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...
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crwiley:10.1111/2041-210x.14112 2024-06-02T08:02:35+00:00 Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach Zhang, Tianqi Liu, Desheng National Science Foundation 2023 http://dx.doi.org/10.1111/2041-210x.14112 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14112 en eng Wiley http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/ Methods in Ecology and Evolution volume 14, issue 7, page 1623-1638 ISSN 2041-210X 2041-210X journal-article 2023 crwiley https://doi.org/10.1111/2041-210x.14112 2024-05-03T10:53:30Z 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 Wiley Online Library Arctic Methods in Ecology and Evolution 14 7 1623 1638 |
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English |
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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. |
author2 |
National Science Foundation |
format |
Article in Journal/Newspaper |
author |
Zhang, Tianqi Liu, Desheng |
spellingShingle |
Zhang, Tianqi Liu, Desheng Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach |
author_facet |
Zhang, Tianqi Liu, Desheng |
author_sort |
Zhang, Tianqi |
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 |
http://dx.doi.org/10.1111/2041-210x.14112 https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.14112 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Methods in Ecology and Evolution volume 14, issue 7, page 1623-1638 ISSN 2041-210X 2041-210X |
op_rights |
http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/ |
op_doi |
https://doi.org/10.1111/2041-210x.14112 |
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Methods in Ecology and Evolution |
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14 |
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7 |
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1623 |
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1638 |
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1800747064029609984 |