<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Institute of Forestry and Rural Engineering

Forestry Studies

This work is licensed under the Creative Commons Attribution 4.0 International License.

Kaugseirel põhinev lahendus Eesti statistilise metsainventuuri jaoks puistute kõrguse, tüvemahu ja liigilise koosseisu kaartide koostamiseks

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

Article Category: Research paper

Pubblicato online: 11 mar 2021

Pagine: 77 - 97

Ricevuto: 19 lug 2020

Accettato: 23 nov 2020

DOI: https://doi.org/10.2478/fsmu-2020-0016

Parole chiave
National Forest Inventory, lidar, multispectral satellite images, sample plots

© 2020 Mait Lang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Article Category: Research paper

Pubblicato online: 11 mar 2021

Pagine: 77 - 97

Ricevuto: 19 lug 2020

Accettato: 23 nov 2020

DOI: https://doi.org/10.2478/fsmu-2020-0016

Parole chiaveNational Forest Inventory, lidar, multispectral satellite images, sample plots

© 2020 Mait Lang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
National Forest Inventory, lidar, multispectral satellite images, sample plots