LostHills_time_series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1\">Copyright 2021, by the California Institute of Technology. ALL RIGHTS RESERVED. United States Government sponsorship acknowledged. Any commercial use must be negotiated with the Office of Technology Transfer at the California Institute of Technology.</font>\n",
    "    \n",
    "<font size=\"1\">This software may be subject to U.S. export control laws and regulations. By accepting this document, the user agrees to comply with all applicable U.S. export laws and regulations. User has the responsibility to obtain export licenses, or other export authority as may be required, before exporting such information to foreign countries or providing access to foreign persons.<font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# InSAR Time Series Analysis using MintPy and ARIA products\n",
    "\n",
    "This notebook is yet another modification of those below. Credit goes to Eric Fielding for providing this example of an OpenSarLabs notebook running in the NISAR On-Demand system. Beyond making the staged data available in a NISAR-friendly bucket, this notebook required little to no changes.\n",
    "\n",
    "#### Kernel: mintpy\n",
    "\n",
    "-----\n",
    "\n",
    "**Author:** Eric Fielding, David Bekaert, Heresh Fattahi and Zhang Yunjun \n",
    "\n",
    " This notebook is a second modification by Eric Fielding from the notebook (https://opensarlab.asf.alaska.edu/user/ejfielding/notebooks/NISAR_ST_demo/ARIA-tools-docs/JupyterDocs/NISAR/L2_interseismic/mintpySF/smallbaselineApp_aria.ipynb) by David Bekaert, Heresh Fattahi and Zhang Yunjun that was originally focused on San Francisco. \n",
    "\n",
    " This notebook is a modification from the [original](https://nbviewer.jupyter.org/github/insarlab/MintPy-tutorial/blob/master/smallbaselineApp_aria.ipynb) by Heresh Fattahi and Zhang Yunjun. \n",
    "\n",
    "\n",
    "**Mapping oilfield motion with InSAR time series**\n",
    "\n",
    "Quick hack to change to Lost Hills oil field area.\n",
    "\n",
    "The Caltech-JPL ARIA project in partnership with NASA Getting Ready for NISAR (GRFN) project has been generated surface displacement products (interferograms) mimicking the NISAR L2 GUNW (Geocoded Unwrapped phase interferograms) product formatting. The interferograms are stored at the NASA ASF DAAC, and are accessible with an Open Source set of tools called ARIA-tools. The Miami Insar Timeseries software in PYthon (MintPy), an open-source package for InSAR time-series analysis, is compatible with the outputs from the ARIA-tools package, and in combination with the ARIA-tools pre-processor can be used to estimate ground displacement time-series. \n",
    "\n",
    "The Jupyter notebook presented here is meant as an practical example on the use of Jupyter for exploring landslide displacements. In the example below, we will demonstrate a time-series derived from ARIA standard InSAR products over the Los Angeles, California area revealing landslide motion on the Palos Verdes peninsula. \n",
    "\n",
    "See the other tutorials on ARIA-tools to learn more about how to use that package.\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "<b>To save time, we have pre-ran the ARIA-tools package</b> \n",
    "\n",
    "    ariaDownload.py -b '35.4 35.7 -119.8 -119.6' --track 144\n",
    "    \n",
    "    ariaTSsetup.py -f 'products/*.nc' -b '35.4 35.7 -119.8 -119.6' --mask Download\n",
    "\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    " \n",
    "<b>The staged data was uploaded in a NISAR S3 data bucket and can be downloaded by running the following commands:</b>\n",
    "\n",
    "    cd notebook_output/LostHills_time_series\n",
    "    \n",
    "    aws s3 cp s3://nisar-dev-ondemand/LostHills_time_series/losthills_stack.tar.gz losthills_stack.tar.gz\n",
    "    \n",
    "    tar -xzvf losthills_stack.tar.gz\n",
    "\n",
    "    rm losthills_stack.tar.gz\n",
    "    \n",
    "These steps are provided for you in cell **0.1 Unpackage Staged Data**</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Notebook setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below is required to be ran each time the notebook is started and ensures correct set-up of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# this block makes sure the directory set-up/change is only done once and relative to the notebook's directory\n",
    "try:\n",
    "    start_dir\n",
    "except NameError:\n",
    "    start_dir = os.getcwd()\n",
    "\n",
    "work_dir = os.path.join(start_dir, \"notebook_output\",\"LostHills_time_series\")\n",
    "!mkdir -p $work_dir\n",
    "os.chdir(work_dir)\n",
    "print(\"Work directory: \", work_dir)\n",
    "\n",
    "support_dir = os.path.join(start_dir,\"support_docs\",\"LostHills_time_series\")\n",
    "print(\"Supporting docs directory: \", support_dir)\n",
    "\n",
    "# define the MintPy time-series directory\n",
    "mint_dir = os.path.join(work_dir,'MintPy')\n",
    "print(\"MintPy directory: \", mint_dir)\n",
    "!mkdir -p $mint_dir\n",
    "\n",
    "# verify if mintpy install is complete:\n",
    "try:\n",
    "    import numpy as np\n",
    "    from mintpy import view, tsview, plot_network, plot_transection, plot_coherence_matrix\n",
    "except:\n",
    "    print(\"Looks like mintPy is not fully installed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### 0.1 Unpackage staged data\n",
    "\n",
    "This step is performed if the staged data has been downloaded from S3. Be sure your AWS credentials are valid (using *aws-login* from a terminal window if necessary). Once this step is completed, proceed to **Step 1. - smallbaselineApp.py overview**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd $work_dir\n",
    "if not os.path.isfile('losthills_stack.tar.gz'):\n",
    "    !aws s3 cp s3://nisar-dev-ondemand/LostHills_time_series/losthills_stack.tar.gz losthills_stack.tar.gz \n",
    "    print('File downloaded.')\n",
    "    \n",
    "# verify if download was succesfull\n",
    "if os.path.isfile('losthills_stack.tar.gz'):\n",
    "    !tar -xzf losthills_stack.tar.gz\n",
    "    print('S3 pre-staged data retrieval was successfull')\n",
    "    !rm losthills_stack.tar.gz\n",
    "else:\n",
    "    print('Download of staged data failed.')          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0.1b - Download the raw data\n",
    "\n",
    "#### Skip this step if you have successfully downloaded the staged data in step 0.1.\n",
    "\n",
    "The following command will download all the ARIA standard products from track 144 over the Lost Hills area, which is 795 products at the time of this writing (last update January 2021). **This will take more than two hours to complete if the data is not already downloaded.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download data for descending track 144 over Lost Hills area\n",
    "!ariaDownload.py -b '35.4 35.7 -119.8 -119.6' --track 144"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Also skip this step if you have successfully downloaded the staged data in step 0.1.\n",
    "\n",
    "\n",
    "The following ARIA time-series setup `ariaTSsetup.py` extracts the data that covers only a small area around the Lost Hills and South Belridge oil fields to speed the time-series processing, which we specify with the bounding box. We also download the water mask to avoid using data over any water in this area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ariaTSsetup.py -f 'products/*.nc' -b '35.4 35.7 -119.8 -119.6' --mask Download"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. smallbaselineApp.py overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This application provides a workflow which includes several steps to invert a stack of unwrapped interferograms and apply different corrections to obtain ground displacement timeseries.  \n",
    "The workflow consists of two main blocks:\n",
    "\n",
    "* correcting unwrapping errors and inverting for the raw phase time-series (blue ovals),\n",
    "* correcting for noise from different sources to obtain the displacement time-series (green ovals).\n",
    "\n",
    "Some steps are optional, which are switched off by default (marked by dashed boundaries). Configuration parameters for each step are initiated with default values in a customizable text file: [smallbaselineApp.cfg](https://github.com/insarlab/MintPy/blob/master/mintpy/defaults/smallbaselineApp.cfg). In this notebook, we will walk through some of these steps, for a complete example see the [MintPy repository](https://github.com/insarlab/MintPy).\n",
    "\n",
    "<p align=\"left\">\n",
    "  <img width=\"600\" src=\"support_docs/LostHills_time_series/smallbaselineApp_workflow.png\">\n",
    "</p>     \n",
    "<p style=\"text-align: center;\">\n",
    "    (Figure from Yunjun et al., 2019)\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Processing steps of smallbaselineApp.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MintPy **smallbaselineApp.py** application provides a workflow to invert a stack of unwrapped interferograms and apply different (often optional) corrections to obtain ground displacement timeseries. A detailed overview of the options can be retrieved by involking the help option:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!smallbaselineApp.py --help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Configuring processing parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The processing parameters for the **smallbaselineApp.py** are controlled through a configuration file. If no file is provided the default [smallbaselineApp.cfg](https://github.com/insarlab/MintPy/blob/master/mintpy/defaults/smallbaselineApp.cfg) configuration is used. Here we use `LHSenDT144.txt`, which already constains selected, manually modified configuration parameters for this time-series analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Small Baseline Time Series Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1. Loading ARIA data into MintPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [ARIA-tools package](https://github.com/aria-tools/ARIA-tools) is used as a pre-processor for MintPY. It has a download tool that wraps around the ASF DAAC API, and includes tools for stitching/cropping and time-series preparation. The output of the time-series preparation is compatible with the [data directory](https://mintpy.readthedocs.io/en/latest/dir_structure/) structure from MintPy. To save time, we have already pre-ran these steps. The commands used were:\n",
    "\n",
    "```\n",
    "!ariaDownload.py -b '33.5 34.5 -119.0 -117.9' --track 71\n",
    "!ariaTSsetup.py -f 'products/*.nc' -b '33.65 33.9 -118.45 -118.15' --mask Download\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ariaTSsetup.py` step above (or the pre-processed Stack.zip) extracted the data for the subset we specified and found a total of 439 products that cover our study area. Now we load the data for the subset area into MintPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd $work_dir\n",
    "\n",
    "# copy the configuration template file\n",
    "!cp $support_dir/LHSenDT144.txt MintPy\n",
    "\n",
    "print('Go to work directory: {}'.format(mint_dir))\n",
    "os.chdir(mint_dir)  \n",
    "\n",
    "!smallbaselineApp.py --dostep load_data LHSenDT144.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1b Verify That The Processing Inputs Exist\n",
    "**Resume here if you have downloaded the staged data from S3**\n",
    "\n",
    "The output of the loading step is an \"inputs\" directory containing two HDF5 files:\n",
    "- ifgramStack.h5: This file contains 6 dataset cubes (e.g. unwrapped phase, coherence, connected components etc.) and multiple metadata.  \n",
    "- geometryGeo.h5: This file contains geometrical datasets (e.g., incidence/azimuth angle, masks, etc.). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%cd $mint_dir\n",
    "!ls inputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<b>info.py :</b> \n",
    "To get general infomation about a MintPy product, run info.py on the file.   \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mintpy\n",
    "print(mintpy.__file__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# determine location of mintpy to add to PATH\n",
    "import os\n",
    "import mintpy\n",
    "mintpy_dir = os.path.dirname(mintpy.__file__)\n",
    "\n",
    "os.environ['PATH'] = f\"{os.environ['PATH']}:{mintpy_dir}\"\n",
    "!info.py inputs/ifgramStack.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!info.py inputs/geometryGeo.h5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2. Plotting the interferogram network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running **plot_network.py** gives an overview of the network and the average coherence of the stack. The program creates multiple files as follows:\n",
    "- ifgramStack_coherence_spatialAvg.txt: Contains interferogram dates, average coherence temporal and spatial baseline separation.\n",
    "- Network.pdf: Displays the network of interferograms on time-baseline coordinates, colorcoded by avergae coherence of the interferograms. \n",
    "- CoherenceMatrix.pdf shows the avergae coherence pairs between all available pairs in the stack."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!smallbaselineApp.py --dostep modify_network LHSenDT144.txt  \n",
    "plot_network.main(['inputs/ifgramStack.h5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3.  Mask generation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mask files can be can be used to mask pixels in the time-series processing. Below we generate a mask file based on the connected components, which is a metric for unwrapping quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!generate_mask.py  inputs/ifgramStack.h5  --nonzero  -o maskConnComp.h5  --update\n",
    "view.main(['maskConnComp.h5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## reference_point\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interferometric phase is relative observation by nature. The phases of each unwrapped interferogram are relative with respect to an arbitrary pixel. Therfore we need to reference all interferograms to a common reference pixel.\n",
    "The step \"reference_point\" selects a common reference pixel for the stack of interferograms. The default approach of mintpy is to choose a pixel with highest spatial coherence in the stack. Other options include specifying the longitude and latitude of the desired reference pixel or the line and column number of the refence pixel.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!smallbaselineApp.py --dostep reference_point LHSenDT144.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running the \"reference_step\" adds additional attributes \"REF_X, REF_Y\" and \"REF_LON, REF_LAT\" to the ifgramStack.h5 file. To see the attributes of the file run info.py:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!info.py inputs/ifgramStack.h5 | egrep 'REF_'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, I set the reference point latitude and longitude to be in a location close to the Portuguese Bend Landslide, and MintPy calculated the X and Y locations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4. Inverting of the Small Baseline network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next step we invert the network of differential unwrapped interferograms to estimate the time-series of unwrapped phase with respect to a reference acquisition date. By default mintpy selects the first acquisition. The estimated time-series is converted to distance change from radar to target and is provided in meters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!smallbaselineApp.py --start reference_point LHSenDT144.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!smallbaselineApp.py --dostep invert_network LHSenDT144.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The timeseries file contains three datasets:\n",
    "- the \"time-series\" which is the interferometric range change for each acquisition relative to the reference acquisition,\n",
    "- the \"date\" dataset which contains the acquisition date for each acquisition,\n",
    "- the \"bperp\" dataset which contains the timeseries of the perpendicular baseline.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5. Estimating the long-term velocity rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ground deformation caused by many geophysical or anthropogenic processes are linear at first order approximation. Therefore it is common to estimate the rate of the ground deformation which is the slope of linear fit to the time-series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!smallbaselineApp.py --dostep velocity LHSenDT144.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scp_args = 'velocity.h5 velocity -v -10 10'\n",
    "view.main(scp_args.split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "<b>Note :</b> \n",
    "Negative values indicates that target is moving away from the radar (i.e., Subsidence in case of vertical deformation).\n",
    "Positive values indicates that target is moving towards the radar (i.e., uplift in case of vertical deformation). \n",
    "    The line of sight (LOS) for this descending Sentinel-1 track is up and east from ground to radar.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Obvious features in the estimated velocity map:\n",
    "\n",
    "1) There are several features with significant velocity in this area. \n",
    "\n",
    "2) The negative LOS feature on the Palos Verdes peninsula (center left of map) is the Portuguese Bend Landslide, moving down and southwest toward the sea.\n",
    "\n",
    "3) There are areas of positive and negative LOS change in the area of Long Beach (east part of map). These are due to the extraction of oil and injection of water in oilfields beneath the city and out in the harbor.\n",
    "\n",
    "4) The block box at 33.76N, 118.36W is the reference pixel for this map, just north of the Portuguese Bend Landslide. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Error analysis (what is signal, what is noise!)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncertainty of the ground displacement products derived from InSAR time-series, depends on the quality of the inversion of the stack of interferograms and the accuracy in separating the ground displacement from other components of the InSAR data. Therefore the definition of signal vs noise is different at the two main steps in mintpy:  \n",
    "\n",
    "1) During the inversion: \n",
    "    At this step all systematic components of the interferometric phase (e.g., ground displacement, propagation delay, geometrical residuals caused by DEM or platform's orbit inaccuracy) are considered signal, while the interferometric phase decorrelation, phase unwrapping error and phase inconsistency are considered noise. \n",
    "    \n",
    "2) After inversion: the ground displacement component of the time-serieses is signal, and everything else (including the propagation delay and geometrical residuals) are considered noise\n",
    "\n",
    "Therefore we first discuss the possible sources of error during the inversion and the existing ways in MintPy to evaluate the quality of inversion and to improve the uncertainty of the inversion. Afterwards we explain the different components of the time-series and the different processing steps in MintPy to separate them from ground displacement signal.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Quality of the inversion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main sources of noise during the time-series inversion includes decorrelation, phase unwrapping error and the inconsistency of triplets of interferofgrams. Here we mainly focus on the decorrelation and unwrapping errors. We first show the existing quantities in MintPy to evaluate decorrelation and unwrapping errors and then discuss the existing ways in MintPy to reduce the decorrelation and unwrapping errors on the time-series inversion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.1 Average spatial coherence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mintpy computes temporal average of spatial coherence of the entire stack as a potential ancillary measure to choose reliable pixels after time-series inversion. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "view.main(['avgSpatialCoh.h5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1.2 Temporal coherence\n",
    "\n",
    "In addition to timeseries.h5 which contains the time-series dataset, invert_network produces other quantities, which contain metrics to evaluate the quality of the inversion including temporalCoherence.h5. Temporal coherence represents the consistency of the timeseries with the network of interferograms. \n",
    "\n",
    "Temporal coherence varies from 0 to 1. Pixels with values closer to 1 are considered reliable and pixels with values closer to zero are considered unreliable. For a dense network of interferograms, a threshold of 0.7 may be used (Yunjun et al, 2019)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "view.main(['temporalCoherence.h5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With both the spatial coherence and temporal coherence, we can see that the InSAR in the ports of Long Beach and Los Angeles have unstable phase, and the InSAR measurements there will be low quality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2. Velocity error analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The estimated velocity also comes with an expression of unecrtainty which is simply based on the goodness of fit while fitting a linear model to the time-series. This quantity is saved in \"velocity.h5\" under the velocityStd dataset. \n",
    "\n",
    "**Mintpy supports additional corrections in its processing not included in this demo:**\n",
    "- Unwrapping error correction\n",
    "- Tropospheric delay correction\n",
    "- deramping\n",
    "- Topographic residual correction\n",
    "- Residual RMS for noise evaluation\n",
    "- Changing the reference date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scp_args = 'velocity.h5 velocityStd -v 0 0.2'\n",
    "view.main(scp_args.split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the plot above is the velocity error, not the velocity. The errors generally increase with distance from the reference point and also increase for points with elevations different from the reference point because of topographically correlated water vapor variations that are especially strong in this area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# complete the processing steps\n",
    "!smallbaselineApp.py --start geocode LHSenDT144.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Plotting a Landslide Motion Transect "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scp_args = 'velocity.h5 —start-lalo 35.41 -119.74  --end-lalo 35.50 -119.70'\n",
    "plot_transection.main(scp_args.split())\n",
    "\n",
    "!smallbaselineApp.py smallbaselineApp.cfg --dostep google_earth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " On this transect, the Portuguese Bend Landslide has an average velocity over the last five years that reaches more than 2.5 cm/year in the descending track radar LOS direction (note that the \"zero\" is about -5 so we have to subtract that first). By analyzing the velocity on the ascending track and combining the two LOS directions, then we could find out that the displacments are largely horizontal, westward (and southward that we can't see with InSAR)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reference material"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Original Notebook withe detailed description by Yunjun and Fattahi at: https://nbviewer.jupyter.org/github/insarlab/MintPy-tutorial/blob/master/smallbaselineApp_aria.ipynb\n",
    "\n",
    "- Mintpy reference: *Yunjun, Z., H. Fattahi, F. Amelung (2019), Small baseline InSAR time series analysis: unwrapping error correction and noise reduction, preprint doi:[10.31223/osf.io/9sz6m](https://eartharxiv.org/9sz6m/).*\n",
    "\n",
    "- University of Miami online time-series viewer: https://insarmaps.miami.edu/\n",
    "\n",
    "- Mintpy Github repository: https://github.com/insarlab/MintPy\n",
    "\n",
    "- ARIA-tools Github Repository: https://github.com/aria-tools/ARIA-tools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"1\">This notebook is compatible with NISAR Jupyter Server Stack v1.7.1 and above</font>"
   ]
  }
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