Remote sensing of boreal forest biophysical and inventory parameters: A review

Canadian Journal of Remote Sensing

Volumn 34, Issue , 2008, Pages S286-S313

  Lutz, David A ^a   Washington Allen, Robert A ^a,b,c   Shugart, Herman H ^a  

^a UNIVERSITY OF VIRGINIA   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

^c UNIVERSITY OF TENNESSEE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EARTH ATMOSPHERE; ECOSYSTEMS; FORESTRY; PARAMETER EXTRACTION; SATELLITE IMAGERY; SENSORS; VEGETATION;

ANALYSIS TECHNIQUES; BOREAL FORESTS; CHANGE DETECTIONS; DERIVED PRODUCTS; EXTRACTION OF INFORMATIONS; FOREST ECOSYSTEMS; FOREST PARAMETERS; FOREST REMOTE SENSING; FORESTED LANDSCAPES; GLOBAL ECONOMIES; LAND COVERS; MULTIPLE SENSORS; REMOTE SENSING INSTRUMENTS; REMOTE SENSING PLATFORMS; REMOTE SENSING TECHNIQUES; REMOTE SENSING TECHNOLOGIES; REMOTELY SENSED IMAGERIES; SATELLITE SENSORS; SCIENTIFIC LITERATURES; SPATIAL SCALES; VEGETATION RESOURCES;

REMOTE SENSING;

ECOSYSTEMS; FORESTRY; IMAGE ANALYSIS; PLANTS; REMOTE SENSING; SATELLITES; SENSORS;

EID: 57849116967     PISSN: 07038992     EISSN: 17127971     Source Type: Journal    
DOI: 10.5589/m08-057     Document Type: Article

References (227)

1. Abuelgasim, A.A., Fernandes, R.A., and Leblanc, S.G. 2006. Evaluation of national and global LAI products derived from optical remote sensing instruments over Canada. IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 7(1), pp. 1872-1884.
2. Adams, J.B., and Smith, M.O. 1986. Spectral mixture modeling: a new analysis of rock and soil types on the Viking Lander 1 site. Journal of Geophysical Research, Vol. 91, pp. 8098-8112.
3. Anderson, J., Martin, M.E., Smith, M.-L., Dubayah, R.O., Hofton, M.A., Hyde, P., et al. 2006. The use of waveform lidar to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire. Remote Sensing of Environment, Vol. 105, pp. 248-261.
4. Andreae, M.O., Atlas, E., Cachier, H., Cofer, III W.R., Harris, G.W., Helas, G., et al. 1996. Trace gas and aerosol emissions from savanna fires. In Biomass burning. Edited by J.S. Levine. MIT Press, Cambridge, pp. 278-295.
5. Asner, G.P., Palace, M., Keller, M., Periera Jr., R., Silva, J.N.M., and Zweede, J.C. 2002. Estimating canopy structure in an Amazon forest from Laser Range Finder and IKONOS satellite observations. Biotropica, Vol. 34, No. 4, pp. 483-492.
6. Awaya, Y., and Tanaka, N. 1993. Vegetation change detection with a Laplacian filter using Landsat data: a case study in boreal forest-tundra transition area. In 1993 International Geoscience and Remote Sensing Symposium, 18-21 August 1993, Tokyo, Japan. IGARSS ‘93. Vol. 2, pp. 742-746.
7. Balzter, H. 2001. Forest mapping and monitoring with interferometric synthetic aperture radar (InSAR). Progress in Physical Geography, Vol. 25, pp. 159-177.
8. 2 flux and soil functional groups in recently burned boreal forest. Global Change Biology, Vol. 10, pp. 1996-2004.
9. Birth, G.S., and McVey, G. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agronomy Journal, Vol. 60, pp. 640-643.
10. Bonan, G.B. 1993. Importance of leaf area index and forest type when estimating photosynthesis in boreal forests. Remote Sensing of Environment, Vol. 43, No. 3, pp. 303-314.
11. Bonan, G.B., and Shugart, H.H. 1989. Environmental factors and ecological processes in boreal forests. Annual Review of Ecology and Systematics, Vol. 20, pp. 1-28.
12. Boschetti, M., Boschetti, L., Oliveri, S., Casati, L., and Canova, I. 2007. Tree species mapping with airborne hyper-spectral MIVIS data: the Ticino Park study case. International Journal of Remote Sensing, Vol. 28, No. 6, pp. 1251-1261.
13. Brandtberg, T. 2007. Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 61, pp. 325-340.
14. Brown, L., Chen, J.M., Leblanc, S.G., and Cihlar, J. 2000. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests: an image and model analysis. Remote Sensing of Environment, Vol. 71, pp. 16-25.
15. Buddenbaum, H., Schlerf, M., and Hill, J. 2005. Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods. International Journal of Remote Sensing, Vol. 26, No. 24, pp. 5453-5465.
16. Callaghan, T.V., Werkman, B.R., and Crawford, R.M.M. 2002. The tundrataiga interface and its dynamics: concepts and applications. AMBIO Special Report, Vol. 12, pp. 3-14.
17. Campbell, J.B. 2006. Introduction to remote sensing. Guilford Press, N.Y.
18. Chand, T.R.K., and Badarinath, K.V.S. 2007. Analysis of ENVISAT ASAR data for forest parameter retrieval and forest type classification—a case study over deciduous forests of central India. International Journal of Remote Sensing, Vol. 28, No. 22, pp. 4985-4999.
19. Chen, J.M., and Black, T.A. 1992. Defining leaf area index for non-flat leaves. Plant, Cell & Environment, Vol. 15, No. 4, pp. 421-429.
20. Chen, J.M., and Cihlar, J. 1996. Retrieving leaf area index of boreal conifer forests using Landsat images. Remote Sensing of Environment, Vol. 55, pp. 153-162.
21. Chen, J.M., Pavlic, G., Brown, L., Cihlar, J., Leblanc, S.G., White, H.P., et al. 2002. Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote Sensing ofEnvironment, Vol. 80, No. 1, pp. 165-184.
22. Chen J.M., Menges, C.H., and Leblanc, S.G. 2005. Global mapping of foliage clumping index using multi-angular satellite data. Remote Sensing of Environement, Vol. 97. No. 4, pp. 447-457.
23. Chen, X., Vierling, L., and Deering, D. 2005a. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment, Vol. 98, pp. 63-79.
24. Chen, X., Vierling, L., Deering, D., and Conley, A. 2005b. Monitoring boreal forest leaf area index across a Siberian burn chronosequence: a MODIS validation study. International Journal ofRemote Sensing, Vol. 26, No. 24, pp. 5433-5451.
25. Chubey, M.S., Franklin, S.E., and Wulder, M.A. 2006. Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering and Remote Sensing, Vol. 72, No. 4, pp. 383-394.
26. Clark, M.L., Roberts, D.A., and Clark, D.B. 2005. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment, Vol. 96, pp. 375-398.
27. Cohen, W.B., Spies, T.A., and Fiorella, M. 1995. Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, USA. International Journal of Remote Sensing, Vol. 16, pp. 721-746.
28. Colombo, R., Bellingeri, D., Fasolini, D., and Marino, C.M. 2003. Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Sensing of Environment, Vol. 86, pp. 120-131.
29. Coops, N.C., Hilker, T., Wulder, M.A., St-Onge, B., Newnham, G., Siggins, A., and Trofymow, J.A. 2007. Estimating canopy structure and function of Douglas-fir forest stands from discrete-return lidar. Trees - Structure and Function, Vol. 21, No. 3, pp. 295-310.
30. Coppin, P., and Bauer, M. 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Review, Vol. 13, pp. 207-234.
31. Curran, PJ., Dungan, J.L., and Gholz, H.L. 1992. Seasonal LAI in slash pine estimated with Landsat TM. Remote Sensing of Environment, Vol. 39, pp. 2-13.
32. Dalponte, M., Bruzzone, L., and Gianelle, D. 2008. Fusion of hyperspectral and lidar remote sensing data for classification of complex forest areas. IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 5, pp. 1416-1427.
33. de Jong, S.M., and van der Meer, F. 2004. Remote sensing image analysis: including the spatial domain. Springer, N.Y.
34. Deering, D.W. 1978. Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. Ph.D. thesis. Texas A&M University, College Station, Tex. 338 pp.
35. Dong, J., Kaufmann, R.K., Myneni, R.B., Tucker, C.J., Kauppi, P.E., Liski, J., et al. 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources and sinks. Remote Sensing of Environment, Vol. 84, pp. 393-410.
36. Donoghue, D.N.M., and Watt, PJ. 2006. Using lidar to compare forest height estimates from IKONOS and Landsat ETM+ data in Sitka spruce plantation forests. International Journal of Remote Sensing, Vol. 27, No. 11, pp. 2161-2175.
37. Dubayah, R.O., and Drake, J.B. 2000. Lidar remote sensing for forestry. Journal of Forestry, Vol. 98, No. 6, pp. 44-46.
38. Dymond, C.C., Mladenoff, D.J., and Radeloff, V.C. 2002. Phenological differences in Tasseled Cap indices improve deciduous forest classification. Remote Sensing of Environment, Vol. 80, pp. 460-472.
39. Eastwood, J.A., Plummer, S.E., Wyatt, B.K., and Stocks, B.J. 1998. The potential of SPOT-vegetation data for fire scar detection in boreal forests. International Journal ofRemote Sensing, Vol. 19, No. 18, pp. 3681-3687.
40. Eklundh, L., Harrie, L., and Kuusk, A. 2001. Investigating relationships between Landsat ETM+ sensor data and leaf area index in a boreal conifer forest. Remote Sensing of Environment, Vol. 78, pp. 239-251.
41. Eriksson, H.M., Eklundh, L., Kuusk, A., and Nilson, T. 2006. Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates. Remote Sensing of Environment, Vol. 103, No. 4, pp. 408-418.
42. Ekstrand, S. P. 1990. Detection of moderate damage on Norway spruce using Landsat TM and digital stand data. IEEE Transactions on Geoscience and Remote Sensing, Vol. 28, No. 4, pp. 685-692.
43. Evans, D.L., Roberts, S.D., and Parker, R.C. 2006. Lidar - a new tool for forests measurements? The Forestry Chronicle, Vol. 82, No. 2, pp. 211-218.
44. Falkowski, M.J., Smith, A.M.S., Hudak, A.T., Gessler, P.E., Vierling, L.A., and Crookston, N.L. 2006. Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data. Canadian Journal of Remote Sensing, Vol. 32, No. 2, pp. 153-161.
45. Field, C.B., Randerson, J.T., and Malmström, C.M. 1995. Global net primary production: combining ecology and remote sensing. Remote Sensing of Environment, Vol. 51, pp. 74-88.
46. Foody, G.M., Lucas, R.M., Curran, P.J., and Honzak, M. 1997. Non-linear mixture modeling without end-members using an artificial neural network. International Journal of Remote Sensing, Vol. 18, pp. 937-953.
47. Fournier, R., Edwards, G., and Eldridge, N. 1995. A catalogue of potential spatial discriminators for high spatial resolution digital images of individual crowns. Canadian Journal of Remote Sensing, Vol. 21, pp. 285-298.
48. Franklin, J. 1986. Thematic Mapper analysis of coniferous structure and composition. International Journal of Remote Sensing, Vol. 7, pp. 1287-1301.
49. Franklin, J. 2001. Geographic information science and ecological assessment. In An integrated ecological assessment protocols guidebook. Edited by P Bourgeron, M. Jensen, and G. Lessard. Springer-Verlag, N.Y. pp. 154-187.
50. Franklin, S.E. 1989. Classification of hemlock looper defoliation using SPOT HRV imagery. Canadian Journal of Remote Sensing, Vol. 15, pp. 178-182.
51. 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, Vol. 60, No. 10, pp. 1233-1241.
52. Franklin, S.E. 2001. Remote sensing for sustainable forest management. CRC Press, Boca Raton, Fla.
53. Franklin, S.E., Hall, R.J., Moskal, L.M., Maudie, A.J., and Lavigne, M.B. 2000. Incorporating texture into classification of forest species composition from airborne multispectral images. International Journal of Remote Sensing, Vol. 21, No. 1, pp. 61-79.
54. Franklin, S.E., Hall, R.J., Smith, L., and Gerylo, G.R. 2003. Discrimination of conifer height, age and crown closure classes using Landsat-5 TM imagery in the Canadian Northwest Territories. International Journal of Remote Sensing, Vol. 24, No. 9, pp. 1823-1834.
55. Fraser, R.H., and Li, Z. 2002. Estimating fire-related parameters in boreal forest using SPOT VEGETATION. Remote Sensing of Environment, Vol. 82, pp. 95-110.
56. Fraser, R.H., Li, Z., and Cihlar, J. 2000. Hotspot and NDVI Differencing Synergy (HANDS) - a new technique for burned area mapping over boreal forest. Remote Sensing of Environment, Vol. 74, No. 3, pp. 362-376.
57. Fuentes, D.A., Gamon, J.A., Qui, H., Sims, D.A., and Roberts, D.A. 2001. Mapping Canadian boreal forest vegetation using pigment and water absorption features derived from the AVIRIS sensor. Journal of Geophysical Research, Vol. 106, No. D24, pp. 33565-33577.
58. Gamon, J.A., Huemmrich, K.F., Peddle, D.R., Chen, J., Fuentes, D., Hall, F.G., et al. 2004. Remote sensing in BOREAS: lessons learned. Remote Sensing of Environment, Vol. 89, No. 2, pp. 139-162.
59. 2 in Central Siberia. Forest Ecology and Management, Vol. 174, pp. 65-75.
60. Gemmell, F., Varjo, J., and Strandstrom, M. 2001. Estimating forest cover in a boreal forest test site using Thematic Mapper data from two dates. Remote Sensing of Environment, Vol. 77, No. 2, pp. 197-215.
61. Gerylo, G.R., Hall, R.J., Franklin, S.E., and Smith, L. 2002. Empirical relations between Landsat TM spectral response and forest stands near Fort Simpson, Northwest Territories, Canada. Canadian Journal of Remote Sensing, Vol. 28, No. 1, pp. 68-79.
62. Giglio, L., Descloitres, J., Justice, C.O., and Kaufman, Y.J. 2003. An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, Vol. 87, pp. 273-282.
63. Goel, N. 1987. Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data. Remote Sensing Reviews, Vol. 3, pp. 1-212.
64. Goel, N.S. 2000. Land surface bidirectional reflectance distribution function (BRDF): recent advances and future prospects. Remote Sensing Reviews, Vol. 18, Nos. 2-4.
65. Goetz, S., and Prince, S.D. 1996. Remote sensing of net primary production in boreal forest stands. Agricultural and Forest Meteorology, Vol. 78, Nos. 34, pp. 149-179.
66. Goetz, S, Steinberg, D., Dubayah, R., and Blair, B. 2007. Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA. Remote Sensing of Environment, Vol. 108, No. 3, pp. 254-263.
67. Gong, P., Pu, R., and Yu, B. 1997. Conifer species recognition: an exploratory analysis of in situ hyperspectral data. Remote Sensing of Environment, Vol. 62, pp. 189-200.
68. Goodenough, D.G., Dyk, A., Niemann, K.O., Peralman, J.S., Hao, C., Tian, H., et al. 2003. Processing Hyperion and ALI for forest classification. IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, No. 6, pp. 1321-1331.
69. Goodwin, N.R., Coops, N.C., and Culvenor, D.S. 2006. Assessment of forest structure with airborne lidar and the effects of platform altitude. Remote Sensing of Environment, Vol. 103, pp. 140-152.
70. APAR, and net primary production of terrestrial ecosystems. Remote Sensing of Environment, Vol. 70, pp. 29-51.
71. Gower, S., and Norman, J.M. 1991. Rapid estimation of leaf area index in conifer and broad-leaf plantations. Ecology, Vol. 72, No. 5, pp. 1896-1900.
72. Grandell, J., Pulliainen, J., and Hallikainen, M. 1998. Subpixel land use classification and retrieval of forest stem volume in the boreal forest zone by employing SSM/I data. Remote Sensing of the Environment, Vol. 63, pp. 140-154.
73. Gu, Y., Belair, S., Mahfouf, J., and Deblonde, G. 2006. Optimal interpolation analysis of leaf area index using MODIS data. Remote Sensing of Environment, Vol. 104, No. 3, pp. 283-296.
74. Hall, F.G., and Kerr, Y.H. 2004. Remote sensing and land surface experiments. Springer-Verlag, N.Y.
75. Hall, F.G., Botkin, D.B., Strebel, D.E., Woods, K.D., and Goetz, S.J. 1991. Large-scale patterns of forest succession as determined by remote sensing. Ecology, Vol. 72, No. 2, pp. 628-640.
76. Hall, F.G., Knapp, D.E., and Huemmrich, K.F. 1997. Physically based classification and satellite mapping of biophysical characteristics in the southern boreal forest. Journal of Geophysical Research, Vol. 102, No. D24, pp. 29 567-29 580.
77. Hall, F.G., Betts, A.K., Frolking, S., Brown, R., Halldin, S., Lettenmaier, D., et al. 2004. The boreal ecosystem and land surface/climate interactions, vegetation, water, humans and the climate. Springer-Verlag, N.Y.
78. Hall, R.J., Crown, P.H., Titus, S.J., and Volney, W.J.A. 1995. Evaluation of Landsat Thematic Mapper data for mapping top kill caused by jack pine budworm defoliation. Canadian Journal ofRemote Sensing, Vol. 21, No. 4, pp. 388-399.
79. Hall, R.J., Skakun, R.S., Arsenault, E.J., and Case, B.S. 2006. Modeling forest structure attributes using Landsat ETM+ data: application to mapping of aboveground biomass and stand volume. Forest Ecology and Management, Vol. 225, Nos. 1-3, pp. 378-390.
80. Hame, T., Salli, A., Andersson, K., and Lohi, A. 1997. A new methodology for the estimation of biomass of conifer-dominated boreal forest using NOAA AVHRR data. International Journal of Remote Sensing, Vol. 18, No. 15, pp. 3211-3243.
81. Harrell, P.A., Bourgeau-Chavez, L.L., Kasischke, E.S., French, N.H.F., and Christensen, Jr., N.L. 1995. Sensitivity of ERS-1 and JERS-1 radar datato biomass and stand structure in Alaskan boreal forest. Remote Sensing of Environment, Vol. 54, pp. 247-260.
82. Heinsch, F.A., Reeves, M.C., Votava, P., Kang, S., Milesi, C., Zhao, M., et al. 2003. User's guide: GPP and NPP (MOD17A2/A3) products, NASA MODIS Land Algorithm. University of Montana, Missoula, Mont.
83. Heiskanen, J. 2006. Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. International Journal of Remote Sensing, Vol. 27, No. 6, pp. 1135-1158.
84. Heiskanen, J., and Kivinen, S. 2008. Assessment of multispectral, -temporal and -angular MODIS data for tree cover mapping in the tundra-taiga transition zone. Remote Sensing ofEnvironment, Vol. 112, No. 5, pp. 2367-2380.
85. Hese, S., Lucht, W., Schmullius, C., Barnsley, M., Dubayah, R., Knorr, D., et al. 2005. Global biomass mapping for an improved understanding of the CO2 balance—the Earth observation mission Carbon-3D. Remote Sensing of Environment, Vol. 94, No. 1, pp. 94-104.
86. Hicke, J.A., Asner, G.P., Kasischke, E.S., French, N.H.F., Randerson, J.T., Collatz, G.J., etal. 2003. Postfire response of North American boreal forest net primary productivity analyzed with satellite observations. Global Change Biology, Vol. 9, pp. 1145-1157.
87. Hilker, T., Coops, N.C., Wulder, M.A., Black, T.A., and Guy, R.D. 2007. The use of remote sensing in light use efficiency based models of gross primary production: a review of current status and future requirements. Science of the Total Environment. In press.
88. Hilker, T., Wulder, M.A., and Coops, N.C. 2008. Update of forest inventory data with lidar and high spatial resolution satellite imagery. Canadian Journal of Remote Sensing, Vol. 34, No. 1, pp. 5-12.
89. Hill, R.A., and Thomson, A.G. 2005. Mapping woodland species composition structure using airborne spectral and lidar data. International Journal of Remote Sensing, Vol. 2005, No. 17, pp. 3763-3779.
90. Holmgren, P., and Thuresson, T. 1998. Satellite remote sensing for forestry planning - a review. Scandinavian Journal of Forest Research, Vol. 13, No. 1, pp. 90-110.
91. Howard, J.A. 1991. Remote sensing of forest resources: theory and application. Chapman and Hall, London.
92. Hudak, A.T., Lefsky, M.A., Cohen, W.B., and Berterretche, M. 2002. Integration of lidar and Landsat ETM+ data for estimating and mapping forest canopy height. Remote Sensing of Environment, Vol. 82, pp. 397-416.
93. Hyde, P., Nelson, R., Kimes, D., and Levine, E. 2007. Exploring LiDAR-RaDAR synergy — predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR. Remote Sensing of Environment, Vol. 106, pp. 28-38.
94. Hyde, P., Dubayah, R., Walkder, W., Blair, J.B., Hofton, M., and Hunsaker, C. 2006. Mapping forest structure for wildlife habitat analysis using multisensor (LiDAR, SAR/InSAR, ETM+, QuickBird) synergy. Remote Sensing of Environment, Vol. 102, pp. 63-73.
95. Hyvonen, R., Agren, G., Linder, S., Persson, T., Cotrufo, M.F., Ekblad, A., et al. 2007. The likely impact of elevated [CO2], nitrogen deposition, increased temperature and management on carbon sequestration in temperate and boreal forest ecosystems: a literature review. New Phytologist, Vol. 173, No. 3, pp. 463-480.
96. Hyyppa, J., Hyyppa, H., Inkinen, M., Engdahl, M., Linko, S., and Zhu, Y. 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, Vol. 128, pp. 109-120.
97. Jensen, J.R. 2007. Remote sensing of the environment: an Earth resource perspective. 2nd ed. Pearson Prentice Hall, Upper Saddle River, N.J.
98. Jensen, J.R., Qiu, F., and Ji, M. 1999. Predictive modeling of coniferous forest age using statistical and artificial neural network approaches applied to remote sensor data. International Journal of Remote Sensing, Vol. 20, No. 14, pp. 2805- 2822.
99. Johnson, C.J., Alexander, N.D., Wheate, R.D., and Parker, K.L. 2003. Characterizing woodland caribou habitat in sub-boreal and boreal forests. Forest Ecology and Management, Vol. 180, pp. 241-248.
100. Jordan, C.F. 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology, Vol. 50, No. 4, pp. 663-666.
101. Joria, P.E., and Ahearn, S.C. 1991. A comparison of the SPOT and Landsat Thematic Mapper satellite systems for detecting gypsy moth defoliation in Michigan. Photogrammetric Engineering and Remote Sensing, Vol. 57, No. 12, pp. 1605-1612.
102. Justice, C.O., and Dowty, P. 1994. IGBP-DIS satellite fire detection algorithm workshop technical report. IGBP-DIS Working Paper 9, NASA/GSFC, Greenbelt, Md., February 1993.
103. Justice, C.O., Giglio, L., Korontzi, S., Owens, J., Morisette, J.T., Roy, D., et al. 2002. The MODIS fire products. Remote Sensing ofEnvironment, Vol. 83, No. 1, pp. 244-262.
104. Kasischke, E.S., and Johnstone, J.F. 2005. Variation in post-fire organic layer thickness in a black spruce forest complex in Interior Alaska and its effects on soil temperature and moisture. Canadian Journal of Forest Resources, Vol. 35, pp. 2164-2177.
105. Kayitakire, F., Hamel, C., and Defourny, P. 2006. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery. Remote Sensing of Environment, Vol. 102, Nos. 3-4, pp. 390-401.
106. Kerr, J.T., and Ostrovsky, M. 2003. From space to species: ecological applications for remote sensing. TRENDS in Ecology and Evolution, Vol. 18, No. 6, pp. 299-305.
107. Kharuk, V.I., Ranson, K.J., Kuz'michev, V.V., and Im, S.T. 2003. Landsat-based analysis of insect outbreaks in southern Siberia. Canadian Journal of Remote Sensing, Vol. 29, No. 2, pp. 286-297.
108. Kharuk, V.I., Ranson, K.J., and Fedotova, E.V. 2007. Spatial pattern of Siberian silkmoth outbreak and taiga mortality. Scandinavian Journal of Forest Research, Vol. 22, No. 6, pp. 531-536.
109. King, D.J. 1995. Airborne multispectral digital camera and video sensors: a critical review of system designs and applications. Canadian Journal of Remote Sensing, Vol. 21, No. 3, pp. 245-273.
110. Knyazikhin, Y., Martonchik, J.V., Myneni, R.B., Diner, D.J., and Running, S.W. 1998. Synergistic algorithm for estimating vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from MODIS and MISR data. Journal of Geophysical Research, Vol. 103, No. D24, pp. 32257-32276.
111. Knyazikhin, Y., Glassy, J., Privette, J.L., Tian, Y., Lotsch, A., Zhang, Y., et al. 1999. MODIS leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by vegetation (FPAR) product (MOD15) algorithm. Theoretical Basis Document. NASA Goddard Space Flight Center, Greenbelt, Md.
112. Koukalas, S., and Blackburn, G.A. 2005. Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LIDAR and multi-spectral remotely sensed data. International Journal of Remote Sensing, Vol. 26, No. 3, pp. 431-455.
113. Kovacs, K., Ranson, K.J., and Kharuk, V.I. 2005. Detecting Siberian silk moth damage in central Siberia using multi-temporal MODIS data. In 2005 International Workshop on the Analysis ofMulti-Temporal Remote Sensing Images, 16-18 May 2005, Biloxi, Miss. pp. 25-29.
114. Kumar, M., and Monteith, J.L. 1982. Remote sensing of plant growth. In Plants and the daylight spectrum. Edited by H. Smith. Academic Press, London. pp. 133-144.
115. Kurnoven, L., Pullianinen, J., and Hallikainen, M. 1999. Retrieval of biomass in boreal forests from multitemporal ERS-1and JERS-1 SAR images. IEEE Transaction on Geoscience and Remote Sensing, Vol. 37, No. 1, pp. 198-205.
116. Kuusk, A., and Nilson, T. 2000. A directional multispectral forest reflectance model. Remote Sensing of Environment, Vol. 72, pp. 244-252.
117. Labrecque, S., Fournier, R.A., Luther, J.E., and Piercey, D. 2006. A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. Forest Ecology and Management, Vol. 226, Nos. 1-3, pp. 129-144.
118. Landhaeusser, S.M., and Wein, R.W. 1993. Post-fire vegetation recovery and tree establishment at the Arctic treeline: climactic change vegetation response hypothesis. Journal of Ecology, Vol. 81, pp. 665-672.
119. Lark, R.M. 1996. Geostatistical description of texture on an aerial photograph for discriminating classes of land cover. International Journal of Remote Sensing, Vol. 17, pp. 2115-2133.
120. Larsen, M. 2007. Single tree species classification with a hypothetical multi-spectral satellite. Remote Sensing ofEnvironment, Vol. 110, pp. 523-532.
121. Le Toan, T., Beaudoin, A., Riom, J., and Guyon, D. 1992. Relating forest biomass to SAR data. IEEE Transactions on Geoscience and Remote Sensing, Vol. 30, No. 2, pp. 403-411.
122. Leboeuf, A., Beaudoin, A., Fournier, R.A., Guindon, L., Luther, J.E., and Lamber, M.-C. 2007. A shadow fraction method for mapping biomass of northern boreal black spruce forests using QuickBird imagery. Remote Sensing of Environment, Vol. 110, pp. 488-500.
123. Lefsky, M.A., Cohen, W.B., Acker, S.A., Parker, G.G., Spies, T.A., and Harding, D. 1999. Lidar remote sensing of the canopy structure and biophysical properties of Douglas-Fire Western Hemlock forests—concepts and management. Remote Sensing ofEnvironment, Vol. 70, No. 3, pp. 339-361.
124. Lefsky, M.A., Cohen, W.B., Harding, D.J., Parker, G.G., Acker, S.A., and Gower, S.T. 2002a. Lidar remote sensing of above-ground biomass in three biomes. Global Ecology & Biogeography, Vol. 11, pp. 393-399.
125. Lefsky, M.A., Cohen, W.B., Parker, G.G., and Harding, D.J. 2002b. Lidar remote sensing for ecosystem studies. Bioscience, Vol. 52, No. 1, pp. 1930.
126. Li, Z., Nadon, S., Cihlar, J., and Stocks, B. 2000. Satellite-based mapping of Canadian boreal forest fires: evaluation and comparison of algorithms. International Journal ofRemote Sensing, Vol. 21, No. 16, pp. 3071-3082.
127. Liang, S., Zheng, T., Liu, R., Fang, H., Tsay, S.-C., and Running, S.W. 2006. Estimation of incident photosynthetically active radiation from moderate resolution imaging spectrometer data. Journal of Geophysical Research, Vol. 111, No. D15. doi: 10.1029/2005JD006730.
128. Lim, K., Treitz, P., Wulder, M., St-Onge, B., and Flood, M. 2003. Lidar remote sensing of forest structure. Progress in Physical Geography, Vol. 27, No. 1, pp. 88-106.
129. Liu, Q., Li, X., Ma, Z., and Takeuchi, N. 2005. Monitoring forest dynamics using satellite imagery - a case study in the natural reserve of Changbai Mountain in China. Forest Ecology and Management, Vol. 210, pp. 25-37.
130. Lobo, A. 1997. Image segmentation and discriminant analysis for the identification of land cover units in ecology. IEEE Transactions on Geoscience and Remote Sensing, Vol. 35, pp. 1136-1145.
131. Lu, D. 2005. Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon Basin. International Journal of Remote Sensing, Vol. 26, pp. 2509-2525.
132. Lu, D. 2006. The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, Vol. 27, No. 7, pp. 1297-1328.
133. Lu, D., and Batistella, M. 2005. Exploring TM image texture and its relationships with biomass estimation in Rondonia, Brazilian Amazon. Acta Amazonica, Vol. 35, pp. 261-268.
134. Lucas, R.M., Cronin, N., Lee, A., Moghaddam, M., Witte, C., and Tickle, P. 2006. Empirical relationships between AIRSAR backscatter and LiDAR-derived forest biomass, Queensland, Australia. Remote Sensing of Environment, Vol. 100, pp. 407-425.
135. Luther, J.E., Fournier, R.A., Piercey, D.E., Guindon G, and Hall, R.J. 2006. Biomass mapping using forest type and structure derived from Landsat TM imagery. International Journal of Applied Earth Observation and Geoinformation, Vol. 8, pp. 173-187.
136. McAllister, D.M., and Valeo, C. 2007. A robust new method for the remote estimation of LAI in montane and boreal forests. International Journal of Remote Sensing, Vol. 28, No. 8, pp. 1891-1905.
137. Makela, H., and Pekkarinen, A. 2004. Estimation of forest stand volumes by Landsat TM imagery and stand-level field-inventory data. Forest Ecology and Management, Vol. 196, Nos. 2-3, pp. 245-255.
138. Maltamo, M., Packalen, P., Yu, X., Eerikainen, K., Hyyppa, J., and Pitkanen, J. 2005. Identifying and quantifying structural characteristics of heterogeneous boreal forests using laser scanner data. Forest Ecology and Management, Vol. 216, pp. 41-50.
139. Manninen, T., Stenberg, P., Rautiainen, M., Voipio, P., and Smolander, H. 2005. Leaf area index estimation of boreal forest using ENVISAT ASAR. IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 11, pp. 2627-2635.
140. Martin, M.E., Newman, S.D., Aber, J.D., and Congalton, R.G. 1998. Determining forest species composition using high spectral resolution remote sensing data. Remote Sensing ofEnvironment, Vol. 65, pp. 249-254.
141. Masek, J.G. 2001. Stability of boreal forest stands during recent climate change: evidence from Landsat satellite imagery. Journal of Biogeography, Vol. 28, pp. 967-976.
142. Means, J.E., Acker, S.A., Harding, D.J., Blair, J.B., Lefsky, M.A., Cohen, W.B., et al. 1999. Use of large-footprint scanning airborne lidar to estimate forest stand characteristics in the Western Cascades of Oregon. Remote Sensing of Environment, Vol. 67, pp. 298-308.
143. 2 and sea temperature. Quarterly Journal of the Royal Meteorological Society, Vol. 109, pp. 113-152.
144. Monteith, J.L. 1972. Solar radiation and productivity in tropical ecosystems. The Journal of Applied Ecology, Vol. 9, No. 3, pp. 747-766.
145. Monteith, J.L. 1977. Climate and the efficiency of crop production in Britian. Philosophical Transactions of the Royal Society of London, Vol. 281, No. 980, pp. 277-294.
146. Morsdorf, F., Kotz, B., Meier, E., Itten, K.I., and Allgower, B. 2006. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sensing of Environment, Vol. 104, No. 1, pp. 50-61.
147. Murakami, T. 2004. Seasonal variation in classification accuracy of forest-cover types examined by a single band or band combinations. Journal of Forestry Research, Vol. 9, pp. 211-215.
148. Muukkonen, P., and Heiskanen, J. 2005. Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data. Remote Sensing of Environment, Vol. 99, pp. 434-447.
149. Muukkonen, P., and Heiskanen, J. 2007. Biomass estimation over a large area based on standwise forest inventory data and ASTER and MODIS satellite data: a possibility to verify carbon inventories. Remote Sensing of Environment, Vol. 107, pp. 617-624.
150. Nasset, E., and Økland, T. 2002. Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve. Remote Sensing of Environment, Vol. 79, pp. 105-115.
151. Nelson, R.F., Hyde, P., Johnson, P., Emessiene, B., Imhoff, M.L., Campbell, R., and Edwards, W. 2007. Investigating radar-lidar synergy in a North Carolina pine forest. Remote Sensing ofEnvironment, Vol. 110, pp. 98-108.
152. Niemann, K.O. 1995. Remote sensing of forest stand age using airborne spectrometer data. Photogrammetric Engineering and Remote Sensing, Vol. 61, pp. 1119-1127.
153. Okland, T. 1996. Vegetation-environment relationships of boreal spruce forests in ten monitoring reference areas in Norway. Sommerfeltia, Vol. 22, pp. 1-349.
154. Packalen, P., and Maltamo, M. 2007. The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs. Remote Sensing of Environment, Vol. 109, pp. 328-341.
155. Palace, M., Keller, M., Asner, G.P., Hagen, S., and Braswell, B. 2008. Amazon forest structure from IKONOS satellite data and the automated characterization of forest canopy properties. Biotropica, Vol. 40, No. 2, pp. 141-150.
156. Papathanassiou, K.P., and Cloude, S.R. 2001. Single-baseline polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 11, pp. 2352-2363.
157. Peddle, D.R., Hall, F.G., and LeDrew, E.F. 1999. Spectral mixture analysis and geometric-optical reflectance modeling of boreal forest biophysical structure - the influence of canopy closure, understorey vegetation and background reflectance. Remote Sensing of Environment, Vol. 67, No. 3, pp. 288-297.
158. Pisek, J., and Chen, M. 2007. Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America. Remote Sensing of Environment, Vol. 109, No. 1, pp. 81-94.
159. Popescu, S.C. 2007. Estimating biomass of individual pine trees using airborne lidar. Biomass & Bioenergy, Vol. 31, pp. 646-655.
160. Prince, S.D., and Goward, S.N. 1995. Global primary production: a remote sensing approach. Journal of Biogeography, Vol. 22, Nos. 4/5, pp. 815-835.
161. Pu, R., Yu, Q., Gong, P., and Biging, G.S. 2005. EO-1 Hyperion, ALI and Landsat 7 ETM+ data comparison for estimating forest crown closure and leaf area index. International Journal of Remote Sensing, Vol. 26, No. 3, pp. 457-474.
162. Puhr, C.B., and Donoghue, D.N.M. 2000. Remote sensing of upland conifer plantations using Landsat TM data: a case study from Galloway, south-west Scotland. International Journal ofRemote Sensing, Vol. 21, No. 4, pp. 633-646.
163. Pulliainen, J., Engdahl, M., and Hallikainen, M. 2003. Feasibility of multitemporal interferometric SAR data for stand-level estimation of boreal forest stem volume. Remote Sensing of Environment, Vol. 85, No. 4, pp. 397-409.
164. Radeloff, V.C., Mladenoff, D.J., and Boyce, M.S. 1999. Detecting jack pine budworm defoliation using spectral mixture analysis: separating effects from determinants. Remote Sensing of Environment, Vol. 69, No. 2, pp. 156-169.
165. Rahman, A.F., Sims, D.A., Cordova, V.D., and El-Masri, B.Z. 2005. Potential of MODIS EVI and surface temperature for directly estimating per-pixel ecosystem C fluxes. Geophysical Research Letters, Vol. 32, L19404. doi: 10.1029/2005GL024127.
166. Ranson, K.J., Saatchi, S., and Sun, G. 1995. Boreal forest ecosystem characterization with SIR-C/XSAR. IEEE Transactions on Geoscience and Remote Sensing, Vol. 33, No. 4, pp. 867-876.
167. Ranson, K.J., Kovacs, K., Sun, G., and Kharuk, V.I. 2003. Disturbance recognition in the boreal forest using radar and Landsat-7. Canadian Journal of Remote Sensing, Vol. 29, No. 1, pp. 271-285.
168. Ranson, K.J., Sun, G., Kovacs, K., and Kharuk, V.I. 2004. Assessing tundrataiga boundary with multi-sensor satellite data. Remote Sensing of Environment, Vol. 93, pp. 283-295.
169. Rauste, Y. 2005. Multi-temporal JERS SAR data in boreal forest biomass mapping. Remote Sensing of Environment, Vol. 97, No. 2, pp. 263-275.
170. Rautianen, M. 2005. Retrieval of leaf area index for a coniferous forest by inverting a forest reflectance model. Remote Sensing of Environment, Vol. 99, No. 3, pp. 295-303.
171. Reitberger, J., Krzystek, P., and Stilla, U. 2008. Analysis of full waveform lidar data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing, Vol. 29, No. 5, pp. 1407-1431.
172. Richards, J.A. 1999. Remote sensing digital image analysis: an introduction. Springer, N.Y.
173. Richter, D.D., O’Neill, K.P., and Kasischke, E.S. 2000. Post-fire stimulation of microbial decomposition in black spruce (Picea mariana L.) forest soils: a hypothesis. In Fire, climate change and carbon cycling in the boreal forest. Edited by E. S. Kasischke and B. J. Stocks. Springer-Verlag, N.Y. pp. 197-213.
174. Rignot, E., Way, J., Williams, C., and Viereck, L. 1994a. Radar estimates of aboveground biomass in boreal forests of Interior Alaska. IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 5, pp. 1117-1124.
175. Rignot, E.J.M., Williams, C.L., Way, J., and Viereck, L.A. 1994b. Mapping of forest types in Alaskan boreal forests using SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 5, pp. 1051-1058.
176. Roberts, S.J., Husmeier, D., Rezek, I., and Penny, W. 1998. Bayesian approaches to Gaussian mixture modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 11, pp. 1133-1142.
177. Rosenquist, A., Milne, T., Lucas, R., Imhoff, M., and Dobson, C. 2003. A review of remote sensing technology in support of the Kyoto Protocol. Environmental Science and Policy, Vol. 6, No. 5, pp. 441-455.
178. Running, S.W., Peterson, D.L., Panner, M.A., and Teuber, K.B. 1986. Remote sensing of coniferous forest leaf area. Ecology, Vol. 67, No. 1, pp. 273-276.
179. Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M., Reeves, M., and Hashimoto, H. 2004. A continuous satellite-derived measure of global terrestrial production. BioScience, Vol. 54, pp. 547-560.
180. Saatchi, S.S., and Moghaddam, M. 2000. Estimation of crown and stem water content and biomass of boreal forest using polarimetric SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 2, pp. 697-709.
181. Saatchi, S.S., and Rignot, E. 1997. Classification of boreal forest cover types using SAR images. Remote Sensing ofEnvironment, Vol. 60, pp. 270-281.
182. Santoro, M., Askne, J., Smith, G., and Fransson, J.E.S. 2002. Stem volume retrieval in boreal forests from ERS-1/2 interferometry. Remote Sensing of Environment, Vol. 81, No. 1, pp. 19-35.
183. Santoro, M., Shvidenko, A., McCallum, I., Askne, J., and Schmullius, C. 2007. Properties of ERS-1/2 coherence in the Siberian boreal forest and implications for stem volume retrieval. Remote Sensing of Environment, Vol. 106, pp. 154-172.
184. Schaphoff, S., Lucht, W., Gerten, D., Sitch, S., Cramer, W., and Prentice, C. 2006. Terrestrial biosphere carbon storage under alternative climate projections. Climatic Change, Vol. 74, Nos. 1-3, pp. 97-122.
185. Sellers, P.J., Meeson, B.W., and Hall, F.G. 1995. Remote sensing of land surface for studies of global change: models-algorithms-experiments. Remote Sensing of Environment, Vol. 51, No. 1, pp. 3-26.
186. Shugart, H.H., Bourgeau-Chavez, L., and Kasishke, E.S. 2000. Determination of stand properties in boreal and temperate forests using high-resolution imagery. Forest Science, Vol. 46, No. 4, pp. 478-486.
187. Shugart, H.H., Leemans, R., and Bonan, G.B. (Editors). 2005. A Systems Analysis of the Global Boreal Forest. Cambridge University Press, Cambridge.
188. Smith, G., Dammert, P.B.G., Santoro, M., Fransson, J.E.S., Wegmüller, R., and Askne, J. 1998. Biomass retrieval in boreal forest using ERS and JERS SAR. European Space Agency Publications, Vol. 441, pp. 293-300.
189. Soja, A.J., Sukhinin, A.I., Cahoon, D.R., Shugart, H.H., and Stackhouse, P.W. 2004. AVHRR-derived fire frequency, distribution, and area burned in Siberia. International Journal of Remote Sensing, Vol. 25 No. 10, pp. 1939-1960.
190. Song, C. 2007. Estimating tree crown size with spatial information of high resolution optical remotely sensed imagery. International Journal of Remote Sensing, Vol. 28, No. 15, pp. 3305-3322.
191. Soudani, K., François, C., le Maire, G., Le Dantec, V., and Dufrêne, E. 2006. Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment, Vol. 103, No. 4, pp. 161-175.
192. Spanner, M.A., Pierce, L.L., Running, S.W., and Peterson, D.L. 1990. The seasonality of AVHRR data of temperate coniferous forests: relationship with leaf area index. Remote Sensing of Environment, Vol. 33, pp. 97-112.
193. Spanner, M., Johnson, L., Miller, J., McCreight, R., Freemandle, J., Runyon, J., and Gong, P. 1994. Remote sensing of seasonal leaf area index across the Oregon transect. Ecological Applications, Vol. 4, No. 2, pp. 258-271.
194. St-Onge, B., and Cavayas, F. 1997. Automated structure mapping from high resolution imagery based on directional semivariogram estimates. Remote Sensing of Environment, Vol. 61, pp. 82-95.
195. Stellingwerf, D.A., and Hussin, Y.A. 1997. Measurements and estimations of forest stand parameters using remote sensing. Brill Academic Publishers.
196. Stenberg, P., Rautianinen, M., Manninen, T., Voipio, P., and Smolander, H. 2004. Reduced simple ratio better than NDVI for estimating LAI in Finnish pine and spruce stands. Silva Fennica, Vol. 38, No. 1, pp. 3-14.
197. Sukhinin, A.I., French, N.H.F., Kasischke, E.S., Hewson, J.H., Soja, A.J., Csiszar, I.A., et al. 2004. AVHRR-based mapping of fires in Russia: new products for fire management and carbon cycle studies. Remote Sensing of Environment, Vol. 93, pp. 546-564.
198. Sun, G., Ranson, K.J., and Kharuk, V.I. 2002. Radiometric slope correction for forest biomass estimation from SAR data in the Western Sayani Mountains, Siberia. Remote Sensing of Environment, Vol. 79, No. 2, pp. 279-287.
199. Sun, G., Ranson, K.J., Kimes, D.S., Blair, J.B., and Kovacs, K. 2008. Forest vertical structure from GLAS: an evaluation using LVIS and SRTM data. Remote Sensing of Environment, Vol. 112, pp. 107-117.
200. Sun, R., Chen, J.M., Zhu, Q., Zhou, Y., Liu, J., Li, J., et al. 2004. Spatial distribution of net primary productivity and evapotranspiration in Changbaishan Natural Reserve, China, using Landsat ETM+ data. Canadian Journal of Remote Sensing, Vol. 30, No. 5, pp. 731-742.
201. Tansey, K.J., Luckman, A.J., Skinner, L., Balzter, H., Strozzie, T., and Wagner, W. 2004. Classification of forest volume resources using ERS tandem coherence and JERS backscatter data. International Journal of Remote Sensing, Vol. 20, pp. 751-768.
202. Tateishi, R., Shimazake, Y., and Gunin, P.D. 2004. Spectral and temporal linear mixing model for vegetation classification. International Journal of Remote Sensing, Vol. 25, No. 20, pp. 4203-4218.
203. Thomas, V., Treitz, P., McCaughey, J.H., and Morrison, I. 2006. Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density. Canadian Journal of Forest Research, Vol. 36, No. 1, pp. 34-47.
204. Treitz, P.M., and Howarth, P.J. 1999. Hyperspectral remote sensing for estimating biophysical parameters of forest ecosystems. Progress in Physical Geography, Vol. 23, No. 3, pp. 359-390.
205. Treitz, P., and Rogan, J. 2004. Remote sensing for mapping and monitoring land-cover and land-use change — an introduction. Progress in Planning, Vol. 61, No. 4, pp. 269-279.
206. Treuhaft, R.N., Law, B.E., and Asner, G.P. 2004. Forest attributes from radar interferometric structure and its fusion with optical remote sensing. BioScience, Vol. 54, No. 6, pp. 561-571.
207. Trotter, C.M., and Dymond, J.R. 1997. Estimation of timber volume in a coniferous plantation forest using Landsat TM. International Journal of Remote Sensing, Vol. 18, No. 10, pp. 2209-2223.
208. Tucker, C.J. 1979. Red and photographic infrared linear combination for monitoring vegetation. Remote Sensing of Environment, Vol. 8, pp. 127-150.
209. Turner, D.P., Ritts, W.D., Cohen, W.B., Gower, S.T., Running, S.W., Zhao, M., et al. 2006. Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment, Vol. 102, pp. 282-292.
210. Ustin, S.L., Smith, M.O., Jacquemoud, S., Verstraete, M., and Govaerts, Y. 1999. Geobotany: vegetation mapping for Earth sciences. In Remote sensingfor the Earth Sciences: manual of remote sensing. 3rd ed. Edited by A.N. Rencz. John Wiley and Sons, N.Y.
211. Ustin, S.L., and Xiao, Q.F. 2001. Mapping successional boreal forests in interior central Alaska. International Journal of Remote Sensing, Vol. 22, No. 6, pp. 1779-1797.
212. Van Laake, P.E., and Sanchez-Azofeifa, G.A. 2005. Mapping PAR using MODIS atmosphere products. Remote Sensing of Environment, Vol. 94, No. 4, pp. 554-563.
213. Verbyla, D.L. 1995. Satellite remote sensing of natural resources. Lewis, Boca Raton, Fla.
214. Verbyla, D.L. 2005. Assessment of the MODIS leaf area index product (MOD15) in Alaska. International Journal of Remote Sensing, Vol. 26, No. 6, pp. 1277-1284.
215. Wagner, W., Luckman, A., Vietmeier, J., Tansey, K., Balzter, H., Schmullius, C., et al. 2003. Large-scale mapping of boreal forest in SIBERIA using ERS tandem coherence and JERS backscatter data. Remote Sensing of Environment, Vol. 85, pp. 125-144.
216. Wang, Y., Woodcock, C.E., Buermann, W., Stenberg, P., Voipio, P., Smolander, H., et al. 2004. Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland. Remote Sensing of Environment, 91, 114-127.
217. Whittaker, R.H., and Likens, G.E. 1975. The biosphere and man. In Primary productivity of the biosphere. Edited by H. Lieth and R.H. Whittaker. Springer-Verlag, N.Y. pp. 339.
218. Widlowski, J.L., Pinty, B., Gobron, N., Verstraete, M.M., Diner, D.J., and Davis, A.B. 2004. Canopy structure parameters derived from multi-angular remote sensing data for terrestrial carbon studies. Climatic Change, Vol. 67, pp. 403-415.
219. Wiegand, C.L., Richardson, A.J., and Kanemasu, E.T. 1979. Leaf area index estimates for wheat from Landsat and their implications for evapotranspiration and crop modeling. Agronomy Journal, Vol. 71, pp. 336-342.
220. Wolter, P.T., Mladenoff, D.J., Host, G.E., and Crow, T.R. 1995. Improved forest classifications in the northern lake states using multi-temporal Landsat imagery. Photogrammetric Engineering and Remote Sensing, Vol. 61, pp. 1129-1143.
221. Wulder, M. 1998. Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography, Vol. 22, No. 4, pp. 449-476.
222. Wulder, M.A., and Franklin, S.E. 2003. Remote sensing of forest environments: concepts and case studies. Kluwer Academic Publishers, Norwell, Mass.
223. Xiao, X., Boles, S., Liu, J., Zhuang, S., and Liu, M. 2002. Characterization of forest types in northeastern China using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sensing of Environment, Vol. 82, pp. 335-348.
224. Zarco-Tejada, PJ., and Millar, J.R. 1999. Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery. Journal of Geophysical Research, Vol. 104, No. D22, pp. 921-927.
225. Zhang, Q., Pavlic, G., Chen, W., Latifovic, R., Fraser, R., and Cihlar, J. 2004. Deriving stand age distribution in boreal forests using SPOT VEGETATION and NOAA AVHRR imagery. Remote Sensing of Environment, Vol. 91, pp. 405-418.
226. Zhang, X., and Kondragunta, S. 2006. Estimating forest biomass in the USA using generalized allometric models and MODIS land products. Geophysical Research Letters, Vol. 33, L09402. doi: 10.1029/2006GL025879.
227. Zheng, D., Rademacher, J., Chen, J., Crow, T., Bresee, M., Le Moine, J., and Ryu, S. 2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sensing of Environment, Vol. 93, pp. 402-411.