Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators

Irulappa-Pillai-Vijayakumar, Dinesh Babu; Renaud, Jean-Pierre; Morneau, François; McRoberts, Ronald E.; Vega, Cédric

doi:10.3390/rs11080991

Cited by 12 publications

(6 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A common theme in these studies is the direct examination of synthetic model prediction errors (e.g., using cross validation) without formulating the models in a design-based or composite modeling framework to mitigate potential synthetic estimator bias (cf. McRoberts et al, 2013;Irulappa-Pillai-Vijayakumar et al, 2019;McConville et al, 2020). In model-assisted applications the usual goal is to increase the precision of population-level estimates, and less often to produce estimates for domains that divide a larger population into small areas where direct estimator instability can be a concern.…”

Section: Model-assisted Estimationmentioning

confidence: 99%

Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory

Dettmann¹,

Radtke²,

Coulston³

et al. 2022

Front. For. Glob. Change

View full text Add to dashboard Cite

Small area estimation is a growing area of research for making inferences over geographic, demographic, or temporal domains smaller than those in which a particular survey data set was originally intended to be used. We aimed to review a body of literature to summarize the breadth and depth of small area estimation and related estimation strategies in forest inventory and management to-date, as well as the current state of terminology, methods, concerns, data sources, research findings, challenges, and opportunities for future work relevant to forestry and forest inventory research. Estimation methodologies explored include direct, indirect, and composite estimation within design-based and model-based inference bases. A variety of estimation methods in forestry have been applied to extensive multi-resource inventory systems like national forest inventories to increase the precision of estimates on small domains or subsets of the overall populations of interest. To avoid instability and large variances associated with small sample sizes when working with small area domains, forest inventory data are often supplemented with information from auxiliary sources, especially from remote sensing platforms and other geospatial, map-based products. Results from many studies show gains in precision compared to direct estimates based only on field inventory data. Gains in precision have been demonstrated in both project-level applications and national forest inventory systems. Potential gains are possible over varying geographic and temporal scales, with the degree of success in reducing variance also dependent on the types of auxiliary information, scale, strength of model relationships, and methodological alternatives, leaving considerable opportunity for future research and growth in small area applications for forest inventory.

show abstract

Section: Model-assisted Estimationmentioning

confidence: 99%

Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory

Dettmann¹,

Radtke²,

Coulston³

et al. 2022

Front. For. Glob. Change

View full text Add to dashboard Cite

show abstract

“…The NFI data came from 755 plots measured from 2010 to 2014. Irulappa-Pillai-Vijayakumar et al (2019) goes into considerable detail about the significant effort invested and difficulties encountered in: (1) transforming digital aerial photography and ALS into 3D digital terrain models that could be used to estimate canopy height models for two different time periods (2008 and 2014); and (2) using the estimated changes in height between 2008 and 2014 to estimate changes in other forest attributes, such as stand density, basal area and several different types of volume. Finally, auxiliary data for all 11 variables were converted to a spatial resolution of 30 m to conform with the spatial resolution of the TM sensor aboard LANDSAT.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…Their SE estimates varied from 2% to 4%, although SE estimates were not reported for specific forest species. Irulappa-Pillai-Vijayakumar et al [65] reported V estimates for a French region using NFI field plot data and remotely sensed data. They reported SE estimates of approximately 3% for oak V estimates and 5.76% for PS.…”

Section: Effects Of Sampling Variability For the Model Calibration Datasmentioning

confidence: 99%

A Model-Based Volume Estimator that Accounts for Both Land Cover Misclassification and Model Prediction Uncertainty

et al. 2020

Self Cite

View full text Add to dashboard Cite

Forest/non-forest and forest species maps are often used by forest inventory programs in the forest estimation process. For example, some inventory programs establish field plots only on lands corresponding to the forest portion of a forest/non-forest map and use species-specific area estimates obtained from those maps to support the estimation of species-specific volume (V) totals. Despite the general use of these maps, the effects of their uncertainties are commonly ignored with the result that estimates might be unreliable. The goal of this study is to estimate the effects of the uncertainty of forest species maps used in the sampling and estimation processes. Random forest (RF) per-pixel predictions were used with model-based inference to estimate V per unit area for the six main forest species of La Rioja, Spain. RF models for predicting V were constructed using field plot information from the Spanish National Forest Inventory and airborne laser scanning data. To limit the prediction of V to pixels classified as one of the main forest species assessed, a forest species map was constructed using Landsat and auxiliary information. Bootstrapping techniques were implemented to estimate the total uncertainty of the V estimates and accommodated both the effects of uncertainty in the Landsat forest species map and the effects of plot-to-plot sampling variability on training data used to construct the RF V models. Standard errors of species-specific total V estimates increased from 2–9% to 3–22% when the effects of map uncertainty were incorporated into the uncertainty assessment. The workflow achieved satisfactory results and revealed that the effects of map uncertainty are not negligible, especially for open-grown and less frequently occurring forest species for which greater variability was evident in the mapping and estimation process. The effects of forest map uncertainty are greater for species-specific area estimation than for the selection of field plots used to calibrate the RF model. Additional research to generalize the conclusions beyond Mediterranean to other forest environments is recommended.

show abstract

Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators

Cited by 12 publications

References 84 publications

Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory

Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory

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

A Model-Based Volume Estimator that Accounts for Both Land Cover Misclassification and Model Prediction Uncertainty

Contact Info

Product

Resources

About