Use of partial-coverage UAV data in sampling for large scale forest inventories

Remote Sensing of Environment

Volumn 194, Issue , 2017, Pages 115-126

  Puliti, Stefano ^a   Ene, Liviu Theodor ^a   Gobakken, Terje ^a   Næsset, Erik ^a  

^a NORWEGIAN UNIVERSITY OF LIFE SCIENCES   (Norway)

Author keywords

Airborne laser scanning; Forest inventory; Hybrid inference; Structure from motion; Unmanned aerial vehicle

Indexed keywords

COST BENEFIT ANALYSIS; COST EFFECTIVENESS; COST ESTIMATING; COSTS; EFFICIENCY; FORESTRY; LASER APPLICATIONS; NATURAL RESOURCES; SURFACE ANALYSIS;

AIRBORNE LASER SCANNING; FOREST INVENTORY; FOREST RESOURCE ASSESSMENTS; HYBRID INFERENCE; LARGE-SCALE APPLICATIONS; PROBABILISTIC SAMPLING; SIMPLE RANDOM SAMPLING; STRUCTURE FROM MOTION;

UNMANNED AERIAL VEHICLES (UAV);

FOREST INVENTORY; LASER METHOD; PRECISION; PROBABILITY; SCANNER; UNMANNED VEHICLE;

NORWAY;

EID: 85016397393     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2017.03.019     Document Type: Article

References (54)

1. Agisoft Agisoft Photoscan User Manual, Agisoft LLC: St. Petersburg, Russia. Available at: http://www.agisoft.com/pdf/photoscan-pro_1_1_en.pdf, 2014.
2. Andersen, H.E., Strunk, J., Temesgen, H., Using airborne light detection and ranging as a sampling tool for estimating forest biomass resources in the Upper Tanana Valley of Interior Alaska. West. J. Appl. For., 2011, 26.
3. Anon, Quantum Gis Geographic Information System. Open Source Geospatial Foundation Project. Available at: http://www.qgis.org/it/site/, 2014.
4. Bohlin, J., Wallerman, J., Fransson, J.E., Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution Dem. Scand. J. For. Res., 27, 2012.
5. Braastad, H., Volume Tables for Birch. Vol. 21, 1966, Meddr norske SkogforsVes, 23–78.
6. Brantseg, A., Volume Functions and Tables for Scots Pine. 22, 1967, Meddr norske SkogforsVes, South Norway, 689–739.
7. Breidenbach, J., Astrup, R., Small area estimation of forest attributes in the Norwegian National Forest Inventory. Eur. J. For. Res. 131 (2012), 1255–1267.
8. Corona, P., Fattorini, L., Franceschi, S., Scrinzi, G., Torresan, C., Estimation of standing wood volume in forest compartments by exploiting airborne laser scanning information: model-based, design-based, and hybrid perspectives. Can. J. For. Res. 44 (2014), 1303–1311.
9. Dandois, J.P., Ellis, E.C., High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens. Environ. 136 (2013), 259–276.
10. Dandois, J., Olano, M., Ellis, E., Optimal altitude, overlap, and weather conditions for computer vision Uav estimates of forest structure. Remote Sens., 7, 2015, 13895.
11. DigiKom Ltd. User's Guide for Surveyingcalculation Plugin for Qgis 2, 0.2, Available at: (03.05.2015) digikom.hu/SurveyingCalculation/usersguide.pdf, 2015.
12. Ene, L.T., Næsset, E., Gobakken, T., Gregoire, T.G., Ståhl, G., Nelson, R., Assessing the accuracy of regional Lidar-based biomass estimation using a simulation approach. Remote Sens. Environ., 2012, 123.
13. Ene, L.T., Næsset, E., Gobakken, T., Gregoire, T.G., Ståhl, G., Holm, S., A simulation approach for accuracy assessment of two-phase post-stratified estimation in large-area Lidar biomass surveys. Remote Sens. Environ. 133 (2013), 210–224.
14. Ene, L.T., Næsset, E., Gobakken, T., Simulation-based assessment of sampling strategies for large-area biomass estimation using Wall-to-Wall and partial coverage airborne laser scanning surveys. Remote Sens. Environ. 176 (2016), 328–340.
15. Ene, L.T., Næsset, E., Gobakken, T., Mauya, E., Bollandsas, O.M., Gregoire, T.G., Ståhl, G., Zahabu, E., Large-scale estimation of change in aboveground biomass in Miombo woodlands using airborne laser scanning and National Forest Inventory Data. Remote Sens. Environ. 186 (2016), 626–636.
16. Fitje, A., Vestjordet, E., Stand Height Curves and New Tariff Tables for Norway Spruce. Vol. 34, 1977, Meddr norske SkogforsVes, 23–62.
17. Floreano, D., Wood, R.J., Science, technology and the future of small autonomous drones. Nature 521 (2015), 460–466.
18. senseFly, Ebee Extended User Manual; Lausanne. Available at: https://www.sensefly.com/fileadmin/user_upload/documents/manuals/Extended_User_Manual_eBee_and_eBee_Ag_v16.pdf, 2014.
19. Gobakken, T., Næsset, E., Nelson, R., Bollandsås, O.M., Gregoire, T.G., Ståhl, G., Holm, S., Ørka, H.O., Astrup, R., Estimating biomass in Hedmark County, Norway using national forest inventory field plots and airborne laser scanning. Remote Sens. Environ. 123 (2012), 443–456.
20. Gobakken, T., Korhonen, L., Næsset, E., Laser-Assisted Selection of Field Plots for an Area-Based forest Inventory. 47, 2013, Silva Fennica, 1–20.
21. Gobakken, T., Bollandsås, O.M., Næsset, E., Comparing biophysical forest characteristics estimated from photogrammetric matching of aerial images and airborne laser scanning data. Scand. J. For. Res., 2014, 1–37.
22. Grafström, A., Ringvall, A.H., Improving forest field inventories by using remote sensing data in novel sampling designs. Can. J. For. Res. 43 (2013), 1015–1022.
23. Grafström, A., Saarela, S., Ene, L.T., Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space. Can. J. For. Res., 44, 2014.
24. Gregoire, T.G., Valentine, H.T., Sampling Strategies for Natural Resources and the Environment. 2008, Chapman & Hall/CRC, Boca Raton, Florida.
25. Gregoire, T.G., Ståhl, G., Næsset, E., Gobakken, T., Nelson, R., Holm, S., Model-assisted estimation of biomass in a LiDAR sample survey in Hedmark County, Norway. Can. J. For. Res. 41 (2011), 83–95.
26. Gregoire, T.G., Næsset, E., McRoberts, R.E., Ståhl, G., Andersen, H.-E., Gobakken, T., Ene, L., Nelson, R., Statistical rigor in Lidar-assisted estimation of aboveground forest biomass. Remote Sens. Environ. 173 (2016), 98–108.
27. Hansen, E., Gobakken, T., Solberg, S., Kangas, A., Ene, L., Mauya, E., Næsset, E., Relative efficiency of Als and Insar for biomass estimation in a Tanzanian rainforest. Remote Sens., 7, 2015, 9865.
28. Hawbaker, T.J., Keuler, N.S., Lesak, A.A., Gobakken, T., Contrucci, K., Radeloff, V.C., Improved estimates of forest vegetation structure and biomass with a Lidar-optimized sampling design. J. Geophys. Res. Biogeo., 2009, 114.
29. Jaakkola, A., Hyyppä, J., Kukko, A., Yu, X., Kaartinen, H., Lehtomäki, M., Lin, Y., A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS J. Photogramm. Remote Sens. 65 (2010), 514–522.
30. Lisein, J., Pierrot-Deseilligny, M., Bonnet, S., Lejeune, P., A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests 4 (2013), 922–944.
31. Lumley, T., Miller, A., Leaps: Regression Subset Selection. Available at: http://cran.r-project.org/web/packages/leaps/leaps.pdf, 2009.
32. McRoberts, R.E., Probability-and model-based approaches to inference for proportion forest using satellite imagery as ancillary data. Remote Sens. Environ., 2010, 114.
33. McRoberts, R.E., Næsset, E., Gobakken, T., Inference for Lidar-assisted estimation of forest growing stock volume. Remote Sens. Environ. 128 (2013), 268–275.
34. McRoberts, R.E., Andersen, H.-E., Næsset, E., Using airborne laser scanning data to support forest sample surveys. Forestry Applications of Airborne Laser Scanning, 2014, Springer, 269–292.
35. McRoberts, R.E., Næsset, E., Gobakken, T., Estimation for inaccessible and non-sampled forest areas using model-based inference and remotely sensed auxiliary information. Remote Sens. Environ. 154 (2014), 226–233.
36. Næsset, E., Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ. 80 (2002), 88–99.
37. Næsset, E., Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand. J. For. Res. 19 (2004), 164–179.
38. 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. 130 (2013), 108–120.
39. Næsset, E., Ørka, H.O., Solberg, S., Bollandsås, O.M., Hansen, E.H., Mauya, E., Zahabu, E., Malimbwi, R., Chamuya, N., Olsson, H., Gobakken, T., Mapping and estimating forest area and aboveground biomass in Miombo woodlands in Tanzania using data from airborne laser scanning, tandem-X, Rapideye, and global forest maps: a comparison of estimated precision. Remote Sens. Environ. 175 (2016), 282–300.
40. Puliti, S., Ørka, H.O., Gobakken, T., Næsset, E., Inventory of small forest areas using an unmanned aerial system. Remote Sens., 7, 2015, 9632.
41. Roze, A., Zufferey, J.-C., Beyeler, A., McClellan, A., Ebee Rtk Accuracy Assessment; Lausanne, Switzerland. Available at: (10.03.2016) https://www.sensefly.com/fileadmin/user_upload/sensefly/documents/eBee-RTK-Accuracy-Assessment.pdf, 2016.
42. Saarela, S., Schnell, S., Grafström, A., Tuominen, S., Nordkvist, K., Hyyppä, J., Kangas, A., Ståhl, G., Effects of sample size and model form on the accuracy of model-based estimators of growing stock volume. Can. J. For. Res. 45 (2015), 1524–1534.
43. Saarela, S., Holm, S., Grafström, A., Schnell, S., Næsset, E., Gregoire, T.G., Nelson, R.F., Ståhl, G., Hierarchical model-based inference for forest inventory using three sources of information. Ann. For. Sci. 73 (2016), 895–910.
44. Särndal, C.E., Swensson, B., Wretman, J., Model Assisted Survey Sampling. 1992, Springer, New York.
45. SenseFly, User Manual Ixus/Elph Camera. Available at: (18.01.2016) http://www.mencisoftware.com/documentUAV/camera%20manual/[ENG]_2014_user_manual_ixuselph_v2.pdf, 2014.
46. Snowdon, P., A ratio estimator for bias correction in logarithmic regressions. Can. J. For. Res. 21 (1991), 720–724.
47. Ståhl, G., Holm, S., Gregoire, T.G., Gobakken, T., Næsset, E., Nelson, R., Model-based inference for biomass estimation in a Lidar sample survey in Hedmark County, Norway. Can. J. For. Res. 41 (2011), 96–107.
48. Ståhl, G., Saarela, S., Schnell, S., Holm, S., Breidenbach, J., Healey, S.P., Patterson, P.L., Magnussen, S., Næsset, E., McRoberts, R.E., Gregoire, T.G., Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation. Forest Ecosystems 3 (2016), 1–11.
49. Strimbu, V.F., Ene, L.T., Næsset, E., Spatially consistent imputations of forest data under a semivariogram model. Can. J. For. Res. 46 (2016), 1145–1156.
50. Takasu, T., Rtklib: Open Source Program Package for Rtk-Gps. 2009, FOSS4G, Tokyo, Japan Available at: http://www.rtklib.com/.
51. Terrasolid Terrascan User's Guide, Terrasolid Ltd.: Helsinki, Available at: https://www.terrasolid.com/download/tscan.pdf, 2012.
52. Vestjordet, E., Functions and Tables for Volume of Standing Trees. Vol. 22, 1967, Meddr norske SkogforsVes, Norway Spruce, 539–574.
53. Vestjordet, E., Volum Av Nyttbart Virke Hos Gran Og Furu Basert På Relativ Høyde Og Diameter I Brysthøyde Eller Ved 2,5 M Fra Stubbeavskjær [Merchantable Volume of Norway Spruce and Scots Pine Based on Relative Height and Diameter at Breast Height or 2.5 M above Stump Level]. Meddr norske SkogforsVes 25 (1968), 411–459.
54. Whitehead, K., Hugenholtz, C.H., Myshak, S., Brown, O., LeClair, A., Tamminga, A., Barchyn, T.E., Moorman, B., Eaton, B., Remote sensing of the environment with small unmanned aircraft systems (Uass), part 2: scientific and commercial applications. J. Unmanned Veh. Sys. 02 (2014), 86–102.