Fusing GEDI with earth observation data for large area aboveground biomass mapping

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2022, Номер 115, С. 103108 - 103108

Опубликована: Ноя. 17, 2022

An accurate and spatially explicit estimation of biomass is required for sustainable forest management, prevention biodiversity loss, carbon accounting climate change mitigation. This study offers a methodology to generate wall-to-wall aboveground 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, land cover produce Australia the United States 2020. The gradient boosting machine learning framework was applied predict AGBD its uncertainty at resolutions 100 m 200 m. performance models based (1) imagery (2) combination elevation compared. most model identified using Bayesian hyperparameter optimization 5-fold cross-validation. analysis resulted in estimated coefficient determination (R2) 0.61 – 0.71, root-mean-square error (RMSE) 59 86 Mg/ha, relative (RMSE%) 45 80%. accuracy improved addition data: R2 0.66 0.74, RMSE 55 81 RMSE% 41 77%. It found cover-derived predictors important estimating annual AGBD. proposed method also reduced saturation effect, which common high areas when predicting satellite imagery. Prediction produced this could serve as baseline current AGB stocks forested lands equal 9.8 Pg 37.1 States, respectively. Overall, research highlights methodological opportunities combining EO yield more globally applicable through fusion.

Язык: Английский

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Factors affecting relative height and ground elevation estimations of GEDI among forest types across the conterminous USA

GIScience & Remote Sensing, Год журнала: 2022, Номер 59(1), С. 975 - 999

Опубликована: Июнь 13, 2022

The Global Ecosystem Dynamics Investigation (GEDI), a new spaceborne LiDAR system of the National Aeronautics and Space Administration (NASA), has potential to revolutionize global measurements vertical vegetation structure. However, GEDI performance among different forest types factors influencing needs be evaluated against similar from existing airborne platforms. Ideally, comparisons across diverse will inform future work quantifying biomass or mapping species habitats. Thus, we compared second version L2A product (GEDI V2) with Airborne Observation Platform (AOP) leaf-on data 33 Ecological Network (NEON) sites. Comparisons were made for ground elevation relative height (RH) simulated laser scanning (ALS) waveforms discrete point cloud LiDAR. Results indicated that V2 obtained high accuracy on RH100 estimations (3σ) RMSEs 1.38 m 2.62 m, respectively. produced (RH100) all 12 %RMSE below 25%. RHs sensitive finding accuracy, RH estimation varied profiles types. For performance, greater than 21% RH95 33% variations can explained by land surface attributes, observing sensor characteristics, collection time differences between NEON Furthermore, geolocation error remains an essential factor affecting which varies cover types, especially canopy estimation. findings reported here provide insights guide enhance GEDI-based structure applications.

Язык: Английский

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Nationwide native forest structure maps for Argentina based on forest inventory data, SAR Sentinel-1 and vegetation metrics from Sentinel-2 imagery

Remote Sensing of Environment, Год журнала: 2022, Номер 285, С. 113391 - 113391

Опубликована: Ноя. 30, 2022

Язык: Английский

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Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel

Remote Sensing, Год журнала: 2022, Номер 14(20), С. 5158 - 5158

Опубликована: Окт. 15, 2022

Continuous large-scale mapping of forest canopy height is crucial for estimating and reporting carbon content, analyzing degradation restoration, or to model ecosystem variables such as aboveground biomass. Over the last years, spaceborne Light Detection Ranging (LiDAR) sensor specifically designed acquire structure information, Global Ecosystem Dynamics Investigation (GEDI), has been used extract information over large areas. Yet, GEDI no spatial coverage most forested areas in Canada other high latitude regions. On hand, LiDAR called Ice, Cloud, Land Elevation Satellite-2 (ICESat-2) provides a global but was not specially developed study ecosystems. Nonetheless, both sensors obtain point-based making spatially continuous estimation very challenging. This compared performance LiDAR, ICESat-2, combined with ALOS-2/PALSAR-2 Sentinel-1 -2 data produce maps year 2020. A set-aside dataset airborne (ALS) from national campaign were accuracy assessment. Both overestimated relation ALS data, had better than ICESat-2 mean difference (MD) 0.9 m 2.9 m, root square error (RMSE) 4.2 5.2 respectively. However, have hemi-boreal forests, captures tall heights expected these forests GEDI. PALSAR-2 HV polarization important covariate predict height, showing great potential L-band comparison C-band optical Sentinel-2. The approach proposed here can be operationally annual that lack coverage.

Язык: Английский

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First validation of GEDI canopy heights in African savannas

Remote Sensing of Environment, Год журнала: 2022, Номер 285, С. 113402 - 113402

Опубликована: Дек. 12, 2022

Язык: Английский

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Modeling carbon storage in urban vegetation: Progress, challenges, and opportunities

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2022, Номер 114, С. 103058 - 103058

Опубликована: Окт. 19, 2022

Urban vegetation (UV) and its carbon storage capacity are critical for terrestrial cycling global sustainable development goals (SDGs). With complex spatial distribution, composition ecological functions, UV is essential climate change. Therefore, improving modeling a research hotspot that deserves extensive investigation. However, the uniqueness of lead to great challenges in modeling, including (1) limitations data algorithms due sensitive urban environments; (2) severe scarcity in-city field observation (e.g., EC towers surveys); (3) difficulty parameter inversion canopy height, LAI, etc.); (4) poor transferability when migrating estimation models from natural scenarios. The progress settings reviewed, with detailed discussions on methods major challenges. We then propose strategies overcome existing challenges, implementing novel improved remote sensing (RS) techniques hyper-spectral, LiDAR, satellites, etc.) obtain enhanced structural functional information UV; nodes earth sensor network, especially distribution settings; leveraging "Model-Data Fusion" technology by integrating big reduce uncertainty estimations. This review provides new insights expected help community achieve better understanding towards neutrality.

Язык: Английский

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Aboveground biomass mapping by integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and topographic information in Mediterranean forests

GIScience & Remote Sensing, Год журнала: 2022, Номер 59(1), С. 1509 - 1533

Опубликована: Сен. 20, 2022

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Язык: Английский

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Assessment of terrain elevation estimates from ICESat-2 and GEDI spaceborne LiDAR missions across different land cover and forest types

Science of Remote Sensing, Год журнала: 2022, Номер 6, С. 100067 - 100067

Опубликована: Сен. 5, 2022

Язык: Английский

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Consistency analysis of forest height retrievals between GEDI and ICESat-2

Remote Sensing of Environment, Год журнала: 2022, Номер 281, С. 113244 - 113244

Опубликована: Сен. 15, 2022

Язык: Английский

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Mapping fine-scale building heights in urban agglomeration with spaceborne lidar

Remote Sensing of Environment, Год журнала: 2022, Номер 285, С. 113392 - 113392

Опубликована: Дек. 6, 2022

Язык: Английский

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