Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images

This study evaluated the suitability of different airborne laser scanning (ALS) datasets for the prediction of forest canopy fuel parameters in managed boreal forests in Finland. The ALS data alternatives were leaf-off and leaf-on unispectral and leaf-on multispectral data, alone and combined with a...

Full description

Saved in:
Bibliographic Details
Published in:European journal of remote sensing 2020-01, Vol.53 (1), p.245-257
Main Authors: Maltamo, Matti, Räty, J., Korhonen, L., Kotivuori, E., Kukkonen, M., Peltola, H., Kangas, J., Packalen, P.
Format: Article
Language:English
Subjects:
Airborne lasers
Alternative fuels
Biomass
Boreal forests
Canopies
Datasets
Dominant species
Fertility
Forest canopy
Forest fire
forest fuel parameters
Forest management
forest structure
Forests
fuel models
Laser applications
Lasers
Leaves
LiDAR
Parameters
Regression analysis
Scanning
Understory
Weight
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study evaluated the suitability of different airborne laser scanning (ALS) datasets for the prediction of forest canopy fuel parameters in managed boreal forests in Finland. The ALS data alternatives were leaf-off and leaf-on unispectral and leaf-on multispectral data, alone and combined with aerial images. Canopy fuel weight, canopy base height, biomass of living and dead trees, and height and biomass of the understory tree layer were predicted using regression analysis. The considered categorical forest parameters were dominant tree species, site fertility and vertical forest structure layers. The canopy fuel weight was modeled based on crown biomass with an RMSE% value of 20-30%. The canopy base heights were predicted separately for pine and spruce stands with satisfactory results the RMSE% values being 9-10% and 15-17%, respectively. Following the initial classification of the existence of an understory layer (with kappa-values of 0.47-0.53), the prediction of understory height performed well (RMSE% 20-25%) but the understory biomass was predicted with larger RMSE% values (about 60-70%). Site fertility was classified with kappa-values of 0.5-0.6. The most accurate results were obtained using multispectral ALS data, although the differences between the datasets were minor.
ISSN:2279-7254
2279-7254
DOI:10.1080/22797254.2020.1816142