| 1. Identity statement | ||||
| Reference Type | Conference Paper (Conference Proceedings) | |||
| Site | mtc-m21c.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34R/3U5UNPP | |||
| Repository | sid.inpe.br/mtc-m21c/2019/09.30.13.02 | |||
| Metadata Repository | sid.inpe.br/mtc-m21c/2019/09.30.13.02.03 | |||
| Metadata Last Update | 2020:01.06.11.42.22 (UTC) administrator | |||
| Secondary Key | INPE--PRE/ | |||
| Citation Key | AlmeidaGaArOmJaPeSa:2019:CoReTe | |||
| Title | Comparison of regression techniques for LiDAR-derived aboveground biomass estimation in the Amazon  | |||
| Year | 2019 | |||
| Access Date | 2024, Apr. 26 | |||
| Secondary Type | PRE CI | |||
| 2. Context | ||||
| Author | 1 Almeida, Catherine Torres de 2 Galvão, Lênio Soares 3 Aragão, Luiz Eduardo Oliveira e Cruz de 4 Ometto, Jean Pierre Henry Balbaud 5 Jacon, Aline Daniele 6 Pereira, Francisca Rocha de Souza 7 Sato, Luciane Yumie | |||
| Resume Identifier | 1 2 8JMKD3MGP5W/3C9JHLF | |||
| Group | 1 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 2 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 3 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 4 COCST-COCST-INPE-MCTIC-GOV-BR 5 6 DIDSR-CGOBT-INPE-MCTIC-GOV-BR 7 COCST-COCST-INPE-MCTIC-GOV-BR | |||
| Affiliation | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 Instituto Nacional de Pesquisas Espaciais (INPE) 5 Instituto Nacional de Pesquisas Espaciais (INPE) 6 Instituto Nacional de Pesquisas Espaciais (INPE) 7 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Author e-Mail Address | 1 catherine.almeida@inpe.br 2 lenio.galvao@inpe.br 3 luiz.aragao@inpe.br 4 jean.ometto@inpe.br 5 6 francisca.pereira@inpe.br 7 luciane.sato@inpe.br | |||
| Conference Name | Congresso Mundial da IUFRO | |||
| Conference Location | Curitiba, PR | |||
| Date | 29 set. - 05 out. | |||
| History (UTC) | 2019-09-30 13:02:03 :: simone -> administrator :: 2019-10-01 16:31:11 :: administrator -> simone :: 2019 2019-12-06 19:28:34 :: simone -> administrator :: 2019 2020-01-06 11:42:22 :: administrator -> simone :: 2019 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Abstract | Light Detection And Ranging (LiDAR) is an active remote sensor that has been successfully applied for characterizing canopy structure, especially to estimate aboveground biomass (AGB). Parametric models, mainly the linear regression with stepwise feature selection (LMstep), are the most common approaches used for estimating AGB. However, non-parametric machine learning techniques, such as Support Vector Regression (SVR), Stochastic Gradient Boosting (SGB), and Random Forest (RF), can better address complex relationships between biomass and remote sensing variables. Therefore, it is desirable to assess the performance of different regression strategies. This study aims to compare eight regression techniques for LiDAR-based AGB estimation: LMstep, Linear Models with Regularization (LMR), Partial Least Squares (PLS), K-Nearest Neighbor (KNN), SVR, RF, SGB, and Cubist. For this purpose, 34 LiDAR metrics were regressed against AGB from 147 inventory plots across the Brazilian Amazon Biome. Models performance were evaluated by the average Root Mean Squared Error (RMSE) and R2 from a 5-fold cross-validation strategy with 10 repetitions. The Kruskal-Wallis test was used to evaluate statistical differences among models. Results showed that LMstep presented the highest RMSE (68.85 Mg.ha-1) and lowest R2 (0.66), while SVR had the lowest RMSE (65.23 Mg.ha-1) and highest R2 (0.69). However, the differences in performance of the models were not statistically significant. Thus, we confirmed the results of previous studies that showed that simple approaches, such as linear regression models, performed just as well as advanced machine learning methods for estimating AGB based on LiDAR data. | |||
| Area | SRE | |||
| Arrangement 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Comparison of regression... | |||
| Arrangement 2 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Comparison of regression... | |||
| 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 | ||||
| Language | en | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/3ER446E 8JMKD3MGPCW/3F3T29H | |||
| Host Collection | urlib.net/www/2017/11.22.19.04 | |||
| 6. Notes | ||||
| Empty Fields | archivingpolicy archivist booktitle callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn keywords label lineage mark mirrorrepository nextedition notes numberoffiles numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readpermission 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 | ||||