Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data

Remote Sensing of Environment

Volumn 164, Issue , 2015, Pages 36-42

  McRoberts, Ronald E ^a   Næsset, Erik ^b   Gobakken, Terje ^b   Bollandsås, Ole Martin ^b  

^a USDA FOREST SERVICE   (United States)

^b NORWEGIAN UNIVERSITY OF LIFE SCIENCES   (Norway)

Author keywords

Model assisted regression estimator; Simple random sampling estimator; Stratified estimator

Indexed keywords

BIOMASS; ESTIMATION; LASER APPLICATIONS; REGRESSION ANALYSIS; REMOTE SENSING; SAMPLING; SURFACE ANALYSIS;

AIRBORNE LASER SCANNING; AUXILIARY INFORMATION; INDEPENDENT VARIABLES; PREDICTOR VARIABLES; REGRESSION ESTIMATORS; SIMPLE RANDOM SAMPLING; STATISTICAL ESTIMATORS; STRATIFIED ESTIMATOR;

FORESTRY;

DATA SET; ECOLOGICAL MODELING; ESTIMATION METHOD; FOREST INVENTORY; PHYTOMASS; PRECISION; REMOTE SENSING;

RANDOM PROCESSES; REGRESSION ANALYSIS; SAMPLING;

NORWAY;

EID: 84924995686     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2015.02.018     Document Type: Article

References (17)

1. Andersen H.-E., Reutebuch S.E., McGaughey R., d'Oliveira M., Keller K. Monitoring selective logging in western Amazonia with repeat LIDAR flights. Remote Sensing of Environment 2014, 151:157-165.
2. 2. The American Statistician 1994, 48(2):113-117.
3. Bollandsås O.M., Gregoire T.G., Næsset E., Øyen B.-H. Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data. Statistical Methods and Applications 2013, 22:113-129.
4. Cochran W.G. Sampling techniques 1977, Wiley, New York, 428 p. 3rd edition.
5. Efroymson M.A. Multiple regression analysis. Mathematical methods for digital computers 1960, 191-203. Wiley, New York. A. Ralston, H.S. Wilf (Eds.).
6. Fuller R.M., Smith G.M., Devereux B.J. The characterisation and measurement of land cover change through remote sensing: Problems in operational applications?. International Journal of Applied Earth Observation and Geoinformation 2003, 4(3):243-253.
7. GFOI Integrating remote-sensing and ground-based observations for estimation of emissions and removals of greenhouse gases in forests: Methods and guidance from the Global Forest Observations Initiative 2013, Group on Earth Observations, Geneva.
8. Gobakken T., Næsset E. Assessing effects of laser point density, ground sampling intensity, and field plot sample size on biophysical stand properties derived from airborne laser scanner data. Canadian Journal of Forest Research 2008, 38:1095-1109.
9. 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.
10. Maltamo M., Eerikainen K., Packalén P., Hyyppä J. Estimation of stem volume using laser scanning-based canopy height metrics. Forestry 2006, 79:217-229.
11. 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).
12. McRoberts R.E., Westfall J.A. The effects of uncertainty in model predictions of individual tree volume on large area volume estimates. Forest Science 2014, 60:34-43.
13. Næsset E., Bollandsås O.M., Gobakken T., Gregoire T.G., Ståhl G. 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 2013, 128:299-314.
14. Särndal C. Design-consistent versus model-dependent estimators for small domains. Journal of the American Statistical Association 1984, 79:624-631.
15. Särndal C.-E. Combined inference in survey sampling. Pakistan Journal of Statistics 2011, 27(4):359-370.
16. Särndal C., Swensson B., Wretman J. Model-assisted survey sampling 1992, Springer-Verlag, New York.
17. Skowronski N.S., Clark K.L., Gallagher M., Birdsey R.A., Hom J.L. Airborne laser scanner-assisted estimation of aboveground biomass change in a temperate oak pine forest. Remote Sensing of Environment 2014, 151:166-174.