Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach
Abstract Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale...
Published in: | Methods in Ecology and Evolution |
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ftdoajarticles:oai:doaj.org/article:880eb30d40db4369a1b6135e6710e7cb 2023-08-27T04:08:10+02:00 Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach Tianqi Zhang Desheng Liu 2023-07-01T00:00:00Z https://doi.org/10.1111/2041-210X.14112 https://doaj.org/article/880eb30d40db4369a1b6135e6710e7cb EN eng Wiley https://doi.org/10.1111/2041-210X.14112 https://doaj.org/toc/2041-210X 2041-210X doi:10.1111/2041-210X.14112 https://doaj.org/article/880eb30d40db4369a1b6135e6710e7cb Methods in Ecology and Evolution, Vol 14, Iss 7, Pp 1623-1638 (2023) artificial neural networks boreal forests canopy height model gradient boosting machine ICESat‐2 multivariate quality control Ecology QH540-549.5 Evolution QH359-425 article 2023 ftdoajarticles https://doi.org/10.1111/2041-210X.14112 2023-08-06T00:47:09Z Abstract Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale. However, with a photon‐counting laser system, ICESat‐2 contains high uncertainties in the estimated canopy heights, requiring appropriate quality control before being applied to canopy height modelling. We adopted a multivariate quality control approach (i.e. the Cook's distance) to improve the quality of ICESat‐2 samples. The controlled ICESat‐2 data were then input as the response variable for predicting boreal forest heights based on spatially continuous satellite data and machine learning (ML) regression models. The examined ML regressors include artificial neural networks (ANN), gradient boosting machine (GBM), random forest (RF) and support vector regression (SVR). The proposed quality control effectively removes low‐quality ICESat‐2 samples and enhances the correlations between ICESat‐2 and airborne laser scanning (ALS) observations. Moreover, the controlled ICESat‐2 samples help achieve a trade‐off between sample quality and quantity for all ML regressors, generating close canopy heights to ALS‐derived counterparts. Overall, RF and GBM make better canopy height predictions than ANN and SVR. Of all explanatory variables, the normalized difference vegetation index calculated based on the first red edge band of Sentinel‐2 (NDVIredEdge1) is considered the most important. The proposed quality control on ICESat‐2 sample selection and canopy height model (CHM) development workflow will greatly benefit forest structure investigations in the Arctic community. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Methods in Ecology and Evolution 14 7 1623 1638 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
artificial neural networks boreal forests canopy height model gradient boosting machine ICESat‐2 multivariate quality control Ecology QH540-549.5 Evolution QH359-425 |
spellingShingle |
artificial neural networks boreal forests canopy height model gradient boosting machine ICESat‐2 multivariate quality control Ecology QH540-549.5 Evolution QH359-425 Tianqi Zhang Desheng Liu Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach |
topic_facet |
artificial neural networks boreal forests canopy height model gradient boosting machine ICESat‐2 multivariate quality control Ecology QH540-549.5 Evolution QH359-425 |
description |
Abstract Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale. However, with a photon‐counting laser system, ICESat‐2 contains high uncertainties in the estimated canopy heights, requiring appropriate quality control before being applied to canopy height modelling. We adopted a multivariate quality control approach (i.e. the Cook's distance) to improve the quality of ICESat‐2 samples. The controlled ICESat‐2 data were then input as the response variable for predicting boreal forest heights based on spatially continuous satellite data and machine learning (ML) regression models. The examined ML regressors include artificial neural networks (ANN), gradient boosting machine (GBM), random forest (RF) and support vector regression (SVR). The proposed quality control effectively removes low‐quality ICESat‐2 samples and enhances the correlations between ICESat‐2 and airborne laser scanning (ALS) observations. Moreover, the controlled ICESat‐2 samples help achieve a trade‐off between sample quality and quantity for all ML regressors, generating close canopy heights to ALS‐derived counterparts. Overall, RF and GBM make better canopy height predictions than ANN and SVR. Of all explanatory variables, the normalized difference vegetation index calculated based on the first red edge band of Sentinel‐2 (NDVIredEdge1) is considered the most important. The proposed quality control on ICESat‐2 sample selection and canopy height model (CHM) development workflow will greatly benefit forest structure investigations in the Arctic community. |
format |
Article in Journal/Newspaper |
author |
Tianqi Zhang Desheng Liu |
author_facet |
Tianqi Zhang Desheng Liu |
author_sort |
Tianqi Zhang |
title |
Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach |
title_short |
Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach |
title_full |
Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach |
title_fullStr |
Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach |
title_full_unstemmed |
Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach |
title_sort |
improving icesat‐2‐based boreal forest height estimation by a multivariate sample quality control approach |
publisher |
Wiley |
publishDate |
2023 |
url |
https://doi.org/10.1111/2041-210X.14112 https://doaj.org/article/880eb30d40db4369a1b6135e6710e7cb |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Methods in Ecology and Evolution, Vol 14, Iss 7, Pp 1623-1638 (2023) |
op_relation |
https://doi.org/10.1111/2041-210X.14112 https://doaj.org/toc/2041-210X 2041-210X doi:10.1111/2041-210X.14112 https://doaj.org/article/880eb30d40db4369a1b6135e6710e7cb |
op_doi |
https://doi.org/10.1111/2041-210X.14112 |
container_title |
Methods in Ecology and Evolution |
container_volume |
14 |
container_issue |
7 |
container_start_page |
1623 |
op_container_end_page |
1638 |
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1775348868246929408 |