Browsing by Author "Mauya, Ernest William"

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Carbon stocks for different land cover types in Mainland Tanzania
Malimbwi, Rogers; Zahabu, Eliakimu; Njana, Marco Andrew; Mugasha, Wilson Ancelm; Mauya, Ernest William
Carbon stocks for different land cover types in Mainland Tanzania
(2019-04) Malimbwi, Rogers; Zahabu, Eliakimu; Njana, Marco Andrew; Mugasha, Wilson Ancelm; Mauya, Ernest William
Background: Developing countries participating in the mitigation mechanism of reducing emissions from defor- estation and forest degradation (REDD+), are required to establish a forest reference emission level (FREL), if they wish to seek financial support to reduce carbon emissions from deforestation and forest degradation. However, establish- ment of FREL relies heavily on the accurate estimates of carbon stock as one of the input variable for computation of the emission factors (EFs). The product of an EF and activity data, such as the area of deforestation, results in the total emissions needed for establishment of FREL. This study presents the carbon stock estimates for different land cover classes based on an analysis of Tanzania’s national forest inventory data generated through the National Forest Resources Monitoring and Assessment (NAFORMA). Results: Carbon stocks were estimated in three carbon pools, namely aboveground, belowground, and deadwood for each of the three land cover classes (i.e. Forest, non-forest, and wetland). The weighted average carbon stock was 33.35 t C ha −1 for forest land, 4.28 t ha −1 for wetland and 5.81 t ha −1 for non-forest land. The uncertainty values were 0.9% for forest land, 11.3% for wetland and 1.8% for non-forest land. Average carbon stocks for land cover sub-classes, which make up the above mentioned major land cover classes, are also presented in our study. Conclusions: The values presented in this paper correspond to IPCC tier 3 and can be used for carbon estimation at the national scale for the respective major primary vegetation type for various purposes including REDD+. However, if local based estimates values are needed, the use of auxiliary data to enhance the precision of the area of interest is recommended. Keywords: Carbon stock, REDD+, FREL, Emission factor, Uncertainty
Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania
(Springer) Mauya, Ernest William; Hansen, Endre Hofstad; Gobakken, Terje; Bollandsås, Ole Martin; Malimbwi, Rogers Ernest; Næsset, Erik
Effects of field plot size on prediction accuracy of aboveground biomass in airborne laser scanning-assisted inventories in tropical rain forests of Tanzania
(Springer, 2015) Mauya, Ernest William; Hansen, Endre Hofstad; Gobakken, Terje; Bollandsås, Ole Martin; Malimbwi, Rogers Ernest; Næsset, Erik
Background: Airborne laser scanning (ALS) has recently emerged as a promising tool to acquire auxiliary information for improving aboveground biomass (AGB) estimation in sample-based forest inventories. Under design-based and model-assisted inferential frameworks, the estimation relies on a model that relates the auxiliary ALS metrics to AGB estimated on ground plots. The size of the field plots has been identified as one source of model uncertainty because of the so-called boundary effects which increases with decreasing plot size. Recent re- search in tropical forests has aimed to quantify the boundary effects on model prediction accuracy, but evidence of the consequences for the final AGB estimates is lacking. In this study we analyzed the effect of field plot size on model prediction accuracy and its implication when used in a model-assisted inferential framework. Results: The results showed that the prediction accuracy of the model improved as the plot size increased. The adjusted R 2 increased from 0.35 to 0.74 while the relative root mean square error decreased from 63.6 to 29.2%. Indicators of boundary effects were identified and confirmed to have significant effects on the model residuals. Variance estimates of model-assisted mean AGB relative to corresponding variance estimates of pure field-based AGB, decreased with increasing plot size in the range from 200 to 3000 m 2 . The variance ratio of field-based esti- mates relative to model-assisted variance ranged from 1.7 to 7.7. Conclusions: This study showed that the relative improvement in precision of AGB estimation when increasing field-plot size, was greater for an ALS-assisted inventory compared to that of a pure field-based inventory.
Linking ground forest inventory and NDVI in mapping above ground carbon stock in kasane forest reserve, Botswana
(Scientific Research Publishing) Basalumi, Lesika; Kilawe, Charles Joseph; Mauya, Ernest William
Linking ground forest inventory and NDVI in mapping above ground carbon stock in kasane forest reserve, Botswana
(Scientific Research Publishing, 2018) Basalumi, Lesika; Kilawe, Charles Joseph; Mauya, Ernest William
Quantification of the above ground carbon stock (AGC) is important in sus- tainable forest management and policy advice on climate change mitigation. Traditional ground vegetation survey methods have been used to provide data for estimation of AGC stock but constrained by inadequate time and often too costly. Remote sensing when combined with few ground collected data has the potential of improving forest resource assessment even though this opportu- nity has not well been utilised. In this study, we mapped AGC through com- bination of ground survey data collected from 51 permanent sapling plots with Normalized Difference Vegetation Index (NDVI) derived from Landsat 5 Thematic Mapper image. Linkage of the two data sources was made during a training stage of supervised classification. The overall classification accuracy was 98%, suggesting that reliable estimate of AGC for a large area can be made through combination of medium resolution satellite imagery and few samples from the ground.
Mapping and estimating the total living biomass and carbon in low‐biomass woodlands using landsat 8 CDR data
(CrossMark) Gizachew, Belachew; Solberg, Svein; Næsset, Erik; Gobakken, Terje; Bollandsås, Ole Martin; Breidenbach, Johannes; Zahabu, Eliakimu; Mauya, Ernest William
Mapping and estimating the total living biomass and carbon in low‐biomass woodlands using landsat 8 CDR data
(CrossMark, 2016-06-24) Gizachew, Belachew; Solberg, Svein; Næsset, Erik; Gobakken, Terje; Bollandsås, Ole Martin; Breidenbach, Johannes; Zahabu, Eliakimu; Mauya, Ernest William
Background: 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.
Methods for estimating volume, biomass and tree species diversity using field inventory and airborne laser scanning in the tropical forests of Tanzania.
(Norwegian University of Life Sciences, 2015) Mauya, Ernest William
Deforestation and forest degradation in the tropical countries have reduced the extent of forest and woodlands, which conserve biodiversity, provide essential resources to people and help in mitigating climate change through carbon sequestration. Forest conservation projects need methods for estimating tree species diversity to effectively generate information necessary for implementing biodiversity management plans, while greenhouse gas reduction programmes such REDD* (Reducing Emissions from Deforestation and Forest Degradation) require robust methods to estimate volume and aboveground biomass (AGB). Such methods are also needed in the context of general forest management planning. The four papers included in this thesis are aimed to test and evaluate methods for estimating volume. AGB. and tree species diversity using field and remotely sensed data in the tropical forests and woodlands of Tanzania. In paper 1. tree models for estimating total, merchantable stem, and branch volume applicable for the entire miombo woodlands of Tanzania were developed. In Paper II. Ill. and IV the potential of airborne laser scanning (AI.S) data for predicting AGB and measures of tree species diversity was tested and evaluated. The results have shown that ALS data can be used for predicting AGB with reasonable accuracy by using both parametric and nonparametric approaches. Effects of plot size on the AGB estimates were investigated and the results indicated that the prediction accuracy of AGB in ALS-assisted inventories improved as the plot size increased. Finally, the results showed that measures of tree species diversity and particularly tree species richness and Shannon diversity index, can potentially be predicted by using ALS data.
Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania
(Springer) Mauya, Ernest William; Ene, Liviu Theodor; Bollandsås, Ole Martin; Gobakken, Terje; Næsset, Erik; Malimbwi, Rogers Ernest; Zahabu, Eliakimu
Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania
(Springer, 2015) Mauya, Ernest William; Ene, Liviu Theodor; Bollandsås, Ole Martin; Gobakken, Terje; Næsset, Erik; Malimbwi, Rogers Ernest; Zahabu, Eliakimu
Background: Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies for estimating aboveground biomass (AGB) in forests. Use of ALS data in area-based forest inventories relies on the development of statistical models that relate AGB and metrics derived from ALS. Such models are firstly calibrated on a sample of corresponding field- and ALS observations, and then used to predict AGB over the entire area covered by ALS data. Several statistical methods, both parametric and non-parametric, have been applied in ALS-based forest inventories, but studies that compare different methods in tropical forests in particular are few in number and less fre- quent than studies reported in temperate and boreal forests. We compared parametric and non-parametric methods, specifically linear mixed effects model (LMM) and k-nearest neighbor (k-NN). Results: The results showed that the prediction accuracy obtained when using LMM was slightly better than when using the k-NN approach. Relative root mean square errors from the cross validation was 46.8 % for the LMM and 58.1 % for the k-NN. Post-stratification according to vegetation types improved the prediction accuracy of LMM more as compared to post-stratification by using land use types. Conclusion: Although there were differences in prediction accuracy between the two methods, their accuracies indicated that both of methods have potentials to be used for estimation of AGB using ALS data in the miombo woodlands. Future studies on effects of field plot size and the errors due to allometric models on the prediction accu- racy are recommended. Keywords: Parametric models, Prediction accuracy, Non-parametric models, LMM, k-NN, Sampling design
Modelling and predicting measures of tree species diversity using airborne laser scanning data in miombo woodlands of Tanzania
(Tanzania Journal of Forestry and Nature Conservation, 2021) Mauya, Ernest William
In the recent decade, remote sensing techniques had emerged as one among the best options for quantification of measures of tree species diversity. In this study, potential of using remotely sensed data derived from airborne laser scanning (ALS) for predicting tree species richness and Shannon diversity index was evaluated. Two modelling approaches were tested: linear mixed effects modelling (LMM), by which each of the measures was modelled separately, and the k-nearest neighbour technique (k-NN), by which both measures were jointly modelled (multivariate approach). For both methods, the effect of vegetation type on the prediction accuracies of tree species richness and Shannon diversity index was tested. Separate predictions for richness and Shannon diversity index using LMM resulted in relative root mean square errors (RMSEcv) of 40.7%, and 39.1%, while for the k-NN they were 41.4% and 39.1%, respectively. Inclusion of dummy variables representing vegetation types to the LMM improved the prediction accuracies of tree species richness (RMSEcv = 40.2%) and Shannon diversity index (RMSEcv = 38.0%). The study concluded that ALS data has a potential for modelling and predicting measures of tree species diversity in the miombo woodlands of Tanzania.
Monitoring forest carbon in a Tanzanian woodland using interferometric SAR: a novel methodology for REDD+
(Springer) Solberg, Svein; Gizachew, Belachew; Næsset, Erik; Gobakken, Terje; Bollandsås, Ole Martin; Mauya, Ernest William; Olsson, Håkan; Malimbwi, Rogers; Zahabu, Eliakimu