LiDAR data transects: a sampling strategy to estimate aboveground biomass in forest areas
The estimation and mapping of aboveground biomass over large areas can be done using the remote sensing tools. The objective of this study was to estimate the aboveground biomass of two types of tropical forest: semi-evergreen (SETF) and semi-deciduous tropical forest (SDTF) in the Yucatan Peninsula...
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Online |
| Sprache: | spa |
| Veröffentlicht: |
Instituto de Ecología, A.C.
2019
|
| Online Zugang: | https://myb.ojs.inecol.mx/index.php/myb/article/view/e2531872 |
| _version_ | 1799769367722852352 |
|---|---|
| author | Ortiz-Reyes, Alma Delia Valdez-Lazalde, José René Ángeles-Pérez, Gregorio De los Santos-Posadas, Héctor M. Schneider, Laura Aguirre-Salado, Carlos Arturo Peduzzi, Alicia |
| author_facet | Ortiz-Reyes, Alma Delia Valdez-Lazalde, José René Ángeles-Pérez, Gregorio De los Santos-Posadas, Héctor M. Schneider, Laura Aguirre-Salado, Carlos Arturo Peduzzi, Alicia |
| author_sort | Ortiz-Reyes, Alma Delia |
| collection | MYB |
| description | The estimation and mapping of aboveground biomass over large areas can be done using the remote sensing tools. The objective of this study was to estimate the aboveground biomass of two types of tropical forest: semi-evergreen (SETF) and semi-deciduous tropical forest (SDTF) in the Yucatan Peninsula, Mexico, using metrics obtained from LiDAR (Light Detection and Ranging) data. Data from 365 plots of the National Forest and Soils Inventory of Mexico were used to calibrate aboveground biomass models using multiple linear regression and Random Forest. These models were used for mapping aboveground biomass along LiDAR strips. The transformed regression model explained the variance by 62% (RMSE = 41.44 Mg ha-1 for SETF & 36.60 Mg ha-1 for SDTF) for both types of vegetation. The models of Random Forest explained the variance by 57% (RMSE = 40.73 Mg ha-1) for SETF and only 52% (RMSE = 35.10 Mg ha-1) by SDTF. The mismatch between the field data and LiDAR data, as well as the error in the precision of the coordinates of the inventory plots, were recognized as factors that influenced on the results. Despite the above, the estimates obtained could serve as a basis to estimate the complete biomass inventory in the study area by incorporating spectral data derived from a remote sensor that covers the entire area. |
| format | Online |
| id | oai:oai.myb.ojs.inecol.mx:article-1872 |
| institution | Madera y Bosques |
| language | spa |
| publishDate | 2019 |
| publisher | Instituto de Ecología, A.C. |
| record_format | ojs |
| spelling | oai:oai.myb.ojs.inecol.mx:article-18722022-11-29T22:45:43Z LiDAR data transects: a sampling strategy to estimate aboveground biomass in forest areas Transectos de datos LiDAR: una estrategia de muestreo para estimar biomasa aérea en áreas forestales Ortiz-Reyes, Alma Delia Valdez-Lazalde, José René Ángeles-Pérez, Gregorio De los Santos-Posadas, Héctor M. Schneider, Laura Aguirre-Salado, Carlos Arturo Peduzzi, Alicia LiDAR strips forest inventory sampling Yucatan Peninsula franjas LiDAR inventario forestal muestreo península de Yucatán The estimation and mapping of aboveground biomass over large areas can be done using the remote sensing tools. The objective of this study was to estimate the aboveground biomass of two types of tropical forest: semi-evergreen (SETF) and semi-deciduous tropical forest (SDTF) in the Yucatan Peninsula, Mexico, using metrics obtained from LiDAR (Light Detection and Ranging) data. Data from 365 plots of the National Forest and Soils Inventory of Mexico were used to calibrate aboveground biomass models using multiple linear regression and Random Forest. These models were used for mapping aboveground biomass along LiDAR strips. The transformed regression model explained the variance by 62% (RMSE = 41.44 Mg ha-1 for SETF & 36.60 Mg ha-1 for SDTF) for both types of vegetation. The models of Random Forest explained the variance by 57% (RMSE = 40.73 Mg ha-1) for SETF and only 52% (RMSE = 35.10 Mg ha-1) by SDTF. The mismatch between the field data and LiDAR data, as well as the error in the precision of the coordinates of the inventory plots, were recognized as factors that influenced on the results. Despite the above, the estimates obtained could serve as a basis to estimate the complete biomass inventory in the study area by incorporating spectral data derived from a remote sensor that covers the entire area. La estimación y el mapeo de la biomasa aérea sobre áreas extensas puede realizarse haciendo uso de las herramientas que ofrece la percepción remota. El objetivo de este estudio fue estimar la biomasa aérea de dos tipos de selva mediana: subperennifolia (SMSP) y subcaducifolia (SMSC) en la península de Yucatán, México, empleando métricas generadas a partir de datos Light Detection and Ranging (LiDAR). Se usaron datos de 365 unidades de muestreo del Inventario Nacional Forestal y de Suelos (INFyS) de México para calibrar modelos de biomasa aérea usando regresión lineal múltiple y Random Forest (RF). Con estos modelos se mapeó la biomasa aérea sobre franjas de datos LiDAR. El modelo de regresión transformado logró explicar la varianza en un 62% (RMSE = 41.44 Mg ha-1 para SMSP y 36.60 Mg ha-1 para SMSC) para ambos tipos de vegetación. Los modelos generados a través de RF lograron explicar la varianza en un 57% (RMSE = 40.73 Mg ha-1) para la SMSP y solo de 52% (RMSE = 35.10 Mg Ha-1) para la SMSC. El desfase entre la toma de datos en campo y LiDAR, así como el error en la precisión de las coordenadas de los sitios de inventario, son factores reconocidos que influyeron en los resultados. A pesar de lo anterior, las estimaciones obtenidas podrían servir de base para estimar el inventario completo de biomasa en el área de estudio incorporando datos espectrales derivados de un sensor remoto que cubra la totalidad de esta. Instituto de Ecología, A.C. 2019-11-13 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artículo evaluado por pares application/pdf application/xml https://myb.ojs.inecol.mx/index.php/myb/article/view/e2531872 10.21829/myb.2019.2531872 Madera y Bosques; Vol. 25 No. 3 (2019): Otoño 2019 Madera y Bosques; Vol. 25 Núm. 3 (2019): Otoño 2019 2448-7597 1405-0471 spa https://myb.ojs.inecol.mx/index.php/myb/article/view/e2531872/1987 https://myb.ojs.inecol.mx/index.php/myb/article/view/e2531872/2021 Derechos de autor 2019 Madera y Bosques |
| spellingShingle | Ortiz-Reyes, Alma Delia Valdez-Lazalde, José René Ángeles-Pérez, Gregorio De los Santos-Posadas, Héctor M. Schneider, Laura Aguirre-Salado, Carlos Arturo Peduzzi, Alicia LiDAR data transects: a sampling strategy to estimate aboveground biomass in forest areas |
| title | LiDAR data transects: a sampling strategy to estimate aboveground biomass in forest areas |
| title_full | LiDAR data transects: a sampling strategy to estimate aboveground biomass in forest areas |
| title_fullStr | LiDAR data transects: a sampling strategy to estimate aboveground biomass in forest areas |
| title_full_unstemmed | LiDAR data transects: a sampling strategy to estimate aboveground biomass in forest areas |
| title_short | LiDAR data transects: a sampling strategy to estimate aboveground biomass in forest areas |
| title_sort | lidar data transects: a sampling strategy to estimate aboveground biomass in forest areas |
| url | https://myb.ojs.inecol.mx/index.php/myb/article/view/e2531872 |
| work_keys_str_mv | AT ortizreyesalmadelia lidardatatransectsasamplingstrategytoestimateabovegroundbiomassinforestareas AT valdezlazaldejoserene lidardatatransectsasamplingstrategytoestimateabovegroundbiomassinforestareas AT angelesperezgregorio lidardatatransectsasamplingstrategytoestimateabovegroundbiomassinforestareas AT delossantosposadashectorm lidardatatransectsasamplingstrategytoestimateabovegroundbiomassinforestareas AT schneiderlaura lidardatatransectsasamplingstrategytoestimateabovegroundbiomassinforestareas AT aguirresaladocarlosarturo lidardatatransectsasamplingstrategytoestimateabovegroundbiomassinforestareas AT peduzzialicia lidardatatransectsasamplingstrategytoestimateabovegroundbiomassinforestareas AT ortizreyesalmadelia transectosdedatoslidarunaestrategiademuestreoparaestimarbiomasaaereaenareasforestales AT valdezlazaldejoserene transectosdedatoslidarunaestrategiademuestreoparaestimarbiomasaaereaenareasforestales AT angelesperezgregorio transectosdedatoslidarunaestrategiademuestreoparaestimarbiomasaaereaenareasforestales AT delossantosposadashectorm transectosdedatoslidarunaestrategiademuestreoparaestimarbiomasaaereaenareasforestales AT schneiderlaura transectosdedatoslidarunaestrategiademuestreoparaestimarbiomasaaereaenareasforestales AT aguirresaladocarlosarturo transectosdedatoslidarunaestrategiademuestreoparaestimarbiomasaaereaenareasforestales AT peduzzialicia transectosdedatoslidarunaestrategiademuestreoparaestimarbiomasaaereaenareasforestales |
