Efficiency of post-stratification for a large-scale forest inventory—case Finnish NFI

Haakana, Helena; Heikkinen, Juha; Katila, Matti; Kangas, Annika

doi:10.1007/s13595-018-0795-6

Cited by 19 publications

(13 citation statements)

References 42 publications

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“…The stratum boundaries were 68, 130, and 202 m 3 •ha -1 . PS of a smaller area could result in more precise volume estimates for municipalities, but applying the same PS for the whole of southern Finland and of northern Finland is more operational (Haakana et al 2019).…”

Section: Auxiliary Information and Psmentioning

confidence: 99%

“…PS estimators (Cochran 1977;Haakana et al 2019) were used for forest areas and total volumes. Forest areas and volumes and their variances were first estimated for each stratum in each municipality and then aggregated to the municipalities.…”

Section: Estimation Of Forest Characteristics and Sampling Variancesmentioning

confidence: 99%

“…264-269). PS using remotely sensed auxiliary information has been shown to improve the precision at the national and regional levels compared with estimation based on field sample plots only (McRoberts et al 2002(McRoberts et al , 2006Nilsson et al 2003Nilsson et al , 2005Pulkkinen et al 2018;Haakana et al 2019). Accordingly, it is applied in operational NFIs in several countries, including France, Netherlands, Slovakia, Sweden, Switzerland, and the United States (Barrett et al 2016;Pulkkinen et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The potential of PS in small-area estimation has received less attention than the gain in efficiency in large-area inventories (McRoberts 2010;Haakana et al 2019). In Sweden, forest statistics for small areas (subcounty level) are estimated using PS; fullcoverage forest maps, estimated with k-NN, or other map products are used for stratification (Fridman et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…In many NFIs, continuous field inventories and remotely sensed data acquired on a regular basis provide external information; data from a previous inventory can be used in fitting the model (Pulkkinen et al 2018;Haakana et al 2019). For example, in Finland, forest resources maps based on NFI sample plot data, satellite images, and numerical map data, extending over the whole country, are produced by the multisource NFI (MS-NFI) method every 2 years (Tomppo et al 2008;Mäkisara et al 2016Mäkisara et al , 2019.…”

Section: Introductionmentioning

confidence: 99%

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Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory

et al. 2020

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National forest inventories (NFIs) are designed to provide accurate information on forest resources at the national and regional levels, but there is also a demand for such information at smaller spatial scales. Auxiliary data such as satellite imagery have been used to facilitate small-area estimation. The commonly used method, k-nearest neighbour (k-NN), provides a model-based estimator for small areas, but a design-unbiased estimator for the mean square error is not available. Post-stratification (PS) is an alternative approach to using auxiliary information that allows for design-based variance estimation. In a case study using real inventory data of the Finnish NFI, we applied this method to the municipality level to explore the lower limit to the area for which the key forest parameters, forest area and growing stock volumes, can be estimated with sufficient precision. For PS, we employed exogenous forest resources maps based on the previous NFI round. In the municipalities of the two study provinces, the relative standard errors of total volume estimates ranged from 2.3% to 26.9%. They were smaller than 10% for municipalities with an area of 390 km2 or larger, corresponding to approximately 100 or more sample plots on forestland. We also demonstrated the usefulness of design-unbiased variance estimation in showing discrepancies between design-based PS and model-based k-NN estimates.

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Section: Auxiliary Information and Psmentioning

confidence: 99%

Section: Estimation Of Forest Characteristics and Sampling Variancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory

et al. 2020

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Spatial predictions of tree density and tree height across Mexico forests using ensemble learning and forest inventory data

Barreras,

Alanís de la Rosa,

Mayorga

et al. 2023

Ecology and Evolution

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The National Forestry Commission of Mexico continuously monitors forest structure within the country's continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall‐to‐wall spatial predictions of both attributes in 1‐km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., mean precipitation, surface temperature, canopy cover). Training data is from the 2009 to 2014 cycle (n > 26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r2 = .35 [.12, .51] (mean [min, max]) than for tree density r2 = .23 [.05, .42]. The best predictive performance when mapping tree height was for broadleaf and coniferous‐broadleaf forests (model explained ~50% of variance). The best predictive performance when mapping tree density was for tropical forest (model explained ~40% of variance). Although most forests had relatively low uncertainty for tree height predictions, e.g., values <60%, arid and semiarid ecosystems had high uncertainty, e.g., values >80%. Uncertainty values for tree density predictions were >80% in most forests. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision‐making and future of the National Forest and Soils Inventory. This work highlights the need for analytical tools that help us exploit the full potential of the Mexican forest inventory datasets.

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Effect of permanent plots on the relative efficiency of spatially balanced sampling in a national forest inventory

Räty

Kangas

2019

Annals of Forest Science

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& Key message Using spatially balanced sampling utilizing auxiliary information in the design phase can enhance the design efficiency of national forest inventory. These gains decreased with increasing proportion of permanent plots in the sample. Using semi-permanent plots, changing every nth inventory round, instead of permanent plots, reduced this phenomenon. Further studies for accounting the permanent sample when selecting temporary sample are needed. & Context National forest inventories (NFIs) produce national-and regional-level statistics for sustainability assessment and decision-making. Using an interpreted satellite image as auxiliary information in the design phase improved the relative efficiency (RE). Spatially balanced sampling through local pivotal method (LPM) used for selection of clusters of sample plots is designed for temporary sample; thus, the method was tested in a NFI design with both permanent and temporary clusters. & Aims We estimated LPM method and stratified sampling for a NFI designed for successive occasions, where the clusters are permanent, semi-permanent, or temporary being replaced: never, every nth, and every inventory round, respectively. & Methods REs of sampling designs against systematic sampling were studied with simulations of inventory sampling. & Results The larger the proportion of permanent clusters the smaller benefits gained with LPM. REs of stratified sampling were not depending on the proportion of permanent clusters. The semi-permanent sampling with LPM removed the previously described decrease and resulted in the largest REs. & Conclusion Sampling strategies with semi-permanent clusters were the most efficient, yet not necessarily optimal for all inventory variables. Further development of method to simultaneously take into account the distribution of permanent sample when selecting temporary or semi-temporary sample is desired since it could increase the design efficiency.

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Efficiency of post-stratification for a large-scale forest inventory—case Finnish NFI

Cited by 19 publications

References 42 publications

Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory

Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory

Spatial predictions of tree density and tree height across Mexico forests using ensemble learning and forest inventory data

Effect of permanent plots on the relative efficiency of spatially balanced sampling in a national forest inventory

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