Evaluation of remotely-sensed reflected and emitted energy for monitoring woodland carbon in Liwale and Kilwa in Tanzania
| dc.contributor.author | Makandi, Harun Atupele | |
| dc.date.accessioned | 2021-08-30T07:31:19Z | |
| dc.date.available | 2021-08-30T07:31:19Z | |
| dc.date.issued | 2019 | |
| dc.description | Available in print form, East Africana collection, Dr. Wilbert Chagula Library, class mark (THS EAF SD387.B48T34M342) | en_US |
| dc.description.abstract | A functional, cost-effective, and comprehensive system for repetitive measurement, reporting and verification (MRV) of forest carbon is important for sustainable forest management. Optical remote sensing datasets are critical for the development of such a system because they are free, and have a wall-to-wall and repetitive coverage. However, their accuracy in estimating woodland above-ground biomass and carbon (AGB and C) using mainstream methods is limited. One such method is using the magnitude of woodland greenness quantified using the normalised difference vegetation index (NDVI) drawn from the imagery. NDVI saturates with increasing AGB and C, thereby limiting the range of estimations and accuracy. Also, the greenness fluctuates seasonally in tropical woodlands and evaluates the variable canopy moisture than the otherwise stable AGB and C. Cloud contamination on the datasets is another limitation. There is a need to enhance the accuracy of the estimations to leverage the strengths of optical satellite data. The objective of this study was to develop a Forest Biomass Index (FoBI) tomodel the magnitude of the latent and sensible thermal fluxes prevalent in woodland conditions. The satellite-derived surface temperature (Ts) and NDVI were combined in the modeling using the index. The magnitude of the fluxes correlates better with woodland AGC and is less prone to seasonal fluctuations than does that of the commonly used woodland greenness. The resulting FoBI maps were regressed with plot-based AGC measurements to estimate the AGC in Liwale and Kilwa districts in 2014 and 2018 and its change between the two years. The regression of the FoBI maps of 2014 and 2018 with plot-based AGC returned R2 of 0.52 and 0.58 respectively. This comparedfavourably to R2 of 0.44 from pairing the annual NDVI map of 2014 with plot estimates. Also, the range of estimation of FoBI map was from 0 to 266 t ha-1 C, over twice that of NDVI. FoBI’s extended range indicates the elimination of the saturation problem at least for estimations of AGC in miombo woodlands. Cloud cover was also eliminated by compositing multiple Ts and NDVI layers using the maximum value compositing (MVC) method. Using the regressed FoBI maps of 2014 and 2018, the mean carbon stock density in the study area was estimated to be 44 t ha-1 at 95% confidence level in both years. The total AGC was about 220 Mt in 2014 and 213 in 2018. Change analysis shows a decline of 6.6 Mt (ca. 3%) of total AGC between the two years, indicating general stability of the AGC pools in Liwale and Kilwa. The developed FoBI enhances the accuracy of comprehensive and repetitive estimations of woodland AGC using free and widely available optical satellite datasets by eliminating the main cited problems with using them. Using FoBI, the monitoring, reporting, and verification of woodland carbon stocking meeting international standards of reporting can be done. | en_US |
| dc.identifier.citation | Makandi, H. A (2019) Evaluation of remotely-sensed reflected and emitted energy for monitoring woodland carbon in Liwale and Kilwa in Tanzania, Doctoral dissertation , University of Dar es Salaam, Dar es Salaam. | en_US |
| dc.identifier.uri | http://41.86.178.5:8080/xmlui/handle/123456789/15506 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of Dar es salaam | en_US |
| dc.subject | Woodlands | en_US |
| dc.subject | Forest biomas | en_US |
| dc.subject | Remote serving | en_US |
| dc.subject | Liwale | en_US |
| dc.subject | Kilwa | en_US |
| dc.subject | Tanzania | en_US |
| dc.title | Evaluation of remotely-sensed reflected and emitted energy for monitoring woodland carbon in Liwale and Kilwa in Tanzania | en_US |
| dc.type | Thesis | en_US |