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...
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Online Access: | http://dx.doi.org/10.1080/22797254.2020.1816142 |
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doaj-636f8e52e5d1468aa5139cd30538cc4a2021-01-04T18:22:11ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542020-01-0153124525710.1080/22797254.2020.18161421816142Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial imagesMatti Maltamo0J. Räty1L. Korhonen2E. Kotivuori3M. Kukkonen4H. Peltola5J. Kangas6P. Packalen7University of Eastern FinlandNorwegian Institute of Bioeconomy Research (NIBIO)University of Eastern FinlandUniversity of Eastern FinlandUniversity of Eastern FinlandUniversity of Eastern FinlandUniversity of Eastern FinlandUniversity of Eastern FinlandThis 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.http://dx.doi.org/10.1080/22797254.2020.1816142forest fireforest structureboreal forestsfuel modelsforest fuel parameterslidar |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Matti Maltamo J. Räty L. Korhonen E. Kotivuori M. Kukkonen H. Peltola J. Kangas P. Packalen |
spellingShingle |
Matti Maltamo J. Räty L. Korhonen E. Kotivuori M. Kukkonen H. Peltola J. Kangas P. Packalen Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images European Journal of Remote Sensing forest fire forest structure boreal forests fuel models forest fuel parameters lidar |
author_facet |
Matti Maltamo J. Räty L. Korhonen E. Kotivuori M. Kukkonen H. Peltola J. Kangas P. Packalen |
author_sort |
Matti Maltamo |
title |
Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images |
title_short |
Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images |
title_full |
Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images |
title_fullStr |
Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images |
title_full_unstemmed |
Prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images |
title_sort |
prediction of forest canopy fuel parameters in managed boreal forests using multispectral and unispectral airborne laser scanning data and aerial images |
publisher |
Taylor & Francis Group |
series |
European Journal of Remote Sensing |
issn |
2279-7254 |
publishDate |
2020-01-01 |
description |
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. |
topic |
forest fire forest structure boreal forests fuel models forest fuel parameters lidar |
url |
http://dx.doi.org/10.1080/22797254.2020.1816142 |
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