a b s t r a c t a r t i c l e i n f oTo solve current environmental challenges such as biodiversity loss, climate change, and rapid conversion of natural areas due to urbanization and agricultural expansion, researchers are increasingly leveraging large, multiscale, multi-temporal, and multi-dimensional geospatial data. In response, a rapidly expanding array of collaborative geospatial tools is being developed to help collaborators share data, code, and results. Successful navigation of these tools requires users to understand their strengths, synergies, and weaknesses. In this paper, we identify the key components of a collaborative Spatial Data Science workflow to develop a framework for evaluating the various functional aspects of collaborative geospatial tools. Using this framework, we then score thirty-one existing collaborative geospatial tools and apply a cluster analysis to create a typology of these tools. We present this typology as a map of the emergent ecosystem and functional niches of collaborative geospatial tools. We identify three primary clusters of tools composed of eight secondary clusters across which divergence is driven by required infrastructure and user involvement. Overall, our results highlight how environmental collaborations have benefitted from the use of these tools and propose key areas of future tool development for continued support of collaborative geospatial efforts.

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Recent advances in high-performance computing (HPC) have promoted the creation of standardized remotely sensed products that map annual vegetation disturbance through two primary methods:(1) conventional approaches that integrate remote sensing-derived vegetation indices with field data and other data on disturbance events reported by public agencies on a year-to-year basis, and (2) ''big'' data approaches using HPC to automate algorithms and workflows across an entire time series. Given the recent proliferation of these annual products and their potential utility for understanding vegetation dynamics, it is important for product end users (that is, practitioners and researchers in domains other than remote sensing) to understand the differences in their representa-ference in reported disturbance between LAND-FIRE and GFC/NAFD was greater for fire disturbance than for non-fire disturbance; LANDFIRE reported more than double the total amounts of fire disturbance of GFC and NAFD in the study period. Based on our results, we encourage end users to choose the appropriate disturbance product based not only on spatial extent and habitat but also on the disturbance type of interest (that is, fire and non-fire). Overall, rather than focusing on accuracy, our study quantifies the extent to which the products exhibited differences in the amounts and locations of reported disturbance to provide insight into these products' representations of disturbance and help end users evaluate and choose the most appropriate product for their needs.

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