Sample-Based Estimation of Greenhouse Gas Emissions From Forests—A New Approach to Account for Both Sampling and Model Errors

Ståhl, Göran; Heikkinen, Juha; Petersson, Hans; Repola, Jaakko; Holm, Sören

doi:10.5849/forsci.13-005

Cited by 69 publications

(59 citation statements)

References 16 publications

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“…This highlights the need for a model-dependent, three-phase estimator that will link the three sources of data (ground-air-satellite). Such work is being undertaken and progress has been made (Ståhl et al 2014). However, the limited number of ground plots and the consequent lack of stratification by cover type or ecoregion for these eastern Eurasia estimates are also possible reasons for the higher uncertainty.…”

Section: Additional Sources Of Errormentioning

confidence: 99%

Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America

et al. 2015

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Abstract:We report estimates of the amount, distribution, and uncertainty of aboveground biomass (AGB) of the different ecoregions and forest land cover classes within the North American boreal forest, analyze the factors driving the error estimates, and compare our estimates with other reported values. A three-phase sampling strategy was used (i) to tie ground plot AGB to airborne profiling lidar metrics and (ii) to link the airborne estimates of AGB to ICESat-GLAS lidar measurements such that (iii) GLAS could be used as a regional sampling tool. We estimated the AGB of the North American boreal forest at 21.8 Pg, with relative error of 1.9% based on 256 GLAS orbits (229 086 pulses). The distribution of AGB was 46.6% for western Canada, 43.7% for eastern Canada, and 9.7% for Alaska. With a single exception, relative errors were under 4% for the three regions and for the major cover types and under 10% at the ecoregion level. The uncertainties of the estimates were calculated using a variance estimator that accounted for only sampling error, i.e., the variability among GLAS orbital estimates, and airborne to spaceborne regression error, i.e., the uncertainty of the model coefficients. Work is ongoing to develop robust statistical techniques for integrating other sources of error such as ground to air regression error and allometric error. Small ecoregions with limited east-west extents tended to have fewer GLAS orbits and a greater percent sampling error. AGB densities derived from GLAS agreed closely with the estimates derived from both forest inventories (<17%) and a MODIS-based interpolation technique (<26%) for more southern, well-inventoried ecoregions, whereas differences were much greater for unmanaged northern and (or) mountainous ecoregions.Key words: aboveground biomass, lidar, North American boreal forest, ICESat-GLAS, Landsat, MODIS, forest inventory, kNN.Résumé : Nous présentons les estimations de quantité, distribution et incertitude de la biomasse aérienne de différentes régions écologiques et de différentes classes de couverts forestiers en Amérique du Nord, analysons les facteurs contrôlant les erreurs d'estimation, et comparons nos résultats avec ceux de la littérature. Un dispositif d'échantillonnage en trois étapes a été utilisé (i) pour associer la biomasse mesurée dans des placettes d'inventaire aux profils de lidar aéroporté et (ii) pour relier les estimations lidar aéroporté de biomasse aux mesures d'ICESat-GLAS, de sorte que (iii) GLAS puisse être utilisé comme outil d'échantillonnage régional. À partir de 156 orbites GLAS (229 086 impulsions), nous avons estimé la biomasse aérienne de la forêt boréale d'Amérique du Nord à 21.8 Pg avec une erreur relative de 1.9 %. La distribution de la biomasse aérienne était de 46.6 % pour l'ouest du Canada, 43.7 % pour l'est du Canada, et 9.7 % pour l'Alaska. Avec une seule exception, les erreurs relatives étaient inférieures à 4 % pour les trois régions et pour les principaux types de couvert, et inférieures à 10 % à l'échelle des régions écologiques...

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Section: Additional Sources Of Errormentioning

confidence: 99%

Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America

et al. 2015

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“…In such cases a sample of auxiliary data can be selected, upon which the auxiliary variable totals and means can be estimated and used together with model predictions that link the auxiliary variables with the target variable. The approach so far appears to have been applied only in a limited number of forest inventories, although implicitly it has been used for a long time in forest inventories where models (such as volume, biomass and growth models) have been applied based on data from forest plots (Ståhl et al 2014).…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2016

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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 designbased 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, modelbased, 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.

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“…While this approach fails to account for model errors in the uncertainty assessment, recent studies (e.g. Ståhl et al 2014) have shown that the magnitude of these errors is fairly small. Different techniques have been applied for assigning characteristics to sample trees.…”

Section: Estimation Principlesmentioning

confidence: 99%

Adapting National Forest Inventories to changing requirements – the case of the Swedish National Forest Inventory at the turn of the 20th century

Fridman¹,

Holm²,

Nilsson³

et al. 2014

Silva Fenn.

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• National Forest Inventories supply invaluable long term time series of forest state. Recent developments and international harmonization of modern NFIs widen the scope to even include ecosystem goods, e.g. biodiversity and carbon sequestration. The combination of NFI field data with remote sensing techniques can give good estimates for areas smaller than national and regional level. AbstractNational Forest Inventories (NFIs) are becoming increasingly important worldwide in order to provide information about the multiple functions of forests, e.g. their provision of raw materials to industry, biodiversity and their capacity to store carbon for mitigating climate change. In several countries the history of NFIs is very long. For these countries a specific challenge is to keep the inventories up-to-date without sacrificing the advantages associated with long time series. At the turn of the 20th century European NFIs faced some major challenges. In this article we describe the history and the recent developments of the Swedish NFI as an example from which general observations are made and discussed. The Swedish NFI started in 1923 and has evolved from an inventory with a narrow focus on wood resources to an inventory today which aims to provide information about all major forest ecosystem services. It can be concluded that the traditional approaches of most European NFIs, e.g. to collect data through sample plot field inventories, has proved to be applicable even for a wide range of new information requirements. Specifically, detailed data about land use, trees, vegetation, and soils has found new important uses in connection with biodiversity assessments and the estimation of greenhouse gas emissions. Though time-consuming and difficult, making NFI information comparable across countries through harmonization appears to be a useful approach. The European National Forest Inventory Network (ENFIN) was formed in 2003 and has been successful in pan-European NFI harmonization.

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Sample-Based Estimation of Greenhouse Gas Emissions From Forests—A New Approach to Account for Both Sampling and Model Errors

Cited by 69 publications

References 16 publications

Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America

Combining satellite lidar, airborne lidar, and ground plots to estimate the amount and distribution of aboveground biomass in the boreal forest of North America

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

Adapting National Forest Inventories to changing requirements – the case of the Swedish National Forest Inventory at the turn of the 20th century

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