Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands

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dc.contributor.authorEgberth, M.
dc.contributor.authorNyberg, G.
dc.contributor.authorNæsset, E.
dc.contributor.authorGobakken, T.
dc.contributor.authorMauya, E
dc.contributor.authorMalimbwi, R.
dc.contributor.authorKatani, J.
dc.contributor.authorChamuya, N.
dc.contributor.authorBulenga, G.
dc.contributor.authorOlsson, H.
dc.date.accessioned2018-07-13T05:58:40Z
dc.date.available2018-07-13T05:58:40Z
dc.date.issued2017-04-17
dc.descriptionCarbon balance manage, 2017; 12 (8)en_US
dc.description.abstractBackground: Soil carbon and biomass depletion can be used to identify and quantify degraded soils, and by using remote sensing, there is potential to map soil conditions over large areas. Landsat 8 Operational Land Imager satellite data and airborne laser scanning data were evaluated separately and in combination for modeling soil organic carbon, above ground tree biomass and below ground tree biomass. The test site is situated in the Liwale district in southeastern Tanzania and is dominated by Miombo woodlands. Tree data from 15 m radius field-surveyed plots and samples of soil carbon down to a depth of 30 cm were used as reference data for tree biomass and soil carbon estimations. Results: Cross-validated plot level error (RMSE) for predicting soil organic carbon was 28% using only Landsat 8, 26% using laser only, and 23% for the combination of the two. The plot level error for above ground tree biomass was 66% when using only Landsat 8, 50% for laser and 49% for the combination of Landsat 8 and laser data. Results for below ground tree biomass were similar to above ground biomass. Additionally it was found that an early dry season satellite image was preferable for modelling biomass while images from later in the dry season were better for modelling soil carbon. Conclusion: The results show that laser data is superior to Landsat 8 when predicting both soil carbon and biomass above and below ground in landscapes dominated by Miombo woodlands. Furthermore, the combination of laser data and Landsat data were marginally better than using laser data only.en_US
dc.identifier.citationEgberth, M., Nyberg, G., Næsset, E., Gobakken, T., Mauya, E., Malimbwi, R., Katani, J., Chamuya, N., Bulenga, G. and Olsson, H. (2017). Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands. Carbon Balance Manage 12:8. DOI 10.1186/s13021-017-0076-y.en_US
dc.identifier.otherDOI 10.1186/s13021-017-0076-y
dc.identifier.urihttps://www.suaire.sua.ac.tz/handle/123456789/2510
dc.language.isoen_USen_US
dc.publisherSpringer Openen_US
dc.subjectSoil carbonen_US
dc.subjectBiomassen_US
dc.subjectAirborne laseren_US
dc.subjectMiombo woodlandsen_US
dc.subjectLandsat 8 OLIen_US
dc.titleCombining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlandsen_US
dc.typeArticleen_US
dc.urlhttps://link.springer.com/article/10.1186/s13021-017-0076-yen_US

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