Home Forest Ecosystems Article
PDF (800.4 KB)
Cite
EndNote(RIS) BibTeX
Collect
Discussion | Open Access

Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation

Göran Ståhl1, Svetlana Saarela1 (), Sebastian Schnell1, Sören Holm1, Johannes Breidenbach2, Sean P. Healey3, Paul L. Patterson3, Steen Magnussen4, Erik Næsset5, Ronald E. McRoberts3, Timothy G. Gregoire6
Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden
Norwegian Institute for Bioeconomy Research, Ås, Norway
USDA Forest Service, Washington, D.C., USA
Canadian Forest Service, Pacific Forestry Centre, British Columbia, Canada
Norwegian University of Life Sciences, Ås, Norway
School of Forestry and Environmental Studies, Yale University, New Haven, CT, USA
Show Author Information

Abstract

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role: model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design-based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data..We review studies on large-area forest surveys based on model-assisted, model-based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

Keywords

Design-based inferenceModel-assisted estimationModel-based inferenceHybrid inferenceNational forest inventoryRemote sensingSampling

References

 
Andersen HE, Barrett T, Winterberger K, Strunk J, Temesgen H (2009) 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. In: Proceedings of the IUFRO Division 4 Conference: "Extending Forest Inventory and Monitoring over Space and Time"., pp 19-22
 

Andersen HE, Strunk J, Temesgen H (2011) 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 Forestry 26:157-164

Crossref Google Scholar
 

Armston JD, Denham RJ, Danaher TJ, Scarth PF, Moffiet TN (2009) Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery. J Appl Remote Sensing 3:33540-33540, doi:10.1117/1.3216031

Crossref Google Scholar
 

Asner GP, Powell GV, Mascaro J, Knapp DE, Clark JK, Jacobson J, Hughes RF (2010) High-resolution forest carbon stocks and emissions in the Amazon. Proc Natl Acad Sci 107:16738-16742, doi:10.1073/pnas.1004875107

Crossref Google Scholar
 

Asner GP, Mascaro J, Muller-Landau HC, Vieilledent G, Vaudry R, Rasamoelina M, Hall S, van Breugel M (2012) A universal airborne LiDAR approach for tropical forest carbon mapping. Oecologia 168:1147-1160, doi:10.1007/s00442-011-2165-z

Crossref Google Scholar
 

Baccini A, Laporte N, Goetz SJ, Sun M, Dong H (2008) A first map of tropical Africa's above-ground biomass derived from satellite imagery. Environ Res Lett 3:9

Crossref Google Scholar
 

Baffetta F, Fattorini L, Franceschi S, Corona P (2009) Design-based approach to k-nearest neighbours technique for coupling field and remotely sensed data in forest surveys. Remote Sensing Environ 113(3):463-475, doi:10.1016/j.rse.2008.06.014

Crossref Google Scholar
 

Baffetta F, Corona P, Fattorini L (2011) Design-based diagnostics for k-NN estimators of forest resources. Can J Forest Res 41:59-72

Crossref Google Scholar
 

Bohlin J, Wallerman J, Fransson JE (2012) Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM. Scand J Forest Res 27:692-699, doi:10.1080/02827581.2012.686625

Crossref Google Scholar
 

Bollandsås OM, Gregoire TG, Næsset E, Øyen B-H (2013) Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data. Stat Methods Appl 22:113-129, doi:10.1007/s10260-012-0220-5

Crossref Google Scholar
 

Boudreau J, Nelson RF, Margolis HA, Beaudoin A, Guindon L, Kimes DS (2008) Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. Remote Sensing Environ 112:3876-3890, doi:10.1016/j.rse.2008.06.003

Crossref Google Scholar
 

Breidenbach J, Astrup R (2012) Small area estimation of forest attributes in the Norwegian National Forest Inventory. Eur J Forest Res 131:1255-1267, doi:10.1007/s10342-012-0596-7

Crossref Google Scholar
 
Breidenbach J, McRoberts RE, Astrup R (2015) Empirical coverage of model-based variance estimators for remote sensing assisted estimation of stand-level timber volume. Remote Sensing Environ (in press). doi: 10.1016/j.rse.2015.07.026https://doi.org/10.1016/j.rse.2015.07.026
Crossref
 

Breidt FJ, Opsomer JD (2000) Local polynomial regression estimators in survey sampling. Ann Stat 2000:1026-1053

Crossref Google Scholar
 

Breidt FJ, Claeskens G, Opsomer JD (2005) Model-assisted estimation for complex surveys using penalised splines. Biometrika 92:831-846, doi:10.1093/biomet/92.4.831

Crossref Google Scholar
 
Cassel CM, Särndal CE, Wretman JH (1977) Foundations of inference in survey sampling. Wiley, New York
 
Chambers R, Clark R (2012) An introduction to model-based survey sampling with applications. Oxford University Press. doi: 10.1093/acprof:oso/9780198566625.001.0001https://doi.org/10.1093/acprof:oso/9780198566625.001.0001
Crossref
 

Chirici G, McRoberts RE, Fattorini L, Mura M, Marchetti M (2016) Comparing echo-based and canopy height model-based metrics for enhancing estimation of forest aboveground biomass in a model-assisted framework. Remote Sensing Environ 174:1-9, doi:10.1016/j.rse.2015.11.010

Crossref Google Scholar
 

Corona P, Fattorini L, Franceschi S, Scrinzi G, Torresan C (2014) Estimation of standing wood volume in forest compartments by exploiting airborne laser scanning information: model-based, design-based, and hybrid perspectives. Can J Forest Res 44:1303-1311, doi:10.1139/cjfr-2014-0203

Crossref Google Scholar
 

Corona P, Fattorini L, Pagliarella MC (2015) Sampling strategies for estimating forest cover from remote sensing-based two-stage inventories. Forest Ecosystems 2(1):1-12, doi:10.1186/s40663-015-0042-7

Crossref Google Scholar
 

Ene LT, Næsset E, Gobakken T, Gregoire TG, Ståhl G, Nelson R (2012) Assessing the accuracy of regional LiDAR-based biomass estimation using a simulation approach. Remote Sensing Environ 123:579-592, doi:10.1016/j.rse.2012.04.017

Crossref Google Scholar
 

Fattorini L, Marcheselli M, Pisani C (2006) A three-phase sampling strategy for large-scale multiresource forest inventories. J Agric Biol Environ Stat 11(3):296-316, doi:10.1198/108571106X130548

Crossref Google Scholar
 

Fattorini L, Franceschi S, Pisani C (2009) A two-phase sampling strategy for large-scale forest carbon budgets. J Stat Plann Inference 139(3):1045-1055, doi:10.1016/j.jspi.2008.06.014

Crossref Google Scholar
 

Gobakken T, Næsset E, Nelson R, Bollandsås OM, Gregoire TG, Ståhl G, Holm S, Ørka HO, Astrup R (2012) Estimating biomass in Hedmark County, Norway using national forest inventory field plots and airborne laser scanning. Remote Sensing Environ 123:443-456, doi:10.1016/j.rse.2012.01.025

Crossref Google Scholar
 

Grafström A, Saarela S, Ene LT (2014) Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space. Can J Forest Res 44:1156-1164, doi:10.1139/cjfr-2014-0202

Crossref Google Scholar
 

Gregoire TG (1998) Design-based and model-based inference in survey sampling: appreciating the difference. Can J Forest Res 28:1429-1447, doi:10.1139/x98-166

Crossref Google Scholar
 
Gregoire TG, Valentine HT (2008) Sampling strategies for natural resources and the environment. CRC Press, Taylor & Francis Group, Boca Ratonhttps://doi.org/10.1201/9780203498880
Crossref
 

Gregoire TG, Ståhl G, Næsset E, Gobakken T, Nelson R, Holm S (2011) Model-assisted estimation of biomass in a LiDAR sample survey in Hedmark County, Norway This article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time. Can J Forest Res 41:83-95, doi:10.1139/X10-195

Crossref Google Scholar
 
Hansen MH, Madow WG, Tepping BJ (1978) On inference and estimation from sample surveys. In: Proceedings of the Survey Research Methods Section., pp 82-107
 

Hansen MH, Madow WG, Tepping BJ (1983) An evaluation of model-dependent and probability-sampling inferences in sample surveys. J Am Stat Assoc 78:776-793, doi:10.1080/01621459.1983.10477018

Crossref Google Scholar
 

Healey SP, Patterson PL, Saatchi S, Lefsky MA, Lister AJ, Freeman EA (2012) A sample design for globally consistent biomass estimation using lidar data from the Geoscience Laser Altimeter System (GLAS). Carbon Balance Manage 7:1-9, doi:10.1186/1750-0680-7-10

Crossref Google Scholar
 

Helmer EH, Ruzycki TS, Wunderle JM, Vogesser S, Ruefenacht B, Kwit C, Ewert DN (2010) Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat. Remote Sensing Environ 114:2457-2473, doi:10.1016/j.rse.2010.05.021

Crossref Google Scholar
 

Köhl M, Brassel P (2001) Zur Auswirkung der Hangneigungskorrektur auf Schätzwerte im Schweizerischen Landesforstinventar (LFI) [Investigation of the effect of the slope correction method as applied in the Swiss National Forest Inventory of estimates.]. Schweizerische Zeitschrift fur Forstwesen 152(6):215-225, doi:10.3188/szf.2001.0215

Crossref Google Scholar
 

Magnussen S (2015) Arguments for a model-dependent inference? Forestry 88(3):317-325, doi:10.1093/forestry/cpv002

Crossref Google Scholar
 
Magnussen S, Tomppo E (2015) Model-calibrated k-nearest neighbor estimators. Scandinavian J Forest Res 1-11. doi: 10.1080/02827581.2015.1073348https://doi.org/10.1080/02827581.2015.1073348
Crossref
 

Magnussen S, Næsset E, Gobakken T (2014) An estimator of variance for two-stage ratio regression estimators. Forest Sci 60(4):663-676, doi:10.5849/forsci.12-163

Crossref Google Scholar
 

Magnussen S, Næsset E, Gobakken T (2015) LiDAR-supported estimation of change in forest biomass with time-invariant regression models. Can J Forest Res 45(999):1514-1523, doi:10.1139/cjfr-2015-0084

Crossref Google Scholar
 

Mandallaz D (2013) Design-based properties of some small-area estimators in forest inventory with two-phase sampling. Can J Forest Res 43:441-449, doi:10.1139/cjfr-2012-0381

Crossref Google Scholar
 

Margolis HA, Nelson RF, Montesano PM, Beaudoin A, Sun G, Andersen HE, Wulder M (2015) Combining satellite lidar, airborne lidar and ground plots to estimate the amount and distribution of aboveground biomass in the Boreal forest of North America. Can J Forest Res 45(7):838-855, doi:10.1139/cjfr-2015-0006

Crossref Google Scholar
 

Massey A, Mandallaz D, Lanz A (2014) Integrating remote sensing and past inventory data under the new annual design of the Swiss National Forest Inventory using three-phase design-based regression estimation. Can J Forest Res 44:1177-1186, doi:10.1139/cjfr-2014-0152

Crossref Google Scholar
 

McRoberts RE (2006) A model-based approach to estimating forest area. Remote Sensing Environ 103:56-66, doi:10.1016/j.rse.2006.03.005

Crossref Google Scholar
 

McRoberts RE (2010) Probability-and model-based approaches to inference for proportion forest using satellite imagery as ancillary data. Remote Sensing Environ 114:1017-1025, doi:10.1016/j.rse.2009.12.013

Crossref Google Scholar
 

McRoberts RE, Tomppo EO, Finley AO, Heikkinen J (2007) Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery. Remote Sensing Environ 111:466-480

Crossref Google Scholar
 
McRoberts RE, Bollandsås OM, Næsset E (2014) Modeling and estimating change. In: Maltamo M, Næsset E, Vauhkonen J. (eds) Forestry Applications of Airborne Laser Scanning. Concepts and Case Studies. Springer, pp. 293-314. doi: 10.1007/978-94-017-8663-8_15https://doi.org/10.1007/978-94-017-8663-8_15
Crossref
 

McRoberts RE, Næsset E, Gobakken T, Bollandsås OM (2015) Indirect and direct estimation of forest biomass change using forest inventory and airborne laser scanning data. Remote Sensing Environ 164:36-42, doi:10.1016/j.rse.2015.02.018

Crossref Google Scholar
 

Melville GJ, Welsh AH, Stone C (2015) Improving the efficiency and precision of tree counts in pine plantations using airborne LiDAR data and flexible-radius plots: model-based and design-based approaches. J Agric Biol Environ Stat 20(2):229-257, doi:10.1007/s13253-015-0205-6

Crossref Google Scholar
 

Næsset E (1997) Estimating timber volume of forest stands using airborne laser scanner data. Remote Sensing Environ 61:246-253, doi:10.1016/S0034-4257(97)00041-2

Crossref Google Scholar
 

Næsset E (2002a) Determination of mean tree height of forest stands by means of digital photogrammetry. Scand J Forest Res 17: 446-459. doi:10.1080/028275802320435469

Crossref Google Scholar
 

Næsset E (2002b) Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sensing Environ 80: 88-99. doi:10.1016/S0034-4257(01)00290-5

Crossref Google Scholar
 

Næsset E (2004) Accuracy of forest inventory using airborne laser scanning: evaluating the first Nordic full-scale operational project. Scand J Forest Res 19:554-557, doi:10.1080/02827580410019544

Crossref Google Scholar
 

Næsset E, Gobakken T, Solberg S, Gregoire TG, Nelson R, Ståhl G, Weydahl D (2011) Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area. Remote Sensing Environ 115:3599-3614, doi:10.1016/j.rse.2011.08.021

Crossref Google Scholar
 

Næsset E, Bollandsås OM, Gobakken T, Gregoire TG, Ståhl G (2013a) 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 Environ 128: 299-314. doi:10.1016/j.rse.2012.10.008

Crossref Google Scholar
 

Næsset E, Gobakken T, Bollandsås OM, Gregoire TG, Nelson R, Ståhl G (2013b) Comparison of precision of biomass estimates in regional field sample surveys and airborne LiDAR-assisted surveys in Hedmark County, Norway. Remote Sensing Environ 130: 108-120. doi:10.1016/j.rse.2012.11.010

Crossref Google Scholar
 

Næsset E, Bollandsås OM, Gobakken T, Solberg S, McRoberts RE (2015) The effects of field plot size on model-assisted estimation of aboveground biomass change using multitemporal interferometric SAR and airborne laser scanning data. Remote Sensing Environ 168:252-264, doi:10.1016/j.rse.2015.07.002

Crossref Google Scholar
 

Nelson R, Krabill W, Maclean G (1984) Determining forest canopy characteris-tics using airborne laser data. Remote Sensing Environ 15:201-212, doi:10.1016/0034-4257(84)90031-2

Crossref Google Scholar
 

Nelson R, Krabill W, Tonelli J (1988) Estimating forest biomass and volume using airborne laser data. Remote Sensing Environ 24:247-267, doi:10.1016/0034-4257(88)90028-4

Crossref Google Scholar
 

Nelson R, Boudreau J, Gregoire TG, Margolis H, Næsset E, Gobakken T, Ståhl G (2009) Estimating Quebec provincial forest resources using ICESat/GLAS. Can J Forest Res 39:862-881, doi:10.1139/X09-002

Crossref Google Scholar
 

Nelson R, Gobakken T, Næsset E, Gregoire TG, Ståhl G, Holm S, Flewelling J (2012) Lidar sampling - using an airborne profiler to estimate forest biomass in Hedmark County, Norway. Remote Sensing Environ 123:563-578, doi:10.1016/j.rse.2011.10.036

Crossref Google Scholar
 

Neyman J (1934) On the two different aspects of the representative method: the method of stratified sampling and the method of purposive selection. J R Stat Soc 97:558-606, doi:10.2307/2342192

Crossref Google Scholar
 

Opsomer JD, Breidt FJ, Moisen GG, Kauermann G (2007) Model-assisted estimation of forest resources with generalized additive models. J Am Stat Assoc 102:400-409, doi:10.1198/016214506000001491

Crossref Google Scholar
 

Reese H, Nilsson M, Sandström P, Olsson H (2002) Applications using estimates of forest parameters derived from satellite and forest inventory data. Comput Electron Agric 37:37-55, doi:10.1016/S0168-1699(02)00118-7

Crossref Google Scholar
 

Saarela S, Grafström A, Ståhl G, Kangas A, Holopainen M, Tuominen S, Nordkvist K, Hyyppä, J (2015a) Model-assisted estimation of growing stock volume using different combinations of LiDAR and Landsat data as auxiliary information. Remote Sensing Environ 158: 431-440. doi:10.1016/j.rse.2014.11.020

Crossref Google Scholar
 

Saarela S, Schnell S, Grafström A, Tuominen S, Nordkvist K, Hyyppä J, Kangas A, Ståhl G (2015b) Effects of sample size and model form on the accuracy of model-based estimators of growing stock volume in Kuortane, Finland. Can J Forest Re 45:1524-1534. doi:10.1139/cjfr-2015-0077

Crossref Google Scholar
 

Saarela S, Schnell S, Tuominen S, Balazs A, Hyyppä J, Grafström A, Ståhl G (2016) Effects of positional errors in model-assisted and model-based estimation of growing stock volume. Remote Sensing Environ 172:101-108, doi:10.1016/j.rse.2015.11.002

Crossref Google Scholar
 

Sannier C, McRoberts RE, Fichet LV, Makaga EMK (2014) Using the regression estimator with Landsat data to estimate proportion forest cover and net proportion deforestation in Gabon. Remote Sensing Environ 151:138-148, doi:10.1016/j.rse.2013.09.015

Crossref Google Scholar
 

Särndal CE (1978) Design-based and model-based inference in survey sampling [with discussion and reply]. Scand J Stat 5(1):27-52

Google Scholar
 
Särndal CE, Swensson B, Wretman J (1992) Model Assisted Survey Sampling. Springer. doi: 10.1007/978-1-4612-4378-6https://doi.org/10.1007/978-1-4612-4378-6
Crossref
 

Skowronski NS, Clark KL, Gallagher M, Birdsey RA, Hom JL (2014) Airborne laser scanner-assisted estimation of aboveground biomass change in a temperate oak-pine forest. Remote Sensing Environ 151:166-174, doi:10.1016/j.rse.2013.12.015

Crossref Google Scholar
 

Solberg S, Astrup R, Bollandsås OM, Næsset E, Weydahl DJ (2010) Deriving forest monitoring variables from X-band InSAR SRTM height. Can J Remote Sensing 36:68-79, doi:10.5589/m10-025

Crossref Google Scholar
 

Ståhl G, Holm S, Gregoire TG, Gobakken T, Næsset E, Nelson R (2011) Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, Norway. Can J Forest Res 41:96-107, doi:10.1139/X10-161

Crossref Google Scholar
 

Ståhl G, Heikkinen J, Petersson H, Repola J, Holm S (2014) Sample-based estimation of greenhouse gas emissions from forests - A new approach to account for both sampling and model errors. Forest Sci 60:3-13, doi:10.5849/forsci.13-005

Crossref Google Scholar
 

Stephens PR, Kimberley MO, Beets PN, Paul TS, Searles N, Bell A, Brack C, Broadley J (2012) Airborne scanning LiDAR in a double sampling forest carbon inventory. Remote Sensing Environ 117:348-357, doi:10.1016/j.rse.2011.10.009

Crossref Google Scholar
 

Strunk JL, Reutebuch SE, Andersen HE, Gould PJ, McGaughey RJ (2012a) Model-assisted forest yield estimation with light detection and ranging. West J Appl Forestry 27: 53-59. doi:10.5849/wjaf.10-043

Crossref Google Scholar
 

Strunk J, Temesgen H, Andersen HE, Flewelling JP, Madsen L (2012b) Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables. Can J Remote Sensing 38: 644-654. doi:10.5589/m12-052

Crossref Google Scholar
 
Tomppo E. Katila M (1991) Satellite image-based national forest inventory of Finland for publication in the IGARSS'91 digest. In: Geoscience and Remote Sensing Symposium, 1991. IGARSS'91. Remote Sensing: Global Monitoring for Earth Management., International (Vol. 3, pp. 1141-1144). doi: 10.1109/igarss.1991.579272https://doi.org/10.1109/IGARSS.1991.579272
Crossref
 

Tomppo E, Olsson H, Ståhl G, Nilsson M, Hagner O, Katila M (2008) Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sensing Environ 112(5):1982-1999

Crossref Google Scholar
 
Tomppo E, Gschwantner T, Lawrence M, McRoberts RE, Gabler K, Schadauer K, Vidal C, Lanz A, Ståhl G, Cienciala E (2010) National forest inventories. Pathways for Common Reporting. Springer, 541-553. doi: 10.1007/978-90-481-3233-1https://doi.org/10.1007/978-90-481-3233-1
Crossref
Forest Ecosystems
Volume 3 Issue 2,
June 2016
Article number: 5
DOI: 10.1186/s40663-016-0064-9
Cite this article:
Ståhl G, Saarela S, Schnell S, et al. Use of models in large-area forest surveys: comparing model-assisted, model-based and hybrid estimation. Forest Ecosystems, 2016, 3(2): 5. https://doi.org/10.1186/s40663-016-0064-9
Metrics & Citations  
Article History
Copyright
Rights and Permissions
Return