A Novel GNSS Technique for Predicting Boreal Forest Attributes at Low Cost

IEEE Transactions on Geoscience and Remote Sensing

Volumn 55, Issue 9, 2017, Pages 4855-4867

  Liu, Jingbin ^a,b   Hyyppa, Juha ^b   Yu, Xiaowei ^b   Jaakkola, Anttoni ^b   Kukko, Antero ^b   Kaartinen, Harri ^b   Zhu, Lingli ^b   Liang, Xinlian ^b   Wang, Yunsheng ^b   Hyyppa, Hannu ^b,c  

^a WUHAN UNIVERSITY   (China)

^b FINNISH GEOSPATIAL RESEARCH INSTITUTE   (Finland)

^c AALTO UNIVERSITY   (Finland)

Author keywords

Crowdsourcing; forestry; global navigation satellite systems (GNSSs); laser scanning; mobile mapping; radio propagation losses

Indexed keywords

AIR NAVIGATION; BIOMASS; COMMUNICATION SATELLITES; COSTS; CROWDSOURCING; DATA ACQUISITION; DATA FLOW ANALYSIS; DECISION TREES; FORECASTING; FORESTRY; GLOBAL POSITIONING SYSTEM; MOBILE RADIO SYSTEMS; RADIO NAVIGATION; RADIO WAVES; RECEIVERS (CONTAINERS); REMOTE SENSING; TIMBER; VEGETATION;

DATA COLLECTION; GEO-SPATIAL ANALYSIS; GLOBAL NAVIGATION SATELLITE SYSTEMS; LASER SCANNING; MOBILE MAPPING; PROPAGATION LOSS;

COST BENEFIT ANALYSIS;

ABOVEGROUND BIOMASS; ALGORITHM; BOREAL FOREST; COST ANALYSIS; CROWDSOURCING; DIAMETER; FOREST INVENTORY; GNSS; HEIGHT; LASER METHOD; MAPPING; MOBILE PHONE; RADIO WAVE; REMOTE SENSING; SCANNER; SIGNAL; WAVE PROPAGATION;

EID: 85028916086     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2017.2650944     Document Type: Article

References (35)

1. Global Forest Resources Assessment 2010: Main Report: FAO Forestry Paper 163, Food Agriculture Organization, Rome, Italy, 2010.
2. C. Beer et al., "Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate, " Science, vol. 329, pp. 834-838, Aug. 2010.
3. Y. Pan, R. A. Birdsey, O. L. Phillips, and R. B. Jackson, "The structure, distribution, and biomass of the world's forests, " Annu. Rev. Ecol. Evol. Syst., vol. 44, pp. 593-622, Nov. 2013.
4. G. Kindermann, I. Mccallum, S. Fritz, and M. Obersteiner, "A global forest growing stock, biomass and carbon map based on FAO statistics, " Silva Fennica, vol. 42, no. 3, pp. 387-396, 2008.
5. B. Koch, "Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment, " ISPRS J. Photogram. Remote Sens., vol. 65, no. 6, pp. 581-590, 2010.
6. J. Rahlf, J. Breidenbach, S. Solberg, E. Næsset, and R. Astrup, "Comparison of four types of 3D data for timber volume estimation, " Remote Sens. Environ., vol. 155, pp. 325-333, Dec. 2014.
7. E. Tomppo et al., "Application of ERS-1 SAR data in large area forest inventory, " in Proc. 2nd ERS Appl. Workshop, London, U. K., Dec. 1995, pp. 103-108.
8. J. Hyyppä, H. Hyyppä, M. Inkinen, M. Engdahl, S. Linko, and Y. H. Zhu, "Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes, " Forest Ecol. Manage., vol. 128, no. 1, pp. 109-120, 2000.
9. H. J. Hyyppä and J. M. Hyyppä, "Effects of stand size on the accuracy of remote sensing-based forest inventory, " IEEE Trans. Geosci. Remote Sens., vol. 39, no. 12, pp. 2613-2621, Dec. 2001.
10. P. S. Thenkabail, Land Resources Monitoring, Modeling, and Mapping With Remote Sensing. Boca Raton, FL, USA: CRC Press, 2015.
11. X. Yu et al., "Comparison of laser and stereo optical, SAR and InSAR point clouds from air-and space-borne sources in the retrieval of forest inventory attributes, " Remote Sens., vol. 7, no. 12, pp. 15933-15954, 2015, doi: 10. 3390/rs71215809.
12. M. Holopainen, M. Vastaranta, and J. Hyyppä, "Outlook for the next generation's precision forestry in Finland, " Forests, vol. 5, no. 7, pp. 1682-1694, 2014.
13. X. Liang, J. Hyyppä, A. Kukko, H. Kaartinen, A. Jaakkola, and X. Yu, "The use of a mobile laser scanning system for mapping large forest plots, " IEEE Geosci. Remote Sens. Lett., vol. 11, no. 9, pp. 1504-1508, Sep. 2014, doi: 10. 1109/LGRS. 2013. 2297418.
14. S. Fritz et al., "Geo-Wiki. Org: The use of crowdsourcing to improve global land cover, " Remote Sens., vol. 1, no. 3, pp. 345-354, 2009.
15. X. Liang et al., "Forest data collection using terrestrial image-based point clouds from a handheld camera compared to terrestrial and personal laser scanning, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 9, pp. 5117-5132, Sep. 2015, doi: 10. 1109/TGRS. 2015. 2417316.
16. C. Heipke, "Crowdsourcing geospatial data, " ISPRS J. Photogram. Remote Sens., vol. 65, no. 6, pp. 550-557, 2010.
17. A. Hudson-Smith, M. Batty, A. Crooks, and R. Milton, "Mapping for the masses: Accessing Web 2. 0 through crowdsourcing, " Social Sci. Comput. Rev., vol. 27, no. 4, pp. 524-538, Aug. 2008.
18. GNSS Market Report: Issue 3, European GNSS Agency, Prague, Czech Republic, Oct. 2013.
19. P. Ferrazzoli, L. Guerriero, N. Pierdicca, and R. Rahmoune, "Forest biomass monitoring with GNSS-R: Theoretical simulations, " Adv. Space Res., vol. 47, no. 10, pp. 1823-1832, 2010.
20. A. Egido et al., "Airborne GNSS-R polarimetric measurements for soil moisture and above-ground biomass estimation, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 5, pp. 1522-1532, May 2014.
21. H. Kaartinen et al., "Accuracy of kinematic positioning using global satellite navigation systems under forest canopies, " Forests, vol. 6, no. 9, pp. 3218-3236, 2015.
22. J. Liu et al., "Can global navigation satellite system signals reveal the ecological attributes of forests?" Int. J. Appl. Earth Observat. Geoinf., vol. 50, pp. 74-79, Aug. 2016. [Online]. Available: http://dx. doi. org/10. 1016/j. jag. 2016. 03. 007
23. M. Holopainen, R. Haapanen, S. Tuominen, and R. Viitala, "Performance of airborne laser scanning-and aerial photograph-based statistical and textural features in forest variable estimation, " in Proc. SilviLaser, 8th Int. Conf. LiDAR Appl. Forest Assessment Inventory, Edinburgh, U. K., Sep. 2008, pp. 105-112.
24. L. Breiman, "Random forests, " Mach. Learn., vol. 45, no. 1, pp. 5-32, 2001.
25. J. Hyyppä, H. Hyyppä, D. Leckie, F. Gougeon, X. Yu, and M. Maltamo, "Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests, " Int. J. Remote Sens., vol. 29, no. 5, pp. 1339-1366, 2008.
26. X. Yu, J. Hyyppä, M. Holopainen, and M. Vastaranta, "Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes, " Remote Sens., vol. 2, no. 6, pp. 1481-1495, 2010, doi: 10. 3390/rs2061481.
27. X. Yu, J. Hyyppä, M. Vastaranta, M. Holopainen, and R. Viitala, "Predicting individual tree attributes from airborne laser point clouds based on the random forests technique, " ISPRS J. Photogram. Remote Sens., vol. 66, no. 1, pp. 28-37, 2011.
28. Y. Wu, S. G. Jin, Z. M. Wang, and J. B. Liu, "Cycle slip detection using multi-frequency GPS carrier phase observations: A simulation study, " Adv. Space Res., vol. 46, no. 2, pp. 144-149, 2010.
29. J. Liu, R. Chen, Z. Wang, and H. Zhang, "Spherical cap harmonic model for mapping and predicting regional TEC, " GPS Solutions, vol. 15, no. 2, pp. 109-119, 2011.
30. "Influences of terrain irregularities and vegetation on troposphere propagation, " CCIR, Geneva, Switzerland, Tech. Rep. 235-236, 1986.
31. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. New York, NY, USA: Springer-Verlag, 2009.
32. L. Breiman and A. Cutler. (2008). Random Forests, accessed on Mar. 16, 2016. [Online]. Available: http://www. stat. berkeley. edu/ users/breiman/RandomForests/cc-home. htm
33. D. R. Cutler et al., "Random forests for classification in ecology, " Ecology, vol. 88, no. 11, pp. 2783-2792, 2007.
34. J. Liu, R. Chen, J. An, Z. Wang, and J. Hyyppä, "Spherical cap harmonic analysis of the Arctic ionospheric TEC for one solar cycle, " J. Geophys. Res. Space Phys., vol. 119, no. 1, pp. 601-619, 2014, doi: 10. 1002/2013JA019501.
35. K. Karila, M. Vastaranta, M. Karjalainen, and S. Kaasalainen, "Tandem-X interferometry in the prediction of forest inventory attributes in managed boreal forests, " Remote Sens. Environ., vol. 159, pp. 259-268, Mar. 2015.