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Spatially explicit information on forest management at a global scale is critical for understanding the current status of forests for sustainable forest management and restoration. Whereas remotely sensed based datasets, developed by applying ML and AI algorithms, can successfully depict tree cover and other land cover types, it has not yet been used to depict untouched forest and different degrees of forest management. We show for the first time that with sufficient training data derived from very high-resolution imagery a differentiation within the tree cover class of various levels of forest management is possible.In this session, we would like to present our approach for labeling forest related training data by using Geo-Wiki application (https://www.geo-wiki.org/). Moreover, we would like to share a new open global training data set on forest management we collected from a series of Geo-Wiki campaigns. In February 2019, we organized an expert workshop to (1) discuss the variety of forest management practices that take place in different parts of the world; (2) generalize the definitions for the application at global scale; (3) finalize the Geo-Wiki interface for the crowdsourcing campaigns; and (4) build a data set of control points (or the expert data set), which we used later to monitor the quality of the crowdsourced contributions by the volunteers. We involved forest experts from different regions around the world to explore what types of forest management information could be collected from visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, in combination with Sentinel time series and Normalized Difference Vegetation Index (NDVI) profiles derived from Google Earth Engine (GEE). Based on the results of this analysis, we expanded these campaigns by involving a broader group of participants, mainly people recruited from remote sensing, geography and forest research institutes and universities.In total, we collected forest data for approximately 230 000 locations globally. These data are of sufficient density and quality and therefore could be used in many ML and AI applications for forests at regional and local scale. We also provide an example of ML application, a remotely sensed based global forest management map at a 100 m resolution (PROBA-V) for the year 2015. It includes such classes as intact forests, forests with signs of human impact, including clear cuts and logging, replanted forest, woody plantations with a rotation period up to 15 years, oil palms and agroforestry. The results of independent statistical validation show that the map’s overall accuracy is 81%.

Geo-Wiki is an online platform for involving citizens in the visual interpretation of very high-resolution satellite imagery to collect reference data on land cover and land use. Instead of being an ongoing citizen science project, short intensive campaigns are organized in which citizens participate. The advantage of this approach is that large amounts of data are collected in a short amount of time with a clearly defined data collection target to reach. Participants can also schedule their time accordingly, with their past feedback indicating that this intensive approach was preferred. The reference data are then used in further scientific research to answer a range of questions such as: How much of the land’s surface is wild or impacted by humans? What is the size of agricultural fields globally? The campaigns are organized as competitions with prizes that include Amazon vouchers and co-authorship on a scientific publication. The scientific publication is the mechanism by which the data are openly shared so that other researchers can use this reference data set in other applications. The publication is usually in the form of a data paper, which explains the campaign in detail along with the data set collected. The data are uploaded to a repository such as Pangaea, ZENODO or IIASA’s own data repository, DARE. This approach from data collection, to opening up the data, to documentation via a scientific data paper also ensures transparency in the data collection process. There have been several Geo-Wiki citizen science campaigns that have been run over the last decade. Here we provide examples of experiences from five recent campaigns: (i) the Global Cropland mapping campaign to build a cropland validation data set; (ii) the Global Field Size campaign to characterize the size of agricultural fields around the world; (iii) the Human Impact on Forests campaign to produce the first global map of forest management; (iv) the Global Built-up Surface Validation campaign to collect data on built-up surfaces for validation of global built-up products such as the Global Human Settlement Layer (https://ghsl.jrc.ec.europa.eu/); and (v) the Drivers of Tropical Forest Loss campaign, which collected data on the main causes of deforestation in the tropics. In addition to outlining the campaign, the data sets collected and the sharing of the data online, we provide lessons learned from these campaigns, which have built upon experiences collected over the last decade. These include insights related to the quality and consistency of the classifications of the volunteers including different volunteer behaviors; best practices in creating control points for use in the gamification and quality assurance of the campaigns; different methods for training the volunteers in visual interpretation; difficulties in the interpretation of some features, which may need expert input instead as well as the inability of some features to be recognized from satellite imagery; and limitations in the approach regarding change detection due to temporal availability of open satellite imagery, among several others.

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.

Increased efforts are required to prevent further losses to terrestrial biodiversity and the ecosystem services that it provides1,2. Ambitious targets have been proposed, such as reversing the declining trends in biodiversity3; however, just feeding the growing human population will make this a challenge4. Here we use an ensemble of land-use and biodiversity models to assess whether—and how—humanity can reverse the declines in terrestrial biodiversity caused by habitat conversion, which is a major threat to biodiversity5. We show that immediate efforts, consistent with the broader sustainability agenda but of unprecedented ambition and coordination, could enable the provision of food for the growing human population while reversing the global terrestrial biodiversity trends caused by habitat conversion. If we decide to increase the extent of land under conservation management, restore degraded land and generalize landscape-level conservation planning, biodiversity trends from habitat conversion could become positive by the mid-twenty-first century on average across models (confidence interval, 2042–2061), but this was not the case for all models. Food prices could increase and, on average across models, almost half (confidence interval, 34–50%) of the future biodiversity losses could not be avoided. However, additionally tackling the drivers of land-use change could avoid conflict with affordable food provision and reduces the environmental effects of the food-provision system. Through further sustainable intensification and trade, reduced food waste and more plant-based human diets, more than two thirds of future biodiversity losses are avoided and the biodiversity trends from habitat conversion are reversed by 2050 for almost all of the models. Although limiting further loss will remain challenging in several biodiversity-rich regions, and other threats—such as climate change—must be addressed to truly reverse the declines in biodiversity, our results show that ambitious conservation efforts and food system transformation are central to an effective post-2020 biodiversity strategy. To promote the recovery of the currently declining global trends in terrestrial biodiversity, increases in both the extent of land under conservation management and the sustainability of the global food system from farm to fork are required.

There are currently no spatially explicit, openly accessible data available on forest certification below national level, so understanding the drivers of certification in the past, examining the scope for further certification and using this information for development of future sustainable forest management strategies is challenging. Hence, this paper presents a methodology for the development of a global map of certified forest areas at 1 km resolution in order to satisfy this information need. Validation of the map with certified areas in Russia showed reasonable results, but the lack of openly accessible data requires broadening the strategy for improving the global certification map in the future. Thus, the second aim of the paper is to present an online tool for visualization and interactive improvement of the global forest certification product through collaborative mapping, aiming at a range of stakeholders including third-party certifiers, green NGOs, forestry organizations, decision-makers, scientists and local experts. Such an approach can help to make more accurate information on forest certification available, promote the sharing of data and encourage more transparent and sustainable forest management, i.e. both producers and users can benefit from this online tool.

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

Knowledge of the spatial distribution of agricultural abandonment following the collapse of the Soviet Union is highly uncertain. To help improve this situation, we have developed a new map of arable and abandoned land for 2010 at a 10 arc-second resolution. We have fused together existing land cover and land use maps at different temporal and spatial scales for the former Soviet Union (fSU) using a training data set collected from visual interpretation of very high resolution (VHR) imagery. We have also collected an independent validation data set to assess the map accuracy. The overall accuracies of the map by region and country, i.e. Caucasus, Belarus, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine, are 90±2%, 84±2%, 92±1%, 78±3%, 95±1%, 83±2%, respectively. This new product can be used for numerous applications including the modelling of biogeochemical cycles, land-use modelling, the assessment of trade-offs between ecosystem services and land-use potentials (e.g., agricultural production), among others.

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.