FOSS4G 2018

The GEO-ICT is an institutional cooperation project aiming at improving the quality and societal relevance of geospatial and ICT research and education at the Universities of Dar es Salaam (UDSM), Ardhi University (ARU), State University of Zanzibar (SUZA) and Sokoine University of Agriculture (SUA). Jointly with the University of Turku (UTU) Finland, the institutions focus on strengthening geospatial and ICT skills of the staff, establishing new curricula, upgrading supportive infrastructures and creating synergetic cooperation modalities with different stakeholders in the society. In this paper we share how our practical experiences of the cooperation, including what is the background for the establishment of institutional cooperation between these universities, how is the project operating in practice, what type of activities and cooperation modalities we do together and how do we think institutional cooperation contributes to the advancement of geospatial expertise in Tanzania and what is the role of open source solutions in this development.</p

Impact of mechanized logging operations on wet and dry soils of Sao Hill Forest Plantations, Tanzania

Mechanization of timber harvesting operations in Tanzania involves use of machinery such as feller bunchers, skidders and tractors which are generally heavy in weight ranging from 12 to 16 tones in unloaded state. The movements of these machines induce soil compaction owing to the exerted normal pressure, vibrations and shear stress. But little literaturehas quantified such phenomena in Tanzania. This paper reports results of soil disturbance caused by timber harvesting machinery in Sao Hill Forest Plantation in wet and dry seasons. Soil characteristics were recorded two years after the plots were harvested using both visual classification and soil strength measurements. The results indicate that bulk densities of the upper 20 cm of soil on a plot logged in wet season increased by an  average of 60.5% to 1.61 g/cm3 while for a plot harvested in dry season the increase was 28.7% to 1.28 g/cm3 compared to those of adjacent undisturbed soils. Porosity of the soil reduced by 31.5% and 14.3% for the area harvested in wet and dry seasons respectively. In the top 35 cm of soil depth, the soil penetration resistance increased by 192% and 112% for the area harvested in wet and dry season respectively. The penetrationresistances for both areas exceeded the USDA allowable limits. In addition, the results indicate that logging in wet season can lead to restricted root growth (1.61g/cm3) while logging in dry season may only affect root growth (1.21 g/cm3). Compaction is a concern on Sao Hill forest soils especially  where fully mechanized logging occurs during moist antecedent soil  conditions. Compaction can be minimized by logging during dry soilconditions.Key words: Soil penetration resistance; USDA forest soil compaction standard; growth limiting bulky densit

Stump diameter: potential predictor of removed biomass through tree cutting

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Biomass and volume models for different vegetation types of Tanzania

Climate change and high rates of global carbon dioxide (CO2) emissions have increased the attention paid to the need for high-quality monitoring systems to assess how much carbon (C) is present in terrestrial systems and how these change over time. The choice of a system to adopt relies heavily on the accuracy of the method for quantifying biomass and volume as important primary variables for computing C stock and changes over time. Methods based on ground forest inventory and remote sensing data have commonly been applied in the recent decade to estimate biomass and volume in the tropical forests. However, regardless of the method, accurate tree level biomass and volume models are needed to translate field or remotely sensed data into estimates of forest biomass and volume. Therefore, the main goal of this study was to develop biomass and volume models for the forests, woodlands, thickets, agroforestry systems and some selected tree species in Tanzania. Data from destructively sampled trees were used to develop volume and above- and below-ground biomass models. Different statistical criteria, including coefficient of determination (R2), relative root mean square error (RMSE %) and Akaike Information Criterion (AIC), were used to assess the quality of the model fits. The models selected showed good prediction accuracy and, therefore, are recommended not only to support the ongoing initiatives on forest C Measurement, Reporting and Verificatio (MRV) processes but also for general forest management in Tanzania

Biomass and volume models for different vegetation types of Tanzania

Climate change and high rates of global carbon dioxide (CO2) emissions have increased the attention paid to the need for high-quality monitoring systems to assess how much carbon (C) is present in terrestrial systems and how these change over time. The choice of a system to adopt relies heavily on the accuracy of the method for quantifying biomass and volume as important primary variables for computing C stock and changes over time. Methods based on ground forest inventory and remote sensing data have commonly been applied in the recent decade to estimate biomass and volume in the tropical forests. However, regardless of the method, accurate tree level biomass and volume models are needed to translate field or remotely sensed data into estimates of forest biomass and volume. Therefore, the main goal of this study was to develop biomass and volume models for the forests, woodlands, thickets, agroforestry systems and some selected tree species in Tanzania. Data from destructively sampled trees were used to develop volume and above- and below-ground biomass models. Different statistical criteria, including coefficient of determination (R2), relative root mean square error (RMSE %) and Akaike Information Criterion (AIC), were used to assess the quality of the model fits. The models selected showed good prediction accuracy and, therefore, are recommended not only to support the ongoing initiatives on forest C Measurement, Reporting and Verificatio (MRV) processes but also for general forest management in Tanzania