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jpswinskijpswinski
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updated multi-mission grand mesa with sampling on gedi apis
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‎examples/multi_mission_grand_mesa.ipynb‎

Lines changed: 68 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -52,7 +52,7 @@
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},
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"outputs": [],
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"source": [
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"sliderule.init(\"slideruleearth.io\", verbose=True, organization=\"developers\")"
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"sliderule.init(\"slideruleearth.io\", verbose=True)"
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]
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},
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{
@@ -72,16 +72,60 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6c8189bb-3f36-44ce-8e03-e1a270daa800",
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"id": "d923a9e7-d634-4cb2-99ae-42f6d1f166a5",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"asset = \"icesat2\"\n",
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"resource = \"ATL03_20220105023009_02111406_005_01.h5\"\n",
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"resource = \"ATL03_20220105023009_02111406_005_01.h5\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "636f5e23-76c0-492b-9301-c47c8d39c81b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"region = sliderule.toregion('grandmesa.geojson')\n",
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"catalog = earthdata.stac(short_name=\"HLS\", polygon=region[\"poly\"], time_start=\"2022-01-01T00:00:00Z\", time_end=\"2022-03-01T00:00:00Z\", as_str=True)\n",
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"catalog = earthdata.stac(short_name=\"HLS\", polygon=region[\"poly\"], time_start=\"2022-01-01T00:00:00Z\", time_end=\"2022-03-01T00:00:00Z\", as_str=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d8c1e30a-fd05-4652-8d5b-f8bc6bb30d78",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"samples = {\n",
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" \"landsat\": {\n",
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" \"asset\": \"landsat-hls\",\n",
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" \"catalog\": catalog,\n",
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" \"closest_time\": \"2022-01-05T00:00:00Z\", \n",
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" \"bands\": [\"NDVI\"]\n",
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" },\n",
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" \"gedi\": {\n",
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" \"asset\": \"gedil4b\"\n",
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" } \n",
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"}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "00b01ed7-c5dc-4e72-ac43-cb195b1641ab",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"parms = { \n",
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" \"poly\": region['poly'],\n",
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" \"ats\": 5.0,\n",
@@ -100,17 +144,7 @@
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" \"use_abs_h\": False, \n",
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" \"send_waveform\": False\n",
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" },\n",
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" \"samples\": {\n",
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" \"landsat\": {\n",
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" \"asset\": \"landsat-hls\",\n",
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" \"catalog\": catalog,\n",
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" \"closest_time\": \"2022-01-05T00:00:00Z\", \n",
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" \"bands\": [\"NDVI\"]\n",
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" },\n",
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" \"gedi\": {\n",
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" \"asset\": \"gedil4b\"\n",
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" } \n",
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" } \n",
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" \"samples\": samples\n",
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"}"
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]
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},
@@ -141,7 +175,7 @@
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"id": "b779ddf2-f9ea-41c2-bb9a-1db92e277fe7",
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"metadata": {},
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"source": [
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"#### Display GeoDataFrame\n",
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"#### Display ATL08 GeoDataFrame\n",
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"Notice the columns that start with \"landsat\" and \"gedi\""
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]
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},
@@ -210,20 +244,31 @@
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" \"poly\": region[\"poly\"],\n",
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" \"degrade_flag\": 0,\n",
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" \"l2_quality_flag\": 1,\n",
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" \"beam\": 0\n",
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" \"beam\": 0,\n",
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" \"samples\": samples\n",
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"}\n",
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"\n",
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"# Turn verbose off\n",
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"sliderule.set_verbose(False)\n",
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"#sliderule.set_verbose(False)\n",
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"\n",
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"# Request GEDI L4A Data\n",
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"gedi04a = gedi.gedi04ap(parms) "
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]
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},
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{
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"cell_type": "markdown",
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"id": "3c54c72b-23ee-4fad-b230-2b848c3b9739",
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"metadata": {
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"tags": []
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},
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"source": [
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"#### Display GEDI 04A GeoDataFrame"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4a091f74-f1da-43fe-99d8-5f3d15fde59d",
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"id": "ebbe90d5-d695-4818-a20a-9670dccfbff2",
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"metadata": {
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"tags": []
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},
@@ -333,23 +378,23 @@
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"\n",
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"# Filter DataFrame\n",
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"df = mmds[(mmds['rgt'] == 211) & (mmds['gt'] == 30) & (mmds['cycle'] == 14)]\n",
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"df = df[df[\"landsat.value\"] < 100]\n",
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"df = df[df[\"gedi.value\"] > -100]\n",
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"df = df[df[\"landsat.value_gedi04a\"] < 100]\n",
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"df = df[df[\"gedi.value_gedi04a\"] > -100]\n",
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"\n",
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"# Plot SlideRule ATL08 Vegetation Photon Counts\n",
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"sc1 = ax.scatter(df.index.values, df[\"veg_ph_count\"].values, c='red', s=2.5)\n",
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"legend_elements.append(matplotlib.lines.Line2D([0], [0], color='red', lw=6, label='ATL08'))\n",
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"\n",
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"# Plot GEDI L4B AGBD\n",
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"sc2 = ax.scatter(df.index.values, df[\"gedi.value\"].values, c='blue', s=2.5)\n",
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"sc2 = ax.scatter(df.index.values, df[\"gedi.value_gedi04a\"].values, c='blue', s=2.5)\n",
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"legend_elements.append(matplotlib.lines.Line2D([0], [0], color='blue', lw=6, label='L4B AGBD'))\n",
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"\n",
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"# Plot GEDI L4A AGBD\n",
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"sc3 = ax.scatter(df.index.values, df[\"agbd\"].values, c='green', s=2.5)\n",
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"legend_elements.append(matplotlib.lines.Line2D([0], [0], color='green', lw=6, label='L4A AGBD'))\n",
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"\n",
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"# Plot LandSat NVDI\n",
352-
"sc3 = ax.scatter(df.index.values, df[\"landsat.value\"].values, c='orange', s=2.5)\n",
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"sc3 = ax.scatter(df.index.values, df[\"landsat.value_gedi04a\"].values, c='orange', s=2.5)\n",
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"legend_elements.append(matplotlib.lines.Line2D([0], [0], color='orange', lw=6, label='HLS NVDI'))\n",
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"\n",
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"\n",

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