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Open AccessArticle

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

1
Institut National de l’Information Géographique et Forestière, Laboratoire d’Inventaire Forestier, 54000 Nancy, France
2
Office National des Forêts, Pôle Recherche Développement Innovation, Site de Nancy-Brabois, 8 allée de Longchamp, 54600 Nancy, France
3
Institut National de l’Information Géographique et Forestière, Service de l’Inventaire Forestier et Environmental, 45290 Nogent-sur-Vernisson, France
4
Department of Forest Resources, University of Minnesota, Saint Paul, MN 55108, USA
5
Northern Research Station, U.S. Forest Service, Saint Paul, MN 55108, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 991; https://doi.org/10.3390/rs11080991
Received: 14 March 2019 / Revised: 18 April 2019 / Accepted: 18 April 2019 / Published: 25 April 2019
(This article belongs to the Special Issue Data Fusion for Improved Forest Inventories and Planning)
Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy height models. On a subset area, changes in canopy height estimated from two successive photogrammetric models were also used. A model-assisted inference framework, using a k nearest-neighbors approach, was used to predict 11 field inventory variables simultaneously. The results showed that among the auxiliary variables tested, 3D metrics improved the precision of dendrometric estimates more than other auxiliary variables. Relative efficiencies (RE) varying from 2.15 for volume to 1.04 for stand density were obtained using all auxiliary variables. Canopy height changes also increased RE from 3% to 26%. Our results confirmed the importance of 3D metrics as auxiliary variables and demonstrated the value of canopy change variables for increasing the precision of estimates of forest structural attributes such as density and quadratic mean diameter. View Full-Text
Keywords: multisource forest inventory; national forest inventory; non-parametric models; statistical inference; digital photogrammetry multisource forest inventory; national forest inventory; non-parametric models; statistical inference; digital photogrammetry
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Irulappa-Pillai-Vijayakumar, D.B.; Renaud, J.-P.; Morneau, F.; McRoberts, R.E.; Vega, C. Increasing Precision for French Forest Inventory Estimates using the k-NN Technique with Optical and Photogrammetric Data and Model-Assisted Estimators. Remote Sens. 2019, 11, 991.

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