{
"cells": [
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"cell_type": "markdown",
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"metadata": {},
"source": [
"# Visualizing multi-dimensional OPERA-DISP with titiler-multidim"
]
},
{
"cell_type": "markdown",
"id": "7ef8a131-64a1-46a6-ad95-c15d7a9e1d19",
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"source": [
"Authors: Henry Rodman(Development Seed), Harshini Girish(UAH), Alex Mandel(Development Seed)\n",
"\n",
"Date: September 25, 2025\n",
"\n",
"Description: TiTiler-MultiDim is a TiTiler application designed to serve multidimensional rasters—like OPERA-DISP NetCDF—directly as web map tiles. It lets you pick a variable (e.g., water_mask or displacement layers) and slice along dimensions (time, burst, polarization, etc.), then renders those selections on-the-fly into XYZ/TileJSON that you can drop into Folium/Leaflet without downloading the file. The notebook centers on this workflow: open a multidim asset, choose variable + dims + styling (colormap, range), and stream dynamic tiles for quick, interactive visualization."
]
},
{
"cell_type": "markdown",
"id": "fa15eafd-bca8-4b0b-8fd3-b917c3c11a60",
"metadata": {},
"source": [
"## Run This Notebook\n",
"To access and run this tutorial within MAAP's Algorithm Development Environment (ADE), please refer to the [\"Getting started with the MAAP\"](https://docs.maap-project.org/en/latest/getting_started/getting_started.html) section of our documentation.\n",
"\n",
"Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors."
]
},
{
"cell_type": "markdown",
"id": "f34058ef-2ff5-40d4-80ce-34c71a0f31df",
"metadata": {},
"source": [
"## Additional Resources\n",
"- [OPERA Surface Displacement (DISP-S1)](https://docs.maap-project.org/en/latest/science/OPERA/OPERA_Surface_Displacement.html) — Overview of the OPERA DISP-S1 product: what it measures, how it’s produced, and how to access/use it on MAAP.\n",
"- [Visualizing with TiTiler-PgSTAC (Python)](https://docs.maap-project.org/en/latest/technical_tutorials/visualization/visualizing_titiler-pgstac.html) — Step-by-step guide to stream STAC assets through TiTiler-PgSTAC and visualize them as web tiles in Python.\n",
"- [Visualizing with TiTiler-PgSTAC (R)](https://docs.maap-project.org/en/latest/technical_tutorials/working_with_r/visualizing_with_titiler-pgstac.html) — R-focused tutorial for rendering STAC items via TiTiler-PgSTAC and building interactive map visualizations.\n"
]
},
{
"cell_type": "markdown",
"id": "2e139427-f3e7-4ffc-9e73-d995d2b1b687",
"metadata": {},
"source": [
"## About the Dataset\n",
"\n",
"> The Level-3 OPERA Sentinel-1 Surface Displacement (DISP) product is generated through interferometric time-series analysis of Level-2 Coregistered Sentinel-1 Single Look Complex (CSLC) datasets. Using a hybrid Persistent Scatterer (PS) and Distributed Scatterer (DS) approach, this product quantifies Earth's surface displacement in the radar line-of-sight. The DISP products enable the detection of anthropogenic and natural surface changes, including subsidence, tectonic deformation, and landslides. \n",
"\n",
"> The OPERA DISP suite comprises complementary datasets derived from Sentinel-1 and NISAR inputs, designated as DISP-S1 and DISP-NI, respectively. Each product, created per acquisition, adheres to a consistent structure, HDF5 file format, file-naming convention, and a 30 m spatial posting. This collection specifically includes DISP-S1 products, derived from Sentinel-1 data. For visualization and quick exploration, the Pangeo Image can be used for these datasets. \n",
"\n",
"Source: [OPERA Surface Displacement from Sentinel-1](https://cmr.earthdata.nasa.gov/search/concepts/C3294057315-ASF.html)"
]
},
{
"cell_type": "markdown",
"id": "6c36cd9f-238c-4087-865a-684f4cfd94a1",
"metadata": {},
"source": [
"## Install/Import Packages\n",
"Make sure the following libraries are installed before running the notebook"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ad3f349c-6286-4f7f-aeaf-84058cc86b9f",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import earthaccess\n",
"import httpx\n",
"from folium import GeoJson, LayerControl, Map, TileLayer\n",
"from pprint import pprint\n",
"from shapely.geometry import box, mapping\n",
"from pathlib import Path\n"
]
},
{
"cell_type": "markdown",
"id": "7db6a652-b34b-483b-a234-4eece3d6d75e",
"metadata": {},
"source": [
"## Searching the Data\n",
"This performs a granule search using the `earthaccess.granule_query()` function on the OPERA Sentinel-1 displacement product collection."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3f8e5c6d-0ce1-4e08-bab1-77d8f2f5c276",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collection: {'ShortName': 'OPERA_L3_DISP-S1_V1', 'Version': '1'}\n",
"Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Latitude': 39.16866, 'Longitude': -121.23333}, {'Latitude': 38.66165, 'Longitude': -124.06639}, {'Latitude': 37.8195, 'Longitude': -123.86328}, {'Latitude': 37.95784, 'Longitude': -122.92612}, {'Latitude': 37.35143, 'Longitude': -122.78615}, {'Latitude': 37.66388, 'Longitude': -120.92917}, {'Latitude': 39.16866, 'Longitude': -121.23333}]}}]}}}\n",
"Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2016-07-05T02:07:28Z', 'EndingDateTime': '2017-01-07T02:06:47Z'}}\n",
"Size(MB): 376.585862159729\n",
"Data: ['https://datapool.asf.alaska.edu/DISP/OPERA-S1/OPERA_L3_DISP-S1_IW_F09157_VV_20160705T020728Z_20170107T020647Z_v1.0_20250408T163918Z.nc', 'https://datapool.asf.alaska.edu/DISP/OPERA-S1/OPERA_L3_DISP-S1_IW_F09157_VV_20160705T020728Z_20170107T020647Z_v1.0_20250408T163918Z.zarr.json.gz', 'https://datapool.asf.alaska.edu/DISP/OPERA-S1/OPERA_L3_DISP-S1_IW_F09157_VV_short_wavelength_displacement.zarr.json.gz']\n"
]
}
],
"source": [
"auth = earthaccess.login()\n",
"granule_query = (\n",
" earthaccess.granule_query()\n",
" .short_name(\"OPERA_L3_DISP-S1_V1\")\n",
" .bounding_box(-121, 38, -120, 39)\n",
")\n",
"\n",
"granules = granule_query.get(1)\n",
"granule = granules[0]\n",
"\n",
"print(granule)\n"
]
},
{
"cell_type": "markdown",
"id": "402cd405-0dd6-4d93-a8b0-767b1197b3d9",
"metadata": {},
"source": [
"## Downloading OPERA-DISP granules\n",
"\n",
"Creates the local folder to fetch the selected OPERA-DISP files.Then the progress indicators for the download tasks.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f9d87914-882f-4567-a895-7a82043f7f2c",
"metadata": {},
"outputs": [],
"source": [
"!mkdir -p /projects/my-public-bucket/opera-disp"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4aad9f3c-1798-4e71-85ce-bf7aa888e0ff",
"metadata": {},
"outputs": [
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"text/plain": [
"QUEUEING TASKS | : 0%| | 0/3 [00:00Make this Notebook Trusted to load map: File -> Trust Notebook
"
],
"text/plain": [
""
]
},
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"metadata": {},
"output_type": "execute_result"
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],
"source": [
"# create an XYZ tile layer for the leaflet map\n",
"tiles = TileLayer(\n",
" name=\"OPERA-DISP\",\n",
" tiles=tilejson[\"tiles\"][0],\n",
" min_zoom=tilejson[\"minzoom\"],\n",
" opacity=1,\n",
" attr=\"NASA\",\n",
" overlay=True,\n",
" control=True,\n",
")\n",
"\n",
"# load the granule bounding box so we can plot it on the map\n",
"geojson = {\"type\": \"Feature\", \"geometry\": mapping(box(*tilejson[\"bounds\"])), \"properties\": {}}\n",
"\n",
"geojson_layer = GeoJson(\n",
" data=geojson,\n",
" style_function=lambda x: {\n",
" \"opacity\": 1,\n",
" \"dashArray\": \"1\",\n",
" \"fillOpacity\": 0,\n",
" \"weight\": 2,\n",
" \"color\": \"orange\",\n",
" },\n",
" name=\"granule bounds\",\n",
" overlay=True,\n",
" control=True,\n",
" show=True,\n",
")\n",
"\n",
"m = Map(\n",
" tiles=\"OpenStreetMap\",\n",
" location=(\n",
" (tilejson[\"bounds\"][1] + tilejson[\"bounds\"][3]) / 2,\n",
" (tilejson[\"bounds\"][0] + tilejson[\"bounds\"][2]) / 2\n",
" ),\n",
" zoom_start=8,\n",
")\n",
"tiles.add_to(m)\n",
"geojson_layer.add_to(m)\n",
"\n",
"LayerControl(collapsed=False).add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"id": "3fcb46b4-ceb1-46c0-a692-70d74dc2565f",
"metadata": {},
"source": [
"**Note:** The plot may take 20–30 seconds to load."
]
}
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