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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "3646013e-5822-4d9e-9758-052b61a67bec",
+   "metadata": {},
+   "source": [
+    "# <center>BioSCape Data Skills Workshop: From the Field to the Image</center>\n",
+    "<center><img src=\"images/121229-87.png\"/></center>\n",
+    "\n",
+    "[**BioSCape**](https://www.bioscape.io/), the Biodiversity Survey of the Cape, is NASA’s first biodiversity-focused airborne and field campaign that was conducted in South Africa in 2023. BioSCape’s primary objective is to study the structure, function, and composition of the region’s ecosystems, and how and why they are changing. \n",
+    "\n",
+    "BioSCape's airborne dataset is unprecedented, with `AVIRIS-NG`, `PRISM`, and `HyTES` imaging spectrometers capturing spectral data across the UV, visible and infrared at high resolution and `LVIS` acquiring coincident full-waveform lidar. BioSCape's `field dataset` is equally impressive, with 18 PI-led projects collecting data ranging from the diversity and phylogeny of plants, kelp and phytoplankton, eDNA, landscape acoustics, plant traits, blue carbon accounting, and more\n",
+    "\n",
+    "This workshop will equip participants with the skills to find, subset, and visualize the various BioSCape field and airborne (imaging spectroscopy and full-waveform lidar) data sets. Participants will learn data skills through worked examples in terrestrial and aquatic ecosystems, including: wrangling lidar data, performing band math calculations, calculating spectral diversity metrics, spectral unmixing, machine learning and image classification, and mapping functional traits using partial least squares regression. The workshop format is a mix of expert talks and interactive coding notebooks and will be run through the BioSCape Cloud computing environment.\n",
+    "\n",
+    "**Date:** October 9 - 11, 2024 Cape Town, South Africa</center>\n",
+    "\n",
+    "**Host:** NASA’s Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), in close collaboration with BioSCape, the South African Environmental Observation Network (SAEON), the University of Wisconsin Madison (Phil Townsend), The Nature Conservancy (Glenn Moncrieff), the University of California Merced (Erin Hestir), the University of Cape Town (Jasper Slingsby), Jet Propulsion Laboratory (Kerry Cawse-Nicholson), and UNESCO.\n",
+    "\n",
+    "**Instructors:** \n",
+    "- In-person contributors: Anabelle Cardoso, Erin Hestir, Phil Townsend, Henry Frye, Glenn Moncrieff, Jasper Slingsby, Michele Thornton, Rupesh Shrestha\n",
+    "- Virtual contributors: Kerry Cawse-Nicholson, Nico Stork, Kyle Kovach\n",
+    "\n",
+    "**Audience:** This training is primarily intended for government natural resource management agency representatives and field technicians in South Africa, as well as local academics and students, especially those connected to the BioSCape Team. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eafb9c74-866b-4d40-af37-9c7714fd81ca",
+   "metadata": {},
+   "source": [
+    "## Calculate Turbidity over a PRISM scene\n",
+    "-----"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5aa52134-af0e-46af-a312-df6990c7a44b",
+   "metadata": {},
+   "source": [
+    "### Overview\n",
+    "-----\n",
+    "Turbidity is a key water quality parameter that measures the clarity or cloudiness of water, primarily caused by the presence of suspended particles. These particles can include silt, clay, organic matter, algae, plankton, and other microscopic organisms. Essentially, turbidity indicates how much material is present in the water that scatters or absorbs light, which directly impacts its transparency.\n",
+    "\n",
+    "![](images/turbidity.png)\n",
+    "Water samples showing increasing turbidity and changes in water color. From https://blogs.worldbank.org/en/water/how-test-water-quality-here-are-some-low-cost-low-tech-options \n",
+    "\n",
+    "As a water quality indicator, turbidity can reveal much about aquatic habitats. High turbidity can reduce light penetration, which affects photosynthesis in aquatic plants and algae. This reduction in primary productivity impacts the entire aquatic food web. High turbidity can also clog fish gills and reduce habitat quality for aquatic organisms. High turbidity often correlates with elevated nutrient levels, particularly phosphorus, which can promote excessive algal growth, leading to eutrophication. This process can deplete oxygen levels in the water, harming fish and other aquatic organisms. In some instances, moderate to high turbidity is necessary for certain fish species, as they are well-adapted for low light environments. \n",
+    "\n",
+    "Additionally, turbidity is a critical parameter for drinking water quality. Elevated turbidity may indicate the presence of pathogens such as bacteria, viruses, and parasites, as these microorganisms can attach to suspended particles. Thus, clear water is generally considered safer for consumption.\n",
+    "\n",
+    "The Portable Remote Imaging Spectrometer (PRISM) is especially designed for aquatic applications, and is well suited to measure water-leaving reflectance, which can be used to calculate turbidity. In this notebook, we will revisit opening a PRISM scene over the Theerwaterskloof Dam, the largest Dam in the province, accounting for 54% of the province's water supply. The region experienced heavy rainfall during the winter and spring of 2023, leading to high river flows and filling the dam to 100% with overspilling by the end of September 2023.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5a239cd-a69a-4af5-ac13-b22c6086f061",
+   "metadata": {},
+   "source": [
+    "### Learning Objectives\n",
+    "1. Mount the BioSCape SMCE S3 object storage in a local environment\n",
+    "2. Examine PRISM data using GDAL\n",
+    "3. Identify band numbers and associated wavelengths\n",
+    "4. Calculate turbidity using a single-band semi-empirical algorithm \n",
+    "5. Create and save a turbidity map as a GeoTIFF file\n",
+    "6. Visualize turbidity maps in QGIS\n",
+    "7. Compare two turbidity maps derived from two different wavelengths "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bcf0f6b-881b-44c8-a608-72e92fc40e50",
+   "metadata": {},
+   "source": [
+    "### Requirements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8937cb6c-0d0a-4f02-a785-7be9a812dbd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import s3fs\n",
+    "import matplotlib.pyplot as plt\n",
+    "from osgeo import gdal\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from os import path\n",
+    "import rioxarray\n",
+    "gdal.UseExceptions()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b156d55-1de9-4d31-9d0a-cd6f39dd879a",
+   "metadata": {},
+   "source": [
+    "### Content\n",
+    "----"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc30c386-adf8-40d4-8b53-110a4682f907",
+   "metadata": {},
+   "source": [
+    "Explore the BioSCape SMCE S3 PRISM data holdings\n",
+    "Let's start by exploring the BioSCape Airborne currently data available on the cloud in Amazon Storage. This AWS S3 storage is specific to the SMCE that provides data access and analytics environment to the BioSCape Science Team as well as interested researchers.\n",
+    "We'll learn how to mount the S3 object storage on our local environment, as well as learning how to bring other data to the analysis.\n",
+    "\n",
+    "SMCE = Science Managed Cloud Environment\n",
+    "S3 = Amazon Simple Storage Service (S3) is a cloud storage service that allows users to store and retrieve data\n",
+    "S3Fs is a Pythonic open source tool that mounts S3 object storage locally. S3Fs provides a filesystem-like interface for accessing objects on S3.\n",
+    "The top-level class S3FileSystem holds connection information and allows typical file-system style operations like ls\n",
+    "ls is a UNIX command to list computer files and directories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1ad24ca1-50a1-4b93-8a62-46fd667544e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['bioscape-data/AVNG',\n",
+       " 'bioscape-data/BioSCapeVegPolys2023_10_18',\n",
+       " 'bioscape-data/BioSCapeVegPolys2023_10_18.geoparquet',\n",
+       " 'bioscape-data/LVIS',\n",
+       " 'bioscape-data/PRISM',\n",
+       " 'bioscape-data/bioscape_avng.yaml']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use S3Fs to list the BioSCape data on the BioSCape SMCE S3 storage\n",
+    "s3 = s3fs.S3FileSystem(anon=False)\n",
+    "files = s3.ls('bioscape-data/')\n",
+    "files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "270baf55-2410-409c-984f-4c78cf7c555d",
+   "metadata": {},
+   "source": [
+    "#### Portable Remote Imaging Spectrometer (PRISM)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1e5550d7-9827-46e6-8c6d-70b30dae496d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['bioscape-data/PRISM/L2']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "PRISM_flightlines = s3.ls('bioscape-data/PRISM')\n",
+    "PRISM_flightlines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "522a0da1-ba15-4e03-8e63-5d24ca24a9c8",
+   "metadata": {},
+   "source": [
+    "Examine PRISM Reflectance Data as a GDAL Raster Dataset\n",
+    "GDAL (Geospatial Data Abstraction Library) is a translator library for raster and vector geospatial data formats\n",
+    "In this step, we will use GDAL to examine the PRISM reflectance data that is in ENVI binary format (a proprietary, but common distribution format)\n",
+    "\n",
+    "We need to configure our S3 credentials for GDAL\n",
+    "The GDAL utility expects S3 links to be formated with the GDAL virtual file system (VSI) S3 path. We therefore have to use the VSI path to access the files with GDAL. We'll substitute the S3 link with the VSI (vsis3) link(s)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2d667702-8462-4bb3-8e34-fd8e48f9aada",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rfl_link = 'bioscape-data/PRISM/L2/prm20231126t080430_rfl_ort'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "075faeff-6bcd-4da1-9342-6bb55886c8a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_open = gdal.Open(path.join('/vsis3', rfl_link))\n",
+    "#image_open.GetMetadata()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "13dcf2cf-10f5-489c-bac6-c28313935ef5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             Band number  Band wavelength (nm)\n",
+      "350.554829             1            350.554829\n",
+      "353.385086             2            353.385086\n",
+      "356.215399             3            356.215399\n",
+      "359.045768             4            359.045768\n",
+      "361.876193             5            361.876193\n",
+      "364.706675             6            364.706675\n",
+      "367.537213             7            367.537213\n",
+      "370.367807             8            370.367807\n",
+      "373.198457             9            373.198457\n",
+      "376.029164            10            376.029164\n",
+      "378.859927            11            378.859927\n",
+      "381.690746            12            381.690746\n",
+      "384.521621            13            384.521621\n",
+      "387.352553            14            387.352553\n",
+      "390.183541            15            390.183541\n",
+      "393.014585            16            393.014585\n",
+      "395.845685            17            395.845685\n",
+      "398.676841            18            398.676841\n",
+      "401.508054            19            401.508054\n",
+      "404.339323            20            404.339323\n",
+      "407.170649            21            407.170649\n",
+      "410.002030            22            410.002030\n",
+      "412.833468            23            412.833468\n",
+      "415.664962            24            415.664962\n",
+      "418.496512            25            418.496512\n",
+      "421.328118            26            421.328118\n",
+      "424.159781            27            424.159781\n",
+      "426.991500            28            426.991500\n",
+      "429.823275            29            429.823275\n",
+      "432.655106            30            432.655106\n",
+      "435.486994            31            435.486994\n",
+      "438.318938            32            438.318938\n",
+      "441.150938            33            441.150938\n",
+      "443.982994            34            443.982994\n",
+      "446.815107            35            446.815107\n",
+      "449.647276            36            449.647276\n",
+      "452.479501            37            452.479501\n",
+      "455.311782            38            455.311782\n",
+      "458.144120            39            458.144120\n",
+      "460.976514            40            460.976514\n",
+      "463.808964            41            463.808964\n",
+      "466.641470            42            466.641470\n",
+      "469.474033            43            469.474033\n",
+      "472.306651            44            472.306651\n",
+      "475.139326            45            475.139326\n",
+      "477.972058            46            477.972058\n",
+      "480.804845            47            480.804845\n",
+      "483.637689            48            483.637689\n",
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+      "489.303545            50            489.303545\n",
+      "492.136557            51            492.136557\n",
+      "494.969626            52            494.969626\n",
+      "497.802751            53            497.802751\n",
+      "500.635932            54            500.635932\n",
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+      "892.154330           192            892.154330\n",
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+      "988.779830           226            988.779830\n",
+      "991.622741           227            991.622741\n",
+      "994.465708           228            994.465708\n",
+      "997.308731           229            997.308731\n",
+      "1000.151810          230           1000.151810\n",
+      "1002.994946          231           1002.994946\n",
+      "1005.838138          232           1005.838138\n",
+      "1008.681386          233           1008.681386\n",
+      "1011.524690          234           1011.524690\n",
+      "1014.368051          235           1014.368051\n",
+      "1017.211468          236           1017.211468\n",
+      "1020.054941          237           1020.054941\n",
+      "1022.898470          238           1022.898470\n",
+      "1025.742056          239           1025.742056\n",
+      "1028.585698          240           1028.585698\n",
+      "1031.429396          241           1031.429396\n",
+      "1034.273150          242           1034.273150\n",
+      "1037.116961          243           1037.116961\n",
+      "1039.960827          244           1039.960827\n",
+      "1042.804750          245           1042.804750\n",
+      "1045.648730          246           1045.648730\n"
+     ]
+    }
+   ],
+   "source": [
+    "# lists of band numbers and band center\n",
+    "band_numbers = [int(b.split(\"_\")[1]) for b in image_open.GetMetadata().keys() if b != \"wavelength_units\"]\n",
+    "band_wavelength = [float(b.split(\" \")[0]) for b in image_open.GetMetadata().values() if b != \"Nanometers\"]\n",
+    "\n",
+    "# data frame describing bands\n",
+    "bands = pd.DataFrame({ \n",
+    "    \"Band number\": band_numbers, \n",
+    "    \"Band wavelength (nm)\": band_wavelength}, index = band_wavelength).sort_index()\n",
+    "\n",
+    "print(bands.to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c76bffc3-1c3c-4546-b25d-33900f14d4d7",
+   "metadata": {},
+   "source": [
+    "Take note of the band numbers and corresponding wavelengths. We will use this information later on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "08584a3e-eb36-4eca-b1b5-c6194b9456d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bands:\t246\n",
+      "Rows:\t2789\n",
+      "Cols:\t655\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Open the PRISM ENVI file and read the file bands, row, cols\n",
+    "#image_open = gdal.Open(gdal_url)\n",
+    "\n",
+    "nbands = image_open.RasterCount\n",
+    "nrows = image_open.RasterYSize\n",
+    "ncols = image_open.RasterXSize\n",
+    "\n",
+    "print(\"\\n\".join([\"Bands:\\t\"+str(nbands), \"Rows:\\t\"+str(nrows), \"Cols:\\t\"+str(ncols)]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a73c127-7409-4e2c-9010-92501f4e3254",
+   "metadata": {},
+   "source": [
+    "### Calculate Turbidity\n",
+    "We are going to use a simple, but globally validated single-band algorithm for turbidity developed by [Nechad et al. (2010)](https://www.sciencedirect.com/science/article/abs/pii/S0034425709003617). It is a semi-empirical water-leaving reflectance based algorithm. It has been found to be particularly useful for coastal, estuarine, and inland waters. It uses the relationship between water-leaving reflectance and turbidity, relying on measurements from visible and near-infrared (NIR) spectral bands to quantify the concentration of suspended particulate matter (SPM).\n",
+    "\n",
+    "$$T=\\frac{A*R_{w}}{1-\\frac{R_{w}}{C}}$$\n",
+    "\n",
+    "Where $R_{w}$ is the water-leaving reflectance at a given wavelength, and A and C are given below. \n",
+    "\n",
+    "The algorithm typically uses red or NIR bands (e.g., around 645 nm or 858 nm). The choice of wavelength depends on the water type; for example, red bands are generally used in relatively clear waters, while NIR bands are more suitable for highly turbid waters. As you have observed, as turbidity, or SPM, increases, water-leaving reflectance tends to increase, and peak reflectance tends to shift to longer wavelengths.\n",
+    "\n",
+    "The coefficients $A$ and $C$ are empirically derived through in-situ measurements of turbidity and corresponding remote sensing data, which helps in tuning the model for different environments.  In this example, we will use parameters taken from [Dogliotti et al. 2011](https://www.sciencedirect.com/science/article/pii/S0034425714003654).\n",
+    "\n",
+    "We are going to implement the algorithm on two wavelengths, and we will compare the results. \n",
+    "\n",
+    "| Wavelength | PRISM band | A     | C      |\n",
+    "| ---------- | ---------- | ----  | ------ |\n",
+    "| 645 nm     | 105        | 228.1 | 0.1641 |\n",
+    "| 858 nm     | 180        | 3078.9| 0.2112 |\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68a9a303-2c31-4664-82fd-9272eb095bad",
+   "metadata": {},
+   "source": [
+    "#### Using GDAL to read a raster file as a numerical array\n",
+    "convert an existing Gdal Dataset or a Band into a numpy array with the ReadAsArray() function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "a0b2b019-9279-4723-bd6d-16e0634c7112",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_red = image_open.GetRasterBand(105).ReadAsArray()  # Band 105 is 645nm \n",
+    "img_nir = image_open.GetRasterBand(180).ReadAsArray()  # Band 181 is 858nm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "89508e0b-6bea-4a66-981a-e089dddefd41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate Turbidity\n",
+    "\n",
+    "# Define the function T\n",
+    "def T(A, C, Rw):\n",
+    "    return (A * Rw) / (1 - (Rw / C))\n",
+    "\n",
+    "turb_red = T(228.1, 0.1641, img_red)\n",
+    "turb_nir = T(3078.9, 0.2112, img_nir)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8d275b4-f9cb-4900-a5a8-9efda960a8a4",
+   "metadata": {},
+   "source": [
+    "#### Explore the Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cbb5ebf3-ccc3-41e0-b2af-3da2a04eff10",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The turbidity (645 nm) value at pixel 1 is 40.846443\n",
+      "The turbidity (645 nm) value at pixel 2 is 25.71505\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Compare turbidity values of two different aquatic plots\n",
+    "# Note here when we print an element in a numpy array, the order is row column \n",
+    "\n",
+    "pixel1 = turb_red[770, 300] #  pixel location: row, col\n",
+    "pixel2 = turb_red[770, 75] # pixel location: row, col\n",
+    "\n",
+    "print(\"The turbidity (645 nm) value at pixel 1 is \" + str(pixel1))\n",
+    "print(\"The turbidity (645 nm) value at pixel 2 is \" + str(pixel2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "9d8d3d2e-195e-4e23-863a-080034427934",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The turbidity (858 nm) value at pixel 3 is 59.01046\n",
+      "The turbidity (858 nm) value at pixel 4 is 41.42754\n"
+     ]
+    }
+   ],
+   "source": [
+    "pixel3 = turb_nir[770, 300] #  pixel location: row, col\n",
+    "pixel4 = turb_nir[770, 75] # pixel location: row, col\n",
+    "\n",
+    "print(\"The turbidity (858 nm) value at pixel 3 is \" + str(pixel3))\n",
+    "print(\"The turbidity (858 nm) value at pixel 4 is \" + str(pixel4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "df7cf292-5700-4c41-8162-a31cfd0b0018",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(300,770, color='red')\n",
+    "plt.scatter(75,770, color='black')\n",
+    "plt.rcParams['figure.figsize'] = [10,10]\n",
+    "plt.rcParams['figure.dpi'] = 100\n",
+    "#plt.imshow(turb_red, vmin=0, vmax=100) # not run\n",
+    "plt.imshow(turb_nir, vmin=0, vmax=100)\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67637a3a-a152-43f0-8a0b-41104d9cf71f",
+   "metadata": {},
+   "source": [
+    "### Export turbidity maps as a stacked projected geoTIFFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7959ec3f-b302-48fb-9bf8-0c5afca8805d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_red = image_open.GetRasterBand(105).ReadAsArray()  # Band 105 is 645nm red\n",
+    "img_nir = image_open.GetRasterBand(181).ReadAsArray()  # Band 70 is 860nm nir\n",
+    "\n",
+    "outfile = ('prism_turbidity.tif')\n",
+    "rows = image_open.RasterYSize\n",
+    "cols = image_open.RasterXSize\n",
+    "datatype = image_open.GetRasterBand(1).DataType\n",
+    "projection = image_open.GetProjection()\n",
+    "transform = image_open.GetGeoTransform()\n",
+    "\n",
+    "driver = gdal.GetDriverByName(\"GTiff\")\n",
+    "DataSetOut = driver.Create(outfile, cols, rows, 2, datatype) # 2 band stack\n",
+    "DataSetOut.GetRasterBand(1).WriteArray(turb_red) # note the order of the band stack\n",
+    "DataSetOut.GetRasterBand(2).WriteArray(turb_nir)\n",
+    "DataSetOut.SetProjection(projection)\n",
+    "DataSetOut.SetGeoTransform(transform)\n",
+    "DataSetOut = None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6200e3d6-e992-4bed-a15f-dfdddd2bd3c9",
+   "metadata": {},
+   "source": [
+    "#### Open QGIS in the Hub Desktop,\n",
+    "- From a Launcher tab, start the Desktop\n",
+    "- At the bottom of the Desktop screen, use the Application Finder to find and launch QGIS\n",
+    "- Using the Home file system, maneuver to the correct file folder containing the prism_turbidity.tif file,\n",
+    "and view the prism_turbidity.tif image you just created !"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e2f135e-3945-48d8-ab3f-a49bf7537215",
+   "metadata": {},
+   "source": [
+    "![QGIS](images/Desktop_applicationfinder_QGIS.PNG)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73c5618e-a339-4933-b6f3-d7b797141501",
+   "metadata": {},
+   "source": [
+    "Note that we have created a two-band image. We can visualize each band to compare the two retrievals. \n",
+    "- Load band 1 (turbidity from the red band)\n",
+    "- Duplicate the layer and load band 2 (turbidity from the nir band)\n",
+    "\n",
+    "HINT: under Symbology, set the Min/Max Value Settings to 0-100 so we are comparing the same scale\n",
+    "\n",
+    "HINT: under Transparency, set the NoData value to 0\n",
+    "\n",
+    "![](images/qgis_turb_red.png)\n",
+    "![](images/qgis_turb_nir.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f2e1b02-3b2b-4d8f-94a1-4418645a78ac",
+   "metadata": {},
+   "source": [
+    "- a copy of the output prism_turbidity.tif file is in the workshop folder `//shared/users/bioscape_ZA24workshop_data/RS_data/PRISM_Notebook`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "global-global-BioSCape",
+   "language": "python",
+   "name": "conda-env-global-global-BioSCape-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/book/tutorials/prism_turbidity/prism_turbidity.tif b/book/tutorials/prism_turbidity/prism_turbidity.tif
new file mode 100644
index 0000000..679f7c6
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