Showing papers by "Linda See published in 2023"

Opening up FAIR in-situ land-use reference data: current gaps, obstacles and future challenges

Abstract: It is becoming increasingly obvious that in order to address current global challenges and achieve the SDGs in the land-use sector, monitoring and evaluation using remote sensing technologies are essential. In particular, with the Copernicus program of the European Union, unprecedented free and open Earth observation data are becoming available. However, in order to improve our remotely sensed based machine learning models, training data in the form of in-situ or annotated land-use or land cover data which are based on the visual interpretation of aerial photographs or very high resolution satellite data are of utmost importance. Without sufficient training data, many land-use and land cover maps lack sufficient quality.The presentation will provide an overview of existing and open in-situ data in the field of land-use science. It will highlight what land-use data are currently available including data collected though crowdsourcing and the Geo-Wiki toolbox. In particular, it will provide insights into current gaps in land cover, land-use, livestock, forest as well as crop type information globally. It will draw on existing global data products such as those from the Copernicus global land monitoring service, and more recently generated products such as WorldCover and WorldCereal. Furthermore, tools to close those data gaps will be shown. The presentation will furthermore explore current obstacles and limitations to data sharing and debunk current arguments that are often put forth for not sharing in-situ data. These arguments include limited resources, quality issues, competition, as well as time constraints, etc. Specific attention will be given to the role of doners and funders in more clearly defining open and FAIR requirements for in-situ data. The presentation will close by making the audience aware of the LUCKINet consortium, which is trying to make more reference data openly accessible and to build a consistent global land-use change dataset as well as work done on in-situ data within the EU LAMASUS and OEMC project. 

Troposphere Monitoring Based on Crowdsourced Smartphone GNSS Data

Abstract: Global Navigation Satellite System (GNSS) is an essential tool for troposphere monitoring. Currently, GNSS meteorology depends mainly on the data from geodetic receivers of global or regional networks. However, these geodetic-grade GNSS stations are costly, and thus cannot be densely deployed, especially in less developed regions. Since the release of the Android 7 operating system in 2016, Android smartphones can be used to collect raw GNSS data. Considering that nowadays there are about 3 billion Android smartphones worldwide, a smartphone GNSS data crowdsourcing campaign was launched on March 17th 2022 as a part of the CAMALIOT project. About 5 TB of raw GNSS observations were collected around the world by more than 12 thousand users of the CAMALIOT Android application. In this contribution, we highlight the results related to the dedicated pipeline developed to process the crowdsourced smartphone GNSS data. Firstly, all the collected data were classified by a machine learning-based model to disregard observations of low quality. It was found that only about 2% of the collected data could potentially be used for troposphere delay estimation. The high-quality observations were then processed in the relative-positioning mode by forming baselines with the nearby geodetic stations. Several crowdsourced data sets were used to demonstrate that the zenith wet delays (ZWD) derived from smartphone data could achieve an accuracy of better than 10 mm. However, uncalibrated phase center variations of the smartphone antennas and multipath errors are still the main limitations to further improve the ZWD estimation. Overall, our study indicates that crowdsourced smartphone GNSS data is promising to densify the existing GNSS networks in terms of troposphere monitoring.

The Contributions of Citizen Science to the United Nations Sustainable Development Goals and Other International Agreements and Frameworks

Abstract: TO CITE THIS ARTICLE: Fraisl, D, See, L, Campbell, J, Danielsen, F and Andrianandrasana, HT. 2023. The Contributions of Citizen Science to the United Nations Sustainable Development Goals and Other International Agreements and Frameworks. Citizen Science: Theory and Practice, 8(1): 27, pp. 1–6. DOI: https://doi.org/10.5334/cstp.643 The Contributions of Citizen Science to the United Nations Sustainable Development Goals and Other International Agreements and Frameworks

Quality Improvement Capacity in a Department of Family Medicine: A Mixed-Methods Study.

Abstract: Background and Objectives Quality improvement capacity is defined as ongoing commitment to sustained quality improvement (QI) and requires knowledge of QI methods and commitment to QI activities from practice leadership and staff. The aim of this project was to identify the major facilitators and barriers to developing quality improvement capacity in a teaching practice of a department of family medicine. Methods We conducted an exploratory, sequential, mixed-methods study, inviting key informants to participate in qualitative interviews and then conducting a survey of faculty, resident physicians, and staff at a community residency teaching practice affiliated with an academic medical center in the Midwest United States. Results Among 12 qualitative key informant interviewees, facilitators of QI capacity included a strong motivation to provide high-quality care and a desire to leverage team-based care in QI interventions. Barriers included competing clinical and educational priorities, lack of faculty expertise in quality and scholarship, and lack of infrastructure to turn QI into scholarship. The survey response rate was 75% (48 of 64 total team members). The most common motivation for participation in QI work was "making a difference" (41, 85%), while the biggest barriers were prioritization of patient care (25, 53%), and teaching (19, 40%). Conclusion This mixed-methods study identified key barriers and facilitators to QI capacity, of which addressing competing priorities, improving QI training, and creating infrastructure for scholarship may improve QI capacity.

Citizen Science: What is in it for the Official Statistics Community?

Abstract: Citizen science data are an example of a non-traditional data source that is starting to be used in the monitoring of the United Nations (UN) Sustainable Development Goals (SDGs) and for national monitoring by National Statistical Systems (NSSs). However, little is known about how the official statistics community views citizen science data, including the opportunities and the challenges, apart from some selected examples in the literature. To fill this gap, this paper presents the results from a survey of NSS representatives globally to understand the key factors in the readiness of national data ecosystems to leverage citizen science data for official monitoring and reporting, and assesses the current awareness and perceptions of NSSs regarding the potential use of these data. The results showed that less than 20% of respondents had direct experience with citizen science data, but almost 50% felt that citizen science data could provide data for SDG and national indicators where there are significant data gaps, listing SDGs 1, 5, and 6 as key areas where citizen science could contribute. The main perceived impediments to the use of citizen science data were lack of awareness, lack of human capacity, and lack of methodological guidance, and several different kinds of quality issues were raised by the respondents, including accuracy, reliability, and the need for appropriate statistical procedures, among many others. The survey was then used as a starting point to identify case studies of successful examples of the use of citizen science data, with follow-up interviews used to collect detailed information from different countries. Finally, the paper provides concrete recommendations targeted at NSSs on how they can use citizen science data for official monitoring.