Fusing GEDI with earth observation data for large area aboveground biomass mapping
An accurate and spatially explicit estimation of biomass is required for sustainable forest management, prevention of biodiversity loss, and carbon accounting for climate change mitigation. This study offers a methodology to generate wall-to-wall aboveground biomass density (AGBD) maps that exclusiv...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222002965 |
| _version_ | 1811190597864652800 |
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| author | Yuri Shendryk |
| author_facet | Yuri Shendryk |
| author_sort | Yuri Shendryk |
| collection | DOAJ |
| description | An accurate and spatially explicit estimation of biomass is required for sustainable forest management, prevention of biodiversity loss, and carbon accounting for climate change mitigation. This study offers a methodology to generate wall-to-wall aboveground biomass density (AGBD) maps that exclusively relies on open access earth observation (EO) data. Specifically, spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR data were fused with Sentinel-1 synthetic-aperture radar, Sentinel-2 multispectral, elevation, and land cover data to produce biomass maps of Australia and the United States for 2020. The gradient boosting machine learning framework was applied to predict AGBD and its uncertainty at the resolutions of 100 m and 200 m. The performance of models based on (1) Sentinel-2 imagery and land cover and (2) a combination of Sentinel-2 and Sentinel-1 imagery with elevation and land cover data were compared. The most accurate gradient boosting model was identified using a Bayesian hyperparameter optimization with a 5-fold cross-validation. The Sentinel-2 imagery and land cover data analysis resulted in AGBD estimated with the coefficient of determination (R2) of 0.61 – 0.71, root-mean-square error (RMSE) of 59 – 86 Mg/ha, and relative root-mean-square error (RMSE%) of 45 – 80%. The accuracy of the models improved with the addition of Sentinel-1 and elevation data: AGBD estimation with R2 of 0.66 – 0.74, RMSE of 55 – 81 Mg/ha, and RMSE% of 41 – 77%. It was found that Sentinel-2 and land cover-derived predictors were the most important in estimating annual AGBD. The proposed method also reduced the saturation effect, which is common in high biomass areas when predicting AGBD using satellite imagery. Prediction maps produced in this study could serve as a baseline for current AGB stocks of forested lands equal to 9.8 Pg and 37.1 Pg in Australia and the United States, respectively. Overall, this research highlights methodological opportunities for combining open access EO data to yield more accurate and globally applicable AGB maps through data fusion. |
| first_indexed | 2024-04-11T14:52:31Z |
| format | Article |
| id | doaj.art-22ccf0daf317491ea633e4f5a4f45b8f |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-11T14:52:31Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-22ccf0daf317491ea633e4f5a4f45b8f2022-12-22T04:17:21ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-12-01115103108Fusing GEDI with earth observation data for large area aboveground biomass mappingYuri Shendryk0Dendra Systems, Sydney, AustraliaAn accurate and spatially explicit estimation of biomass is required for sustainable forest management, prevention of biodiversity loss, and carbon accounting for climate change mitigation. This study offers a methodology to generate wall-to-wall aboveground biomass density (AGBD) maps that exclusively relies on open access earth observation (EO) data. Specifically, spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR data were fused with Sentinel-1 synthetic-aperture radar, Sentinel-2 multispectral, elevation, and land cover data to produce biomass maps of Australia and the United States for 2020. The gradient boosting machine learning framework was applied to predict AGBD and its uncertainty at the resolutions of 100 m and 200 m. The performance of models based on (1) Sentinel-2 imagery and land cover and (2) a combination of Sentinel-2 and Sentinel-1 imagery with elevation and land cover data were compared. The most accurate gradient boosting model was identified using a Bayesian hyperparameter optimization with a 5-fold cross-validation. The Sentinel-2 imagery and land cover data analysis resulted in AGBD estimated with the coefficient of determination (R2) of 0.61 – 0.71, root-mean-square error (RMSE) of 59 – 86 Mg/ha, and relative root-mean-square error (RMSE%) of 45 – 80%. The accuracy of the models improved with the addition of Sentinel-1 and elevation data: AGBD estimation with R2 of 0.66 – 0.74, RMSE of 55 – 81 Mg/ha, and RMSE% of 41 – 77%. It was found that Sentinel-2 and land cover-derived predictors were the most important in estimating annual AGBD. The proposed method also reduced the saturation effect, which is common in high biomass areas when predicting AGBD using satellite imagery. Prediction maps produced in this study could serve as a baseline for current AGB stocks of forested lands equal to 9.8 Pg and 37.1 Pg in Australia and the United States, respectively. Overall, this research highlights methodological opportunities for combining open access EO data to yield more accurate and globally applicable AGB maps through data fusion.http://www.sciencedirect.com/science/article/pii/S1569843222002965GEDIBiomassSatellite imageryData fusionMachine learningRemote sensing |
| spellingShingle | Yuri Shendryk Fusing GEDI with earth observation data for large area aboveground biomass mapping International Journal of Applied Earth Observations and Geoinformation GEDI Biomass Satellite imagery Data fusion Machine learning Remote sensing |
| title | Fusing GEDI with earth observation data for large area aboveground biomass mapping |
| title_full | Fusing GEDI with earth observation data for large area aboveground biomass mapping |
| title_fullStr | Fusing GEDI with earth observation data for large area aboveground biomass mapping |
| title_full_unstemmed | Fusing GEDI with earth observation data for large area aboveground biomass mapping |
| title_short | Fusing GEDI with earth observation data for large area aboveground biomass mapping |
| title_sort | fusing gedi with earth observation data for large area aboveground biomass mapping |
| topic | GEDI Biomass Satellite imagery Data fusion Machine learning Remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222002965 |
| work_keys_str_mv | AT yurishendryk fusinggediwithearthobservationdataforlargeareaabovegroundbiomassmapping |