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

ESA SENTINEL 2 IMAGERY AND GBGEOAPP: INTEGRATED TOOLS FOR THE DEOSAI NATIONAL PARK MANAGEMENT PLAN

Deosai plateau, in the Gilgit-Baltistan Province of Pakistan, for its average elevation of 4,114 meters, is the second highest plateau in the world after Changtang Tibetan Plateau. Two biogeographically important mountain ranges merge in Deosai: the Himalayan and Karakorum–Pamir highlands. The Deosai National Park, with its first recognition in 1993, encompasses an area of about 1620 km2, with the altitude ranging from 3500 to 5200 meters a.s.l. It is known and visited by tourists for the presence of brown bear, but a large number of species of fauna and flora leave, and can be seen during the summer season. This high-altitude ecosystem is particularly fragile and can be considered a sentinel for the effects of climate changes. Due to its geographic position and high altitude, the area of Deosai has never been studied in all its ecosystem components, producing high resolution maps. The first land cover map of Deosai with 10 meters of resolution is discussed in this study. This map has been obtained from Sentinel-2 imagery and improved through the new tool developed in this study: the GBGEOApp. This application for mobile has been done with three main ambitions: the validation of the new land cover map, its improvement with land use information, and the collection of new data in the field. On the basis of the results, the use of the GBGEOApp, as a tool for validation and increasing of environmental data collection, seems to be completely applicable involving the local technicians in a process of data sharing

Producing consistent visually interpreted land cover reference data: learning from feedback

Reference data for large-scale land cover map are commonly acquired by visual interpretation of remotely sensed data. To assure consistency, multiple images are used, interpreters are trained, sites are interpreted by several individuals, or the procedure includes a review. But little is known about important factors influencing the quality of visually interpreted data. We assessed the effect of multiple variables on land cover class agreement between interpreters and reviewers. Our analyses concerned data collected for validation of a global land cover map within the Copernicus Global Land Service project. Four cycles of visual interpretation were conducted, each was followed by review and feedback. Each interpreted site element was labelled according to dominant land cover type. We assessed relationships between the number of interpretation updates following feedback and the variables grouped in personal, training, and environmental categories. Variable importance was assessed using random forest regression. Personal variable interpreter identifier and training variable timestamp were found the strongest predictors of update counts, while the environmental variables complexity and image availability had least impact. Feedback loops reduced updating and hence improved consistency of the interpretations. Implementing feedback loops into the visually interpreted data collection increases the consistency of acquired land cover reference data

MAPPING SPATIAL ACCURACY OF FOREST TYPE CLASSIFICATION IN JAXA’s HIGH-RESOLUTION LAND USE AND LAND COVER MAP

Accuracy assessment of forest type maps is essential to evaluate the classification of forest ecosystems quantitatively. However, map users do not understand in which regions those forest types are well classified from conventional static accuracy measures. Hence, the objective of this study is to unveil spatial heterogeneities of accuracies of forest type classification in a map. Four forest types (deciduous broadleaf forest (DBF), deciduous needleleaf forest (DNF), evergreen broadleaf forest (EBF), and evergreen needleleaf forest (ENF)) found in the JAXA’s land use / cover map of Japan were assessed by a volunteered Site-based dataset for Assessment of Changing LAnd cover by JAXA (SACLAJ). A geographically weighted (GW) correspondence matrix was applied to them to calculate the degree of overall agreements of forest type classes (forest overall accuracy), and the degree of accuracy for each forest class (forest user’s and producer’s accuracies) in a spatially varying way. This study compared spatial surfaces of these measures with static ones of them. The results show that the forest overall accuracy of the forest map tends to be relatively more accurate in the central Japan, while less in the Kansai and Chubu regions and the northern edge of Hokkaido. Static forest user’s accuracy measures for DBF, DNF, and ENF are better than forest producer’s accuracy ones, while the GW approach tells us such characteristics vary spatially and some areas have opposite trends. This kind of spatial accuracy assessment provides a more informative description of the accuracy than the simple use of conventional accuracy measures

Understanding Marketers’ Strategy Development Processes Used for Behavior Change

Studies support the concept that a show of concern by marketing leaders for the consumer equates to consumer loyalty, consumer satisfaction, and positive consumer behavior change. The problem was that processes marketing leaders rely on to develop strategies in California may not show consideration of the campaign’s influence on consumers’ behavior. The purpose of this narrative inquiry was to answer the research question probing for how the processes marketing leaders rely on to develop marketing strategies in California show consideration of the campaigns’ influence on consumers’ behavior. The research question asked about the processes marketing leaders rely on to develop strategies that show consideration of the campaign’s influence on consumers’ behavior. The conceptual framework of consumerism was the lens used to highlight paternalistic processing decisions when analyzing the findings exploring marketing leaders’ strategy development processes. Audio data were collected via video conferencing and telephone interviews followed by member checking. Data were subsequently coded and analyzed for emerging themes or patterns. Findings indicated that all participants showed concern for the consumer, which showed in their strategy development processes choices used for behavior change that resulted from exposure to their marketing campaigns. Having a better understanding of these strategy development processes and decisions may help leaders put mechanisms in place to ensure behavior changes are helpful, thereby creating positive social change, thus benefiting managers, employees, and customers alike

Citizen Science

Citizen science, the active participation of the public in scientific research projects, is a rapidly expanding field in open science and open innovation. It provides an integrated model of public knowledge production and engagement with science. As a growing worldwide phenomenon, it is invigorated by evolving new technologies that connect people easily and effectively with the scientific community. Catalysed by citizens’ wishes to be actively involved in scientific processes, as a result of recent societal trends, it also offers contributions to the rise in tertiary education. In addition, citizen science provides a valuable tool for citizens to play a more active role in sustainable development. This book identifies and explains the role of citizen science within innovation in science and society, and as a vibrant and productive science-policy interface. The scope of this volume is global, geared towards identifying solutions and lessons to be applied across science, practice and policy. The chapters consider the role of citizen science in the context of the wider agenda of open science and open innovation, and discuss progress towards responsible research and innovation, two of the most critical aspects of science today