Mapping and Estimating Above Ground Biomass and Carbon Stock Using Sentinel Imagery in Yayu Forest, South West of Ethiopia
| dc.contributor.advisor | Korme, Tesfaye (PhD) | |
| dc.contributor.author | Muhe, Seid | |
| dc.date.accessioned | 2019-11-18T11:33:53Z | |
| dc.date.accessioned | 2023-11-09T14:10:46Z | |
| dc.date.available | 2019-11-18T11:33:53Z | |
| dc.date.available | 2023-11-09T14:10:46Z | |
| dc.date.issued | 2019-06-02 | |
| dc.description.abstract | Accurate forest above-ground biomass (AGB) and carbon stock estimation is crucial for sustaining forest management and mitigating climate change to support REDD+ (reducing emissions from deforestation and forest degradation, plus the sustainable management of forests, and the conservation and enhancement of forest carbon stocks) processes. However the major challenge for REDD+ is to find an accurate method for biomass estimation. Thus, the main objective of this study is to assess the potential of texture analysis using grey level co-occurrence matrix derived from sentinel 1 C band GRD image and, vegetation indices and vegetation biophysical variables derived from Sentinel-2 medium resolution images in estimating AGB and carbon stock in Yayu tropical forest, south west of Ethiopia. Both Sentinel 1 ground range detected and Sentinel 2 multispectral instrument used for this study were acquired in February, 2018. Twenty two variables from sentinel 1 including polarization (VV and VH) and twenty variables from sentinel 2 including selected bands were used in this study. Forest stand parameter data such as DBH and tree height collected in 2016 were taken from office of Ethiopian Coffee Forest Forum and converted to AGB using available allometric equation. The correlation of biomass value measured in each plot and the radar information extracted using texture analysis of radar images, as well as variables extracted from optical image were assessed by the Pearson correlation coefficients. Regression modelling was applied based on chosen variables to estimate AGB for the whole study area and the estimated result was validated by considering the coefficient of determination between observed and predicted AGB. The strongest correlation (r = 0.65 - 0.74) was identified between sentinel 2 biophysical variables and AGB in the study area. Relatively weak to moderate correlations (r = -0.24 to 0.47) were found between sentinel 1 extracted variables and AGB. Band 4, IRECI, LAI, FCOVER and FAPAR were selected based on their correlation coefficient to develop AGB predictive model. The model has a coefficient of determination value of 0.74 and root mean square error (RMSE) of 0.16 ton/pixel. Forest above ground biomass and carbon stock map were produced by the developed model. Overall Sentinel 2 variables performed better in estimating AGB and carbon stock compared to sentinel 1 GRD image in the study area. Integrating field data with remote sensing method increases the accuracy of estimating forest AGB and carbon stock. | en_US |
| dc.identifier.uri | http://10.90.10.223:4000/handle/123456789/20135 | |
| dc.language.iso | en | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Above Ground Biomass | en_US |
| dc.subject | Sentinel Imagery | en_US |
| dc.subject | Carbon Stock | en_US |
| dc.subject | Texture Analysis | en_US |
| dc.subject | Grey Level Grey Level Co-Occurrence Matrix | en_US |
| dc.subject | Vegetation Indices | en_US |
| dc.subject | Biophysical Variables | en_US |
| dc.title | Mapping and Estimating Above Ground Biomass and Carbon Stock Using Sentinel Imagery in Yayu Forest, South West of Ethiopia | en_US |
| dc.type | Thesis | en_US |