Sparse density, leaf-off airborne laser scanning data in aboveground biomass component prediction

Forests

Volumn 6, Issue 6, 2015, Pages 1839-1857

  Kankare, Ville ^a,b   Vauhkonen, Jari ^b,c   Holopainen, Markus ^a,b   Vastaranta, Mikko ^a,b   Hyyppä, Juha ^b   Hyyppä, Hannu ^b,d,e   Alho, Petteri ^b,d,f  

^a UNIVERSITY OF HELSINKI   (Finland)

^b FINNISH GEODETIC INSTITUTE   (Finland)

^c UNIVERSITY OF EASTERN FINLAND   (Finland)

^d AALTO UNIVERSITY   (Finland)

^e HELSINKI METROPOLIA UNIVERSITY OF APPLIED SCIENCES   (Finland)

^f UNIVERSITY OF TURKU   (Finland)

Author keywords

Forest inventory; LiDAR; Remote sensing

Indexed keywords

FORESTRY; LASER APPLICATIONS; MAPPING; OPTICAL RADAR; REMOTE SENSING; SATELLITE IMAGERY; SCANNING; SURFACE ANALYSIS;

ABOVEGROUND BIOMASS; AIRBORNE LASER SCANNING; CANOPY HEIGHT MODELS; FOREST INVENTORY; NATIONAL FOREST INVENTORIES; PREDICTION ACCURACY; REMOTE SENSING DATA; SCANNING PARAMETERS;

FORECASTING;

ABOVEGROUND BIOMASS; ACCURACY ASSESSMENT; AIRBORNE SENSING; FOREST INVENTORY; LANDSAT THEMATIC MAPPER; LEAF; LIDAR; PREDICTION;

DATA; FORESTS; LASERS; SCANNING;

FINLAND;

EID: 84933530229     PISSN: None     EISSN: 19994907     Source Type: Journal    
DOI: 10.3390/f6061839     Document Type: Article

References (42)

1. Repola, J. Biomass Equations for Birch in Finland. Silva Fenn. 2008, 42, 605-624.
2. Repola, J. Biomass equations for Scots pine and Norway spruce in Finland. Silva Fenn. 2009, 43, 625-647.
3. Tomppo, E.; Olsson, H.; Ståhl, G.; Nilsson, M.; Hagner, O.; Katila, M. Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sens. Environ. 2008, 112, 1982-1999.
4. Lu, D. The potential and challenge of remote sensing - based biomass estimation. Int. J. Remote Sens. 2006, 27, 1297-1328.
5. Tuominen, S.; Eerikäinen, K.; Schibalski, A.; Haakana, M.; Lehtonen, A. Mapping biomass variables with a multi-source forest inventory technique. Silva Fenn. 2010, 44, 109-117.
6. Næsset, E. Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J. Photogramm. Remote Sens. 1997, 52, 49-56.
7. White, J.C.; Wulder, M.A.; Varhola, A.; Vastaranta, M.; Coops, N.C.; Cook, B.D.; Pitt, D.; Woods, M. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. For. Chron. 2013, 89, 722-723.
8. Hyyppä, J.; Inkinen, M. Detecting and estimating attributes for single trees using laser scanner. Photogramm. J. Finl. 1999, 16, 27-42.
9. Hauglin, M.; Gobakken, T.; Astrup, R.; Ene, L.; Næsset, E. Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data. Forests 2014, 5, 384-403.
10. Kankare, V.; Holopainen, M.; Vastaranta, M.; Puttonen, E.; Yu, X.; Hyyppä, J. Individual tree biomass estimation using terrestrial laser scanning. ISPRS J. Photogramm. Remote Sens. 2013, 75, 64-75.
11. Kankare, V.; Räty, M.; Yu, X.; Holopainen, M.; Vastaranta, M.; Kantola, T.; Hyyppä, J.; Hyyppä, H.; Alho, P.; Viitala, R. Single tree biomass modelling using airborne laser scanning. ISPRS J. Photogramm. Remote Sens. 2013, 85, 66-73.
12. Popescu, S.C. Estimating biomass of individual pine trees using airborne lidar. Biomass Bioenergy 2007, 31, 646-655.
13. Hauglin, M.; Dibdiakova, J.; Gobakken, T.; Næsset, E. Estimating single-tree branch biomass of Norway spruce by airborne laser scanning. ISPRS J. Photogramm. Remote Sens. 2013, 79, 147-156.
14. Hauglin, M.; Astrup, R.; Gobakken, T.; Næsset, E. Estimating single-tree branch biomass of Norway spruce with terrestrial laser scanning using voxel-based and crown dimension features. Scand. J. For. Res. 2013, 28, 456-469.
15. Zolkos, S.G.; Goetz, S.J.; Dubayah, R. A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing. Remote Sens. Environ. 2013, 128, 289-298.
16. Popescu, S.C.; Hauglin, M. Estimation of biomass components by airborne laser scanning. In Forestry Applications of Airborne Laser Scanning-Concepts and Case Studies; Maltamo, M.; Næsset, E.; Vauhkonen, J., Eds.; Managing Forest Ecosystems 27; Springer: Dordrecht, The Netherlands, 2014; pp. 157-175.
17. Popescu, S.C.; Wynne, R.H.; Scrivani, J.A. Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot- Level Volume and Biomass in Deciduous. For. Sci. 2004, 50, 551-565.
18. Aardt, J.A.N.; van Wynne, R.H.; Oderwald, R.G. Lidar-Distributional Parameters on a Per-Segment Basis. For. Sci. 2006, 52, 636-649.
19. Næsset, E. Practical Large-Scale Forest Stand Inventory Using a Small-Footprint Airborne Scanning Laser. Scand. J. For. Res. 2004, 19, 164-179.
20. 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, 395-407.
21. Fassnacht, F.E.; Hartig, F.; Lati, H.; Berger, C.; Hernández, J.; Corvalán, P.; Koch, B. Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sens. Environ. 2014, 154, 102-114.
22. Stepper, C.; Straub, C.; Pretzsch, H. Using semi-global matching point clouds to estimate growing stock at the plot and stand levels: Application for a broadleaf-dominated forest in central Europe. Can. J. For. Res. 2015, 123, 111-123.
23. Vastaranta, M.; Kankare, V.; Holopainen, M.; Yu, X.; Hyyppä, J.; Hyyppä, H. Combination of individual tree detection and area-based approach in imputation of forest variables using airborne laser data. ISPRS J. Photogramm. Remote Sens. 2012, 67, 73-79.
24. Kankare, V.; Vastaranta, M.; Holopainen, M.; Räty, M.; Yu, X.; Hyyppä, J.; Hyyppä, H.; Alho, P.; Viitala, R. Retrieval of forest aboveground biomass and stem volume with airborne scanning LiDAR. Remote Sens. 2013, 5, 2257-2274.
25. Tuominen, S.; Haapanen, R. Estimation of forest biomass by means of genetic algorithm-based optimization of airborne laser scanning and digital aerial photograph features. Silva Fenn. 2013, 47, 20.
26. Bohlin, J.; Wallerman, J.; Fransson, J.E.S. Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM. Scand. J. For. Res. 2012, 27, 692-699.
27. Dalponte, M.; Martinez, C.; Rodeghiero, M.; Gianelle, D. The role of ground reference data collection in the prediction of stem volume with LiDAR data in mountain areas. ISPRS J. Photogramm. Remote Sens. 2011, 66, 787-797.
28. Gomez-Gutierrez, A.; Schnabel, S.; Lavado-Contador, F.; Garcia-Marin, R. Testing the quality of open-access DEMs and their derived attributes in Spain: SRTM, GDEM and PNOA DEM. In Proceedings of Geomorphometry 2011 Conference, Redlands, CA, USA, 2011; pp. 53-56.
29. Nord-Larsen, T.; Schumacher, J. Estimation of forest resources from a country wide laser scanning survey and national forest inventory data. Remote Sens. Environ. 2012, 119, 148-157.
30. Villikka, M.; Packalén, P.; Maltamo, M. The Suitability of Leaf-off Airborne Laser Scanning Data in an Area-based Forest Inventory of Coniferous and Deciduous Trees. Silva Fenn. 2012, 46, 99-110.
31. 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.
32. Korhonen, L.; Peuhkurinen, J.; Malinen, J.; Suvanto, A.; Maltamo, M.; Packalén, P.; Kangas, J. The use of airborne laser scanning to estimate sawlog volumes. Forestry 2008, 81, 499-510.
33. Hudak, A.T.; Crookston, N.L.; Evans, J.S.; Hall, D.E.; Falkowski, M.J. Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sens. Environ. 2008, 112, 2232-2245.
34. Crookston, N.L.; Finley, A.O. yaImpute: An R package for kNN imputation. J. Stat. Softw. 2008, 23, 1-16.
35. R Core Team. R: A language and environment for statistical computing. Avaliable online: http://www.r-project.org/ (accessed on 12 January 2014).
36. 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 Sens. Environ. 2004, 92, 1-20.
37. Næsset, E. Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data. Remote Sens. Environ. 2009, 113, 148-159.
38. Kotamaa, E.; Tokola, T.; Maltamo, M.; Packalén, P.; Kurttila, M.; Mäkinen, A. Integration of remote sensing-based bioenergy inventory data and optimal bucking for stand-level decision making. Eur. J. For. Res. 2010, 129, 875-886.
39. Vastaranta, M.; Wulder, M.A.; White, J.C.; Pekkarinen, A.; Tuominen, S.; Ginzler, C.; Kankare, V.; Holopainen, M.; Hyyppa, J.; Hyyppa, H. Airborne laser scanning and digital stereo imagery measures of forest structure: Comparative results and implications to forest mapping and inventory update. Can. J. Remote Sens. 2013, 39, 382-395.
40. He, Q.; Chen, E.; An, R.; Li, Y. Above-Ground Biomass and Biomass Components Estimation Using LiDAR Data in a Coniferous Forest. Forests 2013, 4, 984-1002.
41. Næsset, E.; Gobakken, T. Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sens. Environ. 2008, 112, 3079-3090.
42. Næsset, E.; Gobakken, T.; Bollandsås, O.M.; Gregoire, T.G.; Nelson, R.; Ståhl, G. Comparison of precision of biomass estimates in regional field sample surveys and airborne LiDAR-assisted surveys in Hedmark County, Norway. Remote Sens. Environ. 2013, 130, 108-120.