Patterns of geospatial data and imagery use within coastal communities along the Great Lakes of the United States

Abstract

Listen

Translate article

With expanding technological resources, there has been an increase in both the quantity and quality of coastal data available to researchers and the public. Such quality data, including orthomosaics, digital elevation models, and coastal profiles, are critical to effective coastal management, yet collecting, processing, and analyzing such data represents a barrier for resource-limited communities. Across global landscapes, especially coastal environments, citizen science drone monitoring programs have shown to expand data access. However, differences in understanding may impact capacity for data integration into decision making. This study investigates how capacity to understand and incorporate data into local management practices differs between communities through a survey of decision makers across several Great Lakes communities. The Great Lakes face myriad environmental challenges that threaten the ecological and economic health of the system. Among these are fluctuating lake levels that drive physical changes to the coastline and impact the coupled natural-human system through increased erosion, loss of vegetation, property destruction, and disruption of tourist activities. Exploring the interest and understanding of data use is important given that the Great Lakes account for 20% of the world’s freshwater, over 5,000 miles of U.S. coastline, and have over 52 million people living within the region. Overall, this study found there is an overwhelming agreement among communities’ decision makers that data are important and represent an improvement over current options. However, there were differing patterns of data comprehension when comparing communities. While there was agreement that these data have potential for integration into coastal management if available, in the future it will be important to discern not just the data comprehension and interest but understanding of the preprocessing involved to obtain such data products.