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Thursday 17 November 2016

Plot location errors of National Forest Inventory: related factors and adverse effects on continuity of plot data

Published Date
Volume 21, Issue 6pp 300–305

Short Communication

DOI: 10.1007/s10310-016-0538-1

Cite this article as: 

Nakajima, H. J For Res (2016) 21: 300. doi:10.1007/s10310-016-0538-1

Abstract

Plot location errors (distance between recorded and true coordinates) of National Forest Inventory (NFI) cause a mismatch between plot data and remotely sensed data and might prevent re-measurement because plots with large location errors are hard to find. However, few studies have examined the detailed distribution of errors and related factors, and no studies have verified whether the large errors prevent re-measurement. This study analyzed data related to 101 plots in central Japan that had been established by the First NFI (1999–2003) on a 4-km grid. Plot location errors of these plots were measured by revisiting. Selective Availability (SA) of the Global Positioning System, which degraded location accuracy until May 2000, was the most important factor in increasing plot location errors. The mean errors were 58.6 and 15.0 m, with and without SA, respectively. In 12 plots with large plot location errors (the mean error was 84.6 m), re-measurement in the Second NFI (2004–2008) was not conducted because plot locations could not be found. In these situations, alternative new plots were established; however, their species compositions were significantly different from the initial NFI plots. Plot location errors of NFI adversely affect the continuity of plot data as well as the analysis with remotely sensed data.

References

  1. Adrados C, Girard I, Gendner JP, Janeau G (2002) Global Positioning System (GPS) location accuracy improvement due to Selective Availability removal. C R Biol 325:165–170CrossRefPubMedGoogle Scholar
  2. Bray JR, Curtis JT (1957) An ordination of the upland forest communities of southern Wisconsin. Ecol Monogr 27:325–349CrossRefGoogle Scholar
  3. Deckert C, Bolstad PV (1996) Forest canopy, terrain, and distance effects on global positioning system point accuracy. Photogramm Eng Remote Sens 62:317–321Google Scholar
  4. Fridman J, Holm S, Nilsson M, Nilsson P, Ringvall HA, Ståhl G (2014) Adapting National Forest Inventories to changing requirements—the case of the Swedish National Forest Inventory at the turn of the 20th century. Silva Fenn 48:1095CrossRefGoogle Scholar
  5. Hagner O, Reese H (2007) A method for calibrated maximum likelihood classification of forest types. Remote Sens Environ 110:438–444CrossRefGoogle Scholar
  6. Halme M, Tomppo E (2001) Improving the accuracy of multisource forest inventory estimates to reducing plot location error—a multicriteria approach. Remote Sens Environ 78:321–327CrossRefGoogle Scholar
  7. Hirata Y, Imaizumi Y, Masuyama T, Matsumoto Y, Miyazono H, Goto T (2010) National Forest Inventory Reports: Japan. In: Tomppo E, Gschwantner T, Lawrence M, McRoberts RE (eds) National Forest Inventories: pathways for common reporting. Springer, New York, pp 333–340Google Scholar
  8. Hulbert IAR, French J (2001) The accuracy of GPS for wildlife telemetry and habitat mapping. J Appl Ecol 38:869–878CrossRefGoogle Scholar
  9. Ishihara MI, Suzuki SN, Nakamura M, Enoki T, Fujiwara A, Hiura T et al (2011) Forest stand structure, composition, and dynamics in 34 sites over Japan. Ecol Res 26:1007–1008CrossRefGoogle Scholar
  10. Jung J, Kim S, Hong S, Kim K, Kim E, Im J, Heo J (2013) Effects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm. ISPRS J Photogramm Remote Sens 81:82–92CrossRefGoogle Scholar
  11. Kajisa T, Murakami T, Mizoue N, Kitahara F, Yoshida S (2008) Estimation of stand volumes using the k-nearest neighbors method in Kyushu, Japan. J For Res 13:249–254CrossRefGoogle Scholar
  12. Kitahara F, Mizoue N, Kajisa T, Murakami T, Yoshida S (2010) Positional accuracy of National Forest Inventory plots in Japan. J For Plan 15:73–79Google Scholar
  13. McRoberts RE (2010) The effects of rectification and Global Positioning System errors on satellite image-based estimates of forest area. Remote Sens Environ 114:1710–1717CrossRefGoogle Scholar
  14. McRoberts RE, Holden GR, Nelson MD, Liknes GC, Gormanson DD (2006) Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service. Can J For Res 35:2968–2980CrossRefGoogle Scholar
  15. Morin RS, Liebhold AM (2015) Invasions by two non-native insects alter regional forest species composition and successional trajectories. For Ecol Manag 341:67–74CrossRefGoogle Scholar
  16. Næsset E (1999) Point accuracy of combined pseudorange and carrier phase differential GPS under forest canopy. Can J For Res 29:547–553CrossRefGoogle Scholar
  17. Nakajima H, Ishida M (2014) Decline of Quercus crispula in abandoned coppice forests caused by secondary succession and Japanese oak wilt disease: stand dynamics over twenty years. For Ecol Manag 334:18–27CrossRefGoogle Scholar
  18. Prima ODA, Echigo A, Yokoyama R, Yoshida T (2006) Supervised landform classification of Northeast Honshu from DEM-derived thematic maps. Geomorphology 78:373–386CrossRefGoogle Scholar
  19. Reese H, Nilsson M, Sandström P, Olsson H (2002) Applications using estimates of forest parameters derived from satellite and forest inventory data. Comput Electron Agric 37:37–55CrossRefGoogle Scholar
  20. Rempel RS, Rodgers AR (1997) Effects of differential correction on accuracy of a GPS animal location system. J Wildl Manag 61:525–530CrossRefGoogle Scholar
  21. Rempel RS, Rodgers AR, Abraham KF (1995) Performance of a GPS animal location system under boreal forest canopy. J Wildl Manag 59:543–551CrossRefGoogle Scholar
  22. Saarela S, Schnell S, Tuominen S, Balázs A, Hyyppä J, Grafström A, Ståhl G (2016) Effects of positional errors in model-assisted and model-based estimation of growing stock volume. Remote Sens Environ 172:101–108CrossRefGoogle Scholar
  23. Tanaka S, Takahashi T, Nishizono T, Kitahara F, Saito H, Iehara T, Kodani E, Awaya Y (2015) Stand volume estimation using the k-NN technique combined with forest inventory data, satellite image data and additional feature variables. Remote Sens 7:378–394CrossRefGoogle Scholar
  24. Tokuni M, Hirata M, Suzuki M, Takahashi K, Owari T (2013) Positioning accuracy of handy GNSS receivers in forest environment. Boreal For Res 61:117–120 (in Japanese)Google Scholar
  25. Tomppo E, Gschwantner T, Lawrence M, McRoberts RE (2010) National Forest Inventories: pathways for common reporting. Springer, New YorkCrossRefGoogle Scholar
  26. Wang G, Zhang M, Gertner GZ, Oyana T, McRoberts RE, Ge H (2011) Uncertainties of mapping aboveground forest carbon due to plot locations using national forest inventory plot and remotely sensed data. Scand J For Res 26:360–373CrossRefGoogle Scholar
  27. Wing MG (2011) Consumer-grade GPS receiver measurement accuracy in varying forest conditions. Res J For 5:78–88Google Scholar
  28. Zald HSJ, Ohmann JL, Roberts HM, Gregory MJ, Henderson EB, McGaughey RJ, Braaten J (2014) Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure. Remote Sens Environ 143:26–38CrossRefGoogle Scholar

For further details log on website :
http://link.springer.com/article/10.1007/s10310-016-0542-5

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