Increasing the accuracy of crowdsourced information on land cover via a voting procedure weighted by information inferred from the contributed data

Simple consensus methods are often used in crowdsourcing studies to label cases when data are provided by multiple contributors. A basic majority vote rule is often used. This approach weights the contributions from each contributor equally but the contributors may vary in the accuracy with which they can label cases. Here, the potential to increase the accuracy of crowdsourced data on land cover identified from satellite remote sensor images through the use of weighted voting strategies is explored. Critically, the information used to weight contributions based on the accuracy with which a contributor labels cases of a class and the relative abundance of class are inferred entirely from the contributed data only via a latent class analysis. The results show that consensus approaches do yield a classification that is more accurate than that achieved by any individual contributor. Here, the most accurate individual could classify the data with an accuracy of 73.91% while a basic consensus label derived from the data provided by all seven volunteers contributing data was 76.58%. More importantly, the results show that weighting contributions can lead to a statistically significant increase in the overall accuracy to 80.60% by ignoring the contributions from the volunteer adjudged to be the least accurate in labelling

Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery

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

WeObserve:An Ecosystem of Citizen Observatories for Environmental Monitoring

The last decade has witnessed a rise in the field of citizen science which can be described as a collaborative undertaking between citizens and scientists to help gather data and create new scientific knowledge. In the EU, efforts have been channeled into developing the concept of Citizen Observatories (COs), which have been supported via the Seventh Framework Program (FP7) and continue to be funded in Horizon 2020. COs, often supported by innovative technologies including Earth Observation (EO) and mobile devices, are the means by which communities can monitor and report on their environment and access information that is easily understandable for decision making. To improve the coordination between existing COs and related citizen science activities, the WeObserve project tackles three key challenges that face COs: awareness, acceptability and sustainability. The WeObserve mission is to create a sustainable ecosystem of COs that can systematically address these identified challenges and help move citizen science into the mainstream. The WeObserve approach will apply several key instruments to target, connect and coordinate relevant stakeholders. The first is to develop and foster five communities of practice to strengthen the current knowledge base surrounding COs. Topics will include citizen engagement, the value of COs for governance and CO data interoperability. In co-creating this knowledge base, CO practitioners will have a platform to effectively share best practices and avoid duplication. Secondly, the project will expand the geographical reach of the knowledge base to different target groups via toolkits, a Massive Open Online Course (MOOC), capacity development roadshows and an Open Data Exploitation Challenge, to strengthen the uptake of CO-powered science by public authorities and SMEs. A third mechanism will forge links with GEOSS and Copernicus to demonstrate how COs can complement the EU’s Earth Observation monitoring framework. This paper will describe these various mechanisms and issue a call to bring together diverse stakeholders who share a joint (practice-oriented) interest in citizen science. The WeObserve consortium brings together the current H2020 COs (Ground Truth 2.0, GROW, LandSense, Scent) who will actively open up the citizen science landscape through wide ranging networks, users and stakeholders, including ECSA, GEOSS and Copernicus to foster social innovation opportunities. The WeObserve approach and outcomes have the potential to create a step-change in the Earth Observation sector and make COs a valuable component of Earth system science research to manage environmental challenges and empower resilient communities

Downgrading Recent Estimates of Land Available for Biofuel Production

Recent estimates of additional land available for bioenergy production range from 320 to 1411 million ha. These estimates were generated from four scenarios regarding the types of land suitable for bioenergy production using coarse-resolution inputs of soil productivity, slope, climate, and land cover. In this paper, these maps of land availability were assessed using high-resolution satellite imagery. Samples from these maps were selected and crowdsourcing of Google Earth images was used to determine the type of land cover and the degree of human impact. Based on this sample, a set of rules was formulated to downward adjust the original estimates for each of the four scenarios that were previously used to generate the maps of land availability for bioenergy production. The adjusted land availability estimates range from 56 to 1035 million ha depending upon the scenario and the ruleset used when the sample is corrected for bias. Large forest areas not intended for biofuel production purposes were present in all scenarios. However, these numbers should not be considered as definitive estimates but should be used to highlight the uncertainty in attempting to quantify land availability for biofuel production when using coarse-resolution inputs with implications for further policy development

A global reference database of crowdsourced cropland data collected using the Geo-Wiki platform

A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent

The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

International audienceForest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (aGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. aGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. all plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities

Assessing quality of volunteer crowdsourcing contributions: lessons from the Cropland Capture game

Volunteered geographic information (VGI) is the assembly of spatial information based on public input. While VGI has proliferated in recent years, assessing the quality of volunteer-contributed data has proven challenging, leading some to question the efficiency of such programs. In this paper, we compare several quality metrics for individual volunteers' contributions. The data were the product of the Cropland Capture' game, in which several thousand volunteers assessed 165,000 images for the presence of cropland over the course of 6 months. We compared agreement between volunteer ratings and an image's majority classification with volunteer self-agreement on repeated images and expert evaluations. We also examined the impact of experience and learning on performance. Volunteer self-agreement was nearly always higher than agreement with majority classifications, and much greater than agreement with expert validations although these metrics were all positively correlated. Volunteer quality showed a broad trend toward improvement with experience, but the highest accuracies were achieved by a handful of moderately active contributors, not the most active volunteers. Our results emphasize the importance of a universal set of expert-validated tasks as a gold standard for evaluating VGI quality

Improved Cropland Mapping in Ethiopia. GI_Forum 2013 – Creating the GISociety|

Across the globe, accurate national spatial datasets on cropland extent are lacking. These are necessary for a number of reasons, including accurately monitoring and predicting crop yield, land use, land acquisitions and food security. This study describes the use of crowdsourcing information retrieved over Ethiopia depicting the extent of cropland area. This information has been used to train a classification algorithm in Google Earth Engine to produce a continuous cropland extent map of Ethiopia. Preliminary results of this novel approach are encouraging, with an overall validity of 96%