A new small area estimation algorithm to balance between statistical precision and scale

Vega, Cédric; Renaud, Jean-Pierre; Sagar, Ankit; Bouriaud, Olivier

doi:10.1016/j.jag.2021.102303

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…In this respect, a leading idea is not to reduce, but rather maintain or increase the size of ground-based samples. A dense field network is indeed crucial in the context of monitoring local resources or disturbances, even when multisource NFI is being used, as the number of field plots remains the limiting factor when developing and calibrating multisource models (Vega et al 2021). Also, the performance of the approach depends on the correlations between field forest attributes and auxiliary data.…”

Section: Hybridizing Ground Monitoring and Remote Sensing Information...mentioning

confidence: 99%

Offering the appetite for the monitoring of European forests a diversified diet

Bontemps

Bouriaud

Vega

et al. 2022

Annals of Forest Science

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Key message Forest monitoring in Europe is turning matter of renewed political concern, and a possible role for ICP Forests health monitoring has been suggested to meet this goal (Ann For Sci 78:94, 2021). Multipurpose national forest inventory (NFI) surveys yet offer a sampling effort by two orders of magnitude greater than ICP level 1, have accomplished substantial methodological and harmonization progresses in the recent years, and therefore form a decisive contributor to future European forest monitoring incentives. Possible paths for the future development of a pan-European, comprehensive and more accurate monitoring are designed that stress a crucial need to build on the assets of the existing forest monitoring programs and favor their cooperation, in order to limit the co-existence of distinct forest monitoring processes.

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Section: Hybridizing Ground Monitoring and Remote Sensing Information...mentioning

confidence: 99%

Offering the appetite for the monitoring of European forests a diversified diet

Bontemps

Bouriaud

Vega

et al. 2022

Annals of Forest Science

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“…Then in a second phase, a sample is drawn as a function of land cover types, resulting in about 6,500 plots being measured each year nationwide. Vega et al (2021) introduced a new estimation algorithm to balance between statistical precision and spatial scale. The algorithm identifies the smallest possible groups of domains satisfying prescribed sampling density and estimation error.…”

Section: Rationale For Sae Researchmentioning

confidence: 99%

A Systematic Review of Small Domain Estimation Research in Forestry During the Twenty-First Century From Outside the United States

Guldin¹

2021

Front. For. Glob. Change

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Small domain estimation (SDE) research outside of the United States has been centered in Canada and Europe—both in transnational organizations, such as the European Union, and in the national statistics offices of individual countries. Support for SDE research is driven by government policy-makers responsible for core national statistics across domains. Examples include demographic information about provision of health care or education (a social domain) or business data for a manufacturing sector (economic domain). Small area estimation (SAE) research on forest statistics has typically studied a subset of core environmental statistics for a limited geographic domain. The statistical design and sampling intensity of national forest inventories (NFIs) provide population estimates of acceptable precision at the national level and sometimes for broad sub-national regions. But forest managers responsible for smaller areas—states/provinces, districts, counties—are facing changing market conditions, such as emerging forest carbon markets, and budgetary pressures that limit local forest inventories. They need better estimates of conditions and trends for small sub-sets of a national-scale domain than can be provided at acceptable levels of precision from NFIs. Small area estimation research is how forest biometricians at the science-policy interface build bridges to inform decisions by forest managers, landowners, and investors.

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“…For instance, in Belgium, the sampling rate is usually one plot per 1-10 ha. The sampling rate needs to be adapted according to the expected precision [12,14]. In even-aged forests, traditional FMI data correspond to stand variables like volume, basal area, average size or dominant height estimated using visual assessment or relascope measurements [7].…”

Section: Introductionmentioning

confidence: 99%

Estimating Species-Specific Stem Size Distributions of Uneven-Aged Mixed Deciduous Forests Using ALS Data and Neural Networks

et al. 2022

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Sustainable forest management requires accurate fine-scale description of wood resources. Stem size distribution (SSD) by species is used by foresters worldwide as a representative overview of forest structure and species composition suitable for informing management decisions at shorter and longer terms. In mixed uneven-aged deciduous forests, tree data required for SSD estimation are most often collected in the field through traditional forest management inventories (FMIs), but these are time-consuming and costly with respect to the sampled area. Combining FMIs with remote sensing methods such as airborne laser scanning (ALS), which has high potential for predicting forest structure and composition, and is becoming increasingly accessible and affordable, could provide cheaper and faster SSD data across large areas. In this study, we developed a method for estimating species-specific SSDs by combining FMIs and dual-wavelength ALS data using neural networks (NNs). The proposed method was tested and validated using 178 FMI plots within 22,000 ha of a mixed uneven-aged deciduous forest in Belgium. The forest canopy was segmented, and metrics were derived from the ALS point cloud. A NN with a custom architecture was set up to simultaneously predict the three components required to compute species-specific SSDs (species, circumference, and number of stems) at segment level. Species-specific SSDs were thereafter estimated at stand level by aggregating the estimates for the segments. A robustness test was set up using fully independent plots to thoroughly assess the method precision at stand-level on a larger area. The global Reynolds index for the species-specific SSDs was 21.2 for the training dataset and 54.0 for the independent dataset. The proposed method does not require allometric models, prior knowledge of the structure, or the predefinition of variables; it is versatile and thus potentially adaptable to other forest types having different structures and compositions.

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A new small area estimation algorithm to balance between statistical precision and scale

Cited by 7 publications

References 44 publications

Offering the appetite for the monitoring of European forests a diversified diet

Offering the appetite for the monitoring of European forests a diversified diet

A Systematic Review of Small Domain Estimation Research in Forestry During the Twenty-First Century From Outside the United States

Estimating Species-Specific Stem Size Distributions of Uneven-Aged Mixed Deciduous Forests Using ALS Data and Neural Networks

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