Browsing by Author "Gobakken, T."
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Item Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands(Springer Open, 2017-04-17) Egberth, M.; Nyberg, G.; Næsset, E.; Gobakken, T.; Mauya, E; Malimbwi, R.; Katani, J.; Chamuya, N.; Bulenga, G.; Olsson, H.Background: 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.Item Decision-support tool for management of miombo woodlands: a matrix model approach(SOUTHERN FORESTS, 2017-03-06) Mugasha, W. A.; Bollandsås, O. M.; Gobakken, T.; Zahabu, E.; Katani, J. Z.; Eid, T.Rational forest management planning requires information on the present forest state and on future development. However, forest management planning in Tanzania has often been done without any information on forest development because appropriate tools are lacking. This study presents a matrix model that combines distance-independent growth and mortality models, area-based recruitment models, and allometric models for prediction of volume and biomass. In this way forest development can be simulated according to different treatment options. A shortterm (seven years) test of the matrix model using independent data from permanent sample plots showed that the overall difference between predicted and observed basal area was small (6.5%). Long-term simulations (1 000 years) with the model showed that it was able to attain, irrespective of initial conditions, similar steady-state conditions (i.e. basal area, volume and biomass of 13 m2 ha−1, 130 m3 ha−1 and 90 t ha−1, respectively), which also correspond well to biological expectations in the ‘real’ miombo woodlands of the country. The flexibility of the model as a decision-support tool was demonstrated by simulating three harvesting options aiming at different combinations of charcoal and timber production. The model complexity is well adapted to the data quality and abundance, and it is dependent on proxies of some main drivers of the dynamic processes. The development of the matrix model is a step forward facilitating better decisions in the management of miombo woodlands. However, data ranges used for calibrating the submodels are limited in time and space, and future efforts should focus on tests and recalibrations based on extended data ranges. Presently, therefore, applications of the matrix model should be limited to the data ranges of the modelling data from the Iringa and Manyara regions.