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Remote-sensing support for the Estonian National Forest Inventory, facilitating the construction of maps for forest height, standing-wood volume, and tree species composition

Authors
  • Lang, Mait1, 2
  • Sims, Allan3
  • Pärna, Kalev4
  • Kangro, Raul4
  • Möls, Märt4
  • Mõistus, Marta1
  • Kiviste, Andres2
  • Tee, Mati5
  • Vajakas, Toivo5
  • Rennel, Mattias5
  • 1 University of Tartu, Estonia , (Estonia)
  • 2 Estonian University of Life Sciences, Kreutzwaldi 5 , (Estonia)
  • 3 Estonian Environment Agency, Mustamäe tee 33 , (Estonia)
  • 4 University of Tartu, Narva mnt 18 , (Estonia)
  • 5 Reach-U, Riia 24 , (Estonia)
Type
Published Article
Journal
Forestry Studies
Publisher
Sciendo
Publication Date
Mar 11, 2021
Volume
73
Issue
1
Pages
77–97
Identifiers
DOI: 10.2478/fsmu-2020-0016
Source
De Gruyter
Keywords
License
Green

Abstract

Since 1999, Estonia has conducted the National Forest Inventory (NFI) on the basis of sample plots. This paper presents a new module, incorporating remote-sensing feature variables from airborne laser scanning (ALS) and from multispectral satellite images, for the construction of maps of forest height, standing-wood volume, and tree species composition for the entire country. The models for sparse ALS point clouds yield coefficients of determination of 89.5–94.8% for stand height and 84.2–91.7% for wood volume. For the tree species prediction, the models yield Cohen's kappa values (taking 95% confidence intervals) of 0.69–0.72 upon comparing model results against a previous map, and values of 0.51–0.54 upon comparing model results against NFI sample plots. This paper additionally examines the influence of foliage phenology on the predictions and discusses options for further enhancement of the system.

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