Estimating biomass in Hedmark County, Norway using national forest inventory field plots and airborne laser scanning

被引:98
作者
Gobakken, Terje [1 ]
Naesset, Erik [1 ]
Nelson, Ross [2 ]
Bollandsas, Ole Martin [1 ]
Gregoire, Timothy G. [3 ]
Stahl, Goran [4 ]
Holm, Soren [4 ]
Orka, Hans Ole [1 ]
Astrup, Rasmus [5 ]
机构
[1] Norwegian Univ Life Sci, Dept Ecol & Nat Resource Management, NO-1432 As, Norway
[2] NASA, Biospher Sci Branch 614 4, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA
[3] Yale Univ, Sch Forestry & Environm Studies, New Haven, CT 06511 USA
[4] Swedish Univ Agr Sci, Dept Forest Resource Management, SE-90183 Umea, Sweden
[5] Norwegian Forest & Landscape Inst, NO-1431 As, Norway
关键词
Airborne laser scanning; National forest inventory; Sampling; MODEL-BASED INFERENCE; LIDAR SAMPLE SURVEY; ABOVEGROUND BIOMASS; TREE HEIGHTS; ACCURACY; CANOPY; CARBON; VOLUME; RESOURCES; STANDS;
D O I
10.1016/j.rse.2012.01.025
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this paper two sampling and estimation strategies for regional forest inventory were investigated in detail and results were presented for various geographical scales. Airborne laser scanner (ALS) data were acquired to augment data from a systematic sample of National Forest Inventory (NFI) ground plots in Hedmark County, Norway (27,390 km(2)). Approximately 50% of the NFI field plots were covered by the systematic ALS sample of 53 parallel flight lines spaced 6 km apart. The area was stratified into eight cover classes and independent log-transformed regression models were developed for each class to predict total above-ground dry biomass (AGB). The two laser-ground estimation strategies tested were a model-dependent (MD), two-phase approach that rests on the assumption that the predictive models are correctly specified, and a model-assisted (MA) approach with a two-stage probability sampling design which utilizes design-unbiased estimators. ALS AGB estimates were reported by land cover class and compared to the NH ground estimates. The ALS-based MA and MD mean estimates differed from the NFI AGB estimates by about 2% and 8%, respectively, for the entire County. At the county level the smallest estimated standard error (SE) for the estimates was obtained using the field data alone. However, the SEs calculated from field and ALS data were based on unequal numbers of ground plots. When considering only the NFI plots in the ALS strips, the smallest SEs were obtained using the MD framework. However, we also illustrated the sensitivity of the estimates of applying different plausible models. All the applied estimators assumed simple random sampling while the selection of flight lines as well as ground plots followed a systematic design. Thus, the estimates of SE were most likely conservative. Simulated sampling undertaken in a parallel research effort suggests that the overestimation of the SEs was probably much larger for the ALS-based estimates compared to the NEI estimates. ALS-based estimates were also derived for sub-county political units and thereby demonstrated how limited sample sizes affect the standard error of the biomass estimates. (C) 2012 Elsevier Inc. All rights reserved.
引用
收藏
页码:443 / 456
页数:14
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