Estimating biomass of savanna grasslands as a proxy of carbon stock using multispectral remote sensing
Limited research has been done to estimate the root biomass (belowground biomass) of savanna grasslands. The advent of remote sensing and related products have facilitated the estimation of biomass in terrestrial ecosystems, providing a synoptic overview on ecosystems biomass. Multispectral remote sensing was used in this study to estimate total biomass (belowground and aboveground) of selected tropical savanna grassland species. Total biomass was estimated by assessing the relationship between aboveground and belowground biomass, the Normalised Difference Vegetation Index (NDVI) and belowground biomass, and NDVI and total biomass. Results showed a positive significant relationship (p ¼ 0.005) between belowground and aboveground biomass. NDVI was significantly correlated (p ¼ 0.0386) to aboveground biomass and the Root Mean Square Error (RMSE) was 18.97 whilst the model BIAS was 0.019, values within acceptable ranges. A significant relationship (p ¼ 0) was found between belowground biomass and NDVI and the RMSE was 5.53 and the model BIAS was 0.0041. More so, a significant relationship (p ¼ 0.054) was observed between NDVI and total biomass. The positive relationships between NDVI and total grass biomass and the lack of bias in the model provides an opportunity to routinely monitor carbon stock and assess seasonal carbon storage fluctuations in grasslands. There is great potential in the ability of remote sensing to become an indispensable tool for assessing, monitoring and inventorying carbon stocks in grassland ecosystems under tropical savanna conditions.
| Main Authors: | , , |
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| Format: | Journal Article biblioteca |
| Language: | English |
| Published: |
Elsevier
2020-01
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| Subjects: | carbon stock assessments, savannas, grasslands, biomass, estimation, remote sensing, climate change, greenhouse gas emissions, ecosystems, satellite imagery, landsat, models, |
| Online Access: | https://hdl.handle.net/10568/106471 https://doi.org/10.1016/j.rsase.2019.100275 |
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