Model-assisted estimation of change in forest biomass over an 11year period in a sample survey supported by airborne LiDAR: A case study with post-stratification to provide "activity data"

Remote Sensing of Environment

Volumn 128, Issue , 2013, Pages 299-314

  Næsset, Erik ^a   Bollandsås, Ole Martin ^a   Gobakken, Terje ^a   Gregoire, Timothy G ^b   Ståhl, Göran ^c  

^a NORWEGIAN UNIVERSITY OF LIFE SCIENCES   (Norway)

^b Yale School of the Environment   (United States)

^c SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES   (Sweden)

Author keywords

Airborne LiDAR; Biomass; Change estimation; Estimation; Model assisted; Probability sampling; REDD monitoring

Indexed keywords

AIRBORNE LIDAR; AREA OF INTEREST; ASSESSMENT PROGRAMS; AUXILIARY DATA; AUXILIARY INFORMATION; BOREAL FORESTS; CARBON POOL; CARBON STOCKS; CHANGE ESTIMATION; COLLABORATIVE PROGRAMS; ESTIMATION PROCEDURES; FIELD DATA; FIELD PLOT; FIELD SURVEYS; FOREST BIOMASS; FOREST DEGRADATION; GRID CELLS; HUMAN ACTIVITIES; IN-FIELD; LIDAR DATA; LIDAR REMOTE SENSING; MODEL-ASSISTED; MONITORING PROGRAMS; MULTI-TEMPORAL; MULTINOMIAL LOGISTIC REGRESSION MODELS; NATIONAL LEVEL; NATIONAL POLICIES; PROBABILITY SAMPLING; STANDARD ERRORS; SYSTEMATIC DESIGNS; TEMPORAL RESOLUTION; TREE BIOMASS; UNITED NATIONS; VARIANCE ESTIMATE;

BIOMASS; CARBON; DEFORESTATION; DEVELOPING COUNTRIES; EMISSION CONTROL; LOGISTICS; OPTICAL RADAR; REGRESSION ANALYSIS; REMOTE SENSING; SURVEYS; UNCERTAINTY ANALYSIS; VOLTAGE MEASUREMENT;

ESTIMATION;

AIRBORNE SURVEY; BOREAL FOREST; CARBON SEQUESTRATION; DATA SET; DEFORESTATION; DEVELOPING WORLD; EMISSION CONTROL; ERROR ANALYSIS; ESTIMATION METHOD; FIELD SURVEY; HUMAN ACTIVITY; LIDAR; MONITORING; NATIONAL PLANNING; PHYTOMASS; PROBABILITY; RESOLUTION; SAMPLING; STRATIFICATION; UNCERTAINTY ANALYSIS;

BIOMASS; DEFORESTATION; FORESTS; POLLUTION CONTROL; RADAR; REGRESSION ANALYSIS; REMOTE SENSING; STRATIFICATION;

NORWAY;

EID: 84868647283     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2012.10.008     Document Type: Article

References (68)

1. Andersen H.-E., Barrett T., Winterberger K., Strunk J., Temesgen H. Estimating forest biomass on the western lowlands of the Kenai Peninsula of Alaska using airborne lidar and field plot data in a model-assisted sampling design. Proceedings of the IUFRO Division 4 conference, Extending forest inventory and monitoring over time and space, 19-22 May 2009, Quebec City, Canada 2009, (Available at: http://blue.for.msu.edu/meeting/proceed.php. (last accessed: February 2010)).
2. Andersen H.-E., Breidenbach J. Statistical properties of mean stand biomass estimators in a LIDAR-bases double sampling forest survey design. Proceedings of the ISPRS Workshop Laser Scanning 2007 and SilviLaser 2007. 12-14 September 2007, Espoo, Finland. IAPRS, Volume XXXVI, Part 3/ W52, 2007 2007, 8-13.
3. Anon. SAS/STAT 9.1 user's guide 2004, SAS Institute Inc., Cary, NC..
4. Anon. TerraScan user's guide 2005, Terrasolid Ltd., Jyvaskyla, Finland, (169 pp. (www.terrasolid.fi) (last date accessed: 2 October 2006)).
5. Anon. Memorandum of Understanding between the Government of the Cooperative Republic of Guyana and the Government of the Kingdom of Norway regarding Cooperation on Issues related to the Fight against Climate Change, the Protection of Biodiversity and the Enhancement of Sustainable Development [online] (accessed 19 September 2011). http://www.regjeringen.no/upload/MD/Vedlegg/Klima/klima_skogprosjektet/The%20Memorandum%20of%20Understanding%20Guyana%20Norway%20on%20REDD%20(081109)%20signed%20091109.pdf.
6. Anon. Final draft: Forest carbon partnership facility (FCPF), readiness preparation proposal (R-PP), United Republic of Tanzania [online] (accessed 10 October 2012). http://www.reddtz.org/.
7. Asner G.P., Powell G.V.N., Mascaro J., Knapp D.E., Clark J.K., Jacobson J., et al. High-resolution forest carbon stocks and emissions in the Amazon. Proceedings of the National Academy of Science 2010, 107:16738-16742.
8. Axelsson P. DEM generation from laser scanner data using adaptive TIN models. International Archives of Photogrammetry and Remote Sensing 2000, 33(B4):110-117.
9. Blingsmo K. Tilvekstfunksjoner. Foredrag fra et seminar angående planlegging i skogbruket 1988, (in Norwegian only), Norwegian Forest Research Institute, (Ås, 8 pp.).
10. Bollandsås, O. M., Gregoire, T. G., Næsset, E., & Øyen, B. -H. (2012). Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data. Statistical Models and Applications, http://dx.doi.org/10.1007/s10260-012-0220-5.
11. Breidt F.J., Opsomer J.G. Endogenous post-stratification in surveys: Classifying with a sample-fitted model. The Annals of Statistics 2008, 36:403-427.
12. Clark M.L., Clark D.B., Roberts D.A. Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sensing of Environment 2004, 91:68-89.
13. Cochran W.G. Sampling techniques 1977, Wiley, New York. 3rd edition.
14. Corona P., Fattorini L. Area-based lidar-assisted estimation of forest standing volume. Canadian Journal of Forest Research 2008, 38:2911-2916.
15. Drake J.B., Dubayah R.O., Knox R.G., Clark D.B., Blair J.G. Sensitivity of large-footprint lidar to canopy structure and biomass in a neotropical rainforest. Remote Sensing of Environment 2002, 81:378-392.
16. Drake J.B., Knox R.G., Dubayah R.O., Clark D.B., Condit R., Blair B., et al. Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: Factors affecting the generality of relationships. Global Ecology and Biogeography 2003, 12:141-159.
17. 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 Sensing of Environment 2012, 123:579-592.
18. Frayer W.E. Stratification in double sampling-The easy way out may sometimes be the best way. Resource Inventory Notes BLM-10 1978, Bureau of Land Management, U.S. Department of Interior, Denver, Colorado.
19. Gobakken T., Næsset E. Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data. Canadian Journal of Forest Research 2008, 38:1095-1109.
20. Gobakken T., Næsset E., Nelson R., Bollandsås O.M., Gregoire T.G., Ståhl G., et al. Estimating biomass in Hedmark County, Norway using national forest inventory field plots and airborne laser scanning. Remote Sensing of Environment 2012, 123:443-456.
21. Goeman J.J., Le Cessie S. A goodness-of-fit test for multinomial logistic regression. Biometrics 2006, 62:980-985.
22. 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. Canadian Journal of Forest Research 2011, 41:83-95.
23. Gregoire T.G., Valentine H.T. Sampling strategies for natural resources and the environment 2008, Chapman & Hall/CRC, Boca Raton, Fla, (474 pp.).
24. Harter H.L. Order statistics and their use in testing and estimation 1970, Vol. II. US Government Printing Office, Washington D.C., (172 pp.).
25. Holmgren J. Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scandinavian Journal of Forest Research 2004, 19:543-553.
26. Hopkinson C., Chasmer L., Hall R.J. The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets. Remote Sensing of Environment 2008, 112:1168-1180.
27. Lefsky M.A., Cohen W.B., Harding D.J., Parker G.G., Acker S.A., Gower S.T. Lidar remote sensing of above-ground biomass in three biomes. Global Ecology and Biogeography 2002, 11:393-399.
28. Magnussen S. Effect of plot size on estimates of top height in Douglas-fir. Western Journal of Applied Forestry 1999, 14:17-27.
29. Maltamo M., Eerikainen K., Packalén P., Hyyppä J. Estimation of stem volume using laser scanning-based canopy height metrics. Forestry 2006, 79:217-229.
30. Mandallaz D. Sampling techniques for forest inventories 2008, Chapman & Hall/CRC, (256 pp.).
31. Marklund L.G. Biomass functions for pine, spruce and birch in Sweden 1988, Swedish University of Agricultural Sciences, Department of Forest Survey, Umeå, (In Swedish.).
32. McRoberts R.E. Probability- and model-based approaches to inference for proportion forest using satellite imagery and ancillary data. Remote Sensing of Environment 2010, 114:1017-1025.
33. McRoberts R.E. Satellite image-based maps: Scientific inference or pretty pictures?. Remote Sensing of Environment 2011, 115:715-724.
34. McRoberts R.E., Cohen W.B., Næsset E., Stehman S.V., Tomppo E.O. Using remotely sensed data to construct and assess forest attribute maps and related spatial products. Scandinavian Journal of Forest Research 2010, 25:368-381.
35. McRoberts R.E., Gobakken T., Næsset E. Post-stratified estimation of forest area and growing stock volume using lidar-based stratifications. Remote Sensing of Environment 2012, 125:157-166.
36. McRoberts R.E., Næsset E., Gobakken T. Inference for lidar-assisted estimates of growing stock volume. Remote Sensing of Environment 2013, 128(21):268-275.
37. Mukama K., Mustalahti I., Zahabu E. Participatory forest carbon assessment and REDD+: Learning from Tanzania. International Journal of Forestry Research 2012, (14 pp.). 10.1155/2012/126454.
38. Næsset E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing of Environment 2002, 80:88-99.
39. Næsset E. Accuracy of forest inventory using airborne laser-scanning: Evaluating the first Nordic full-scale operational project. Scandinavian Journal of Forest Research 2004, 19:554-557.
40. Næsset E. Airborne laser scanning as a method in operational forest inventory: Status of accuracy assessments accomplished in Scandinavia. Scandinavian Journal of Forest Research 2007, 22:433-442.
41. Næsset E. Estimating above-ground biomass in young forests with airborne laser scanning. International Journal of Remote Sensing 2011, 32:473-501.
42. Næsset E., Bjerknes K.-O. Estimating tree heights and number of stems in young forest stands using airborne laser scanner data. Remote Sensing of Environment 2001, 78:328-340.
43. Næsset E., Bollandsås O.M., Gobakken T. Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data. Remote Sensing of Environment 2005, 94:541-553.
44. Næsset E., Gobakken T. Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sensing of Environment 2005, 80:88-99.
45. Næsset E., Gobakken T. Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sensing of Environment 2008, 112:3079-3090.
46. Næsset E., Gobakken T., Solberg S., Gregoire T.G., Nelson R., Ståhl G., et al. Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area. Remote Sensing of Environment 2011, 115:3599-3614.
47. Nelson R. Modeling forest canopy heights: The effects of canopy shape. Remote Sensing of Environment 1997, 60:327-334.
48. Nelson R., Gobakken T., Næsset E., Gregoire T.G., Ståhl G., Holm S., et al. Lidar sampling-Using an airborne profiler to estimate forest biomass in Hedmark County, Norway. Remote Sensing of Environment 2012, 123:563-578.
49. Nelson R., Oderwald R., Gregoire T.G. Separating the ground and airborne laser sampling phases to estimate tropical forest basal area, volume, and biomass. Remote Sensing of Environment 1997, 60:311-326.
50. Parker R.C., Evans D.L. An application of LiDAR in a double-sampling forest inventory. Western Journal of Applied Forestry 2004, 19:95-101.
51. Pesonen A., Maltamo M., Kangas A. The comparison of airborne laser scanning-based probability layers as auxiliary information for assessing coarse woody debris. International Journal of Remote Sensing 2010, 31:1245-1259.
52. Pigeon J.G., Heyse J.F. An improved goodness-of-fit statistic for probability prediction models. Biometrical Journal 1999, 41:71-82.
53. Rypdal K., Bloch V.V.H., Flugsrud K., Gobakken T., Hoem B., Tomter S.M., et al. Emissions and removals of greenhouse gases from land use, land-use change and forestry in Norway. NIJOS report 11/05, Ås, Norway 2005, (105 pp. Ås).
54. Särndal C.-E. Design-consistent versus model-dependent estimation for small domains. Journal of the American Statistical Association 1984, 79:624-631.
55. Särndal C.-E. Combined inference in survey sampling. Pakistan Journal of Statistics 2011, 27:359-370.
56. Särndal C.-E., Swensson B., Wretman J. Model assisted survey sampling 1992, Springer-Verlag, Inc., New York, (694 pp.).
57. Stehman S.V. Model-assisted estimation as a unifying framework for estimating the area of land cover and land-cover change from remote sensing. Remote Sensing of Environment 2009, 113:2455-2462.
58. Stephens P.R., Kimberley M.O., Beets P.N., Paul T.S.H., Searls N., Bell A., et al. Airborne scanning LiDAR in a double sampling forest carbon inventory. Remote Sensing of Environment 2012, 117:348-357.
59. St-Onge B., Vepakomma U. Assessing forest gap dynamics and growth using multi-temporal laser-scanner data. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 2004, Vol. XXXVI, Part 8/W2:173-178. M. Thies, B. Koch, H. Spiecker, H. Weinacker (Eds.).
60. Thompson S.K. Sampling 2002, John Wiley & Sons, Inc., New York, (367 pp.). 2nd ed.
61. National forest inventories-Pathways for common reporting 2010, Springer, (612 pp.). E. Tomppo, T. Gschwantner, M. Lawrence, R.E. McRoberts (Eds.).
62. UNFCCC Kyoto protocol reference manual on accounting of emissions and assigned amounts (last date accessed: 10 December 2010). http://unfccc.int/kyoto_protocol/items/3145.php.
63. Weishampel J.F., Blair J.B., Knox R.G., Dybayah R., Clark D.B. Volumetric lidar return patterns from old-growth tropical rainforest canopy. International Journal of Remote Sensing 2000, 21:409-415.
64. Woodbury P.B., Smith J.E., Heath L.S. Carbon sequestration in the U.S. forest sector from 1990 to 2010. Forest Ecology and Management 2007, 241:14-27.
65. Yu X., Hyyppä J., Kaartinen H., Hyyppä H., Maltamo M., Rönnholm P. Measuring the growth of individual trees using multi-temporal airborne laser scanning point clouds. Proceedings of the ISPRS Workshop Laser Scanning 2005, 12-15 September 2005, Enschede, the Netherlands 2005, 36:204-208.
66. Yu X., Hyyppä J., Kaartinen H., Maltamo M. Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sensing of Environment 2004, 90:451-462.
67. Yu X., Hyyppä J., Kaartinen H., Maltamo M., Hyyppä H. Obtaining plotwise mean height and volume growth in boreal forests using multi-temporal laser surveys and various change detection techniques. International Journal of Remote Sensing 2008, 29:1367-1386.
68. Yu X., Hyyppä J., Kukko A., Maltamo M., Kaartinen H. Change detection techniques for canopy height growth measurements using airborne laser scanner data. Photogrammetric Engineering and Remote Sensing 2006, 72:1339-1348.