roadmap and homepage

content/_index.md

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+---
+title: Schmidt DSE Disturbance Recovery Tool
+width: full
+---
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+{{< callout >}}
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+##### This tool, jointly developed between The [Eric and Wendy Schmidt Center for Data Science and Environment](https://dse.berkeley.edu/) and [The National Parks Service](https://www.nps.gov), is designed to allow users to explore the impact of disturbances on a geographic area of interest, using Sentinel-2 satellite imagery.
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+{{< /callout >}}
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+{{< cards >}}
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+{{< card link="/dse-nps-landing-page/about" title="About" image="https://source.unsplash.com/featured/800x600?landscape" subtitle="About this tool" >}}
+{{< card link="/dse-nps-landing-page/about/how_to" title="How To" image="https://source.unsplash.com/featured/800x600?landscape" subtitle="How to get started" >}}
+{{< card link="/dse-nps-landing-page/upload" title="Upload" image="https://source.unsplash.com/featured/800x600?landscape" subtitle="Upload a fire perimeter" >}}
+
+{{< /cards >}}

content/roadmap.md

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 title: Roadmap
 ---
 
-**[To be updated before WxNW]**
+NPS park managers confronting ecosystem vulnerability in a changing climate, need to navigate a wide range of academic literature, expert opinion, inventory and monitoring data, research results, and public opinion when making decisions regarding vegetation management - especially post-fire, or other large scale disturbance.
+
+DSE’s intent is to create a “toolbox” of analytical approaches (referred to as “modules”) to synthesize these various information sources to support NPS managers in making resource-efficient decisions regarding post-disturbance vegetation management. The modules will be:
+
+- **Flexible** - allow users to quickly and easily import their own data sources, export intermediate steps, and modify analysis approaches for their particular need.
+- **Scalable** - designed to work across environments, park unit sizes, and resource constraints.
+- **Modular** - each work in isolation, or in any combination, adding to one another when used together.
+
+This work will effectively synthesize several data streams to enable land managers to better evaluate landscape recovery strategies in a world of rapidly changing conditions. By employing contemporary data science and analytical approaches, this work will also demonstrate options to adapt existing I&M science approaches to better accommodate changing conditions within ecological monitoring study designs.