Usability of VGI for validation of land cover maps

Volunteered Geographic Information (VGI) represents a growing source of potentially valuable data for many applications, including land cover map validation. It is still an emerging field and many different approaches can be used to take value from VGI, but also many pros and cons are related to its use. Therefore, since it is timely to get an overview of the subject, the aim of this article is to review the use of VGI as reference data for land cover map validation. The main platforms and types of VGI that are used and that are potentially useful are analysed. Since quality is a fundamental issue in map validation, the quality procedures used by the platforms that collect VGI to increase and control data quality are reviewed and a framework for addressing VGI quality assessment is proposed. A review of cases where VGI was used as an additional data source to assist in map validation is made, as well as cases where only VGI was used, indicating the procedures used to assess VGI quality and fitness for use. A discussion and some conclusions are drawn on best practices, future potential and the challenges of the use of VGI for land cover map validation

Assessing positional accuracy of drainage networks extracted from ASTER, SRTM and OpenStreetMap

This study intends to evaluate the positional accuracy and compare the completeness of the drainage networks extracted from three sources of free geographic data, namely from the Digital Elevation Models ASTER and SRTM and the collaborative project OpenStreetMap (OSM), in an area included in the basin of Mondego river, located in the centre of continental Portugal. The drainage networks extracted from ASTER and SRTM are generated considering several values of flow accumulation as the critical level to identify the water courses and the feature “waterway” was extracted from OSM. To assess the completeness and positional accuracy of these water courses the drainage network of the 1/25000 topographic map of the Portuguese Army Geographical Institute was used as reference. The distance between the ASTER, SRTM and OSM derived water courses to the reference data was computed as well as the length of the water courses and the results compared

Detecting animals in African Savanna with UAVs and the crowds

Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife monitoring, with several advantages over traditional field-based methods. They have readily been used to count birds, marine mammals and large herbivores in different environments, tasks which are routinely performed through manual counting in large collections of images. In this paper, we propose a semi-automatic system able to detect large mammals in semi-arid Savanna. It relies on an animal-detection system based on machine learning, trained with crowd-sourced annotations provided by volunteers who manually interpreted sub-decimeter resolution color images. The system achieves a high recall rate and a human operator can then eliminate false detections with limited effort. Our system provides good perspectives for the development of data-driven management practices in wildlife conservation. It shows that the detection of large mammals in semi-arid Savanna can be approached by processing data provided by standard RGB cameras mounted on affordable fixed wings UAVs

Mapping and the Citizen Sensor

The role of citizens in mapping has evolved considerably over the last decade. This chapter outlines the background to citizen sensing in mapping and sets the scene for the chapters that follow, which highlight some of the main outcomes of a collaborative programme of work to enhance the role of citizens in mapping

An assessment of citizen contributed ground reference data for land cover map accuracy assessment

It is now widely accepted that an accuracy assessment should be part of a thematic mapping programme. Authoritative good or best practices for accuracy assessment have been defined but are often impractical to implement. Key reasons for this situation are linked to the ground reference data used in the accuracy assessment. Typically, it is a challenge to acquire a large sample of high quality reference cases in accordance to desired sampling designs specified as conforming to good practice and the data collected are normally to some degree imperfect limiting their value to an accuracy assessment which implicitly assumes the use of a gold standard reference. Citizen sensors have great potential to aid aspects of accuracy assessment. In particular, they may be able to act as a source of ground reference data that may, for example, reduce sample size problems but concerns with data quality remain. The relative strengths and limitations of citizen contributed data for accuracy assessment are reviewed in the context of the authoritative good practices defined for studies of land cover by remote sensing. The article will highlight some of the ways that citizen contributed data have been used in accuracy assessment as well as some of the problems that require further attention, and indicate some of the potential ways forward in the future

Satellite remote sensing to monitor species diversity: potential and pitfalls

open9siDR was partially funded by: (i) the EU BON (Building the European Biodiversity Observation Network) project, funded by the European Union under the 7th Framework programme, Contract No. 308454 and (ii) by the ERA-Net BiodivERsA, with the national funders ANR, BelSPO and DFG, part of the 2012–2013 BiodivERsA call for research proposals. This work benefited from support from EU COST Action TD1202 ‘Mapping and the Citizen Sensor’.Assessing the level of diversity in plant communities from field-based data is difficult for a number of practical reasons: (1) establishing the number of sampling units to be investigated can be difficult; (2) the choice of sample design can impact on results; and (3) defi ning the population of concern can be challenging. Satellite remote sensing (SRS) is one of the most cost-effective approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field-based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, SRS provides repeated measures, thus making it possible to study temporal changes in biodiversity. Here, we provide a concise review of the potential of satellites to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of satellite imagery to predict species diversity. Our work shows that, while the assessment of alpha-diversity is relatively straightforward, calculation of beta-diversity (variation in species composition between adjacent locations) is challenging, making it difficult to reliably estimate gamma-diversity (total diversity at the landscape or regional level). We conclude that an increased collaboration between the remote sensing and biodiversity communities is needed in order to properly address future challenges and developmentsopenRocchini D.; Boyd D. S.; Féret J. B.; Foody G. M.; He K. S.; Lausch A.; Nagendra H.; Wegmann M.; Pettorelli N.Rocchini D.; Boyd D. S.; Féret J. B.; Foody G. M.; He K. S.; Lausch A.; Nagendra H.; Wegmann M.; Pettorelli N

CONSISTENCY AND RELEVANCE OF VGI AVAILABLE FROM SOCIAL NETWORKS FOR EMERGENCY MITIGATION AND MUNICIPAL MANAGEMENT

Volunteered geographical information (VGI) is an increasing source of data for many applications. In order to explore some of these sources of data, an algorithm was conceived and implemented in the ExploringVGI platform enabling the collection of georeferenced data from collaborative projects that provide an Application Programming Interface (API). This paper presents a preliminary study to evaluate the consistency and relevance of VGI extracted from Flickr platform for emergency mitigation and municipal management. The study carried out was based on data extraction and analysis with keywords related to emergency events (“Accident”, “Flood” and “Fire apartment”), and municipal management (“Graffiti” and “Homeless”) in four European cities (Frankfurt, Lisbon, London, and Rome). The proposed approach sets up a region of interest on a map, selects one or more keywords for the search, and carries out a search using the Flickr API. Data detected and extracted were then loaded into a database and further analysed to verify whether they were consistently obtained through consecutive searches at different locations. A statistical analysis performed on data collected for each case provided us with: the total number of data collected for each keyword and location; their relevance in terms of search goal; and the quality of the associate geolocation of the post. Results obtained illustrate the effectiveness of the approach when applied to different scenarios, which contributes to assess the role that VGI available on the Web may have in different events depending on the specific context of a geolocation/keyword(s) combination

Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map

Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR), as well as classification and regression trees (CART). We ran the comparison experiments using two data types: presence/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs

Assessing and Improving the Reliability of Volunteered Land Cover Reference Data

Volunteered geographic data are being used increasingly to support land cover mapping and validation, yet the reliability of the volunteered data still requires further research. This study proposes data-based guidelines to help design the data collection by assessing the reliability of volunteered data collected using the Geo-Wiki tool. We summarized the interpretation difficulties of the volunteers at a global scale, including those areas and land cover types that generate the most confusion. We also examined the factors affecting the reliability of majority opinion and individual classification. The results showed that the highest interpretation inconsistency of the volunteers occurred in the ecoregions of tropical and boreal forests (areas with relatively poor coverage of very high resolution images), the tundra (a unique region that the volunteers are unacquainted with), and savannas (transitional zones). The volunteers are good at identifying forests, snow/ice and croplands, but not grasslands and wetlands. The most confusing pairs of land cover types are also captured in this study and they vary greatly with different biomes. The reliability can be improved by providing more high resolution ancillary data, more interpretation keys in tutorials, and tools that assist in coverage estimation for those areas and land cover types that are most prone to confusion. We found that the reliability of the majority opinion was positively correlated with the percentage of volunteers selecting this choice and negatively related to their self-evaluated uncertainty when very high resolution images were available. Factors influencing the reliability of individual classifications were also compared and the results indicated that the interpretation difficulty of the target sample played a more important role than the knowledge base of the volunteers. The professional background and local knowledge had an influence on the interpretation performance, especially in identifying vegetation land cover types other than croplands. These findings can help in building a better filtering system to improve the reliability of volunteered data used in land cover validation and other applications