Tracing how Landsat data helps foster fire-adapted communities in Colorado through data-driven, science-informed planning.

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Mitigation planning to enhance resilience of communities living near Wildland-Urban Interfaces (WUIs)[edit | edit source]

Shifting patterns of ambient temperature, precipitation, vegetation, and insect pests introduce a new level of uncertainty into risk management practices for communities in the proximity of Wildland-Urban Interfaces (WUIs). Continuous adaptive management to these risks may be the new norm for such communities at risk. The need to regularly re-assess mitigation plans reinforces the value of risk management practices that are, to an extent, repeatable year after year, informed by the best available science and data.

Capturing the provenance of science-informed, data-driven risk management practices facilitates the "swapping out" of outdated components of the process (e.g. data, policies, scientific models, protocols), replacing them with updated ones, re-assessing the risks in advance of a fire season, and taking appropriate actions. This "data-to-decisions" provenance can also be shared and re-purposed and modified for other communities, mitigating the time and expense required to employ solutions. This is basically the idea behind the Resilience Genome.

The narrative below, assembled from draft concept maps developed for the specific purpose of demonstrating the potential for capturing transdisciplinary processes as concept maps, is an informal way to tell the story of how Landsat data can be used to inform sections of a Community Wildfire Protection Plan that describes wildfire hazards threatening a community at risk. A more formal representation of this provenance trace is beyond the scope of this experiment. The first three "steps" in this narrative are similar to the other wildfire-related narrative "Tracing how Landsat data is used to inform burnt acreage in an emergency stabilization Burned Area Emergency Response BAER for the 2012 High Park Fire in Colorado"

However, we hope that this example narrative gives you an idea of how you can assemble concepts in the c-soup repository to tell a story about how data could be used to inform a hazard mitigation plan. In this example, the mitigation plan - a Community Wildfire Protection Plan - has a role to play in a law passed by an act of the US Congress (Healthy Forests Restoration Act of 2003, Public Law 108-148).

Nodes below that are clickable retrieve the underlying concept maps that provide more details relevant to the narrative. These underlying concept maps will not tell the whole story: references in each concept map point to documents and websites for future information. You may also notice that the underlying concept maps use slightly formalized expressions compared to the narrative below. This is because those underlying concept maps are drafted with the intent of possible re-use by machine algorithms in the future. Those maps constitute a "Genome Library" by which an algorithmic "Genome Assembler" can design a risk mitigation plan that is customized for the intended context-of-use.

The nodes below are clickable, but you would be left scrolling around madly within the restricted size window. Click here instead to render the full-scale concept map below in your browser window.