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Annals of Forest Science

, Volume 72, Issue 1, pp 33–45 | Cite as

Assessing forest inventory information obtained from different inventory approaches and remote sensing data sources

  • Even Bergseng
  • Hans Ole Ørka
  • Erik Næsset
  • Terje GobakkenEmail author
Original Paper

Abstract

Context

Evaluations of forest inventories usually end when accuracy and precision have been quantified.

Aims

We aim to value the accuracy of information derived from different remote sensing sensors (airborne laser scanning, aerial multispectral and hyperspectral imagery) and four alternative forest inventory approaches.

Methods

The approaches were (1) mean values or (2) diameter distributions both obtained by the area-based approach (ABA), (3) individual tree crown (ITC) segmentation and (4) an approach called semi-individual tree crown (SITC) segmentation. The estimated tree information was assessed and used to evaluate how erroneous inventory data affect economic value and loss due to suboptimal harvesting decisions. Field measured data used as reference come from 23 field plots collected in a study area in south-eastern Norway typical of managed boreal forests in Norway.

Results

The accuracy of the forest inventory was generally in line with previous studies. Our results show that using mean values from the area-based approach may yield large economic losses, while adding a diameter distribution to the area-based approach yielded less loss than the individual tree crown methods. Adding information from imagery had little effect on the results.

Conclusions

Taking inventory costs into account, diameter distributions from the area-based approach without additional information seems favourable.

Keywords

Aerial imagery Airborne laser scanning Forest management inventory Hyperspectral Multispectral Value of information 

Notes

Acknowledgments

The research was conducted as part of the FlexWood (‘Flexible Wood Supply Chain’) project, funded under the European Union’s and European Atomic Energy Community’s Seventh Framework Programme (FP7/2007–2013; FP7/2007–2011, under Grant Agreement No. 245136). We wish to thank Blom Geomatics (Norway) for providing and processing the ALS data and Terratec AS (Norway) for providing and processing the hyperspectral data.

References

  1. Birchler U, Bütler M (2007) Information economics. Routledge, AbingdonCrossRefGoogle Scholar
  2. Bollandsås OM (2007) Uneven-aged forestry in Norway: inventory and management models [PhD thesis]. Norwegian University of Life SciencesGoogle Scholar
  3. Bollandsås OM, Næsset E (2007) Estimating percentile-based diameter distributions in uneven-sized Norway spruce stands using airborne laser scanner data. Scand J For Res 22:33–47CrossRefGoogle Scholar
  4. Borders BE, Souter RA, Bailey RL, Ware KD (1987) Percentile-based distributions characterize forest stand tables. For Sci 33:570–576Google Scholar
  5. Borders BE, Harrison WM, Clutter ML, Shiver BD, Souter RA (2008) The value of timber inventory information for management planning. Can J For Res 38:2287–2294CrossRefGoogle Scholar
  6. Braastad H (1966) Volumtabeller for bjørk (Volume tables for birch). Meddr norske SkogforsVes 21:23–78Google Scholar
  7. Brandtberg T (2002) Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets. Fuzzy Sets Syst 132:371–387CrossRefGoogle Scholar
  8. Brantseg A (1967) Furu sønnafjells. Kubering av stående skog. Funksjoner og tabeller (Scots pine Southern Norway. Volume functions and tables). Meddr norske SkogforsVes 22:689–739Google Scholar
  9. Breidenbach J, Næsset E, Lien V, Gobakken T, Solberg S (2010) 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 Sens Environ 114:911–924CrossRefGoogle Scholar
  10. Breidenbach J, Næsset E, Gobakken T (2012) Improving k-nearest neighbor predictions in forest inventories by combining high and low density airborne laser scanning data. Remote Sens Environ 117:358–365CrossRefGoogle Scholar
  11. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  12. Cao QV, Burkhart HE (1984) A segmented distribution approach for modelling diameter frequency data. For Sci 30:129–137Google Scholar
  13. Carleer A, Wolff E (2004) Exploitation of very high resolution satellite data for tree species identification. Photogramm Eng Remote Sens 70:135–140CrossRefGoogle Scholar
  14. Chen C, Liaw A, Breiman L (2004) Using random forest to learn imbalanced data. In: Statistics Technical Reports (p. 12). USA: Dept. Statistics, Univ. California BerkeleyGoogle Scholar
  15. Dalponte M, Bruzzone L, Gianelle D (2012) Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sens Environ 123:258–270CrossRefGoogle Scholar
  16. Dalponte M, Ørka HO, Gobakken T, Gianelle D, Næesset E (2013) Tree species classification in boreal forests with hyperspectral data. IEEE Trans Geosci Remote Sens 51:2632–2645CrossRefGoogle Scholar
  17. Eid T (2000) Use of uncertain inventory data in forestry scenario models and consequential incorrect harvest decisions. Silva Fenn 34:89–100CrossRefGoogle Scholar
  18. Eid T (2002) En vurdering av eksisterende diameter- og høydefordelingsmodeller (An evaluation of existing diameter and height distribution models). Rep Skogforsk 4/02:24Google Scholar
  19. Eid T, Gobakken T, Næsset E (2004) Comparing stand inventories for large areas based on photo interpretation and laser scanning by means of cost-plus-loss analyses. Scand J For Res 19:512–523CrossRefGoogle Scholar
  20. Eid T, Gobakken T, Næsset E (2005) Bestemmelse av diameterfordeling i bestand – sammenligninger av ulike takstopplegg (Estimating diameter distribution in forest stands – comparisons of different inventory methods). In: INA report (p. 31). Ås: Department of Ecology and Natural Resource Management, Norwegian University of Life SciencesGoogle Scholar
  21. Ene L, Næsset E, Gobakken T (2012) Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates. Int J Remote Sens 33:5171–5193CrossRefGoogle Scholar
  22. Flewelling J (2008) Probability models for individually segmented tree crown images in a sampling context. In: Proceedings of Silvilaser 2008. Edinburgh, UKGoogle Scholar
  23. Flewelling J (2009) Forest inventory predictions from individual tree crowns: regression modeling within a sample framework. In: Proceedings of the Eighth Annual Forest Inventory and Analysis Symposium 2006 (pp. 16–19)Google Scholar
  24. Gobakken T, Næsset E (2004) Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data. Scand J For Res 19:529–542CrossRefGoogle Scholar
  25. Gobakken T, Næsset E (2005) Weibull and percentile models for LIDAR-based estimation of basal area distribution. Scand J For Res 20:490–502CrossRefGoogle Scholar
  26. Gobakken T, Lexerød NL, Eid T (2008) T: a forest simulator for bioeconomic analyses based on models for individual trees. Scand J For Res 23:250–265CrossRefGoogle Scholar
  27. Haara A, Haarala M (2002) Tree species classification using semi-automatic delineation of trees on aerial images. Scand J For Res 17:556–565CrossRefGoogle Scholar
  28. Hill RA, Thomson AG (2005) Mapping woodland species composition and structure using airborne spectral and LiDAR data. Int J Remote Sens 26:3763–3779CrossRefGoogle Scholar
  29. Hoen HF, Eid T (1990) En modell for analyse av behandlingsalternativer for en skog ved bestandssimulering og lineær programmering (A model for analysing treatment options for a forest using stand simulation and linear programming). Rep Norw For Res Inst 9/90:35 pGoogle Scholar
  30. Holmgren J, Persson Å, Söderman U (2008) Species identification of individual trees by combining high resolution LIDAR data with multi-spectral images. Int J Remote Sens 29:1537–1552CrossRefGoogle Scholar
  31. Holmström H, Kallur H, Ståhl G (2003) Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data. Silva Fenn 37:381–398CrossRefGoogle Scholar
  32. Holte A (1993) Diameter distribution functions for even-aged (Picea abies) stands. Meddr Skogforsk 46:47Google Scholar
  33. Hyde P, Dubayah R, Walker W, Blair JB, Hofton M, Hunsaker C (2006) Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sens Environ 102:63–73CrossRefGoogle Scholar
  34. Hynynen J, Ahtikoski A, Siitonen J, Sievanen R, Liski J (2005) Applying the MOTTI simulator to analyse the effects of alternative management schedules on timber and non-timber production. For Ecol Manag 207:5–18CrossRefGoogle Scholar
  35. Hyyppä H, Hyyppä J (1999) Comparing the accuracy of laser scanner with other optical remote sensing data sources for stand attributes retrieval. Photogramm J Finland 16:5–15Google Scholar
  36. Hyyppä J, Inkinen M (1999) Detecting and estimating attributes for single trees using laser scanner. Photogramm J Finland 16:27–42Google Scholar
  37. Hyyppä J, Kelle O, Lehikoinen M, Inkinen M (2001) A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Trans Geosci Remote Sens 39:969–975CrossRefGoogle Scholar
  38. Kaartinen H, Hyyppä J, Yu X, Vastaranta M, Hyyppä H, Kukko A, Holopainen M, Heipke C, Hirschmugl M, Morsdorf F, Næsset E, Pitkänen J, Popescu S, Solberg S, Wolf BM, Wu J-C (2012) An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens 4:950–974CrossRefGoogle Scholar
  39. Kangas A (2010) Value of forest information. Eur J For Res 129:863–874CrossRefGoogle Scholar
  40. Kangas A, Mehtätalo L, Mäkinen A, Vanhatalo K (2011) Sensitivity of harvest decisions to errors in stand characteristics. Silva Fenn 45:693–709CrossRefGoogle Scholar
  41. Ketzenberg ME, Rosenzweig ED, Marucheck AE, Metters RD (2007) A framework for the value of information in inventory replenishment. Eur J Oper Res 182:1230–1250CrossRefGoogle Scholar
  42. Key T, Warner TA, McGraw JB, Fajvan MA (2001) A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest. Remote Sens Environ 75:100–112CrossRefGoogle Scholar
  43. Korpela I, Ørka HO, Maltamo M, Tokola T, Hyyppä J (2010) Tree species classification using airborne LiDAR—effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. Silva Fenn 44:319–339CrossRefGoogle Scholar
  44. Lindberg E, Holmgren J, Olofsson K, Olsson H, Wallerman J (2008). Estimation of tree lists from airborne laser scanning data using a combination of analysis on single tree and raster cell level. In: Proceedings of the Silvilaser 2008 Conference. Edinburgh, UKGoogle Scholar
  45. Lumley T, Miller A (2009) Leaps: regression subset selection (http://cran.r-project.org/web/packages/leaps/index.html). R package
  46. Mäkinen A, Kangas A, Mehtätalo L (2010) Correlations, distributions, and trends in forest inventory errors and their effects on forest planning. Can J For Res 40:1386–1396CrossRefGoogle Scholar
  47. McCombs JW, Roberts SD, Evans DL (2003) 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 49:457–466Google Scholar
  48. McRoberts RE, Tomppo EO, Næsset E (2010) Advances and emerging issues in national forest inventories. Scand J For Res 25:368–381CrossRefGoogle Scholar
  49. Mønness EN (1982) Diameter distributions and height curves in even-aged stands of Pinus sylvestris L. Meddr Nor Inst Skogforsk 36:43 pGoogle Scholar
  50. Næsset E (2002) Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens Environ 80:88–99CrossRefGoogle Scholar
  51. Næsset E (2004) Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand J For Res 19:164–179CrossRefGoogle Scholar
  52. Næsset E (2007) Airborne laser scanning as a method in operational forest inventory: status of accuracy assessments accomplished in Scandinavia. Scand J For Res 22:433–442CrossRefGoogle Scholar
  53. Næsset E, Gobakken T, Holmgren J, Hyyppä H, Hyyppä J, Maltamo M, Nilsson M, Olsson H, Persson Å, Søderman U (2004) Laser scanning of forest resources: the nordic experience. Scand J For Res 19:482–499CrossRefGoogle Scholar
  54. Næsset E, Gobakken T, Solberg S, Gregoire TG, Nelson R, Ståhl G, Weydahl DJ (2011) Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: a case study from a boreal forest area. Remote Sens Environ 115:3599–3614CrossRefGoogle Scholar
  55. Ørka HO, Næsset E, Bollandsås OM (2009) Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sens Environ 113:1163–1174CrossRefGoogle Scholar
  56. Ørka HO, Næsset E, Bollandsås OM (2010) Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning. Remote Sens Environ 114:1445–1461CrossRefGoogle Scholar
  57. Ørka HO, Gobakken T, Næsset E, Ene L, Lien V (2012) Simultaneously acquired airborne laser scanning and multispectral imagery for individual tree species identification. Can J Remote Sens 38:125–138CrossRefGoogle Scholar
  58. Ørka HO, Dalponte M, Gobakken T, Næsset E, Ene L (2013) Characterizing forest species composition using multiple sensors and inventory approaches. Scand J For Res 28:677–688CrossRefGoogle Scholar
  59. Packalén P, Maltamo M (2006) Predicting the plot volume by tree species using airborne laser scanning and aerial photographs. For Sci 52:611–622Google Scholar
  60. Packalén P, Suvanto A, Maltamo M (2009) A two stage method to estimate species-specific growing stock. Photogramm Eng Remote Sens 75:1451–1460CrossRefGoogle Scholar
  61. Persson Å, Holmgren J, Söderman U (2002) Detecting and measuring individual trees using an airborne laser scanner. Photogramm Eng Remote Sens 68:925–932Google Scholar
  62. Peuhkurinen JPJ, Mehtätalo LML, Maltamo M (2011) Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands. Can J For Res 41:583–598CrossRefGoogle Scholar
  63. Pretzsch H, Biber P, Dursky J (2002) The single tree-based stand simulator SILVA: construction, application and evaluation. For Ecol Manag 162:3–21CrossRefGoogle Scholar
  64. Solberg S, Næsset E, Bollandsås OM (2006) Single tree segmentation using airborne laser scanner data in a structurally heterogeneous spruce forest. Photogramm Eng Remote Sens 72:1369–1378CrossRefGoogle Scholar
  65. Vauhkonen J, Korpela I, Maltamo M, Tokola T (2010) Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics. Remote Sens Environ 114:1263–1276CrossRefGoogle Scholar
  66. Vauhkonen J, Ene L, Gupta S, Heinzel J, Holmgren J, Pitkänen J, Solberg S, Wang Y, Weinacker H, Hauglin KM, Lien V, Packalén P, Gobakken T, Koch B, Næsset E, Tokola T, Maltamo M (2012a) Comparative testing of single-tree detection algorithms under different types of forest. Forestry 85:27–40CrossRefGoogle Scholar
  67. Vauhkonen J, Seppänen A, Packalén P, Tokola T (2012b) Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data. Remote Sens Environ 124:534–541CrossRefGoogle Scholar
  68. Vehmas M, Eerikäinen K, Peuhkurinen J, Packalén P, Maltamo M (2011) Airborne laser scanning for the site type identification of mature boreal forest stands. Remote Sens 3:100–116CrossRefGoogle Scholar
  69. Vestjordet E (1967) Funksjoner og tabeller for kubering av stående gran (Functions and tables for volume of standing trees. Norway spruce). Meddr norske SkogforsVes 22:539–574Google Scholar
  70. Waser LT, Ginzler C, Kuechler M, Baltsavias E, Hurni L (2011) Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sens Environ 115:76–85CrossRefGoogle Scholar
  71. Wikström P, Edenius L, Elfving B, Eriksson LO, Lämås T, Sonesson J, Öhman K, Wallerman J, Waller C, Klintebäck F (2011) The Heureka forestry decision support system: an overview. Math Comput For Nat-Res Sci 3:87–95Google Scholar

Copyright information

© INRA and Springer-Verlag France 2014

Authors and Affiliations

  • Even Bergseng
    • 1
  • Hans Ole Ørka
    • 1
  • Erik Næsset
    • 1
  • Terje Gobakken
    • 1
    Email author
  1. 1.Department of Ecology and Natural Resource ManagementNorwegian University of Life SciencesÅsNorway

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