Inventory and cartography of forest variables derived from LiDAR data: comparison of methods
The most common method to estimate forest variables to a large or small scale is the forest inventory based on field sampling. Currently, remote sensing techniques offer a range of possibilities in forest resources estimation; this is the case of LiDAR (Light Detection And Ranging) that allows the c...
| Príomhchruthaitheoirí: | , , , , , |
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| Formáid: | Online |
| Teanga: | spa |
| Foilsithe / Cruthaithe: |
Instituto de Ecología, A.C.
2016
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| Rochtain ar líne: | https://myb.ojs.inecol.mx/index.php/myb/article/view/461 |
| _version_ | 1799769225918676992 |
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| author | Ortiz-Reyes, Alma Delia Valdez-Lazalde, J. René De los Santos-Posadas, Héctor M. Ángeles-Pérez, Gregorio Paz-Pellat, Fernando Martínez-Trinidad, Tomás |
| author_facet | Ortiz-Reyes, Alma Delia Valdez-Lazalde, J. René De los Santos-Posadas, Héctor M. Ángeles-Pérez, Gregorio Paz-Pellat, Fernando Martínez-Trinidad, Tomás |
| author_sort | Ortiz-Reyes, Alma Delia |
| collection | MYB |
| description | The most common method to estimate forest variables to a large or small scale is the forest inventory based on field sampling. Currently, remote sensing techniques offer a range of possibilities in forest resources estimation; this is the case of LiDAR (Light Detection And Ranging) that allows the characterization forest structure in three-dimensions. We analyzed the relationship between LiDAR and field data to estimate forest variables such as: basal area (AB), total biomass (BT), crown cover (COB) and timber volume (VOL) through four methods: 1) multiple linear regression, 2) non-linear regression, 3) ratio estimators and 4) traditional forest inventory (stratified sampling). Total estimates derived from the ratio estimator were within the 95% confidence interval calculated by traditional inventory for AB, BT and VOL; this estimator showed the closest values and precision to those obtained by traditional forest inventory. In general, estimates through non-linear models were the most optimistic compared to the traditional forest inventory. Our results indicated a good relationship (R2 > 0.50) between LiDAR metrics and field data, particularly the percentiles of height and rates of return on a defined height. From the linear models fit we generated maps for each of the forest variables analyzed. |
| format | Online |
| id | oai:oai.myb.ojs.inecol.mx:article-461 |
| institution | Madera y Bosques |
| language | spa |
| publishDate | 2016 |
| publisher | Instituto de Ecología, A.C. |
| record_format | ojs |
| spelling | oai:oai.myb.ojs.inecol.mx:article-4612022-11-30T05:45:47Z Inventory and cartography of forest variables derived from LiDAR data: comparison of methods Inventario y cartografía de variables del bosque con datos derivados de LiDAR: comparación de métodos Ortiz-Reyes, Alma Delia Valdez-Lazalde, J. René De los Santos-Posadas, Héctor M. Ángeles-Pérez, Gregorio Paz-Pellat, Fernando Martínez-Trinidad, Tomás above-ground biomass ratio and regression estimators mapping spatial modeling total volume biomasa aérea estimadores de razón y regresión mapeo modelación espacial volumen total The most common method to estimate forest variables to a large or small scale is the forest inventory based on field sampling. Currently, remote sensing techniques offer a range of possibilities in forest resources estimation; this is the case of LiDAR (Light Detection And Ranging) that allows the characterization forest structure in three-dimensions. We analyzed the relationship between LiDAR and field data to estimate forest variables such as: basal area (AB), total biomass (BT), crown cover (COB) and timber volume (VOL) through four methods: 1) multiple linear regression, 2) non-linear regression, 3) ratio estimators and 4) traditional forest inventory (stratified sampling). Total estimates derived from the ratio estimator were within the 95% confidence interval calculated by traditional inventory for AB, BT and VOL; this estimator showed the closest values and precision to those obtained by traditional forest inventory. In general, estimates through non-linear models were the most optimistic compared to the traditional forest inventory. Our results indicated a good relationship (R2 > 0.50) between LiDAR metrics and field data, particularly the percentiles of height and rates of return on a defined height. From the linear models fit we generated maps for each of the forest variables analyzed. El método más común para estimar variables dasométricas a gran o pequeña escala es el inventario forestal basado en un muestreo en campo. En la actualidad la teledetección ofrece un abanico de posibilidades para incorporarse en las estimaciones forestales, tal es el caso de LiDAR (Light Detection And Ranging) que permite caracterizar de forma tridimensional el bosque. En este trabajo se estudió la relación entre datos derivados de LiDAR con los datos medidos en campo para estimar variables dasométricas como: área basal (AB), biomasa total (BT), cobertura arbórea (COB) y volumen de madera (VOL), mediante cuatro métodos: 1) regresión lineal múltiple, 2) regresión no lineal, 3) estimador de razón y 4) inventario forestal tradicional (muestreo estratificado). Las estimaciones totales derivadas del estimador de razón se encuentran dentro del intervalo de confianza al 95% calculado mediante inventario tradicional para AB, BT y VOL, siendo este el estimador que arroja los valores más cercanos y precisos a la estimación mediante inventario. En general, las estimaciones de los modelos no lineales fueron los más optimistas con respecto al inventario tradicional. Los resultados indican una buena relación (R2 > 0.50) entre las métricas de LiDAR y datos de campo, principalmente los percentiles de altura y las tasas de retorno sobre una altura definida. A partir de los modelos lineales, se generó la cartografía de cada una de las variables analizadas. Instituto de Ecología, A.C. 2016-02-25 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artículo evaluado por pares application/pdf text/html https://myb.ojs.inecol.mx/index.php/myb/article/view/461 10.21829/myb.2015.213461 Madera y Bosques; Vol. 21 No. 3 (2015): Otoño Madera y Bosques; Vol. 21 Núm. 3 (2015): Otoño 2448-7597 1405-0471 spa https://myb.ojs.inecol.mx/index.php/myb/article/view/461/642 https://myb.ojs.inecol.mx/index.php/myb/article/view/461/640 Derechos de autor 2016 Madera y Bosques |
| spellingShingle | Ortiz-Reyes, Alma Delia Valdez-Lazalde, J. René De los Santos-Posadas, Héctor M. Ángeles-Pérez, Gregorio Paz-Pellat, Fernando Martínez-Trinidad, Tomás Inventory and cartography of forest variables derived from LiDAR data: comparison of methods |
| title | Inventory and cartography of forest variables derived from LiDAR data: comparison of methods |
| title_full | Inventory and cartography of forest variables derived from LiDAR data: comparison of methods |
| title_fullStr | Inventory and cartography of forest variables derived from LiDAR data: comparison of methods |
| title_full_unstemmed | Inventory and cartography of forest variables derived from LiDAR data: comparison of methods |
| title_short | Inventory and cartography of forest variables derived from LiDAR data: comparison of methods |
| title_sort | inventory and cartography of forest variables derived from lidar data: comparison of methods |
| url | https://myb.ojs.inecol.mx/index.php/myb/article/view/461 |
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