Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data
Field measurement of sample plots is a major cost in forest remote sensing. This is also relevant in drone-based forest inventories where the target area is rather small compared to the area used in other remote sensing techniques. Implementation of forest inventories by remote sensing could be stre...
Published in: | International Journal of Applied Earth Observation and Geoinformation |
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Online Access: | https://doi.org/10.1016/j.jag.2021.102484 https://doaj.org/article/a2f2f883d0714e07bfdc27460c684670 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:a2f2f883d0714e07bfdc27460c684670 2023-05-15T17:42:16+02:00 Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data Janne Toivonen Lauri Korhonen Mikko Kukkonen Eetu Kotivuori Matti Maltamo Petteri Packalen 2021-12-01 https://doi.org/10.1016/j.jag.2021.102484 https://doaj.org/article/a2f2f883d0714e07bfdc27460c684670 en eng Elsevier 1569-8432 doi:10.1016/j.jag.2021.102484 https://doaj.org/article/a2f2f883d0714e07bfdc27460c684670 undefined International Journal of Applied Earth Observations and Geoinformation, Vol 103, Iss , Pp 102484- (2021) Airborne laser scanning Area-based approach Drone Forest inventory Remote sensing geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2021 fttriple https://doi.org/10.1016/j.jag.2021.102484 2023-01-22T18:27:14Z Field measurement of sample plots is a major cost in forest remote sensing. This is also relevant in drone-based forest inventories where the target area is rather small compared to the area used in other remote sensing techniques. Implementation of forest inventories by remote sensing could be streamlined by using models fitted elsewhere in a similar type of forest. The main objective of this study was to investigate the accuracy of forest attribute predictions from drone-based image point clouds (DIPC) without locally fitted models. Instead, the models were fitted in 22 inventory areas across Finland using airborne laser scanning (ALS) data. These models were applied to predict dominant height and stem volume for a separate test area located in eastern Finland. In the test area, the predictors were computed from DIPC data for 15 m × 15 m sub-plots that were finally aggregated to full 30 m × 30 m plots. All dominant height models performed well with the test data: the relative root mean square error (RMSE) varied between 3 and 5% and the relative mean difference (MD) values ranged between 0 and 3%. In contrast, the stem volume models fitted in northern Finland performed poorly with the test data. These models produced RMSE values between 40 and 65%, whereas models fitted in other parts of the country produced RMSE values between 20 and 30%. Similarly, MD values associated with the stem volume models fitted in northern Finland ranged between 24 and 51%, whereas MD values associated with models fitted elsewhere in Finland ranged between 3 and 17%. Regional variations in forest structure are the main reason why models fitted in northern Finland did not perform as well as in the test area. Article in Journal/Newspaper Northern Finland Unknown Image Point ENVELOPE(-132.002,-132.002,53.245,53.245) International Journal of Applied Earth Observation and Geoinformation 103 102484 |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
English |
topic |
Airborne laser scanning Area-based approach Drone Forest inventory Remote sensing geo envir |
spellingShingle |
Airborne laser scanning Area-based approach Drone Forest inventory Remote sensing geo envir Janne Toivonen Lauri Korhonen Mikko Kukkonen Eetu Kotivuori Matti Maltamo Petteri Packalen Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data |
topic_facet |
Airborne laser scanning Area-based approach Drone Forest inventory Remote sensing geo envir |
description |
Field measurement of sample plots is a major cost in forest remote sensing. This is also relevant in drone-based forest inventories where the target area is rather small compared to the area used in other remote sensing techniques. Implementation of forest inventories by remote sensing could be streamlined by using models fitted elsewhere in a similar type of forest. The main objective of this study was to investigate the accuracy of forest attribute predictions from drone-based image point clouds (DIPC) without locally fitted models. Instead, the models were fitted in 22 inventory areas across Finland using airborne laser scanning (ALS) data. These models were applied to predict dominant height and stem volume for a separate test area located in eastern Finland. In the test area, the predictors were computed from DIPC data for 15 m × 15 m sub-plots that were finally aggregated to full 30 m × 30 m plots. All dominant height models performed well with the test data: the relative root mean square error (RMSE) varied between 3 and 5% and the relative mean difference (MD) values ranged between 0 and 3%. In contrast, the stem volume models fitted in northern Finland performed poorly with the test data. These models produced RMSE values between 40 and 65%, whereas models fitted in other parts of the country produced RMSE values between 20 and 30%. Similarly, MD values associated with the stem volume models fitted in northern Finland ranged between 24 and 51%, whereas MD values associated with models fitted elsewhere in Finland ranged between 3 and 17%. Regional variations in forest structure are the main reason why models fitted in northern Finland did not perform as well as in the test area. |
format |
Article in Journal/Newspaper |
author |
Janne Toivonen Lauri Korhonen Mikko Kukkonen Eetu Kotivuori Matti Maltamo Petteri Packalen |
author_facet |
Janne Toivonen Lauri Korhonen Mikko Kukkonen Eetu Kotivuori Matti Maltamo Petteri Packalen |
author_sort |
Janne Toivonen |
title |
Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data |
title_short |
Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data |
title_full |
Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data |
title_fullStr |
Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data |
title_full_unstemmed |
Transferability of ALS-based forest attribute models when predicting with drone-based image point cloud data |
title_sort |
transferability of als-based forest attribute models when predicting with drone-based image point cloud data |
publisher |
Elsevier |
publishDate |
2021 |
url |
https://doi.org/10.1016/j.jag.2021.102484 https://doaj.org/article/a2f2f883d0714e07bfdc27460c684670 |
long_lat |
ENVELOPE(-132.002,-132.002,53.245,53.245) |
geographic |
Image Point |
geographic_facet |
Image Point |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_source |
International Journal of Applied Earth Observations and Geoinformation, Vol 103, Iss , Pp 102484- (2021) |
op_relation |
1569-8432 doi:10.1016/j.jag.2021.102484 https://doaj.org/article/a2f2f883d0714e07bfdc27460c684670 |
op_rights |
undefined |
op_doi |
https://doi.org/10.1016/j.jag.2021.102484 |
container_title |
International Journal of Applied Earth Observation and Geoinformation |
container_volume |
103 |
container_start_page |
102484 |
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1766144107504205824 |