Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic
Mapping the spatial distribution of soil organic carbon (SOC) in lands covered by tropical forests is important to understand the relationship and dynamics of SOC in this type of ecosystem. In this study, the Random Forest (RF) algorithm was used to map SOC stocks of topsoil (0–15 cm) in forest land...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
|
| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2022.2045226 |
| _version_ | 1818319467624529920 |
|---|---|
| author | Efraín Duarte Erick Zagal Juan A. Barrera Francis Dube Fabio Casco Alexander J. Hernández |
| author_facet | Efraín Duarte Erick Zagal Juan A. Barrera Francis Dube Fabio Casco Alexander J. Hernández |
| author_sort | Efraín Duarte |
| collection | DOAJ |
| description | Mapping the spatial distribution of soil organic carbon (SOC) in lands covered by tropical forests is important to understand the relationship and dynamics of SOC in this type of ecosystem. In this study, the Random Forest (RF) algorithm was used to map SOC stocks of topsoil (0–15 cm) in forest lands of the Dominican Republic. The methodology was developed using geospatial datasets available in the Google Earth Engine (GEE) platform combined with a set of 268 soil samples. Twenty environmental covariates were analyzed, including climate, topography, and vegetation. The results indicate that Model A (combining all 20 covariates) was only marginally better than Model B (combining topographic and climatic covariates), and Model C (only combining multispectral remote sensing data derived from Landsat 8 OLI images). Model A and Model B yielded SOC mean values of 110.35 and 110.87 Mg C ha−1, respectively. Model A reported the lowest prediction error and uncertainty with an R2 of 0.83, an RMSE of 35.02 Mg C ha−1. There was a strong dependence of SOC stocks on multispectral remote sensing data. Therefore, multispectral remote sensing proved accurate to map SOC stocks in forest ecosystems in the region. |
| first_indexed | 2024-12-13T10:09:35Z |
| format | Article |
| id | doaj.art-910c6d31c0774751a71818534ff1dffa |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-12-13T10:09:35Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-910c6d31c0774751a71818534ff1dffa2022-12-21T23:51:29ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542022-12-0155121323110.1080/22797254.2022.2045226Digital mapping of soil organic carbon stocks in the forest lands of Dominican RepublicEfraín Duarte0Erick Zagal1Juan A. Barrera2Francis Dube3Fabio Casco4Alexander J. Hernández5Department of Soils and Natural Resources, Faculty of Agronomy, University of Concepcion, Chillán, ChileDepartment of Soils and Natural Resources, Faculty of Agronomy, University of Concepcion, Chillán, ChileDepartment of Soils and Natural Resources, Faculty of Agronomy, University of Concepcion, Chillán, ChileDepartment of Silviculture, Faculty of Forest Sciences, University of Concepcion, Concepción, ChileIR3 initiative, Food and Agriculture Organization (FAO) of the United Nations, Tegucigalpa, HondurasUnited States Department of Agriculture, Agricultural Research Service., Utah State University, Logan, Utah, USAMapping the spatial distribution of soil organic carbon (SOC) in lands covered by tropical forests is important to understand the relationship and dynamics of SOC in this type of ecosystem. In this study, the Random Forest (RF) algorithm was used to map SOC stocks of topsoil (0–15 cm) in forest lands of the Dominican Republic. The methodology was developed using geospatial datasets available in the Google Earth Engine (GEE) platform combined with a set of 268 soil samples. Twenty environmental covariates were analyzed, including climate, topography, and vegetation. The results indicate that Model A (combining all 20 covariates) was only marginally better than Model B (combining topographic and climatic covariates), and Model C (only combining multispectral remote sensing data derived from Landsat 8 OLI images). Model A and Model B yielded SOC mean values of 110.35 and 110.87 Mg C ha−1, respectively. Model A reported the lowest prediction error and uncertainty with an R2 of 0.83, an RMSE of 35.02 Mg C ha−1. There was a strong dependence of SOC stocks on multispectral remote sensing data. Therefore, multispectral remote sensing proved accurate to map SOC stocks in forest ecosystems in the region.https://www.tandfonline.com/doi/10.1080/22797254.2022.2045226Random forestlandsatmachine learningtropical forestenvironmental covariatesGoogle Earth Engine |
| spellingShingle | Efraín Duarte Erick Zagal Juan A. Barrera Francis Dube Fabio Casco Alexander J. Hernández Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic European Journal of Remote Sensing Random forest landsat machine learning tropical forest environmental covariates Google Earth Engine |
| title | Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic |
| title_full | Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic |
| title_fullStr | Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic |
| title_full_unstemmed | Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic |
| title_short | Digital mapping of soil organic carbon stocks in the forest lands of Dominican Republic |
| title_sort | digital mapping of soil organic carbon stocks in the forest lands of dominican republic |
| topic | Random forest landsat machine learning tropical forest environmental covariates Google Earth Engine |
| url | https://www.tandfonline.com/doi/10.1080/22797254.2022.2045226 |
| work_keys_str_mv | AT efrainduarte digitalmappingofsoilorganiccarbonstocksintheforestlandsofdominicanrepublic AT erickzagal digitalmappingofsoilorganiccarbonstocksintheforestlandsofdominicanrepublic AT juanabarrera digitalmappingofsoilorganiccarbonstocksintheforestlandsofdominicanrepublic AT francisdube digitalmappingofsoilorganiccarbonstocksintheforestlandsofdominicanrepublic AT fabiocasco digitalmappingofsoilorganiccarbonstocksintheforestlandsofdominicanrepublic AT alexanderjhernandez digitalmappingofsoilorganiccarbonstocksintheforestlandsofdominicanrepublic |