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Recent years have witnessed the practical value of open-access Earth observation data catalogues and software in land and forest mapping. Combined with cloud-based computing resources, and data collection through the crowd, these solutions have substantially improved possibilities for monitoring changes in land resources, especially in areas with difficult accessibility and data scarcity. In this study, we developed and tested a participatory mapping methodology utilizing the open data catalogues and cloud computing capacity to map the previously unknown extent and species composition of forest plantations in the Southern Highlands area of Tanzania, a region experiencing a rapid growth of smallholder-owned woodlots. A large set of reference data, focusing on forest plantation coverage, species and age information distribution, was collected in a two-week participatory GIS campaign where 22 Tanzanian experts interpreted very high-resolution satellite images in Google Earth with the Open Foris Collect Earth tool developed by the Food and Agriculture Organization of the United Nations. The collected samples were used as training data to classify a multi-sensor image stack of Landsat 8 (2013–2015), Sentinel-2 (2015–2016), Sentinel-1 (2015), and SRTM derived elevation and slope data layers into a 30 m resolution forest plantation map in Google Earth Engine. The results show that the forest plantation area was estimated with high overall accuracy (85%). The interpretation accuracy of local experts was high considering general definition of forest plantation declining with increased details in interpretation attributes. The results showcase the unique value of local expert participation, enabling the collection of thousands of reference samples over a large geographical area in a short period of time simultaneously building the capacity of the experts. However, sufficient training prior to the data collection is crucial for the interpretation success especially when detailed interpretation is conducted in complex landscapes. Since the methodology is built on open-access data and software, it presents a highly feasible solution for repetitive land resource mapping applicable at different spatial scales globally.

Digitalizing business analytics by generating relevant information from data to gain valuable insights and to enhance decision-making is a key ability for companies. We describe a potential concept for digitizing company analytics to enable data-driven approach by providing a path from information needs to visualizing the analysis results, including the consideration of analysis logic, relevant data, and necessary data model. Company business processes, IT applications, and data assets provide the foundations to build upon. The developed concept may enable practitioners to consider possible applications and the needed, already existing technologies.

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.

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.

Product portfolio management (PPM) is vital for companies to succeed. It aims to constitute a profitable entity for company products in the long-term. The extant literature on PPM is, however, lacking in IT application-based discussion, which links to the potential of IT system independent analysis and reporting. One potential application is producing accurate product-level profitability calculations. This study aims to conceptualise application-based PPM analysis and identify the related data needs. Additionally, the opportunities and risks related to the implementation of the concept are studied. The study combines a literature review with qualitative company analyses. The results aid in understanding application-based PPM analysis and clarify the related opportunities for its use. Our research appears to indicate that application-based PPM analysis has the potential to improve product portfolio-related decision-making while reducing the manual work needed for profitability reporting.

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, as both methodological issues and substance differences cause variability 2 . Here we show, based on 77 standardized face-to-face interviews of local experts with the best possible knowledge of local land-use economics and sociopolitical 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 dominated by peatlands compared with the average of the eight other landscapes. Hence, if reducing emissions from deforestation and forest degradation (REDD+) and other land-use mitigation efforts are to be distributed evenly across forested countries, for example, for the sake of international equity, their overall effectiveness would be dramatically lower than for a cost-minimizing distribution. The cost of preserving ecosystem storage of carbon varies depending on local land-use and socio-political pressures. A survey of experts suggests a cost-minimizing distribution would be more effective for mitigation than equitable distribution.