M. Dürauer

Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology

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.

Spatial distribution of arable and abandoned land across former Soviet Union countries

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.

Towards pathways bending the curve terrestrial biodiversity trends within the 21st century

Unless actions are taken to reduce multiple anthropogenic pressures, biodiversity is expected to continue declining at an alarming rate. Models and scenarios can be used to help design the pathways to sustain a thriving nature and its ability to contribute to people. This approach has so far been hampered by the complexity associated with combining projections of pressures on, and subsequent responses from, biodiversity. Most previous assessments have projected continuous biodiversity declines and very few have identified pathways for reversing the loss of biodiversity without jeopardizing other objectives such as development or climate mitigation. The Bending The Curve initiative set out to advance quantitative modelling techniques towards ambitious scenarios for biodiversity. In this proof-of-concept analysis, we developed a modelling approach that demonstrates how global land use and biodiversity models can shed light on wedges able to bend the curve of biodiversity trends as affected by land-use change, the biggest current threat to biodiversity. In order to address the uncertainties associated with such pathways we used a multi-model framework and relied on the Shared Socioeconomic Pathway/Representative Concentration Pathway scenario framework. This report describes the details of this modelling approach.