| 1. Identity statement | ||||
| Reference Type | Conference Paper (Conference Proceedings) | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/4878GS5 | |||
| Repository | sid.inpe.br/mtc-m21d/2022/12.13.18.23 | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2022/12.13.18.23.26 | |||
| Metadata Last Update | 2023:07.08.07.14.43 (UTC) administrator | |||
| Secondary Key | INPE--PRE/ | |||
| Citation Key | CsillikKLBBCCDFGJOVV:2022:AmFoSt | |||
| Title | Amazon forest structural diversity estimated using field inventory plots, airborne lidar and GEDI spaceborne lidar  | |||
| Year | 2022 | |||
| Access Date | 2024, May 12 | |||
| Secondary Type | PRE CI | |||
| 2. Context | ||||
| Author | 1 Csillik, Ovidiu 2 Keller, Michael 3 Longo, Marcos 4 Bonal, Damien 5 Burban, Benoit 6 Chave, Jérome 7 Coomes, David A. 8 Derroire, Geraldine 9 Feldpausch, Ted 10 Görgens, Eric Bastos 11 Jackson, Tobias 12 Ometto, Jean Pierre Henry Balbaud 13 Valdivia, María Isabel Vilalba 14 Vincente, Grégoire | |||
| Group | 1 2 3 4 5 6 7 8 9 10 11 12 DIPE3-COGPI-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Jet Propulsion Laboratory 2 NASA Jet Propulsion Laboratory 3 Lawrence Berkeley National Laboratory 4 Université de Lorraine 5 INRAE 6 CNRS 7 University of Cambridge 8 CIRAD 9 University of Exeter 10 Universidade Federal dos Vales do Jequitinhonha e Mucuri 11 University of Cambridge 12 Instituto Nacional de Pesquisas Espaciais (INPE) 13 Jardín Botánico de Missouri 14 University of Montpellier | |||
| Author e-Mail Address | 1 2 3 4 5 6 7 8 9 10 11 12 jean.ometto@inpe.br | |||
| Conference Name | AGU Fall Meeting | |||
| Conference Location | Chicago, IL | |||
| Date | 12-16 Dec. 2022 | |||
| Publisher | AGU | |||
| History (UTC) | 2022-12-13 18:23:26 :: simone -> administrator :: 2023-07-08 07:14:43 :: administrator -> simone :: 2022 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Abstract | Tropical forests store more than half of terrestrial aboveground biomass and are key to understanding the global carbon cycle. Knowing the structure of a forest today is critical to predicting its future structure and productivity. Unfortunately, field inventories of tropical forests are sparse and vast areas have no representative inventory information. Reliance on sparse field measurements can lead to large uncertainties when extrapolating to vast regions such as the Amazon forest. The advent of airborne and spaceborne lidar expands the coverage from local to regional and continental measurements of forest structural properties. We produced Amazon-wide estimates of forest structure (aboveground biomass, basal area, leaf area index, and stem number density) by integrating information from forest inventory plots, airborne lidar and GEDI (Global Ecosystem Dynamics Investigation) spaceborne lidar using a two-step approach. The first step relates forest properties from about one thousand forest inventory plots and relative heights from simulated GEDI metrics based on airborne lidar overlapping the plots. We compared several regression models and machine learning approaches, with best results obtained by Ordinary Least Squares and Random Forests that reached R2 values from 0.69 for stem density (relative RMSE 22%) to 0.79 for basal area (20%), 0.80 for aboveground biomass (24%) and 0.83 for leaf area index (19%). Second, we used 11,280 contemporary co-located GEDI-simulated shots from airborne lidar with spaceborne GEDI shots to build linear models between their corresponding relative heights. Ultimately, we combined the two models to upscale the measurements from field inventory to GEDI data over the Amazon, consisting of more than 250 million quality-filtered shots acquired over 2.5 years. We analyzed environmental and climatic factors that influence regional and local variations in forest structure. Preliminary results indicate that the dry season length was a strong predictor of major gradients of stem number density across the Amazon basin. These results fill a gap where systematic forest inventories are lacking and can be used to study a wide range of ecosystem processes and model predictions of forest carbon budget dynamics. | |||
| Area | CST | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produçăo a partir de 2021 > COGPI > Amazon forest structural... | |||
| doc Directory Content | there are no files | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| User Group | simone | |||
| Visibility | shown | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/46L2FGP | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | archivingpolicy archivist booktitle callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn keywords label language lineage mark mirrorrepository nextedition notes numberoffiles numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle size sponsor subject targetfile tertiarymark tertiarytype type url versiontype volume | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||