Forest structure modeling with combined airborne hyperspectral and LiDAR data

Remote Sensing of Environment

Volumn 121, Issue , 2012, Pages 10-25

  Latifi, Hooman ^a   Fassnacht, Fabian ^a   Koch, Barbara ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Forest structure; GA; Hyperspectral; LiDAR; Spatial models

Indexed keywords

3D INFORMATION; AREA-BASED; ATMOSPHERIC WINDOW; CANONICAL CORRELATIONS; CONIFEROUS SPECIES; DATA SOURCE; DESCRIPTIVE STATISTICS; ENVIRONMENTAL APPLICATIONS; FOREST INVENTORY DATA; FOREST MODELING; FOREST STRUCTURAL ATTRIBUTES; FOREST STRUCTURE; GERMANY; HYPERSPECTRAL; LEAVE-ONE-OUT CROSS VALIDATIONS; LIDAR DATA; LIGHT DETECTION AND RANGING; MULTIPLE SENSORS; ORIGINAL CHANNELS; PREDICTOR VARIABLES; REFLECTANCE CURVE; REMOTE SENSING DATA; RESAMPLES; SCOTS PINE; SPATIAL MODELS; STEM DENSITY; STUDY AREAS; TEMPERATE FORESTS; TOTAL BIOMASS;

GALLIUM; GENETIC ALGORITHMS; OPTICAL RADAR; REFLECTION; REMOTE SENSING; THREE DIMENSIONAL;

FORESTRY;

AIRBORNE SENSING; BIOMASS; CANONICAL ANALYSIS; CONIFEROUS TREE; ECOLOGICAL MODELING; FOREST ECOSYSTEM; FOREST INVENTORY; GENETIC ALGORITHM; HEIGHT; LIDAR; LIGHT INTENSITY; NUMERICAL MODEL; SATELLITE DATA; SPATIAL ANALYSIS; SPECTRAL ANALYSIS; THREE-DIMENSIONAL MODELING; TWO-DIMENSIONAL MODELING;

ALGORITHMS; DATA PROCESSING; GALLIUM; MATHEMATICAL MODELS; OPTICAL ACTIVITY; RADAR; REFLECTION; REMOTE SENSING; THREE DIMENSIONAL DESIGN;

GERMANY;

PINUS SYLVESTRIS;

EID: 84857753065     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2012.01.015     Document Type: Article

References (72)

1. Apan A., Phinn S., Maraseni T. Discrimination of remnant tree species and regeneration stages in Queensland, Australia using hyperspectral imagery hyperspectral image and signal processing. First workshop on evolution in remote sensing, 2009. WHISPERS '09. 26-28 Aug. 2009 2009, 1: 4.
2. Ardö J. Volume quantification of coniferous forest compartments using spectral radiance recorded by Landsat Thematic Mapper. International Journal of Remote Sensing 1992, 13(9):1779-1786.
3. Barros A.S., Rutledge D.N. Genetic algorithm applied to the selection of principal components. Chemometrics and Intelligent Laboratory Systems 1998, 40:65-81.
4. Boyd D.S., Hill R.A. Validation of airborne LiDAR intensity values from a forested landscape using HYMAP data: Preliminary analysis. Proceedings of the ISPRS Workshop 'Laser Scanning 2007 and SilviLaser 2007' Part 3/W52, Espoo- Finland 2007, 71-76pp.
5. Breidenbach J., Næsset E., Lien V., Gobakken T., Solberg S. Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sensing of Environment 2010, 114:911-924.
6. Breidenbach J., Nothdurft A., Kändler G. Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data. European Journal of Forest Research 2010, 129(5):833-846.
7. Buddenbaum H., Schlerf M., Hill J. Classification of coniferous tree species and age classes using hyperspectral data and geostatistical methods. International Journal of Remote Sensing 2005, 26(24):5453-5465.
8. Cadima J., Cerdeira J., Orestes Minhoto M. Computational aspects of algorithms for variable selection in the context of principal components. Computational Statistics and Data Analysis 2004, 47:225-236.
9. Cadima J., Jolliffe I.T. Variable selection and the interpretation of principal 20 subspaces. Journal of Agricultural, Biological, and Environmental Statistics 2001, 6:62-79.
10. Carter G.A. Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. International Journal of Remote Sensing 1994, 15:697-703.
11. Cerdeira J.O., Duarte Silva P., Cadima J., Minhoto M. Package "subselect" userguide. R Development Core Team Available from:, [Accessed 25 August 2010]. http://cran.r26project.org/web/packages/subselect/index.html.
12. Cocks T., Jenssen R., Stewart A., Wilson I., Shields T. The HyMap airborne hyperspectral sensor: The system, calibration and performance. Presented at 1st EARSEL Workshop on Imaging Spectroscopy, Zurich, October 1998 1998.
13. Crookston N.L., Moeur M., Renner D. Users guide to the most similar neighbor imputation program version 2 2002, RMRS-GTR-96, USDA Forest Service Rocky Mountain Research Station, Ogden, UT, 35pp.
14. De Castro H.F., Cavalca K.L. Availability optimization with genetic algorithm. International Journal of Reliability Management 2003, 20(7):847-863.
15. De Castro A.I.G., Yánez J. Optimization of telescope scheduling Algorithmic research and scientific policy. Astronomy and Astrophysics 2003, 403:357-367.
16. Debba P., Cho M., Mathieu R. Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level. Proceedings of IEEE workshop on hyperspectral image and signal processing WHISPERS, 26-28 August, Grenoble, France 2009, 4pp.
17. Estrella A. Generalized canonical regression. FRB of New York staff report no. 288 Available at SSRN:, [Accessed 10 April 2011]. http://ssrn.com/abstract=1003174.
18. Fassnacht F., Weinacker H., Koch B. Automatic forest area extraction from imaging spectroscopy data using an extended NDVI. Proceedings 7th EARSeL Workshop on Imaging Spectroscopy, 11th-13th April, 2011, Edinburgh, Scotland 2011.
19. Foster J.R., Kingdon C.C., Townsend P.A. Predicting tropical forest carbon from EO-1 hyperspectral imagery in Noel Kempff Mercado National Park, Bolivia. IEEE International Geoscience and Remote Sensing Symposium, 2002. IGARSS '02 2002, Vol. 6:3108-3110.
20. Franklin J. Thematic mapper analysis of coniferous forest structure and composition. International Journal of Remote Sensing 1986, 7:1287-1301.
21. Gitelson A.A., Merzlyak M.N. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology 1994, 143:286-292.
22. Glenn E., Nagler P.L., Nelson S.G. Relationship between remotely-sensed vegetation indices, canopy attributes and plant physiological processes: What vegetation indices can and cannot tell us about the landscape. Sensors 2008, 8:2136-2160.
23. Goodenough D.G., Dyk A., Hobart G., Chen Hao Forest information products from hyperspectral data - Victoria and Hoquiam test sites. Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International 23-28 July 2007 2007, 1532-1536pp.
24. Goodenough D.G., Niemann K.O., Dyk A., Hobart G., Gordon P., Loisel M., et al. Comparison of AVIRIS and AISA airborne hyperspectral sensing for above-ground forest carbon mapping. IEEE International Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008 2008, Vol. 2:II-129-II-132.
25. Green A.A., Berman M., Switzer P., Craig M.D. A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geoscience and Remote Sensing 1988, 26(1):65-74.
26. Green R.O., Eastwood M.L., Williams O. Imaging spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Remote Sensing of Environment 1998, 65:227-240.
27. Haapanen R., Ek A.R., Bauer M.E., Finley A.O. Delineation of forest/nonforest land use classes using nearest neighbor methods. Remote Sensing of Environment 2004, 89:265-271.
28. Hosking J.R.M. L-moments: Analysis and estimation of distributions using linear combinations of order statistics. Journal of the Royal Statistical Society 1990, 52(1):105-124.
29. Hudak A., Crookston N., Evans J., Hall D., Falkowski M. Nearest neighbour imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sensing of Environment 2008, 112:2232-2245.
30. Hyyppä J., Hyyppä H., Leckie D., Gougon F., Yu X., Maltamo M. Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. International Journal of Remote Sensing 2008, 29(5):1339-1366.
31. Jolliffe I.T. Principal component analysis. Springer series in statistics 2002, second edition.
32. Kaufman Y.J., Tanre D. Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: From AVHRR to EOS-MODIS. Remote Sensing of Environment 1996, 55:65-79.
33. Koch B. Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment. ISPRS Journal of Photogrammetry and Remote Sensing 2010, 65:581-590.
34. Latifi H., Nothdurft A., Koch B. Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: Application of multiple optical/LiDAR-derived predictors. Forestry 2010, 83(4):395-407.
35. Latifi H., Nothdurft A., Straub C., Koch B. Modelling stratified forest attributes using Optical/LiDAR features in a central European landscape. International Journal of Digital Earth 2012, 5(2):106-132.
36. LeMair G., Francois C., Soudani K., Bervellier D., Pontallier J.Y., Breda N., et al. Calibration and validation of hyperspectral indices for estimation of broadleaved forest, leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopybiomass. Remote Sensing of Environment 2008, 112(10):3846-3864.
37. LeMay V., Temesgen H. Comparison of nearest neighbour methods for estimating basal area and stems per hectare using aerial auxiliary variables. Forest Science 2005, 51(2):109-119.
38. Lu D., Mausel P., Brondízio E., Moran E. Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. Forest Ecology and Management 2004, 198:149-167.
39. Malenovský Z., Ufer C., Lhotákova Z., Clevers J.G.P.W., Schaepman M.E., Albrechtová J., et al. A new hyperspectral index for chlorophyll estimation of a forest canopy: Area under curve normalised to maximal band depth between 650-725nm. EARSeL eProceedings 2006, 5(2/2006):161-172.
40. Maltamo M., Næsset E., Bollandsås O.M., Gobakken T., Packalén P. Non-parametric prediction of diameter distribution using airborne laser scanner data. Scandinavian Journal of Forest Research 2009, 24:541-553.
41. McGaughey R.J. FUSION/LDV: Software for LIDAR data analysis and visualization (Ver. 2.90) 2010, USDA Forest service- Pacific Northwest Research Station, 127pp.
42. McRoberts R.E. Diagnostic tools for nearest neighbors techniques when used with satellite imagery. Remote Sensing of Environment 2009, 113:489-499.
43. Middleton M., Hyvönen E., Arkimaa H., Helminen T., Laitinen J., Kuosmanen V., et al. Analysis of hyperspectral airborne HyMap data for vegetation mapping around Lahnaslampi talc mine, Finland. Proceedings of 3rd EARSeL Workshop on Imaging Spectroscopy, Herrsching, 13-16th of May, 2003 2003.
44. Moeur M., Stage A.R. Most similar neighbour: An improved sampling inference procedure for natural resource planning. Forest Science 1995, 41:337-359.
45. Nothdurft A., Saborowski J., Breidenbach J. Spatial prediction of forest stand variables. European Journal of Forest Research 2009, 128(3):241-251.
46. Packalén P., Maltamo M. Predicting the plot volume by tree species using airborne laser scanning and aerial photographs. Forest Science 2006, 52(6):611-622.
47. Packalén P., Maltamo M. The k-MSN method for the prediction of species-specific stand attributes using airborne laser scanning and aerial photographs. Remote Sensing of Environment 2007, 109:328-341.
48. Papes M., Tupayachi R., Martinez P., Peterson A.T., Powel G.V.N. Using hyperspectral satellite imagery for regional inventories: A test with tropical emergent trees in the Amazon Basin. Journal of Vegetation Science 2010, 21:342-354.
49. Ponzoni F., Galvao J., Soares L., Liesenberg V., Santos J.R. Impact of multi-angular CHRIS/PROB data on their empirical relationships with tropical forest biomass. International Journal of Remote Sensing 2010, 31:5257-5273.
50. Richards J.A. Remote sensing digital image analysis: An introduction 1999, 240. Springer-Verlag, Berlin, Germany.
51. RIEGL RiANALYZE airborne software Available from:, [Accessed 20 January 2011]. http://www.rieglusa.com/software34guide/rianalyze.shtml.
52. Ripple W.G., Wang S., Isaacson W.L., Paine D.P. A preliminary comparison of Landsat Thematic Mapper and SPOT-l HRV multispectral data for estimating coniferous forest volume. International Journal of Remote Sensing 1991, 12(9):1971-1977.
53. Rouse J.W., Haas R.H., Schell J.A., Deering D.W. Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351 I 1973, 309-317.
54. Roy P.S., Ravan S.A. Biomass estimation using satellite remote-sensing data - An investigation on possible approaches for natural forest. Journal of Biosciences 1996, 21:535-561.
55. Schlerf, M. (2006). Determination of structural and chemical forest attributes using hyperspectral remote sensing data- Case studies in Norway Spruce forests. PhD Dissertation at University of Trier, Germany, 203 pp.
56. Schlerf M., Atzberger C., Hill J. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote Sensing of Environment 2005, 95(2):177-194.
57. Schlerf M., Hill J., Koetz B., Kneubuehler M. Retrieving canopy structure from hyperspectral multi-angular satellite data. Proceedings 5th 1519 EARSeL Workshop on 1520 Imaging Spectroscopy 2007, Bruges, Belgium, April 23-25.
58. Smith M.L., Lucie Plourde M.E.M., Ollinger S.V. Analysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: Comparison between an airborne (AVIRIS) and a spaceborne (Hyperion) sensor. IEEE Transactions on Geoscience and Remote Sensing 2003, 41(6):1-7.
59. Stage A.R., Crookston N.L. Measuring similarity in nearest neighbor imputation: Some new alternatives. Symposium on statistics and information technology in forestry, September 8-12, 2002 2002, 91-96. Virginia Polytechnic Institute and State University, Blacksburg, VA, Available online at www.forestry, [Accessed 5 May 2011]. http://ubc.ca/prognosis/documents/MSN_StageCrookston.pdf.
60. State Forest Service of Baden-Württemberg User manual of the forest inventory program Baden-Württemberg BI2005 (in German) 2009.
61. Steininger M.K. Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia. International Journal of Remote Sensing 2000, 21(6&7):1139-1157.
62. Straub C., Weinacker H., Koch B. A comparison of different methods for forest resource estimation using information from airborne laser scanning and CIR orthophotos. European Journal of Forest Research 2010, 129:1069-1080.
63. Svetnik V., Liaw A., Tong C., Cullberson C., Sheridan R.P., Feuston B.P. Random forest: A classification and regression tool for compound classification and QSAR modelling. Journal of Chemical Information and Computer Sciences 2003, 43:1947-1958.
64. Thenkabail P.S., Enclona E.A., Ashtin M.S., Legg C., De Dieu M.J. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rainforests. Remote Sensing of Environment 2003, 90:23-43.
65. Tomppo E., Gagliano C., De Natale F., Katila M., McRoberts R.E. Predicting categorical forest variables using an improved k-Nearest Neighbour estimator and Landsat imagery. Remote Sensing of Environment 2009, 113(3):500-517.
66. Tomppo E., Halme M. Using coarse scale forest variables as ancillary information and weighting of variables in k-NN estimation: A genetic algorithm approach. Remote Sensing of Environment 2004, 92:1-20.
67. Trevino V., Falciani F. GALGO: An R package for multivariate variable 13 selection using genetic algorithms. Bioinformatics 2006, 22(9):1154-1156.
68. Vauhkonen J., Korpela I., Maltamo M., Tokola T. Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics. Remote Sensing of Environment 2010, 114:1263-1276.
69. Vogelmann J.E., Rock B.N., Moss D.M. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 1993, 14:1563-1575.
70. Yu S., De Backer S., Scheunders P. Genetic feature selection combined with composite fuzzy nearest neighbour classifiers for hyperspectral satellite imagery. Pattern Recognition Letters 2002, 23:183-190.
71. Zell, J. (2008). Methoden für die Ermittlung, Modellierung und Prognose der Kohlenstoffspeicherung in Wäldern auf Grundlage permanenter Großrauminventuren. Ph.D thesis, Faculty of Forest and Environmental Studies, University of Freiburg, Germany. 162 pp. (in German with English summary).
72. Zhang H., Hu H., Yao X.g., Zheng K.f. Estimation of above-ground biomass using HJ-1 hyperspectral images in Hangzhou Bay, China. International Conference on Information Engineering and Computer Science, 2009. ICIECS 2009 2009.