Estimating leaf area index in intensively managed pine plantations using airborne laser scanner data

Forest Ecology and Management

Volumn 270, Issue , 2012, Pages 54-65

  Peduzzi, Alicia ^a   Wynne, Randolph H ^a   Fox, Thomas R ^a   Nelson, Ross F ^b   Thomas, Valerie A ^a  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^b NASA GODDARD SPACE FLIGHT CENTER   (United States)

Author keywords

Forest management; Forest mensuration; Loblolly pine; Remote sensing; Silviculture

Indexed keywords

AIRBORNE LASER SCANNERS; DEPENDENT VARIABLES; FIELD MEASUREMENT; IN-SITU; IN-SITU MEASUREMENT; INDEPENDENT VARIABLES; LEAF AREA INDEX; LIDAR DATA; LOBLOLLY PINE; MEAN INTENSITY; NORTH CAROLINA; PENETRATION INDEX; PINE PLANTATIONS; PLANT CANOPIES; PULSE DENSITY; SILVICULTURE; SMALL-FOOTPRINT LIDARS; STAND AGE; STEM DENSITY; VIRGINIA;

LASER APPLICATIONS; OPTICAL RADAR; REMOTE SENSING; VEGETATION;

FORESTRY;

AIRBORNE SENSING; CANOPY ARCHITECTURE; CONIFEROUS FOREST; DATA SET; ESTIMATION METHOD; FOREST MANAGEMENT; IN SITU MEASUREMENT; LEAF AREA INDEX; LIDAR; PLANTATION FORESTRY; STAND DYNAMICS;

NORTH CAROLINA; UNITED STATES; VIRGINIA;

PINUS TAEDA;

EID: 84856577361     PISSN: 03781127     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.foreco.2011.12.048     Document Type: Article

References (68)

1. Akaike H. Fitting autoregressive models for prediction. Ann. Inst. Stat. Math. 1969, 21:243-247.
2. 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. For. Sci. 1998, 44:317-328.
3. Allen, D., 1971. The prediction sum of squares as a criterion for selection of predictor variables. University of Kentucky, Department of Statistics, Tech. Report 23.
4. Anderson M.C., Neale C.M.U., Li F., Norman J.M., Kustas W.P., Jayanthi H., Chavez J. Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery. Remote Sens. Environ. 2004, 92:447-464.
5. Barilotti, A., Turco, S., Napolitano, R., Bressan, E., 2005. La tecnologia LiDAR per lo studio della biomassa negli ecosistemi forestali. In: 15th Meeting of the Italian Society of Ecology, Torino, Italy.
6. Bater C.W., Wulder M.A., Coops N.C., Nelson R.F., Hilker T., Naesset E. Stability of sample-based scanning-LiDAR-derived vegetation metrics for forest monitoring. IEEE Trans. Geosci. Remote Sens. 2011, 49:2385-2392.
7. Belsley D.A. Demeaning conditioning diagnostics through centering (with discussion). Am. Stat. 1984, 38:73-77.
8. Birky A.K. NDVI and a simple model of deciduous forest seasonal dynamics. Ecol. Model. 2001, 143:43-58.
9. Bortolot Z.J., Wynne R.H. Estimating forest biomass using small footprint LiDAR data: an individual tree-based approach that incorporates training data. ISPRS J. Photogramm. Remote Sens. 2005, 59:342-360.
10. Cannell M.G.R. Physiological basis of wood production: a review. Scand. J. For. Res. 1989, 4:459-475.
11. Carlson C.A., Fox T.R., Creighton J., Dougherty P.M., Johnson J.R. Nine-year growth responses to planting density manipulation and repeated early fertilization in a loblolly pine stand in the Virginia Piedmont. South. J. Appl. For. 2009, 33:109-114.
12. Carlyle J.C. Relationships between nitrogen uptake, leaf area, water status and growth in an 11-year-old Pinus radiata plantation in response to thinning, thinning residue, and nitrogen fertiliser. For. Ecol. Manage. 1998, 108:41-55.
13. Curran P.J., Dungan J.L., Gholz H.L. Seasonal LAI in slash pine estimated with Landsat TM. Remote Sens. Environ. 1992, 39:3-13.
14. Dalla-Tea F., Jokela E.J. Needlefall, canopy light interception, and productivity of young intensively managed slash and loblolly pine stands. For. Sci. 1991, 37:1298-1313.
15. Dewey, J.C., Roberts, S.D., Hartley, I., 2006. A comparison of tools for remotely estimating leaf area index in loblolly pine plantations. In: Proceedings of the 13th Biennial Southern Silvicultural Research Conference. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, pp. 71-75.
16. Drake J.B., Knox R.G., Dubayah R.O., Clark D.B., Condit R., Blair B.J., Hofton M.A. Above-ground biomass estimation in closed canopy neotropical forests using lidar remote sensing: factors affecting the generality of relationships. Global Ecol. Biogeogr. 2003, 12:147-159.
17. Eriksson H.M., Eklundh L., Kuusk A., Nilson T. Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates. Remote Sens. Environ. 2006, 103:408-418.
18. Farid A., Rautenkranz D., Goodrich D.C., Marsh S.E., Sorooshian S. Riparian vegetation classification from airborne laser scanning data with an emphasis on cottonwood trees. Can. J. Remote Sens. 2006, 32:15-18.
19. 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. For. Res. 2006, 36:1587-1596.
20. FPC, 2011. Forest Productivity Cooperative. Shaping the Future of Plantation Forestry, Raleigh, NC. (August 2009). http://www.forestnutrition.org/.
21. Gholz H.L., Curran P.J., Kupiec J.A., Smith G.M. Assessing leaf area and canopy biochemistry of Florida pine plantations using remote sensing. The Use of Remote Sensing in the Modeling of Forest Productivity 1997, Kluwer Academic Publishers, Dordrecht, The Netherlands. H.L. Gholz, K. Nakane, H. Shimoda (Eds.).
22. Gresham C.A. Litterfall patterns in mature loblolly and longleaf pine stands in coastal South Carolina. For. Sci. 1982, 28:223-231.
23. Hebert 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. For. Ecol. Manag. 1998, 105:273-282.
24. Hocking R. The analysis and selection of variables in linear regression. Biometrics 1976, 32:1-49.
25. Holmgren J. Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scand. J. For. Res. 2004, 19:543-553.
26. Jensen J.L.R., Humes K.S., Vierling L.A., Hudak A.T. Discrete return lidar-based prediction of leaf area index in two conifer forests. Remote Sens. Environ. 2008, 112:3947-3957.
27. Jensen R.R., Binford M.W. Measurement and comparison of leaf area index estimators derived from satellite remote sensing techniques. Int. J. Remote Sens. 2004, 25:4251-4265.
28. Kwak, D.-A., Lee, W.-K., Cho, H.-K., 2007. Estimation of LAI using lidar remote sensing in forest. In: ISPRS Workshop on Laser Scanning and SilviLaser, Espoo, Finland, pp. 248-252.
29. Lefsky M.A., Cohen W.B., Harding D.J., Parker G.C., Acker S.A., Gower S.T. Lidar remote sensing of above-ground biomass in three biomes. Global Ecol. Biogeogr. 2002, 11:393-399.
30. Lefsky M.A., Cohen W.B., Parker G.G., Harding D.J. Lidar remote sensing for ecosystem studies. Bioscience 2002, 52:19-30.
31. LI-COR, 2010. FV2000 the LAI-2000 data file viewer. In: LI-COR Biosciences, Lincoln, NE, USA, p. 73.
32. Lovell, J.L., Jupp, D.L.B., Culvenor, D.S., Coops, N.C., 2003. Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. In: Workshop on 3-D Analysis of Forest Structure and Terrain Using LiDAR Technology. Canadian Aeronautics Space Inst., Victoria, Canada, pp. 607-622.
33. Lu D. The potential and challenge of remote sensing-based biomass estimation. Int. J. Remote Sens. 2006, 27:1297-1328.
34. Lutz D.A., Washington-Allen R.A., Shugart H.H. Remote sensing of boreal forest biophysical and inventory parameters: a review. Can. J. For. Res. 2008, 34:S286-S313.
35. Magnussen S., Boudewyn P. Derivations of stand heights from airborne laser scanner data with canopy-based quantile estimators. Can. J. For. Res. 1998, 28:1016-1031.
36. Maltamo M., Eerikäinen K., Pitkänen J., Hyyppä J., Vehmas M. Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sens. Environ. 2004, 90:319-330.
37. Marquart D.W. Comment: you should standardize the predictor variables in your regression models. J. Am. Stat. Assoc. 1980, 75:87-91.
38. Martin T.A., Jokela E.J. Developmental patterns and nutrition impact radiation use efficiency components in southern pine stands. Ecol. Appl. 2004, 14:1839-1854.
39. McCombs J.W., Roberts S.D., Evans D.L. Influence of fusing lidar and multispectral imagery on remotely sensed estimates of stand density and mean tree height in a managed loblolly pine plantation. For. Sci. 2003, 49:457-466.
40. Morsdorf F., Kötz B., Meier E., Itten K.I., Allgöwer B. Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction. Remote Sens. Environ. 2006, 104:50-61.
41. Næsset E. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J. Photogramm. Remote Sens. 1997, 52:49-56.
42. Næsset E. Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens. Environ. 1997, 61:246-253.
43. Næsset E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ. 2002, 80:88-99.
44. Nelson R., Krabill W., MacLean G. Determining forest canopy characteristics using airborne laser data. Remote Sens. Environ. 1984, 15:201-212.
45. Nelson R., Oderwald R., Gregoire T.G. Separating the ground and airborne laser sampling phases to estimate tropical forest basal area, volume, and biomass. Remote Sens. Environ. 1997, 60:311-326.
46. Nilsson M. Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens. Environ. 1996, 56:1-7.
47. Ørka H.O., Næsset E., Bollandsås O.M. Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sens. Environ. 2009, 113:1163-1174.
48. Peduzzi A., Allen H.L., Wynne R.H. Leaf area of overstory and understory in pine plantations in the flatwoods. South. J. Appl. For. 2010, 34:154-160.
49. Popescu S.C., Wynne R.H., Nelson R. Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Comput. Electron. Agric. 2002, 37:71-95.
50. Popescu S.C., Wynne R.H., Nelson R.F. Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Can. J. Remote Sens. 2003, 29:564-577.
51. Rawlings J.O., Pantula S.G., Dickey D.A. Applied Regression Analysis: A Research Tool 2001, Springer, Ann Arbor, Michigan. second ed.
52. Riaño D., Valladares F., Condes S., Chuvieco E. Estimation of leaf area index and covered ground from airborne laser scanner (lidar) in two contrasting forests. Agric. For. Meteorol. 2004, 124:269-275.
53. Roberts S.D., Dean T.J., Evans D.L., McCombs J.W., Harrington R.L., Glass P.A. Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions. For. Ecol. Manage. 2005, 213:54-70.
54. 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. For. Res. 2003, 33:2477-2490.
55. Sampson D.A., Allen H.L. Direct and indirect estimates of leaf-area index (lai) for lodgepole and loblolly-pine stands. Trees-Struct. Funct. 1995, 9:119-122.
56. SAS, 2010. The SAS System for Windows Release 9.1.3. SAS Institute Inc., Cary, NC, USA.
57. Sasaki T., Imanishi J., Ioki K., Morimoto Y., Kitada K. Estimation of leaf area index and canopy openness in broad leaved forest using an airborne laser scanner in comparison with high-resolution near-infrared digital photography. Landscape Ecol. Eng. 2008, 4:47-55.
58. Schwarz G. Estimating the dimension of a model. Ann. Stat. 1978, 6:461-464.
59. Soudani K., Trautmann J., Walter J.M.N. Leaf area index and canopy stratification in scots pine (Pinus sylvestris L.) stands. Int. J. Remote Sens. 2002, 23:3605-3618.
60. Spanner M.A., Pierce L.L., Peterson D.L., Running S.W. Remote-sensing of temperate coniferous forest leaf-area index - the influence of canopy closure, understory vegetation and background reflectance. Int. J. Remote Sens. 1990, 11:95-111.
61. Spanner M.A., Pierce L.L., Running S.W., Peterson D.L. The seasonality of AVHRR data of temperate coniferous forests: relationship with leaf area index. Remote Sens. Environ. 1990, 33:97-112.
62. Turner D.P., Cohen W.B., Kennedy R.E., Fassnacht K.S., Briggs J.M. Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sens. Environ. 1999, 70:52-68.
63. van Aardt J., Wynne R.H., Oderwald R.G. Forest volume and biomass estimation using small-footprint lidar-distributional parameters on a per-segment basis. For. Sci. 2006, 52:636-649.
64. Vitousek P.M., Matson P.A. Disturbance, nitrogen availability, and nitrogen losses in an intensively managed loblolly-pine plantation. Ecology 1985, 66:1360-1376.
65. Vose J.M., Allen H.L. Leaf area, stemwood growth, and nutrition relationships in loblolly pine. For. Sci. 1988, 34:547-563.
66. Watson D.J. Comparative physiological studies in the growth of field crops. I. Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Ann. Bot-London 1947, 11:41-76.
67. Welles J.M., Norman J.M. Instrument for indirect measurement of canopy architecture. Agron. J. 1991, 83:818-825.
68. Zhao K., Popescu S. Lidar-based mapping of leaf area index and its use for validating GLOBCARBON satellite LAI product in a temperate forest of the southern USA. Remote Sens. Environ. 2009, 113:1628-1645.