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Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

Main text Slowing the reduction, or increasing the accumulation, of organic carbon stored in biomass and soils has been suggested as a potentially rapid and cost-effective method to reduce the rate of atmospheric carbon increase 1. The costs of mitigating climate change by increasing ecosystem carbon relative to the baseline or business-as-usual scenario has been quantified in numerous studies, but results have been contradictory, with both methodological issues and substance differences causing variability 2. Here we show, based on 77 standardised face-to-face interviews of local experts with best possible knowledge on local land-use economics and socio-political context in ten landscapes around the globe, that the estimated cost of increasing ecosystem carbon varied vastly and was perceived to be 16-27 times cheaper in two Indonesian landscapes compared to the average of the eight other landscapes. Hence, if REDD+ and other land-use mitigation efforts were to be distributed evenly across forested countries, e.g. for the sake of international equity, their overall effectiveness would be dramatically lower than for a cost-minimising distribution. Changes in agriculture, forestry and other land uses are considered central in the mitigation pathways envisioned by the IPCC 6. Because deforestation 'business as usual' tends to benefit forestland holders and often even forested countries 3 , a system of compensated deforestation reduction between poor forested and rich countries has been developed 4. Hundreds of projects aimed at reducing emissions from deforestation and forest degradation (REDD+) and other forest carbon initiatives with similar objectives have been launched 5. Their combined impact on the global carbon cycle has so far remained modest 6 , but this may change thanks to the signing of the Paris Agreement in early 2016 (7). Information on the costs of mitigating climate change is valuable to avoid spending in landscapes with high cost-effectiveness ratios. Forest-based mitigation cost curves have been estimated, from the local to global scale, using household-level field surveys 8 , contracts allocated by inversed auctions 9 , census-based municipal-level data 10 and global simulation models based on national census data 11. For example a recent pantropical household survey across 17 different sites finds the time-discounted value of costs per Mg of carbon to vary by more than two orders of magnitude

<p><strong>Abstract.</strong> There is an increasing amount of open Earth observation (EO) data available, offering solutions to map, assess and monitor natural resources and to obtain answers to global and local societal challenges. With the help of free and open source software (FOSS) and open access cloud computing resources, the remote sensing community can take the full advantage of these vast geospatial data repositories. To empower developing societies, support should be given to higher education institutions (HEIs) to train professionals in using the open data, software and tools. In this paper, we describe a participatory mapping methodology, which utilizes open source software Open Foris and QGIS, various open Earth observation data catalogues, and computing capacity of the free Google Earth Engine cloud platform. Using this methodology, we arranged a collaborative data collection event, Mapathon, in Tanzania, followed by a training of the related FOSS tools for HEIs’ teaching staff. We collected feedback from the Mapathon participants about their learning experiences and from teachers about the usability of the methodology in remote sensing training in Tanzania. Based on our experiences and the received feedback, using a participatory mapping campaign as a training method can offer effective learning about environmental remote sensing through a real-world example, as well as networking and knowledge sharing possibilities for the participating group.</p>

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