Applicability of Satellite Remote Sensing in Accounting Above-Ground Carbon in Miombo Woodlands

Mganga N. D., Lyaruu H.V.M.


The crisis of climate change has drawn attention of ecologists all over the world to explore ways that could effectively enhance the sequestration of carbon in forests and woodlands. This necessitates forest inventory, along with knowledge of techniques that are quick and manageable. The present study was carried out in Miombo woodlands of western Tanzania namely, Kitwe and Mgaraganza. The aim of the present study was to investigate the effectiveness of remote sensing in estimating the stock of carbon in Miombo woodlands. Two types of data namely, ground-truthing and satellite imagery were used. Ground-truthing data were obtained by measuring the diameter at breast height (DBH) of all trees in 30 and 20 concentric plots in Kitwe and Mgaraganza forests, respectively. The average DBH of trees in each forest was fitted in biomass allometric models to estimate the ground-truthing vegetation biomass. On the other hand, Landsat images of the two forests were used to compute the Normalised Vegetation Index (NDVI). The computed NDVI were regressed with the ground-truthing vegetation biomass to get the remotely sensed vegetation biomass which was assumed to be 50% carbon. The coefficients of determination between the ground-truthing above-ground biomass and the NDVI values were statistically significant at P<0.05. The above-ground carbon stock obtained by ground-truthing in Mgaraganza and Kitwe forests was 3 and 2 times higher than that of satellite remotely sensed data respectively. The above-ground carbon stock obtained from satellite remote sensing gives some impression thus a basis for remote sensing in Miombo woodlands.


Biomass; Ground-Truthing; Landsat

Full Text: PDF


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (