Validation of Automatically Generated Global and Regional Cropland Data Sets: The Case of Tanzania

There is a need to validate existing global cropland maps since they are used for different purposes including agricultural monitoring and assessment. In this paper we validate three recent global products (ESA-CCI, GlobeLand30, FROM-GC) and one regional product (Tanzania Land Cover 2010 Scheme II) using a validation data set that was collected by students through the Geo-Wiki tool. The ultimate aim was to understand the usefulness of these products for agricultural monitoring. Data were collected wall-to-wall for Kilosa district and for a sample across Tanzania. The results show that the amount of and spatial extent of cropland in the different products differs considerably from 8% to 42% for Tanzania, with similar values for Kilosa district. The agreement of the validation data with the four different products varied between 36% and 54% and highlighted that cropland is overestimated by the ESA-CCI and underestimated by FROM-GC. The validation data were also analyzed for consistency between the student interpreters and also compared with a sample interpreted by five experts for quality assurance. Regarding consistency between the students, there was more than 80% agreement if one difference in cropland category was considered (e.g., between low and medium cropland) while most of the confusion with the experts was also within one category difference. In addition to the validation of current cropland products, the data set collected by the students also has potential value as a training set for improving future cropland products

Promoting ethical and responsible data management within a toolkit for scaling Citizen Science projects

Citizen Science (CS) has great potential for contributing to the achievement of the UN Sustainable Development Goals (SDGs) by providing data to indicators monitoring and tracking, and implementing the targets [1–3]. However, an outstanding challenge is to demonstrate CS impacts at scale and to devise indicators that are meaningful for stakeholders [4]. Comprehensive assessments that allow researchers to systematically align anticipated outcomes with SDGs, and design for scale in the early phases of a CS project are not currently available. At the same time, research on data ownership, access and use, and ethics in digitizing are becoming relevant and urgent for the inclusion of smallholder farmers and other stakeholders to close the digital divide [5]. An interactive toolkit was developed as a way for researchers and CS teams in agriculture to define scaling ambitions that are sustainable and responsible. A responsible ambition accounts for potential unintended negative effects in the outcome that it might produce [6,7]. The toolkit is based on a logical framework, and integrates both a tool for systems change at scale, and a sustainability assessment [8,9]. The toolkit was tested with senior researchers for content, usability, and preferred format via a hypothetical case, where researchers indicated that the toolkit is of most use in early CS project stages, and that workshops as well as its implementation as a web-based tool would enhance its impact, bringing together as many views and information as possible, and decreasing the inherent subjectivity, which is part of every sustainability assessment. An additional module within the toolkit, called ‘Guidelines for ethical and responsible data management’, is being tested to gather insights into how data management plans can be embedded into a ‘design for scale’ and SDGs-impact process early on when designing a CS project. The module is based on ‘FAIR Guiding Principles for scientific data management and stewardship’, and the Responsible Data Guidelines (CGIAR, 2020) to manage data ethically. For instance, in an app for farmers where personally identifiable information (e.g. name, address) is being collected, practitioners can acquire an overall understanding of the best practices to manage their data. Equally, if georeferenced information is to be collected (e.g. plots, crops), the FAIR Guiding Principles act as a compass. If researchers have identified a potential contribution to a specific SDG indicator, the toolkit suggests to align this plan with the aforesaid guidelines. Researchers are asked to indicate a status against 15 criteria. If there is a defined plan for a given criterion, a green colour is indicated; if there is a plan but it is not completely defined, the criterion is marked yellow, while red is shown if there is no plan. Detailed descriptions of each guideline can be found via links provided within the toolkit. Additional references to external resources are also included. These tools include the Data Ethics Canvas (theodi.org), as well as the data code of conduct developed by leading institutions (EU CoC on agricultural data sharing, 2018). The toolkit and the ethical and responsible data management modules are currently in the form of a spreadsheet, and will be implemented as a web-based tool. Furthermore, its application will be expanded from agricultural to include all kinds of CS projects. Future research could involve matching the proposed guidelines with the required CS data quality criteria defined by local and regional statistical offices that are responsible for SDG monitoring and implementation

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement

A global dataset of crowdsourced land cover and land use reference data

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general

StarBorn : Towards making in-situ land cover data generation fun with a location-based game

University Research Priority Program: Language and Space, University of ZurichPeer reviewedPublisher PD

The Picture Pile Tool for Rapid Image Assessment: A Demonstration using Hurricane Matthew

In 2016, Hurricane Matthew devastated many parts of the Caribbean, in particular the country of Haiti. More than 500 people died and the damage was estimated at 1.9billionUSD. At the time, the Humanitarian OpenStreetMap Team (HOT) activated their network of volunteers to create base maps of areas affected by the hurricane, in particular coastal communities in the path of the storm. To help improve HOT’s information workflow for disaster response, one strand of the Crowd4Sat project, which was funded by the European Space Agency, focussed on examining where the Picture Pile Tool, an application for rapid image interpretation and classification, could potentially contribute. Satellite images obtained from the time that Hurricane Matthew occurred were used to simulate a situation post-event, where the aim was to demonstrate how Picture Pile could be used to create a map of building damage. The aim of this paper is to present the Picture Pile tool and show the results from this simulation, which produced a crowdsourced map of damaged buildings for a selected area of Haiti in 1 week (but with increased confidence in the results over a 3 week period). A quality assessment of the results showed that the volunteers agreed with experts and the majority of individual classifications around 92% of the time, indicating that the crowd performed well in this task. The next stage will involve optimizing the workflow for the use of Picture Pile in future natural disaster situations

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation

We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis