Add visibility analysis tutorial (r.viewshed)

[Valyrian-Code]

Adds a new intermediate tutorial on visibility analysis with
r.viewshed — a topic
not currently covered in the tutorial set.

What it covers

1. A single-observer viewshed, drawn over shaded relief, and measuring the visible area.
2. The effect of observer height — ground level (~6.4 km² visible) vs. a 40 m tower (~46.6 km²).
3. Limiting the search with max_distance (a 3 km radius).
4. Cumulative visibility from five sites summed with r.series, as a starting point for site optimization.

Details

• Uses the standard North Carolina sample dataset (elevation DEM) and the grass.tools API (GRASS 8.5), consistent with the recent NumPy/Landlab tutorial.
• All code was run against GRASS 8.5; the figures are real outputs of the workflow. The tutorial renders cleanly with Quarto (eval: false, following the other Python tutorials).
• Categories: raster, terrain, visualization, intermediate, Python.
• Cross-links to the Terrain/DEMs and Modeling Movement tutorials.

Feedback on scope, wording, or the choice of observer sites is very welcome.

[Copilot]

Copilot was unable to review this pull request because the user who requested the review has reached their quota limit.

[veroandreo]

The example developed is pretty basic, and the images are not visually appealing. They are too big, the legend and scale bar overlap. I would like to read a more compelling story, i.e., the observer in the center of the map is easy, but why are they there? Perhaps you could pick a different dataset or a different area and still make the tutorial reproducible by using r.in.usgs or i.eodag, and place the observer and the towers in places that actually mean something, not just in the center of the region. Please have a look at https://github.com/ncsu-geoforall-lab/grass-gis-workshop-foss4gna-2023, esp. notebooks 2 and 3, which cover similar analysis.

[Valyrian-Code]

Thanks @veroandreo, this was the right push. I reworked the tutorial from scratch around a real story: the historic fire lookout towers of the Nantahala National Forest (Wayah Bald, Wesser Bald, and Albert Mountain, all still standing along the Appalachian Trail near Franklin, NC). The terrain now comes from the USGS via the r.in.usgs addon into a fresh EPSG:32617 project, so it is fully reproducible: one pinned tile of about 58 MB, with a callout on previewing the download with -i first.

The observers now mean something. These towers were sited for visibility, so each section answers a question their builders actually faced:

• Observer height: standing on the bald vs the 16 m cab (165 vs 277 km² visible), which is why the towers were built.
• Earth curvature with -c at a lookout's 32 km range, including why the effect is small in ridge-limited terrain.
• target_elevation as a rising smoke column: detectable area grows from 277 to 412 km², since a lookout watches for smoke, not ground.
• The three-tower network summed with r.series, with per-class coverage reported as a table from r.report JSON output (44% of the region).
• A worked "where should a fourth tower go?" section: the best candidate summit recovers under 4% of the blind area, because the remaining gaps are valley bottoms that no ridgetop tower can see. That diminishing-returns result is, I think, the most interesting number in the tutorial.

On the images: regenerated at 600 px with labeled towers, shaded relief bases, non-overlapping legend and scale bar, and zoomed extents for the single-tower maps. The get-started link now points to the Python quick start, the callouts are spread out, and the area estimation commands are shown in the text.

Happy to adjust anything else.