318
Views
6
Downloads
0
Crossref
0
WoS
0
Scopus
0
CSCD
Estimating amounts of change in forest resources over time is a key function of most national forest inventories (NFI). As this information is used broadly for many management and policy purposes, it is imperative that accurate estimations are made from the survey sample. Robust sampling designs are often used to help ensure representation of the population, but often the full sample is unrealized due to hazardous conditions or possibly lack of land access permission. Potentially, bias may be imparted to the sample if the nonresponse is nonrandom with respect to forest characteristics, which becomes more difficult to assess for change estimation methods that require measurements of the same sample plots at two points in time, i.e., remeasurement. To examine potential nonresponse bias in change estimates, two synthetic populations were constructed: 1) a typical NFI population consisting of both forest and nonforest plots, and 2) a population that mimics a large catastrophic disturbance event within a forested population. Comparisons of estimates under various nonresponse scenarios were made using a standard implementation of post-stratified estimation as well as an alternative approach that groups plots having similar response probabilities (response homogeneity). When using the post-stratified estimators, the amount of change was overestimated for the NFI population and was underestimated for the disturbance population, whereas the response homogeneity approach produced nearly unbiased estimates under the assumption of equal response probability within groups. These outcomes suggest that formal strategies may be needed to obtain accurate change estimates in the presence of nonrandom nonresponse.
Estimating amounts of change in forest resources over time is a key function of most national forest inventories (NFI). As this information is used broadly for many management and policy purposes, it is imperative that accurate estimations are made from the survey sample. Robust sampling designs are often used to help ensure representation of the population, but often the full sample is unrealized due to hazardous conditions or possibly lack of land access permission. Potentially, bias may be imparted to the sample if the nonresponse is nonrandom with respect to forest characteristics, which becomes more difficult to assess for change estimation methods that require measurements of the same sample plots at two points in time, i.e., remeasurement. To examine potential nonresponse bias in change estimates, two synthetic populations were constructed: 1) a typical NFI population consisting of both forest and nonforest plots, and 2) a population that mimics a large catastrophic disturbance event within a forested population. Comparisons of estimates under various nonresponse scenarios were made using a standard implementation of post-stratified estimation as well as an alternative approach that groups plots having similar response probabilities (response homogeneity). When using the post-stratified estimators, the amount of change was overestimated for the NFI population and was underestimated for the disturbance population, whereas the response homogeneity approach produced nearly unbiased estimates under the assumption of equal response probability within groups. These outcomes suggest that formal strategies may be needed to obtain accurate change estimates in the presence of nonrandom nonresponse.
Boose, E.R., Foster, D.R., Fluet, M., 1994. Hurricane impacts to tropical and temperate forest landscapes. Ecol. Monogr. 64(4), 369–400.
Charru, M., Seynave, I., Morneau, F., Bontemps, J.D., 2010. Recent changes in forest productivity: an analysis of national forest inventory data for common beech (Fagus sylvatica L. ) in north-eastern France. For. Ecol. Manag 260(5), 864–874.
Cochran, W.G., 1977. Sampling Techniques, 3rd edn. John Wiley & Sons, New York.
Cohen, W.B., Yang, Z., Kennedy, R., 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — tools for calibration and validation. Remote Sens. Environ. 114(12), 2911–2924.
Connelly, N., Brown, T., 2003. Factors affecting response rates to natural resource - focused mail surveys: empirical evidence of declining rates over time. Soc. Nat. Resour. 16, 541–549.
Corona, P., Chirici, G., Franceschi, S., Maffei, D., Marcheselli, M., Pisani, C., Fattorini, L., 2014. Design-based treatment of missing data in forest inventories using canopy heights from aerial laser scanning. Can. J. For. Res. 44(8), 892–902.
De Grandpré, L., Waldron, K., Bouchard, M., Gauthier, S., Beaudet, M., Ruel, J.C., Hébert, C., Kneeshaw, D.D., 2018. Incorporating insect and wind disturbances in a natural disturbance-based management framework for the boreal forest. Forests 9(8), 471.
Edgar, C.B., Westfall, J.A., Klockow, P.A., Vogel, J.G., Moore, G.W., 2019. Interpreting effects of multiple, large-scale disturbances using national forest inventory data: a case study of standing dead trees in east Texas, USA. For. Ecol. Manag. 437, 27–40.
Fahey, R.T., Atkins, J.W., Campbell, J.L., Rustad, L.E., Duffy, M., Driscoll, C.T., Fahey, T.J., Schaberg, P.G., 2020. Effects of an experimental ice storm on forest canopy structure. Can. J. For. Res. 50(2), 136–145.
Fattorini, L., Franceschi, S., Maffei, D., 2013. Design-based treatment of unit nonresponse in environmental surveys using calibration weighting. Biomed. J. 55(6), 925–943.
Field, S.A., Tyre, A.J., Jonzén, N., Rhodes, J.R., Possingham, H.P., 2004. Minimizing the cost of environmental management decisions by optimizing statistical thresholds. Ecol. Lett. 7(8), 669–675.
Fischer, C., Kleinn, C., Fehrmann, L., Fuchs, H., Panferov, O., 2011. A national level forest resource assessment for Burkina Faso–A field based forest inventory in a semiarid environment combining small sample size with large observation plots. For. Ecol. Manag. 262(8), 1532–1540.
Gschwantner, T., Lanz, A., Vidal, C., Bosela, M., Di Cosmo, L., Fridman, J., Gasparini, P., Kuliešis, A., Tomter, S., Schadauer, K., 2016. Comparison of methods used in European National Forest Inventories for the estimation of volume increment: towards harmonisation. Ann. For. Sci. 73(4), 807–821.
Kalton, G., Kasprzyk, D., 1986. The treatment of survey missing data. Surv. Methodol. 12(1), 1–16.
Legg, C.J., Nagy, L., 2006. Why most conservation monitoring is, but need not be, a waste of time. J. Environ. Manag. 78(2), 194–199.
Magnussen, S., Stinson, G., Boudewyn, P., 2017. Updating Canada's National Forest Inventory with multiple imputations of missing contemporary data. For. Chron. 93(3), 213–225.
McRoberts, R.E., Wendt, D.G., Nelson, M.D., Hansen, M.H., 2002. Using a land cover classification based on satellite imagery to improve the precision of forest inventory area estimates. Remote Sens. Environ. 81(1), 36–44.
Montagné-Huck, C., Brunette, M., 2018. Economic analysis of natural forest disturbances: a century of research. J. For. Econ. 32, 42–71.
Nelson, M.D., Healey, S.P., Moser, W.K., Hansen, M.H., 2009. Combining satellite imagery with forest inventory data to assess damage severity following a major blowdown event in northern Minnesota, USA. Int. J. Rem. Sens. 30(19), 5089–5108.
Nguyen, T.H., Jones, S., Soto-Berelov, M., Haywood, A., Hislop, S., 2019. Landsat time-series for estimating forest aboveground biomass and its dynamics across space and time: a review. Rem. Sens. 12(1), 98.
Park, M.S., Choi, E.S., Youn, Y.C., 2013. REDD+ as an international cooperation strategy under the global climate change regime. For. Sci. Tech. 9(4), 213–224.
Patterson, P.L., Coulston, J.W., Roesch, F.A., Westfall, J.A., Hill, A.D., 2012. A primer for nonresponse in the US forest inventory and analysis program. Environ. Monit. Assess. 184(3), 1423–1433.
Särndal, C. -E., Swensson, B., Wretman, J., 1992. Model Assisted Survey Sampling. Springer-Verlag, New York.
Särndal, C.E., Lundström, S., 2005. Estimation in Surveys with Nonresponse. John Wiley & Sons, Chichester.
Schleeweis, K., Moisen, G., Schroeder, T., Toney, C., Freeman, E., Goward, S., Huang, C., Dungan, J., 2020. US national maps attributing forest change: 1986–2010. Forests 11, 653.
Schreuder, H., Gregoire, T., Wood, G., 1993. Sampling Methods for Multiresource Forest Inventory. John Wiley & Sons, New York.
Scott, C.T., Bechtold, W.A., Reams, G.A., Smith, W.D., Westfall, J.A., Hansen, M.H., Moisen, G.G., 2005. Sample-based Estimators Used by the Forest Inventory and Analysis National Information Management System. U.S. Forest Service, Southern Research Station, . Gen. Tech. Rep. SRS-80, pp. 53–77.
Senf, C., Seidl, R., 2021. Persistent impacts of the 2018 drought on forest disturbance regimes in Europe. Biogeosciences 18(18), 5223–5230.
Smith, R.J., Gray, A.N., 2021. Strategic monitoring informs wilderness management and socioecological benefits. Conserv. Sci. Pract. 3(9), e482.
Stueve, K.M., Perry, C.H., Nelson, M.D., Healey, S.P., Hill, A.D., Moisen, G.G., Cohen, W.B., Gormanson, D.D., Huang, C., 2011. Ecological importance of intermediate windstorms rivals large, infrequent disturbances in the northern Great Lakes. Ecosphere 2(1), 1–21.
Westfall, J.A., 2022. An estimation method to reduce complete and partial nonresponse bias in forest inventory. Eur. J. For. Res. 141, 901–907.
Westfall, J.A., Lister, A.J., Scott, C.T., 2022. Evaluation of mapped-plot variance estimators across a range of partial nonresponse in a post-stratified national forest inventory. Can. J. For. Res. 52(2), 280–285.
Westfall, J.A., Wilson, B.T., 2022. Nonresponse bias in change estimation: a national forest inventory example. Forestry 95(3), 301–311.
Westfall, J.A., Schroeder, T.A., McCollum, J.M., Patterson, P.L., 2022. A spatial and temporal assessment of nonresponse in the national forest inventory of the US. Environ. Monit. Assess. 194, 530.
Wilson, D.C., Morin, R.S., Frelich, L.E., Ek, A.R., 2019. Monitoring disturbance intervals in forests: a case study of increasing forest disturbance in Minnesota. Ann. For. Sci. 76, 78.
Woodcock, C.E., Loveland, T.R., Herold, M., Bauer, M.E., 2020. Transitioning from change detection to monitoring with remote sensing: a paradigm shift. Remote Sens. Environ. 238, 111558.
Zeng, W., Tomppo, E., Healey, S.P., von Gadow, K., 2015. The national forest inventory in China: history-results-international context. For. Ecosyst. 2, 23.
Zhao, M., Yang, J., Zhao, N., Liu, Y., Wang, Y., Wilson, J.P., Yue, T., 2019. Estimation of China's forest stand biomass carbon sequestration based on the continuous biomass expansion factor model and seven forest inventories from 1977 to 2013. For. Ecol. Manag. 448, 528–534.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).