Mapping and estimating the total living biomass and carbon in low‐biomass woodlands using landsat 8 CDR data

Simple item page
dc.contributor.authorGizachew, Belachew
dc.contributor.authorSolberg, Svein
dc.contributor.authorNæsset, Erik
dc.contributor.authorGobakken, Terje
dc.contributor.authorBollandsås, Ole Martin
dc.contributor.authorBreidenbach, Johannes
dc.contributor.authorZahabu, Eliakimu
dc.contributor.authorMauya, Ernest William
dc.date.accessioned2022-06-08T10:57:52Z
dc.date.available2022-06-08T10:57:52Z
dc.date.issued2016-06-24
dc.description.abstractBackground: A functional forest carbon measuring, reporting and verification (MRV) system to support climate change mitigation policies, such as REDD+, requires estimates of forest biomass carbon, as an input to estimate emis- sions. A combination of field inventory and remote sensing is expected to provide those data. By linking Landsat 8 and forest inventory data, we (1) developed linear mixed effects models for total living biomass (TLB) estimation as a function of spectral variables, (2) developed a 30 m resolution map of the total living carbon (TLC), and (3) estimated the total TLB stock of the study area. Inventory data consisted of tree measurements from 500 plots in 63 clusters in a 15,700 km 2 study area, in miombo woodlands of Tanzania. The Landsat 8 data comprised two climate data record images covering the inventory area. Results: We found a linear relationship between TLB and Landsat 8 derived spectral variables, and there was no clear evidence of spectral data saturation at higher biomass values. The root-mean-square error of the values predicted by the linear model linking the TLB and the normalized difference vegetation index (NDVI) is equal to 44 t/ha (49 % of the mean value). The estimated TLB for the study area was 140 Mt, with a mean TLB density of 81 t/ha, and a 95 % confidence interval of 74–88 t/ha. We mapped the distribution of TLC of the study area using the TLB model, where TLC was estimated at 47 % of TLB. Conclusion: The low biomass in the miombo woodlands, and the absence of a spectral data saturation problem sug- gested that Landsat 8 derived NDVI is suitable auxiliary information for carbon monitoring in the context of REDD+, for low-biomass, open-canopy woodlands.en_US
dc.identifier.urihttps://www.suaire.sua.ac.tz/handle/123456789/4229
dc.language.isoenen_US
dc.publisherCrossMarken_US
dc.subjectBiomassen_US
dc.subjectCarbonen_US
dc.subjectModelingen_US
dc.subjectMiombo woodlandsen_US
dc.subjectREDD+en_US
dc.subjectNDVIen_US
dc.titleMapping and estimating the total living biomass and carbon in low‐biomass woodlands using landsat 8 CDR dataen_US
dc.typeArticleen_US
dc.urlDOI 10.1186/s13021-016-0055-8en_US

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Mapping and estimating the total living biomass and carbon in low-biomass woodlands using Landsat 8 CDR data.pdf
Size:
1.94 MB
Format:
Adobe Portable Document Format
Description:
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.67 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Undergraduate Research Projects Collection