Inventory and mapping of forest variables through remote sensors in Quintana Roo state, México
Remote sensors in combination with information derived from forest inventories estimate variables of interest with precision and low cost. The objective was to estimate the basal area (AB), timber volume (VTA) and aboveground biomass (B) in different forest ecosystems using Landsat ETM information a...
| Glavni autori: | , , , , |
|---|---|
| Format: | Online |
| Jezik: | spa |
| Izdano: |
Instituto de Ecología, A.C.
2020
|
| Online pristup: | https://myb.ojs.inecol.mx/index.php/myb/article/view/e2611884 |
| _version_ | 1799769371133870080 |
|---|---|
| author | Hernández-Ramos, Jonathan García-Cuevas, Xavier Peréz-Miranda, Ramiro González-Hernández, Antonio Martínez-Ángel, Luis |
| author_facet | Hernández-Ramos, Jonathan García-Cuevas, Xavier Peréz-Miranda, Ramiro González-Hernández, Antonio Martínez-Ángel, Luis |
| author_sort | Hernández-Ramos, Jonathan |
| collection | MYB |
| description | Remote sensors in combination with information derived from forest inventories estimate variables of interest with precision and low cost. The objective was to estimate the basal area (AB), timber volume (VTA) and aboveground biomass (B) in different forest ecosystems using Landsat ETM information and National Forest and Soil Inventory (INFyS) in Quintana Roo, Mexico. A correlation matrix was generated between INFyS data and spectral information, and later, a multiple linear regression model. With the selected equations, spatial distribution maps of AB (m2 ha-1), VTA (m3 ha-1)and B (Mg ha-1)were generated. The total inventory was estimated using three approaches: i) Reason Estimators (ERaz), ii) Regression Estimators (EReg), and iii) Estimators of Random Simple Sampling. The first two approaches correspond to the alternative inventory using remote sensors and the third corresponds to the traditional inventory. The correlation coefficient was greater than the normalized difference index with 0.35, 0.39 and 0.39 for AB, VTA and B. The regression models had adjusted determination coefficients of 0.28, 0.32 and 0.32 to estimate AB, VTA and B, respectively. The three estimators are statistically different and show that the EReg is the most conservative and with precision in AB, VTA and B of 2.73%, 2.92% and 2.71%, respectively, in addition to confidence intervals of smaller amplitude than the MSA and ERaz. By updating the inventory using remote sensors, the process of evaluating forest resources and their planning is improved. |
| format | Online |
| id | oai:oai.myb.ojs.inecol.mx:article-1884 |
| institution | Madera y Bosques |
| language | spa |
| publishDate | 2020 |
| publisher | Instituto de Ecología, A.C. |
| record_format | ojs |
| spelling | oai:oai.myb.ojs.inecol.mx:article-18842022-11-29T22:40:03Z Inventory and mapping of forest variables through remote sensors in Quintana Roo state, México Inventario y mapeo de variables forestales mediante sensores remotos en el estado de Quintana Roo, México Hernández-Ramos, Jonathan García-Cuevas, Xavier Peréz-Miranda, Ramiro González-Hernández, Antonio Martínez-Ángel, Luis aerial biomass forest structure Landsat regression models remote sensing biomasa aérea estructura forestal Landsat modelos de regresión sensores remotos Remote sensors in combination with information derived from forest inventories estimate variables of interest with precision and low cost. The objective was to estimate the basal area (AB), timber volume (VTA) and aboveground biomass (B) in different forest ecosystems using Landsat ETM information and National Forest and Soil Inventory (INFyS) in Quintana Roo, Mexico. A correlation matrix was generated between INFyS data and spectral information, and later, a multiple linear regression model. With the selected equations, spatial distribution maps of AB (m2 ha-1), VTA (m3 ha-1)and B (Mg ha-1)were generated. The total inventory was estimated using three approaches: i) Reason Estimators (ERaz), ii) Regression Estimators (EReg), and iii) Estimators of Random Simple Sampling. The first two approaches correspond to the alternative inventory using remote sensors and the third corresponds to the traditional inventory. The correlation coefficient was greater than the normalized difference index with 0.35, 0.39 and 0.39 for AB, VTA and B. The regression models had adjusted determination coefficients of 0.28, 0.32 and 0.32 to estimate AB, VTA and B, respectively. The three estimators are statistically different and show that the EReg is the most conservative and with precision in AB, VTA and B of 2.73%, 2.92% and 2.71%, respectively, in addition to confidence intervals of smaller amplitude than the MSA and ERaz. By updating the inventory using remote sensors, the process of evaluating forest resources and their planning is improved. Los sensores remotos en combinación con información derivada de los inventarios forestales estiman variables de interés con precisión y bajo costo. El objetivo de este trabajo fue estimar el área basal (AB), volumen maderable (VTA) y biomasa aérea (B) en diferentes ecosistemas de selvas mediante información Landsat ETM+ e Inventario Nacional Forestal y de Suelos (INFyS) en Quintana Roo, México. Se generó una matriz de correlación entre datos del INFyS e información espectral, posteriormente, un modelo de regresión lineal múltiple. Con las ecuaciones seleccionadas se generaron mapas de distribución espacial de AB (m2 ha-1), VTA (m3 ha-1) y B (Mg ha-1). El inventario total se estimó mediante tres enfoques: i) estimadores de razón (ERaz), ii) estimadores de regresión (EReg) y iii) estimadores del muestreo simple al azar. Los dos primeros enfoques corresponden al inventario alternativo mediante sensores remotos y el tercero al inventario tradicional. El coeficiente de correlación resultó mayor del índice de diferencia normalizada con 0.35, 0.39 y 0.39 para AB, VTA y B. Los modelos de regresión presentaron coeficientes de determinación ajustada de 0.28, 0.32 y 0.32 para estimar AB, VTA y B, respectivamente. Los tres estimadores son estadísticamente diferentes y muestran que el EReg es el más conservador y con precisión en AB, VTA y B de 2.73%, 2.92% y 2.71%, respectivamente, además de intervalos de confianza de menor amplitud que el MSA y ERaz. Con la actualización del inventario mediante sensores remotos se mejora el proceso de evaluación de los recursos forestales y su planificación. Instituto de Ecología, A.C. 2020-03-30 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artículo evaluado por pares application/pdf text/xml https://myb.ojs.inecol.mx/index.php/myb/article/view/e2611884 10.21829/myb.2020.2611884 Madera y Bosques; Vol. 26 No. 1 (2020): Primavera 2020 Madera y Bosques; Vol. 26 Núm. 1 (2020): Primavera 2020 2448-7597 1405-0471 spa https://myb.ojs.inecol.mx/index.php/myb/article/view/e2611884/2059 https://myb.ojs.inecol.mx/index.php/myb/article/view/e2611884/2166 10.21829/myb.2017.232181 Derechos de autor 2020 Madera y Bosques http://creativecommons.org/licenses/by-nc-sa/4.0 |
| spellingShingle | Hernández-Ramos, Jonathan García-Cuevas, Xavier Peréz-Miranda, Ramiro González-Hernández, Antonio Martínez-Ángel, Luis Inventory and mapping of forest variables through remote sensors in Quintana Roo state, México |
| title | Inventory and mapping of forest variables through remote sensors in Quintana Roo state, México |
| title_full | Inventory and mapping of forest variables through remote sensors in Quintana Roo state, México |
| title_fullStr | Inventory and mapping of forest variables through remote sensors in Quintana Roo state, México |
| title_full_unstemmed | Inventory and mapping of forest variables through remote sensors in Quintana Roo state, México |
| title_short | Inventory and mapping of forest variables through remote sensors in Quintana Roo state, México |
| title_sort | inventory and mapping of forest variables through remote sensors in quintana roo state, méxico |
| url | https://myb.ojs.inecol.mx/index.php/myb/article/view/e2611884 |
| work_keys_str_mv | AT hernandezramosjonathan inventoryandmappingofforestvariablesthroughremotesensorsinquintanaroostatemexico AT garciacuevasxavier inventoryandmappingofforestvariablesthroughremotesensorsinquintanaroostatemexico AT perezmirandaramiro inventoryandmappingofforestvariablesthroughremotesensorsinquintanaroostatemexico AT gonzalezhernandezantonio inventoryandmappingofforestvariablesthroughremotesensorsinquintanaroostatemexico AT martinezangelluis inventoryandmappingofforestvariablesthroughremotesensorsinquintanaroostatemexico AT hernandezramosjonathan inventarioymapeodevariablesforestalesmediantesensoresremotosenelestadodequintanaroomexico AT garciacuevasxavier inventarioymapeodevariablesforestalesmediantesensoresremotosenelestadodequintanaroomexico AT perezmirandaramiro inventarioymapeodevariablesforestalesmediantesensoresremotosenelestadodequintanaroomexico AT gonzalezhernandezantonio inventarioymapeodevariablesforestalesmediantesensoresremotosenelestadodequintanaroomexico AT martinezangelluis inventarioymapeodevariablesforestalesmediantesensoresremotosenelestadodequintanaroomexico |
