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This paper focuses on enhancing researchers’ ability to articulate and achieve societal impacts in their work using the ARIS Broad Impact (BI) Toolkit as a jumping off point. The authors conducted a survey of UNM researchers to understand their approach to societal impact and relationship-building in research projects. The key findings suggest that 1) researchers across disciplines and funding sources value societal impacts in their work; 2) most UNM researchers engage in collaborative relationships as part of their efforts to achieve broader societal impacts; and 3) while researchers feel confident in conceptualizing and carrying out activities related to broader societal impacts, they are less confident in writing BI statements for NSF proposals. Based on these findings, the authors recommend that research development professionals 1) emphasize the importance of finding meaning in research through its broader social impacts; 2) develop tools to support more intentional relationship-building in research projects; and 3) provide resources to help researchers translate their ideas into effective BI statements for proposals. The paper concludes by highlighting areas for further research, including student engagement, mentorship, and institutional questions about BI project development and evaluation. The authors argue that addressing these areas is crucial for enhancing the societal value and effectiveness of scholarly work, ultimately driving significant positive change through research. 

Through the voices of both faculty and student authors, we discuss the intentional integration of neurodiversity in an undergraduate, community geography research program. This exploratory case study takes conversations about diversity, equity, and inclusion from theory to practice presenting the development of an inclusive learning community through the lens of education and geoscience education frameworks. Through multiple perspectives advocating for systemic change for inclusive community geography, this paper presents actionable recommendations others in geography can draw from in their own efforts to broaden participation within geography field programs. 

High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data.