Showing papers by "Linda See published in 2018"

WUDAPT: An Urban Weather, Climate, and Environmental Modeling Infrastructure for the Anthropocene

Abstract: The World Urban Database and Access Portal Tools (WUDAPT) is an international community-based initiative to acquire and disseminate climate relevant data on the physical geographies of cities for modeling and analysis purposes. The current lacuna of globally consistent information on cities is a major impediment to urban climate science toward informing and developing climate mitigation and adaptation strategies at urban scales. WUDAPT consists of a database and a portal system; its database is structured into a hierarchy representing different levels of detail, and the data are acquired using innovative protocols that utilize crowdsourcing approaches, Geowiki tools, freely accessible data, and building typology archetypes. The base level of information (L0) consists of local climate zone (LCZ) maps of cities; each LCZ category is associated with a range of values for model-relevant surface descriptors (roughness, impervious surface cover, roof area, building heights, etc.). Levels 1 (L1) and 2 (L2) will provide specific intra-urban values for other relevant descriptors at greater precision, such as data morphological forms, material composition data, and energy usage. This article describes the status of the WUDAPT project and demonstrates its potential value using observations and models. As a community-based project, other researchers are encouraged to participate to help create a global urban database of value to urban climate scientists.

Agricultural diversification as an important strategy for achieving food security in Africa

Abstract: Farmers in Africa have long adapted to climatic and other risks by diversifying their farming activities. Using a multi-scale approach we explore the relationship between farming diversity and food security and the diversification potential of African agriculture and its limits on the household and continental scale. On the household scale we use agricultural surveys from more than 28,000 households located in 18 African countries. In a next step we use the relationship between rainfall, rainfall variability and farming diversity to determine the available diversification options for farmers on the continental scale. On the household scale, we show that households with greater farming diversity are more successful in meeting their consumption needs, but only up to a certain level of diversity per ha cropland and more often if food can be purchased from off-farm income or income from farm sales. More diverse farming systems can contribute to household food security, however the relationship is influenced by other factors e.g. the market-orientation of a household, livestock ownership, non-agricultural employment opportunities and available land resources. On the continental scale, the greatest opportunities for diversification of food crops, cash crops and livestock are located in areas with 500-1000mm annual rainfall and 17-22% rainfall variability. Forty three percent of the African cropland lacks these opportunities at present which may hamper the ability of agricultural systems to respond to climate change. While sustainable intensification practices that increase yields have received most attention to date, our study suggests that a shift in the research and policy paradigm towards agricultural diversification options may be necessary.

Crowdsourcing Methods for Data Collection in Geophysics : State of the Art, Issues, and Future Directions

Abstract: Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

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

Abstract: 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.

Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Abstract: Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

Improved Estimates of Biomass Expansion Factors for Russian Forests

Abstract: Biomass structure is an important feature of terrestrial vegetation. The parameters of forest biomass structure are important for forest monitoring, biomass modelling and the optimal utilization and management of forests. In this paper, we used the most comprehensive database of sample plots available to build a set of multi-dimensional regression models that describe the proportion of different live biomass fractions (i.e., the stem, branches, foliage, roots) of forest stands as a function of average stand age, density (relative stocking) and site quality for forests of the major tree species of northern Eurasia. Bootstrapping was used to determine the accuracy of the estimates and also provides the associated uncertainties in these estimates. The species-specific mean percentage errors were then calculated between the sample plot data and the model estimates, resulting in overall relative errors in the regression model of −0.6%, −1.0% and 11.6% for biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF), and root-to-shoot ratio respectively. The equations were then applied to data obtained from the Russian State Forest Register (SFR) and a map of forest cover to produce spatially distributed estimators of biomass conversion and expansion factors and root-to-shoot ratios for Russian forests. The equations and the resulting maps can be used to convert growing stock volume to the components of both above-ground and below-ground live biomass. The new live biomass conversion factors can be used in different applications, in particular to substitute those that are currently used by Russia in national reporting to the UNFCCC (United Nations Framework Convention on Climate Change) and the FAO FRA (Food and Agriculture Organization’s Forest Resource Assessment), among others.

Integrated Participatory and Collaborative Risk Mapping for Enhancing Disaster Resilience

Abstract: Critical knowledge gaps seriously hinder efforts for building disaster resilience at all levels, especially in disaster-prone least developed countries. Information deficiency is most serious at local levels, especially in terms of spatial information on risk, resources, and capacities of communities. To tackle this challenge, we develop a general methodological approach that integrates community-based participatory mapping processes, one that has been widely used by governments and non-government organizations in the fields of natural resources management, disaster risk reduction and rural development, with emerging collaborative digital mapping techniques. We demonstrate the value and potential of this integrated participatory and collaborative mapping approach by conducting a pilot study in the flood-prone lower Karnali river basin in Western Nepal. The process engaged a wide range of stakeholders and non-stakeholder citizens to co-produce locally relevant geographic information on resources, capacities, and flood risks of selected communities. The new digital community maps are richer in content, more accurate, and easier to update and share than those produced by conventional Vulnerability and Capacity Assessments (VCAs), a variant of Participatory Rural Appraisal (PRA), that is widely used by various government and non-government organizations. We discuss how this integrated mapping approach may provide an effective link between coordinating and implementing local disaster risk reduction and resilience building interventions to designing and informing regional development plans, as well as its limitations in terms of technological barrier, map ownership, and empowerment potential.

Spatial and temporal changes of vegetable production in China

Abstract: Optimizing the distribution of vegetable production requires knowing where vegetables are currently produced and understanding the factors driving structural changes in vegetable production. Revised Gini coefficient and Moran’s Index are used to examine the spatial and temporal changes of vegetable production in China. A spatial Durbin model is applied to investigate the driving forces of these dynamic changes. The results show that: vegetable production in China has become more geographically concentrated in Huang-Huai-Hai region and Yangtze River Basin; Both comparative advantage and New Economic Geography-type mechanisms were drivers of the dynamic changes of vegetable production. Rural labor, road density (low-grade) and urban population growth have significant positive effects on vegetable production, while no significant effects of the climate factors is found. Furthermore, evidence of the existence of the spatial spillover effect was found in vegetable production. More specifically, rural labor and road density (low-grade) have positive externalities on vegetable production in neighboring provinces.

Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data

Abstract: 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.

An Exploration of Some Pitfalls of Thematic Map Assessment Using the New Map Tools Resource

Abstract: A variety of metrics are commonly employed by map producers and users to assess and compare thematic maps’ quality, but their use and interpretation is inconsistent. This problem is exacerbated by a shortage of tools to allow easy calculation and comparison of metrics from different maps or as a map’s legend is changed. In this paper, we introduce a new website and a collection of R functions to facilitate map assessment. We apply these tools to illustrate some pitfalls of error metrics and point out existing and newly developed solutions to them. Some of these problems have been previously noted, but all of them are under-appreciated and persist in published literature. We show that binary and categorical metrics, including information about true-negative classifications, are inflated for rare categories, and more robust alternatives should be chosen. Most metrics are useful to compare maps only if their legends are identical. We also demonstrate that combining land-cover classes has the often-neglected consequence of apparent improvement, particularly if the combined classes are easily confused (e.g., different forest types). However, we show that the average mutual information (AMI) of a map is relatively robust to combining classes, and reflects the information that is lost in this process; we also introduce a modified AMI metric that credits only correct classifications. Finally, we introduce a method of evaluating statistical differences in the information content of competing maps, and show that this method is an improvement over other methods in more common use. We end with a series of recommendations for the meaningful use of accuracy metrics by map users and producers.

Classification of Building Function Using Available Sources of Vgi

Abstract: . This paper examines the feasibility of using data from OpenStreetMap (OSM), Facebook and Foursquare as a source of information on the function of buildings. Such information is rarely openly available and if available, would vary between cities by nomenclature, making comparisons between places difficult. Volunteered Geographic Information (VGI) including data from social media represents new potential sources of building function data that have not yet been exploited for this purpose. Using a part of the city of Milan as the study area, building data from OSM and points of interest (POIs) from OSM, Facebook and Foursquare were extracted to derive the building function. This resulted in the classification of building function for more than 80 % of the buildings and demonstrated that both Facebook and Foursquare can complement the building function derived from OSM, helping to fill in missing gaps. This preliminary study has demonstrated the potential of this approach for deriving building function information from open data in a simple way yet still requires independent validation with alternative sources as well as extension to other areas that have different amounts of OSM and social media coverage.

Developing an Individual-level Geodemographic Classification

Abstract: Geodemographics is a spatially explicit classification of socio-economic data, which can be used to describe and analyse individuals by where they live. Geodemographic information is used by the public sector for planning and resource allocation but it also has considerable use within commercial sector applications. Early geodemographic systems, such as the UK’s ACORN (A Classification of Residential Neighbourhoods), used only area-based census data, but more recent systems have added supplementary layers of information, e.g. credit details and survey data, to provide better discrimination between classes. Although much more data has now become available, geodemographic systems are still fundamentally built from area-based census information. This is partly because privacy laws require release of census data at an aggregate level but mostly because much of the research remains proprietary. Household level classifications do exist but they are often based on regressions between area and household data sets. This paper presents a different approach for creating a geodemographic classification at the individual level using only census data. A generic framework is presented, which classifies data from the UK Census Small Area Microdata and then allocates the resulting clusters to a synthetic population created via microsimulation. The framework is then applied to the creation of an individual-based system for the city of Leeds, demonstrated using data from the 2001 census, and is further validated using individual and household survey data from the British Household Panel Survey.

An Experimental Framework for Integrating Citizen and Community Science into Land Cover, Land Use, and Land Change Detection Processes in a National Mapping Agency

Abstract: Accurate and up-to-date information on land use and land cover (LULC) is needed to develop policies on reducing soil sealing through increased urbanization as well as to meet climate targets. More detailed information about building function is also required but is currently lacking. To improve these datasets, the national mapping agency of France, Institut de l’Information Geographique et Forestiere (IGN France), has developed a strategy for updating their LULC database on a update cycle every three years and building information on a continuous cycle using web, mobile, and wiki applications. Developed as part of the LandSense project and eventually tapping into the LandSense federated authentication system, this paper outlines the data collection campaigns, the key concepts that have driven the system architecture, and a description of the technologies developed for this solution. The campaigns have only just begun, so there are only preliminary results to date. Thus far, feedback on the web and mobile applications has been positive, but still requires a further demonstration of feasibility.

The picture pile tool for rapid image assessment: a demonstration using hurricane matthew

Abstract: In 2016, Hurricane Matthew devastated many parts of the Caribbean, in particular the country of Haiti More than 500 people died and the damage was estimated at 19 billion USD At the time, the Humanitarian OpenStreetMap Team (HOT) activated their network of volunteers to create base maps of areas affected by the hurricane, in particular coastal communities in the path of the storm To help improve HOT’s information workflow for disaster response, one strand of the Crowd4Sat project, which was funded by the European Space Agency, focussed on examining where the Picture Pile Tool, an application for rapid image interpretation and classification, could potentially contribute Satellite images obtained from the time that Hurricane Matthew occurred were used to simulate a situation post-event, where the aim was to demonstrate how Picture Pile could be used to create a map of building damage The aim of this paper is to present the Picture Pile tool and show the results from this simulation, which produced a crowdsourced map of damaged buildings for a selected area of Haiti in 1 week (but with increased confidence in the results over a 3 week period) A quality assessment of the results showed that the volunteers agreed with experts and the majority of individual classifications around 92 % of the time, indicating that the crowd performed well in this task The next stage will involve optimizing the workflow for the use of Picture Pile in future natural disaster situations

WeObserve: An Ecosystem of Citizen Observatories for Environmental Monitoring

Abstract: 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.

Engaging Citizens in Environmental Monitoring via Gaming

Abstract: Citizen science is quickly becoming one of the most effective tools for the rapid and low-cost collection of environmental information, filling a long recognized gap in in-situ data Incentivizing citizens to participate, however, remains a challenge, with gaming being widely recognized as an effective solution to overcome the participation barrier Building upon well-known gaming mechanics, games provide the user with a competitive and fun environment This paper presents three different applications that employ game mechanics and have generated useful information for environmental science Furthermore, it describes the lessons learnt from this process to guide future efforts

Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery for Monitoring Applications

Abstract: . Very high resolution (VHR) satellite imagery from Google Earth and Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, this imagery is used to create detailed time-sensitive maps, e.g. for emergency response purposes, or to validate coarser resolution products such as global land cover maps. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global snapshot of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885767 .

FloodCitiSense: Early Warning Service for Urban Pluvial Floods for and by Citizens and City Authorities

Abstract: FloodCitiSense aims at developing an urban pluvial flood early warning service for, but also by citizens and city authorities, building upon the state-of-the-art knowledge, methodologies and smart technologies provided by research units and private companies FloodCitiSense targets the co-creation of this innovative public service in an urban living lab context with all local actors This service will reduce the vulnerability of urban areas and citizens to pluvial floods, which occur when heavy rainfall exceeds the capacity of the urban drainage system Due to their fast onset and localized nature, they cause significant damage to the urban environment and are challenging to manage Monitoring and management of peak events in cities is typically in the hands of local governmental agencies Citizens most often just play a passive role as people negatively affected by the flooding, despite the fact that they are often the ‘first responders’ and should therefore be actively involved The FloodCitiSense project aims at integrating crowdsourced hydrological data, collaboratively monitored by local stakeholders, including citizens, making use of low-cost sensors and web-based technologies, into a flood early warning system This will enable ‘citizens and cities’ to be better prepared for and better respond to urban pluvial floods Three European pilot cities are targeted: Brussels – Belgium, Rotterdam – The Netherlands and Birmingham – UK

Using Citizen Science to Help Monitor Urban Landscape Changes and Drive Improvements

Abstract: Citizen Science has become a vital source for data collection when the spatial and temporal extent of a project makes it too expensive to send experts into the field. However, involving citizens can go further than that – participatory projects focusing on subjective parameters can fill in the gap between local community needs and stakeholder approaches to tackle key social and environmental issues. LandSense, a Horizon 2020 project that is deeply rooted in environmental challenges and solutions, aims to establish a citizen observatory that will provide data to stakeholders, from researchers to businesses. Within this project, a mobile application has been developed that aims not only to stimulate civic engagement to monitor changes within the urban environment, but also to enable users to drive improvements by providing city planners with information about the public perception of urban spaces. The launch of a public version of such an app requires preparation and testing by focus groups. Recently, a prototype of the app was used by both staff and students from Vienna University of Technology, who contributed valuable insights to help enhance this citizen science tool for engaging and empowering the inhabitants of the city.

The LandSense Engagement Platform: Connecting citizens with earth observation data to promote environmental monitoring

Abstract: The Horizon 2020 project, LandSense, is building a modern citizen observatory for Land Use & Land Cover (LULC) monitoring, by connecting citizens with Earth Observation (EO) data to transform current approaches to environmental decision making. Citizen Observatories are community-driven mechanisms to complement existing environmental monitoring systems and can be fostered through EO-based mobile and web applications, allowing citizens to not only play a key role in LULC monitoring, but also to be directly involved in the co-creation of such solutions. A critical component within the project is the LandSense Engagement Platform, a service platform comprised of highly marketable EO-based solutions that contribute to the transfer, assessment, valuation, uptake and exploitation of LULC data and related results. The platform engages citizens to view, analyze and share data collected from different citizen science campaigns and create their own maps, individually and collaboratively. In addition, citizens can participate in ongoing demonstration pilots using their own devices (e.g. mobile phones and tablets), through interactive reporting and gaming applications, as well as launching their own campaigns. This interaction is achieved by bringing together and extending various key pieces of technology like Geo-Wiki, LACO-Wiki, Geopedia, SentinelHub and the Earth Observation Data Centre. Furthermore, a key pillar of the platform is the LandSense Federation which supports users to authenticate from a variety of login providers using social media (i.e. Facebook and Google) and some 2500 academic institutions globally (eduGAIN). Such a federated approach will promote the awareness, outreach, uptake and ultimately the science of citizen science. Services and solutions from the LandSense Engagement Platform are currrently deployed through a series of citizen science campaigns in Vienna, Toulouse, Amsterdam, Serbia, and Spain covering topics such as urban greenspaces, agricultural management and bird habitat/biodiversity monitoring. The presentation will not only showcase the results from these campaigns, but also highlight how one can link to the platform to exploit its EO services and launch your own citizen science campaigns.

Citizen Science & SDGs: Opportunities, Challenges, Recommendations

Abstract: Tracking and monitoring the implementation of the SDGs is critical for establishing progress, which requires a systematic review of the social, economic and environmental dimensions of the SDGs. As inputs, accurate, accessible, timely and spatially disaggregated data are needed. Even though data availability and quality have improved over the last decade, more data are needed to ensure that “no one is left behind”, which is a key component of the 2030 Agenda for Sustainable Development, while addressing all aspects of the SDGs. Traditional data collection methods, e.g. administrative records, statistical surveys, censuses, etc. need to be strengthened, and a much wider set of data are needed to address the SDGs. To achieve this, new, innovative ways of data production and analysis using Earth Observation (EO), mobile data, social media, sensors, etc. need to be developed and adopted. In addition to EO and other new geospatial data sets such as mobile phone data, another key source of data to support the SDGs is citizen science (CS), which is defined as the involvement of citizens in scientific research. CS can deliver data swiftly, accurately and at a level of granularity not possible with traditional data gathering methods. This session will outline the results from a workshop hosted by IIASA on 3‐5 October in Austria that brought together major global citizen science associations from Europe, Africa, Asia, Australia and the US. Recommendations and best practices will be outlined to address how non‐traditional data sources, citizen science in particular, can contribute to both monitoring and implementation of the SDGs. A conceptual framework will be proposed to identify how creating an enabling environment for the integration of traditional and non‐traditional approaches can leverage the SDG achievement. Additionally, we will showcase examples of success stories, and demonstrate how selected indicators can be monitored via non‐traditional approaches.