Estimating Aboveground Biomass Using Remote Sensing Data and Google Earth Engine
| dc.contributor.author | Wang, Hanyang | |
| dc.contributor.chair | Steenhuis, Tammo | |
| dc.date.accessioned | 2022-03-08T18:35:20Z | |
| dc.date.available | 2022-03-08T18:35:20Z | |
| dc.date.issued | 2021-12 | |
| dc.description.abstract | Grassland is an essential component of terrestrial ecosystems. Biomass is a key indicator of ecosystem quality. It is of great significance to estimate the grass biomass yield effectively and accurately for the grassland management and utilization of grass resources and other related research. In order to pursue efficient and rapid estimation of the above-ground biomass of grassland, this study aims to find the relationship between satellite imagery data and ground survey biomass data through machine learning and Google Earth Engine (GEE) platform; and estimate the aboveground biomass using random forest (RF) regression algorithm and remote sensing data. In this study, a 16-acre research site was used as the research area. After evaluating the RF model performance, the R^2 of this model is about 0.75. The prediction of grass biomass yield in 2021 was presented. Finally, the advantages and disadvantages of the model and improvement methods are discussed. | |
| dc.identifier.uri | https://hdl.handle.net/1813/111043 | |
| dc.language.iso | en_US | |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Estimating Aboveground Biomass Using Remote Sensing Data and Google Earth Engine | |
| dc.type | term paper | |
| thesis.degree.level | Master of Professional Studies |
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