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Almost 6 years ago, the now Center for Earth Observation and Citizen Science (EOCS) at the International Institute for Applied Systems Analysis (IIASA) pioneered a crowdsourcing mobile app that allowed citizens to report land use and land cover at specific locations across Austria. The app is called FotoQuest Austria (and FotoQuest Go Europe when extended outside of Austria) and uses the GPS capabilities of mobile phones to allow citizens to visit locations near to them and then provide information on various land-related characteristics. A subset of the locations in FotoQuest Austria matched those used in the three-yearly Land Use and Coverage Area frame Survey (LUCAS) from Eurostat. The interface was developed to mimic part of the same protocol that LUCAS surveyors use when visiting locations across Europe, but in this case allowing any citizen to record land use and land cover characteristics observed at these locations. Over a period of 4 years, the FotoQuest project continued to improve: In the 2015 FotoQuest Austria version, 76 citizens collected data at over 600 LUCAS locations, although only 300 were used for comparison, mostly due to quality reasons (Laso Bayas et al. 2016). In the 2018 FotoQuest Go Europe campaign, 140 users from 18 different countries visited 1600 locations, with almost 1400 being currently used for analysis. Apart from the increased number of countries and locations, the user interface, experience and interaction with the app was continuously enhanced. Although LUCAS happened only twice in this period (2015 and 2018), FotoQuest had 3 official campaigns, which allowed us to introduce improvements in each campaign, but it also enabled citizens to continue providing land use change information in between campaigns. In 2015, the agreement between the main land cover classes in LUCAS and FotoQuest Austria was 69% whereas in the 2018 FotoQuest Go Europe campaign, it was over 90%. Currently, data from all campaigns are being compiled and will be freely available through the Geo-Wiki open platform (www.geo-wiki.org). The current presentation will describe the development of the FotoQuest project, as an example of a citizen science project that provides open data, including engagement strategies, improvements to the user interface and experience, and the lessons learnt from the uptake and the match of the crowdsourced data against the official LUCAS results. We hope the lessons we have learned during the project can help other citizen science projects share their data more openly and increase citizen participation. Related publication:Laso Bayas, J C, L See, S Fritz, T Sturn, C Perger, M Dürauer, M Karner, et al. 2016. “Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology.” Remote Sensing 8 (11): e905. https://doi.org/10.3390/rs8110905.

The land area covered by freely available very high-resolution (VHR) imagery has grown dramatically over recent years, which has considerable relevance for forest observation and monitoring. For example, it is possible to recognize and extract a number of features related to forest type, forest management, degradation and disturbance using VHR imagery. Moreover, time series of medium-to-high-resolution imagery such as MODIS, Landsat or Sentinel has allowed for monitoring of parameters related to forest cover change. Although automatic classification is used regularly to monitor forests using medium-resolution imagery, VHR imagery and changes in web-based technology have opened up new possibilities for the role of visual interpretation in forest observation. Visual interpretation of VHR is typically employed to provide training and/or validation data for other remote sensing-based techniques or to derive statistics directly on forest cover/forest cover change over large regions. Hence, this paper reviews the state of the art in tools designed for visual interpretation of VHR, including Geo-Wiki, LACO-Wiki and Collect Earth as well as issues related to interpretation of VHR imagery and approaches to quality assurance. We have also listed a number of success stories where visual interpretation plays a crucial role, including a global forest mask harmonized with FAO FRA country statistics; estimation of dryland forest area; quantification of deforestation; national reporting to the UNFCCC; and drivers of forest change. ISSN:0169-3298 ISSN:1573-0956

Citizens are increasingly becoming involved in data collection, whether for scientific purposes, to carry out micro-tasks, or as part of a gamified, competitive application. In some cases, volunteered data collection overlaps with that of mapping agencies, e.g., the citizen-based mapping of features in OpenStreetMap. LUCAS (Land Use Cover Area frame Sample) is one source of authoritative in-situ data that are collected every three years across EU member countries by trained personnel at a considerable cost to taxpayers. This paper presents a mobile application called FotoQuest Austria, which involves citizens in the crowdsourcing of in-situ land cover and land use data, including at locations of LUCAS sample points in Austria. The results from a campaign run during the summer of 2015 suggest that land cover and land use can be crowdsourced using a simple protocol based on LUCAS. This has implications for remote sensing as this data stream represents a new source of potentially valuable information for the training and validation of land cover maps as well as for area estimation purposes. Although the most detailed and challenging classes were more difficult for untrained citizens to recognize, the agreement between the crowdsourced data and the LUCAS data for basic high level land cover and land use classes in homogeneous areas (ca. 80%) shows clear potential. Recommendations for how to further improve the quality of the crowdsourced data in the context of LUCAS are provided so that this source of data might one day be accurate enough for land cover mapping purposes.

A number of global and regional maps of forest extent are available, but when compared spatially, there are large areas of disagreement. Moreover, there is currently no global forest map that is consistent with forest statistics from FAO (Food and Agriculture Organization of the United Nations). By combining these diverse data sources into a single forest cover product, it is possible to produce a global forest map that is more accurate than the individual input layers and to produce a map that is consistent with FAO statistics. In this paper we applied geographically weighted regression (GWR) to integrate eight different forest products into three global hybrid forest cover maps at a 1 km resolution for the reference year 2000. Input products included global land cover and forest maps at varying resolutions from 30 m to 1 km, mosaics of regional land use/land cover products where available, and the MODIS Vegetation Continuous Fields product. The GWR was trained using crowdsourced data collected via the Geo-Wiki platform and the hybrid maps were then validated using an independent dataset collected via the same system. Three different hybrid maps were produced: two consistent with FAO statistics, one at the country and one at the regional level, and a “best guess” forest cover map that is independent of FAO. Independent validation showed that the “best guess” hybrid product had the best overall accuracy of 93% when compared with the individual input datasets. The global hybrid forest cover maps are available at http://biomass.geo-wiki.org .

Accurate geospatial cropland information is one of several critical input parameters needed for the assessment of food security and for achieving sustainable development. However, current land cover products are either not accurate enough or lacking in many food insecure countries. Here we demonstrate how we can use the Geo-Wiki crowdsourcing tool and student volunteers to generate a cropland map of Myanmar, and validate a global hybrid cropland map. The preliminary results clearly indicate that the method could be applied in a similar manner to other countries.