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],
"text/plain": [
":Overlay\n",
" .Polygons.I :Polygons [Longitude,Latitude]\n",
" .WMTS.I :WMTS [Longitude,Latitude]\n",
" .Points.I :Points [Longitude,Latitude] (Beam,Shot Number,Stale Return Flag,Degrade)"
]
},
"execution_count": 25,
"metadata": {
"application/vnd.holoviews_exec.v0+json": {
"id": "1022"
}
},
"output_type": "execute_result"
}
],
"source": [
"# Call the function for plotting the GEDI points\n",
"gv.Polygons(redwoodNP).opts(line_color='red', color=None) * pointVisual(latslons, vdims = vdims)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Above is a good illustration of the full GEDI orbit (GEDI files are stored as one ISS orbit). One of the benefits of using geoviews is the interactive nature of the output plots. Use the tools to the right of the map above to zoom in and find the shots intersecting Redwood National Park. \n",
"> (**HINT**: find where the orbit intersects the west coast of the United States)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Below is a screenshot of the region of interest:\n",
"![alt text](GEDI_Tutorial_11.png \"Sample of GEDI L1B shots in yellow (orbit 2932) plotted over Redwood National Park, USA.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Side Note: Wondering what the 0's and 1's for `stale_return_flag` and `degrade` mean?"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"stale_return_flag: Indicates that a \"stale\" cue point from the coarse search algorithm is being used.\n",
"degrade: Greater than zero if the shot occurs during a degrade period, zero otherwise.\n"
]
}
],
"source": [
"print(f\"stale_return_flag: {gediL1B[b]['stale_return_flag'].attrs['description']}\")\n",
"print(f\"degrade: {gediL1B[b]['geolocation']['degrade'].attrs['description']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### We will show an example of how to quality filter GEDI data in section 5.\n",
"#### After finding one of the shots within Redwood NP, find the index for that shot number so that we can find the correct waveform to visualize in Section 4. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Each GEDI shot has a unique shot identifier (shot number) that is available within each data group of the product. The shot number is important to retain in any data subsetting as it will allow the user to link any shot record back to the original orbit data, and to link any shot and its data between the L1 and L2 products. The standard format for GEDI Shots is as follows:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Shot: 29320619900465601\n",
"> **2932**: Orbit Number \n",
"**06**: Beam Number \n",
"**199**: Minor frame number (0-241) \n",
"**00465601**: Shot number within orbit "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"shot = 29320619900465601"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"465600"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"index = np.where(gediL1B[f'{beamNames[0]}/shot_number'][()]==shot)[0][0] # Set the index for the shot identified above\n",
"index"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"del latslons # No longer need the geodataframe used to visualize the full GEDI orbit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"# 4. Subset and Visualize Waveforms \n",
"#### In this section, learn how to extract and subset specific waveforms and plot them using `holoviews`. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4.1 Import and Extract Waveforms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### In order to find and extract the full waveform for the exact index we are interested in, instead of importing the entire waveform dataset (over 1 billion values!), we will use the `rx_sample_count` and `rx_sample_start_index` to identify the location of the waveform that we are interested in visualizing and just extract those values."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"# From the SDS list, use list comprehension to find sample_count, sample_start_index, and rxwaveform\n",
"sdsCount = gediL1B[[g for g in gediSDS if g.endswith('/rx_sample_count') and beamNames[0] in g][0]]\n",
"sdsStart = gediL1B[[g for g in gediSDS if g.endswith('/rx_sample_start_index') and beamNames[0] in g][0]]\n",
"sdsWaveform = [g for g in gediSDS if g.endswith('/rxwaveform') and beamNames[0] in g][0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Print the description for each of these datasets to better understand how we will use them to extract specific waveforms."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"rxwaveform is The corrected receive (RX) waveforms. Use rx_sample_count and rx_sample_start_index to identify the location of each waveform.\n"
]
}
],
"source": [
"print(f\"rxwaveform is {gediL1B[sdsWaveform].attrs['description']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Next, read how to use the rx_sample_count and rx_sample_start_index layers:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"rx_sample_count is The number of sample intervals (elements) in each RX waveform.\n",
"rx_sample_start_index is The index in the rxwaveform dataset of the first element of each RX waveform. The indices start at 1.\n"
]
}
],
"source": [
"print(f\"rx_sample_count is {sdsCount.attrs['description']}\")\n",
"print(f\"rx_sample_start_index is {sdsStart.attrs['description']}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use `rx_sample_count` and `rx_sample_start_index` to identify the location of each waveform in `rxwaveform`."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"wfCount = sdsCount[index] # Number of samples in the waveform\n",
"wfStart = int(sdsStart[index] - 1) # Subtract one because python array indexing starts at 0 not 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Next grab additional information about the shot, including the unique `shot_number`, and lat/lon location."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"wfShot = gediL1B[f'{beamNames[0]}/shot_number'][index]\n",
"wfLat = gediL1B[f'{beamNames[0]}/geolocation/latitude_bin0'][index]\n",
"wfLon = gediL1B[f'{beamNames[0]}/geolocation/longitude_bin0'][index]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Put everything together to identify the waveform we want to extract:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The waveform located at: 41.28533109208681, -124.0300090357876 (shot ID: 29320619900465601, index 465600) is from beam BEAM0110 and is stored in rxwaveform beginning at index 661152000 and ending at index 661152812\n"
]
}
],
"source": [
"print(f\"The waveform located at: {str(wfLat)}, {str(wfLon)} (shot ID: {wfShot}, index {index}) is from beam {beamNames[0]} \\\n",
" and is stored in rxwaveform beginning at index {wfStart} and ending at index {wfStart + wfCount}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### In order to plot a full waveform, you also need to import the elevation recorded at `bin0` (the first elevation recorded) and `lastbin` or the last elevation recorded for that waveform."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"# Grab the elevation recorded at the start and end of the full waveform capture\n",
"zStart = gediL1B[f'{beamNames[0]}/geolocation/elevation_bin0'][index] # Height of the start of the rx window\n",
"zEnd = gediL1B[f'{beamNames[0]}/geolocation/elevation_lastbin'][index] # Height of the end of the rx window"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extract the full waveform using the index start and count:\n",
"#### Below you can see why it is important to extract the specific waveform that you are interested in: almost 1.4 billion values are stored in the rxwaveform dataset!"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1,391,172,580\n"
]
}
],
"source": [
"print(\"{:,}\".format(gediL1B[sdsWaveform].shape[0]))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# Retrieve the waveform sds layer using the sample start index and sample count information to slice the correct dimensions\n",
"waveform = gediL1B[sdsWaveform][wfStart: wfStart + wfCount]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4.2 Visualize Waveforms\n",
"#### Below, plot the extracted waveform using the elevation difference from bin0 to last_bin on the y axis, and the waveform energy returned or amplitude (digital number = dn) on the x axis. "
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"# Find elevation difference from start to finish and divide into equal intervals based on sample_count\n",
"zStretch = np.add(zEnd, np.multiply(range(wfCount, 0, -1), ((zStart - zEnd) / int(wfCount))))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
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