feat(raster-api): new colormap for NLCD data (#433)

### Issue

#429 

### What?

This PR adds a new colormap for NLCD data (`nlcd`) and documents how the
colormap was created.

### Why?

- The colormap definition for the categorical NLCD data was too long to
bypass firewall url parameter length rules

### Testing?

- See linked issue

Author: anayeaye

raster_api/runtime/src/cmap_data/README.md

@@ -28,3 +28,55 @@ cmap_vals = my_cmap(x)[:, :]
 cmap_uint8 = (cmap_vals * 255).astype('uint8')
 np.save("epa-ghgi-ch4.npy", cmap_uint8)
 ```
+
+##### NLCD colormap
+
+refs: 
+
+- https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description
+- https://github.com/NASA-IMPACT/veda-backend/issues/429
+
+```python
+import rasterio
+from rio_tiler.colormap import parse_color
+import numpy as np
+
+# The COGs in the nlcd-annual-conus collection store an internal colormap
+nlcd_filename = "/vsis3/veda-data-store/nlcd-annual-conus/nlcd_2001_cog_v2.tif"
+
+# These categories are only used to set transparency and document categories defined in colormap
+# https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description
+nlcd_categories = {
+    "11": "Open Water",
+    "12": "Perennial Ice/Snow",
+    "21": "Developed, Open Space",
+    "22": "Developed, Low Intensity",
+    "23": "Developed, Medium Intensity",
+    "24": "Developed, High Intensity",
+    "31": "Barren Land (Rock/Sand/Clay)",
+    "41": "Deciduous Forest",
+    "42": "Evergreen Forest",
+    "43": "Mixed Forest",
+    "51": "Dwarf Scrub",
+    "52": "Shrub/Scrub",
+    "71": "Grassland/Herbaceous",
+    "72": "Sedge/Herbaceous",
+    "73": "Lichens",
+    "74": "Moss",
+    "81": "Pasture/Hay",
+    "82": "Cultivated Crops",
+    "90": "Woody Wetlands",
+    "95": "Emergent Herbaceous Wetlands"
+}
+
+with rasterio.open(nlcd_filename) as r:
+    internal_colormap = r.colormap(1)
+
+cmap = np.zeros((256, 4), dtype=np.uint8)
+cmap[:] = np.array([0, 0, 0, 255])
+for c, v in internal_colormap.items():
+    if str(c) in nlcd_categories.keys():
+        cmap[c] = np.array(parse_color(v))
+
+np.save("nlcd.npy", cmap)
+```