. Approaches for the production and evaluation of fuzzy land cover classifications from remotely sensed data. International Journal of Remote Sensing 17:1317–1340.CrossRefGoogle Scholar
  18. Foody, G.M., and M.K. Arora. 1996. Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications. Pattern Recognition Letters 17:1389–1398.CrossRefGoogle Scholar
  19. Fournier, R., G. Edwards, and N. Eldridge. 1995. A catalogue of potential spatial discriminators for high spatial resolution digital images of individual tree crowns. Canadian Journal of Remote Sensing 21:285–298.Google Scholar
  20. Franklin, J., and CE. Woodcock. 1997. Multiscale vegetation data for the mountains of southern California: spatial and categorical resolution. Pages 141–168 in D.A. Quattrochi, and M.F. Goodchild, eds. Scaling in remote sensing and GIS. CRC Press, Boca Raton, FL.Google Scholar
  21. Franklin, S.E. 1994. Discrimination of subalpine forest species and canopy density using digital CASI, SPOT PLA and Landsat TM data. Photogrammetric Engineering and Remote Sensing 60:1233–1241.Google Scholar
  22. Franklin, S.E., and J.E. Luther. 1995. Satellite remote sensing of balsam fir forest structure, growth and cumulative defoliation. Canadian Journal of Remote Sensing 21:400–411.Google Scholar
  23. Franklin, S.E, R.H. Waring, R. McCreight, W.B. Cohen, and M. Fiorella. 1995. Aerial and satellite sensor detection and classification of western spruce budworm defoliation in a subalpine forest. Canadian Journal of Remote Sensing 21:299–308.Google Scholar
  24. Franklin, S.E, M.B. Lavigne, M.J. Deuling, M.A. Wulder, and E.R. Hunt, Jr. 1997a. Landsat TM-derived forest cover type for use in ecosystem models of net primary production. Canadian Journal of Remote Sensing 23:91–99.Google Scholar
  25. Franklin, S.E, M.B. Lavigne, M.J. Deuling, M.A. Wulder, and E.R. Hunt, Jr. 1997b. Estimation of forest leaf area index using remote sensing and GIS data for modeling net primary production. International Journal of Remote Sensing 18: 3459–3471.CrossRefGoogle Scholar
  26. Frohn, R.C. 1998. Remote sensing for landscape ecology. CRC Press, Boca Raton, FL.Google Scholar
  27. Gemmel, F. 1998. An investigation of terrain effects on the inversion of a forest reflectance model. Remote Sensing of Environment 65:155–169CrossRefGoogle Scholar
  28. Gerylo, G, R.J. Hall, S.E. Franklin, A. Roberts, and E.J. Milton. 1998. Hierarchical image classification and extraction of forest species composition and crown closure from airborne multispectral images. Canadian Journal of Remote Sensing 24:219–232.Google Scholar
  29. Ghitter, G.S, R.J. Hall, and S.E. Franklin. 1995, Variability of Landsat Thematic Mapper data in boreal deciduous and mixedwood stands with conifer understory. International Journal of Remote Sensing 16:2989–3002.CrossRefGoogle Scholar
  30. Glackin, D.L. 1998. International space-based remote sensing overview. Canadian Journal of Remote Sensing 24:307–314.Google Scholar
  31. Gougeon, F.A. 1995a. Comparison of possible multispectral classification schemes for tree crowns individually delineated on high spatial resolution MEIS images. Canadian Journal of Remote Sensing 21:1–9.Google Scholar
  32. Gougeon, F.A. 1995b. A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Canadian Journal of Remote Sensing 21:274–284.Google Scholar
  33. Graetz, R.D. 1990. Remote sensing of terrestrial ecosystem structure: an ecologist’s pragmatic view. Pages 5–30 in R.J. Hobbs, and H.A. Mooney, eds. Remote sensing of Biosphere Functioning. Springer-Verlag, New York.CrossRefGoogle Scholar
  34. Green, R.M., N.S. Lucas, P.J. Curran, and G.M. Foody. 1996. Coupling remotely sensed data to an ecosystem simulation model—an example involving a coniferous plantation in upland Wales. Global Ecology and Biogeography Letters 5: 192–205.CrossRefGoogle Scholar
  35. Gu, D., and A. Gillespie. 1998. Topographic normalization of Landsat TM images of forest based on subpixel sun-canopy-sensor geometry. Remote Sensing of Environment 64:166–175.CrossRefGoogle Scholar
  36. Guindon, B. 1996. Computer-based aerial image understanding: a review and assessment of its application to planimetric information extraction from very high resolution satellite images. Canadian Journal of Remote Sensing 23:38–47.Google Scholar
  37. Hammond, T.O., and D.L. Verbyla. 1996. Optimistic bias in classification accuracy assessment. International Journal of Remote Sensing 17:1261–1266.CrossRefGoogle Scholar
  38. Hunt, E.R., Jr., and S.W. Running. 1992. Simulated dry matter yields for aspen and spruce stands in the North American boreal forest. Canadian Journal of Remote Sensing 18:126–133.Google Scholar
  39. Hunt, E.R., Jr., S.C. Piper, R. Nemani, CD. Keeling, R.D. Otto, and S.W. Running. 1996. Global net carbon exchange and intra-annual atmospheric CO2 concentrations predicted by an ecosystem process model and three-dimensional atmospheric transport model. Global Biogeochemical Cycles 10:431–456.CrossRefGoogle Scholar
  40. Hunt, E.R., Jr., M.B. Lavigne, and S.E. Franklin. 1999. Factors controlling the decline of growth efficiency and net primary production for balsam fir in Newfoundland. Ecological Modeling 122:151–164.CrossRefGoogle Scholar
  41. Hyyppä, J., J. Pullianinen, M. Hallikainene, and A. Saatsi. 1997. Radar-derived standwise forest inventory. IEEE Transactions on Geoscience and Remote Sensing 35:392–404.CrossRefGoogle Scholar
  42. Itten, K.L, and P. Meyer. 1993. Geometric and radiometric correction of TM data of mountainous forested areas. IEEE Transactions on Geoscience and Remote Sensing 31:764–770.CrossRefGoogle Scholar
  43. Jakubauskas, M.E. 1997. Effects of forest succession on texture in Landsat Thematic Mapper imagery. Canadian Journal of Remote Sensing 23:257–263.Google Scholar
  44. Jasinski, M. 1996. Estimation of subpixel vegetation density of natural regions using satellite multispectral imagery. IEEE Transactions on Geoscience and Remote Sensing 34:804–813.CrossRefGoogle Scholar
  45. Kasischke, E., L. Bourgeau-Chavez, N. Christensen, and E. Haney. 1994. Observations on the sensitivity of ERS-1 SAR image intensity to changes in aboveground biomass in young loblolly pine forests. International Journal of Remote Sensing 15:3–16.CrossRefGoogle Scholar
  46. King, D. 1995. Airborne multispectral digital camera and video sensors: a critical review of system designs and applications. Canadian Journal of Remote Sensing 21:245–274.Google Scholar
  47. Landsberg, J., and S.T. Gower. 1997. Applications of physiological ecology to forest production. Academic Press, San Diego, CA.Google Scholar
  48. Landsberg, J., and N.C. Coops. 1999. Modeling forest productivity across large areas and long periods. Natural Resource Modeling 12:1–28.Google Scholar
  49. Leckie, D.G., and M.D. Gillis. 1995. Forest inventory in Canada with an emphasis on map production. The Forestry Chronicle 71:74–88.Google Scholar
  50. Lobo, A. 1997. Image segmentation and discriminant analysis for the identification of land cover units in ecology. IEEE Transactions on Geoscience and Remote Sensing 35:1136–1145.CrossRefGoogle Scholar
  51. Lowell, K.E., and G. Edwards. 1996. Modeling the heterogeneity and change of natural forests. Geomatica 50:425–440.Google Scholar
  52. Luther, J.E., S.E. Franklin, J. Hudak, and J.P. Meades. 1997. Forecasting the susceptibility and vulnerability of balsam fir forests to insect defoliation with satellite remote sensing. Remote Sensing of Environment 59:77–91.CrossRefGoogle Scholar
  53. McCaffrey, T.M., and S.E. Franklin. 1993. Automated training site selection for large-area remote sensing image analysis. Computers and Geosciences 19:1413–1428.CrossRefGoogle Scholar
  54. McNulty, S.G., J.M. Vose, and W.T. Swank. 1997. Scaling predicted pine forest hydrology and productivity across the southern United States. Pages 187–209 in D.A. Quattrochi, and M.F. Goodchild, eds. Scale in remote sensing and GIS. CRC Press, Boca Raton, FL.Google Scholar
  55. Mickelson, J.G., D.L. Civco, and J.A. Silander, Jr. 1998. Delineating forest canopy species in the northeastern United States using multitemporal TM imagery. Photogrammetric Engineering and Remote Sensing 64:891–904.Google Scholar
  56. Milner, K., S.W. Running, and D.W. Coble. 1996. A biophysical soil-site model for estimating potential productivity of forested landscapes. Canadian Journal of Forest Research 26:1174–1186.CrossRefGoogle Scholar
  57. Moore, I.D., P.E. Gessler, G.A. Nielson, and G.A. Peterson. 1993. Soil attribute prediction using terrain analysis. Soil Science Society of America Journal 57:443–452.CrossRefGoogle Scholar
  58. Muchoney, D.M., and B.N. Haack, 1994. Change detection for monitoring forest defoliation. Photogrammetric Engineering and Remote Sensing 60:1243–1251.Google Scholar
  59. Nemani, R., L. Pierce, S. Running, and L. Band. 1993. Forest ecosystem processes at the watershed scale: sensitivity to remotely sensed Leaf Area Index estimates. International Journal of Remote Sensing 14:2519–2534.CrossRefGoogle Scholar
  60. Nilsen, T., and U. Peterson. 1994. Age dependence of forest reflectance: analysis of main driving factors. Remote Sensing of Environment 48:319–333.CrossRefGoogle Scholar
  61. Peddle, D.R., G. Foody, A. Zhang, S.E. Franklin, and E.F. LeDrew. 1994. Multi-source image classification. II: an empirical comparison of the evidential reasoning and neural network approaches. Canadian Journal of Remote Sensing 20:396–407.Google Scholar
  62. Peddle, D., F.G. Hall, and E.F. LeDrew. 1999. Spectral mixture analysis and geometric-optical reflectance modeling of a boreal forest biophysical structure. Remote Sensing of Environment 67:288–297.CrossRefGoogle Scholar
  63. Peterson, D.L., and R.H. Waring. 1994. Overview of the Oregon Transect Ecosystem Research Project. Ecological Applications 4:211–225.CrossRefGoogle Scholar
  64. Peterson, D.L. 1997. Forest structure and productivity along the Oregon transect. Pages 173–218 in H.L. Gholz, K. Nakane, and H. Shimoda, eds. The use of remote sensing in the modeling of forest productivity. Kluwer Academic Publishers, Boston.CrossRefGoogle Scholar
  65. Robinove, C. 1981. The logic of multispectral classification and mapping of land. Remote Sensing of Environment 11:121–130.Google Scholar
  66. Rock, B., T. Hoshizaki, and J.R. Miller. 1988. Comparison of in situ and airborne spectral measurements of the blue shift associated with forest decline. Remote Sensing of Environment 24:109–127.CrossRefGoogle Scholar
  67. Rowe, J.S. 1972. Forest regions of Canada. Canadian Forest Service Publication No. 1300. Environment Canada, Ottawa, Ontario, Canada.Google Scholar
  68. Ruimy, A., B. Saugier, and G. Dedieu. 1994. Methodology for the estimation of net primary production from remotely sensed data. Journal of Geophysical Research 99:5263–5283.CrossRefGoogle Scholar
  69. Running, S., R. Nemani, D.L. Peterson, L.E. Band, D.F. Potts, L.L. Pierce, et al. 1989. Mapping regional forest evapotranspiration and photosynthesis and coupling satellite data with ecosystem simulation. Ecology 70:1090–1101.CrossRefGoogle Scholar
  70. Running, S., and E.R. Hunt, Jr. 1993. Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. Pages 141–157 in J. Ehleringer, and C. Field, eds. Scaling physiological processes: leaf to globe. Academic Press, Toronto.Google Scholar
  71. Running, S.W. 1990. Estimating terrestrial primary productivity by combining remote sensing and ecosystem simulation. Pages 65–86 in R.J. Hobbs, and H.A. Mooney, eds. Remote sensing of biosphere functioning. Springer-Verlag, New York.CrossRefGoogle Scholar
  72. Running, S.W. 1994. Testing FOREST-BGC ecosystem process simulations across a climatic gradient in Oregon. Ecological Applications 4:238–247.CrossRefGoogle Scholar
  73. Spanner, M., L. Johnson, J. Miller, R. McCreight, J. Fremantle, J. Runyon, et al. 1994. Remote sensing of leaf area index across the Oregon Transect. Ecological Applications 4:258–271.CrossRefGoogle Scholar
  74. Spanner, M.L., L. Pierce, D. Peterson, and S.W. Running. 1990. Remote sensing of temperate coniferous forest leaf area index: the influence of canopy closure, understory vegetation and background reflectance. International Journal of Remote Sensing 11:95–111.CrossRefGoogle Scholar
  75. Stehman, S.V., and R.L. Czaplewski. 1998. Design and analysis of thematic map accuracy assessment: fundamental principles. Remote Sensing of Environment 64:331–344.CrossRefGoogle Scholar
  76. Teillet, P.M. 1986. Image corrections for radiometric effects in remote sensing. International Journal of Remote Sensing 7:1637–1651.CrossRefGoogle Scholar
  77. Thomas, I.L., V.M. Benning, and N.P. Ching. 1987. Classification of remotely sensed images. Adam Hilger Publ., Bristol, UK.Google Scholar
  78. Trotter, C, J.R. Dymond, and C.J. Goulding. 1997. Estimation of timber volume in a coniferous forest plantation using Landsat TM. International Journal of Remote Sensing 18:2209–2223.CrossRefGoogle Scholar
  79. Waring, R.H., J.B. Way, E.R. Hunt, L. Morrisey, K.J. Ranson, J. Weishempel, et al. 1995. Imaging radar for ecosystem studies. BioScience 45:715–723.CrossRefGoogle Scholar
  80. Waring, R.H., and S.W. Running. 1998. Forest ecosystems: analysis at multiple scales, second edition. Academic Press, San Diego, CA.Google Scholar
  81. Weishempel, J.F., R.G. Knox, K.J. Ranson, D.L. Williams, and J.A. Smith. 1997. Integrating remotely sensed heterogeneity with a three-dimensional forest succession model. Pages 109–133 in H.L. Gholz, K. Nakane, and H. Shimoda, eds. The use of remote sensing in the modeling of forest productivity. Kluwer Academic Publishers, Boston.CrossRefGoogle Scholar
  82. White, J.D., S.W. Running, R. Nemani, R.E. Keane, and K.C. Ryan. 1997. Measurement and remote sensing of LAI in Rocky Mountain montane ecosystems. Canadian Journal of Forest Research 27:1714–1727.CrossRefGoogle Scholar
  83. Wickham, J.D., R.V. O’Neill, K.H. Riitters, T.G. Wade, and K.B. Jones. 1997. Sensitivity of selected landscape pattern metrics to land-cover misclassification and differences in land-cover composition. Photogrammetric Engineering and Remote Sensing 63:397–402.Google Scholar
  84. Woodcock, C.E., and V.J. Harward. 1992. Nested-hierarchical scene models and image segmentation. International Journal of Remote Sensing 13:3167–3377.CrossRefGoogle Scholar
  85. Woodcock, C.E., J.B. Collins, V.D. Jakabhazy, X. Li, S.A. Macomber, and Y. Wu. 1997. Inversion of the Li-Strahler canopy reflectance model for mapping forest structure. IEEE Transactions of Geoscience and Remote Sensing 35:405–414.CrossRefGoogle Scholar
  86. Wulder, M.A. 1998. Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography 22:449–476.Google Scholar
  87. Wulder, M, E.F. LeDrew, M.B. Lavigne, and S.E. Franklin. 1998. Aerial image texture for improved estimation of LAI in mixedwood stands. Remote Sensing of Environment 64:64–76.CrossRefGoogle Scholar
  88. Zheng, D., E.R. Hunt, Jr., and S.W. Running. 1996. Comparison of available soil water capacity estimated from topography and soil series information. Landscape Ecology 11:3–14.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Steven E. Franklin

There are no affiliations available

Personalised recommendations