From afb76c10737b9c7eabb373eb9db15953db5cc916 Mon Sep 17 00:00:00 2001 From: Elsa Culler Date: Thu, 6 Jun 2024 09:55:06 -0600 Subject: [PATCH 1/3] Create python_basics.md --- docs/resources/python_basics.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) create mode 100644 docs/resources/python_basics.md diff --git a/docs/resources/python_basics.md b/docs/resources/python_basics.md new file mode 100644 index 0000000..3ec78bf --- /dev/null +++ b/docs/resources/python_basics.md @@ -0,0 +1,13 @@ +# Playing with Python on Cyverse + +Today, we will: + + 1. Practice logging into Cyverse and logging into GitHub + 2. Check that everything is set to use Python on the HYR SENSE Cyverse analysis + 3. [Make a map](https://cu-esiil-edu.github.io/esiil-learning-portal/shortcourse/pages/03-git-github/03-github-portfolio/05-map.html) using Open Street Maps to look up places and boundaries + 4. Try some sample analyses from `xarray` + 5. Make some different types of plots: + - [raster images](https://docs.xarray.dev/en/stable/examples/monthly-means.html) + - [hvplot interactive raster image](https://hvplot.holoviz.org/reference/xarray/image.html) + - [seaborn tabular data plots](https://seaborn.pydata.org/examples/index.html) + From 7126c3438b6c097666f66b6929918332d581581c Mon Sep 17 00:00:00 2001 From: serbinsh Date: Thu, 6 Jun 2024 17:38:31 +0000 Subject: [PATCH 2/3] Updates --- images/leaf_anatomy_figure.jpg | Bin 0 -> 66073 bytes images/spectral_signatures.jpg | Bin 0 -> 253805 bytes ...lculate_spectral_vegetation_indices.ipynb} | 42 +++++++++++++++--- ...Download_data_granules_basic_example.ipynb | 23 ++++++++-- notebooks/how_to/Orthorectify_images.ipynb | 2 +- .../spectral_index.cpython-310.pyc | Bin 1138 -> 2200 bytes tools/functions/spectral_index.py | 35 ++++++++++++--- 7 files changed, 85 insertions(+), 17 deletions(-) create mode 100644 images/leaf_anatomy_figure.jpg create mode 100644 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zV83oq=F~5)9pU4*<q)lsdI@`C#8K`vQGWxz0ob#nIq!czfpObIp6L>vlF@GjF(6?9 z@}cyMlnl2(<|esRE57B_(!-Ins9#vruad98Jg=MWYwxy5P;fsmi1nnPO8`I=Zk(pY PMo0An)Bm{AzdrmA{9g3_ literal 0 HcmV?d00001 diff --git a/notebooks/how_to/Calculate_NDVI.ipynb b/notebooks/how_to/Calculate_spectral_vegetation_indices.ipynb similarity index 74% rename from notebooks/how_to/Calculate_NDVI.ipynb rename to notebooks/how_to/Calculate_spectral_vegetation_indices.ipynb index 0a91464..423d6b9 100644 --- a/notebooks/how_to/Calculate_NDVI.ipynb +++ b/notebooks/how_to/Calculate_spectral_vegetation_indices.ipynb @@ -5,7 +5,10 @@ "id": "434fea91-e70c-43db-b29a-1fb36b6bed50", "metadata": {}, "source": [ - "## How To: Calculating spectral vegetation indices with EMIT data " + "## How To: Calculating spectral vegetation indices with EMIT data\n", + "\n", + "This notebook provides examples demonstrating how to create maps of spectral vegetation indices using EMIT images. To explore the utility of high spectral resolution data for calculating SVIs for Earth Science, we can demonstrate how to calculate commonly-used spectral vegetation indices. For reference, here is a recent article describing the use of SVIs in Earth Science <br>\n", + "[https://www.nature.com/articles/s41597-023-02096-0](https://www.nature.com/articles/s41597-023-02096-0)" ] }, { @@ -77,8 +80,27 @@ "## Load the selected image to memory - we will use the scene ID EMIT_L2A_RFL_001_20230729T205630_2321014_019 in this example\n", "img_file = 'EMIT_L2A_RFL_001_20230729T205630_2321014_019_ortho.nc'\n", "img_file_dat = os.path.join(source_file_path,img_file)\n", - "ds_geo = xr.open_dataset(img_file_dat)\n", - "ds_geo" + "ds_geo = xr.open_dataset(img_file_dat)" + ] + }, + { + "cell_type": "markdown", + "id": "6eef8fde-c1f3-45f3-bc35-c0d33a9c1a6f", + "metadata": {}, + "source": [ + "### Step 3: Calculate spectral vegetation indices (SVIs)\n", + "\n", + "Below we will walk through how you calculate SVIs remote sensing data, and EMIT hyperspectral imagery in general, why you might select specific band combinations based on what you are studying, and a few different ways you can calculate SVIs using EMIT data, either in-line, defining functions, or using pre-built functions found in the provided [spectral_index.py](https://github.com/CU-ESIIL/HYR-SENSE/blob/main/tools/functions/spectral_index.py) file\n", + "\n", + "First lets take a moment to consider again what remote sensing imagery like those provided by EMIT can tell us about the underlying vegetation. " + ] + }, + { + "cell_type": "markdown", + "id": "8988a84b-a77b-455c-b626-ac048f015850", + "metadata": {}, + "source": [ + "|![leaf_anatomy.jpg](../../images/leaf_anatomy_figure.jpg) |![spectral_signatures.jpg](../../images/spectral_signatures.jpg)|" ] }, { @@ -86,7 +108,17 @@ "id": "5785a55b-7672-4458-b713-e38290a28399", "metadata": {}, "source": [ - "Here we calculate a simple index, the normalized difference vegetation index" + "**Calculate the Normalized Difference Vegetation Index (NDVI)**\n", + "\n", + "Here we calculate a simple yet powerful index, the normalized difference vegetation index (NDVI). NDVI has been used for over 40 years to study changes on the Earth's surface, specifically related to vegetation, stress, and agriculture. For more information on NDVI, you can explore this article from NASA: [https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_1.php](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_1.php)'" + ] + }, + { + "cell_type": "markdown", + "id": "5d280305-0ae2-4df7-86cb-27dfc7ed8f19", + "metadata": {}, + "source": [ + "To calculate NDVI, we need to select which bands we want to include in the calculation. In general, NDVI is defined using a red and near-infrared band, so lets use a band centered at 650nm and another at 850nm, both squarely within the red and NIR wavelength range as shown in the example spectral graph above" ] }, { @@ -176,7 +208,7 @@ "id": "9d5dc416-f2b5-422b-834f-71433839bec7", "metadata": {}, "source": [ - "We can also use the stored functions in the HYR-SENSE repository" + "We can also use the stored functions in the HYR-SENSE repository. These functions can be found in the spectral_index.py file https://github.com/CU-ESIIL/HYR-SENSE/blob/main/tools/functions/spectral_index.py\n" ] }, { diff --git a/notebooks/how_to/Download_data_granules_basic_example.ipynb b/notebooks/how_to/Download_data_granules_basic_example.ipynb index 5bbabe1..9911983 100644 --- a/notebooks/how_to/Download_data_granules_basic_example.ipynb +++ b/notebooks/how_to/Download_data_granules_basic_example.ipynb @@ -46,10 +46,25 @@ "id": "e57d1067-8502-432c-bd9b-cd5ec28e60c6", "metadata": {}, "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Enter your Earthdata Login username: SerbinFav\n", + "Enter your Earthdata password: ········\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Persisting credentials to .netrc\n" + ] + }, { "data": { "text/plain": [ - "<earthaccess.auth.Auth at 0x7fe02a213910>" + "<earthaccess.auth.Auth at 0x7f29db21ebf0>" ] }, "execution_count": 2, @@ -80,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "id": "7385efaf-6a60-4c3e-a217-dc1f9c668db4", "metadata": {}, "outputs": [ @@ -108,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "id": "873313dc-d84f-4106-968f-e3e6e11d5447", "metadata": {}, "outputs": [], @@ -153,7 +168,7 @@ "text": [ "Granule ID: EMIT_L2A_RFL_001_20230729T205630_2321014_019.nc\n", "Granule ID: EMIT_L2A_RFL_001_20230729T205642_2321014_020.nc\n", - "Total time: 0.0006256103515625\n" + "Total time: 121.57887816429138\n" ] } ], diff --git a/notebooks/how_to/Orthorectify_images.ipynb b/notebooks/how_to/Orthorectify_images.ipynb index f826291..5b02709 100644 --- a/notebooks/how_to/Orthorectify_images.ipynb +++ b/notebooks/how_to/Orthorectify_images.ipynb @@ -110,7 +110,7 @@ "Input Granule ID: EMIT_L2A_RFL_001_20230729T205642_2321014_020.nc\n", "Output Granule ID: EMIT_L2A_RFL_001_20230729T205642_2321014_020_ortho.nc\n", " \n", - "Total time: 16.768723726272583\n" + "Total time: 17.057365894317627\n" ] } ], diff --git a/tools/functions/__pycache__/spectral_index.cpython-310.pyc b/tools/functions/__pycache__/spectral_index.cpython-310.pyc index b9ea72817e10d8de1483913f700a679fa0a8ed1e..b50231792209c4d5865c99d069308c5dde4fa513 100644 GIT binary patch delta 916 zcmds0&1w`u5O#Kh#FZ>A5(x&ekf2%2u4cSN_YziN51P2lLC{NOrrK$knI3z3vW}O1 zhNTgF0na{!7f+si3GwDt5UYD6dh!9B!_d|B^L<rc_4oC6gTC6>=$+&5%U2Zh^;Q3E z=Vxd2e&@2%CRLhZBZZ^{9zLE$t54U5h0-}@@(m@UL<&KgikP4(IZ$H;vPkGP^Z;P> zZO3i~(=~Ut&w?9v8Eh@bJ!e9c5{SCs+Nyw3t5caUge<_YgX<*HdAOWesL=@)3H*VD zzhGN5;V79skYgGHJekg50*9Owj@*AA?zGnJL%haj%iz%&I%>?xkgg8w=iuHl$Cm_g zt+_9gHIS(oP0KB(T<PXMpkc~Xv7hP63KlZUn4bZRV=9er-Zvs@c9dvRFbWWa%(%If z(^IU9TLw;u3I@QocaC8M9(w}~8Q6`Erbk0REqkFe8TeD_$R2>3NNBHiZU&)kIv<09 zopfJy@yEY(J5H5RaZ0(2d9HI387qcPyP214_jo?@aID)ha($f%q-s;vrt&>;RJaF= zRK_WAjMIy+pyHxDB~}yR?r6WYJ!KXLDw&rVX%?90%(FA-g;&DopQ4uS1up;>8%Op^ q@SxqBr@_$e&GzrT`9D-UAGWf7=sv%=%`dNV@2`K!ZmzuuwtfL&vMCP$ delta 192 zcmbOs_=$rrpO=@50SJn{6VhZ^Ch|>Xy3RQH28+sM0aiH{KbJ7i$>FRjYzkZoKrs0y ztL<cKHVqaKZ}LV)Npmi+ct}R7LRx8Fa!F=>o<drFQEp<1LT0goQ(|6<kuHcdRM3a; zw81>C$!Sc+9C-@4sk!+@m6NrZZFve3ixP9K6q107jVIeMt1%i+Uch{qk#}+mOCh7* X<g4t_+~Octg51i-IN6#bhEW6nF-|hb diff --git a/tools/functions/spectral_index.py b/tools/functions/spectral_index.py index 3352f6d..e2fa9b6 100644 --- a/tools/functions/spectral_index.py +++ b/tools/functions/spectral_index.py @@ -2,15 +2,19 @@ def normalized_diff(input_xarray, band1=650, band2=850): """ - This function takes an input xarray image and calculates a NDVI image based on the selected bands. - The assumption is the input image is an EMIT image in xarray format prepared using the emit_xarray() - function from emit_tools + This function takes an input orthorectified EMIT xarray image and calculates a normalized-difference spectral index + image based on the selected bands. The assumption is the input image is an EMIT image in xarray format + prepared using the emit_xarray() function from emit_tools. - The function format is Band2-Band1 / Band2+Band1 + The function provided in the traditional two-band normalized difference index format, i.e. NDI = Band2-Band1 / Band2+Band1 - param: input_xarray is the input EMIT xarray in memory + Function parameters: + param: input_xarray is the input EMIT xarray image currently located in memory; at present this will not accept + the full path to a file that hasn't been loaded into memory yet (TODO) + param: band1 the EMIT band number to use for band 1 - param: band3 the EMIT band number to use for band 3 + + param: band2 the EMIT band number to use for band 2 """ reflb1 = input_xarray.sel(wavelengths=band1, method='nearest') @@ -21,8 +25,25 @@ def normalized_diff(input_xarray, band1=650, band2=850): def pri2(input_xarray, band1=531, band2=570): + """ + This function takes an input orthorectified EMIT xarray image and calculates a Photochemical Reflectance Index (PRI) + image based on the selected bands. This function outputs the PRI2, which is the PRI scaled on a 0-1 scale + for simpler interpretation of the variation in PRI.The assumption is the input image is an EMIT image in xarray + format prepared using the emit_xarray() function from emit_tools. + + Function parameters: + param: input_xarray is the input EMIT xarray image currently located in memory; at present this will not accept + the full path to a file that hasn't been loaded into memory yet (TODO) + + param: band1 the EMIT band number to use for band 1 + + param: band2 the EMIT band number to use for band 2 + """ + reflb1 = input_xarray.sel(wavelengths=band1, method='nearest') reflb2 = input_xarray.sel(wavelengths=band2, method='nearest') pri = (reflb1-reflb2)/(reflb1+reflb2) pri2_output = (pri+1)/2 - return(pri2_output) \ No newline at end of file + return(pri2_output) + + From 27445a038f91728cb31eb8a154418a20e9816b9f Mon Sep 17 00:00:00 2001 From: serbinsh <shawn.p.serbin@nasa.gov> Date: Thu, 6 Jun 2024 21:24:21 +0000 Subject: [PATCH 3/3] Updates --- ...alculate_spectral_vegetation_indices.ipynb | 128 ++++++++++++++---- .../spectral_index.cpython-310.pyc | Bin 2200 -> 3079 bytes tools/functions/spectral_index.py | 39 +++++- 3 files changed, 137 insertions(+), 30 deletions(-) diff --git a/notebooks/how_to/Calculate_spectral_vegetation_indices.ipynb b/notebooks/how_to/Calculate_spectral_vegetation_indices.ipynb index 423d6b9..4eb1905 100644 --- a/notebooks/how_to/Calculate_spectral_vegetation_indices.ipynb +++ b/notebooks/how_to/Calculate_spectral_vegetation_indices.ipynb @@ -92,7 +92,7 @@ "\n", "Below we will walk through how you calculate SVIs remote sensing data, and EMIT hyperspectral imagery in general, why you might select specific band combinations based on what you are studying, and a few different ways you can calculate SVIs using EMIT data, either in-line, defining functions, or using pre-built functions found in the provided [spectral_index.py](https://github.com/CU-ESIIL/HYR-SENSE/blob/main/tools/functions/spectral_index.py) file\n", "\n", - "First lets take a moment to consider again what remote sensing imagery like those provided by EMIT can tell us about the underlying vegetation. " + "First, let's take a moment to consider again what remote sensing imagery like those provided by EMIT can tell us about the underlying vegetation. The internal structure and biochemistry of leaves (A) within a canopy control the optical signatures observed by remote sensing instrumentation (B). The amount of incident radiation that is reflected by, transmitted through, or absorbed by leaves within a canopy is regulated by these structural and biochemical properties of leaves. For example, leaf properties such as a thick cuticle layer, high wax, and/or a large amount of leaf hairs can significantly influence the amount of first-surface reflectance (that is the reflected light directly off the outer leaf layer that does not interact with the leaf interior), causing less solar radiation to penetrate into the leaf. The thickness of the mesophyll layer associated with other properties, such as thicker leaves, can cause higher degree of internal leaf scattering, less transmittance through the leaf, and higher absorption in some wavelengths. Importantly, the diffuse reflectance out of the leaf is that modified by internal leaf properties and contains useful for mapping functional traits (B). High spectral resolution measurements of leaves and plant canopies enable the indirect, non-contact measurement of key structural and chemical absorption features that are associated with the physiological and biochemical properties of plants (B)\n" ] }, { @@ -100,7 +100,24 @@ "id": "8988a84b-a77b-455c-b626-ac048f015850", "metadata": {}, "source": [ - "|![leaf_anatomy.jpg](../../images/leaf_anatomy_figure.jpg) |![spectral_signatures.jpg](../../images/spectral_signatures.jpg)|" + "**A)** ![leaf_anatomy.jpg](../../images/leaf_anatomy_figure.jpg) **B)** ![spectral_signatures.jpg](../../images/spectral_signatures.jpg)" + ] + }, + { + "cell_type": "markdown", + "id": "b9911dec-f068-4e87-b1b8-c3edc67b7497", + "metadata": {}, + "source": [ + "We can make use of all of the information contained within the emergent spectral signatures provided by vegetation. We can do this by directly utilizing the spectral profiles of a leaf or an EMIT pixel, or we can instead target specific wavebands provided by data like EMIT to calculate a spectral vegetation index (SVI). SVIs range widely in the wavelengths/bands, structure, and applications. This article provides some background information ([https://www.nature.com/articles/s41597-023-02096-0](https://www.nature.com/articles/s41597-023-02096-0)) but you can find comprehensive list of SVIs [here](https://www.indexdatabase.de/)\n", + "\n", + "\n", + "For more information, you can review these select articles and resources that discuss how leaf and canopy structure, leaf chemical properties, and stress can alter the spectral signatures we see with remote sensing data like those provided by EMIT and how SVIs provide us a way to easily probe the properties of vegetation remotely.\n", + "\n", + "[Sources of variability in canopy reflectance and the convergent properties of plants](https://doi.org/10.1111/j.1469-8137.2010.03536.x)\n", + "\n", + "[Retrieval of foliar information about plant pigment systems from high resolution spectroscopy](https://doi.org/10.1016/j.rse.2008.10.019)\n", + "\n", + "[Scaling Functional Traits from Leaves to Canopies](https://link.springer.com/chapter/10.1007/978-3-030-33157-3_3)\n" ] }, { @@ -110,7 +127,7 @@ "source": [ "**Calculate the Normalized Difference Vegetation Index (NDVI)**\n", "\n", - "Here we calculate a simple yet powerful index, the normalized difference vegetation index (NDVI). NDVI has been used for over 40 years to study changes on the Earth's surface, specifically related to vegetation, stress, and agriculture. For more information on NDVI, you can explore this article from NASA: [https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_1.php](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_1.php)'" + "Here we calculate a simple yet powerful index, the normalized difference vegetation index (NDVI). NDVI has been used for over 40 years to study changes on the Earth's surface, specifically related to vegetation, stress, and agriculture. For more information on NDVI, you can explore this article from NASA: [https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_1.php](https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_1.php)" ] }, { @@ -118,7 +135,9 @@ "id": "5d280305-0ae2-4df7-86cb-27dfc7ed8f19", "metadata": {}, "source": [ - "To calculate NDVI, we need to select which bands we want to include in the calculation. In general, NDVI is defined using a red and near-infrared band, so lets use a band centered at 650nm and another at 850nm, both squarely within the red and NIR wavelength range as shown in the example spectral graph above" + "To calculate NDVI, we need to select which bands we want to include in the calculation. In general, NDVI is defined using a red and near-infrared band, so lets use a band centered at 650nm and another at 850nm, both squarely within the red and NIR wavelength range as shown in the example spectral graph above\n", + "\n", + "The basic structure of the NDVI is: NDVI = (NIR−Red)/(NIR + Red)" ] }, { @@ -128,9 +147,13 @@ "metadata": {}, "outputs": [], "source": [ + "### To calculate NDVI, starting with an EMIT xarray image we can select out the two bands using the example below\n", + "\n", "refl650 = ds_geo.sel(wavelengths=650, method='nearest')\n", "refl850 = ds_geo.sel(wavelengths=850, method='nearest')\n", - "ndvi = (refl850-refl650)/(refl850+refl650)" + "ndvi = (refl850-refl650)/(refl850+refl650)\n", + "\n", + "# Note: The 'nearest' option allows you to define a specific wavelength you want to use and it will use the closest EMIT band to that wavelength" ] }, { @@ -140,6 +163,7 @@ "metadata": {}, "outputs": [], "source": [ + "### Now lets view the resulting NDVI image stretech to display between 0-1\n", "ndvi.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', aspect = 'equal', frame_width=720, clim=(0,1)).opts(title=\"NDVI Image\")" ] }, @@ -162,16 +186,19 @@ "source": [ "def normalized_diff_demo(input_xarray = ds_geo, band1=650, band2=850):\n", " \"\"\"\n", - " This function takes an input xarray image and calculates a NDVI image based on the selected bands. The assumption is the input image is an EMIT image in \n", - " xarray format prepared using the emit_xarray() function from emit_tools\n", + " This function takes an input orthorectified EMIT xarray image and calculates a normalized-difference spectral index \n", + " image based on the selected bands. The assumption is the input image is an EMIT image in xarray format \n", + " prepared using the emit_xarray() function from emit_tools.\n", "\n", - " The function format is Band2-Band1 / Band2+Band1\n", + " The function provided in the traditional two-band normalized difference index format, i.e. NDI = Band2-Band1 / Band2+Band1\n", "\n", - " param: input_xarray is the input EMIT xarray in memory\n", + " Function parameters:\n", + " param: input_xarray is the input EMIT xarray image currently located in memory; at present this will not accept \n", + " the full path to a file that hasn't been loaded into memory yet (TODO)\n", + " \n", " param: band1 the EMIT band number to use for band 1\n", - " param: band3 the EMIT band number to use for band 3\n", - "\n", " \n", + " param: band2 the EMIT band number to use for band 2\n", " \"\"\"\n", " \n", " reflb1 = input_xarray.sel(wavelengths=band1, method='nearest')\n", @@ -194,10 +221,8 @@ { "cell_type": "code", "execution_count": null, - "id": "e65d363f-03ce-4efc-b826-aebc3a21d8ab", - "metadata": { - "scrolled": true - }, + "id": "2ec41d17-14de-4af5-9c49-d3c19f0bafd9", + "metadata": {}, "outputs": [], "source": [ "ndvi2.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', aspect = 'equal', frame_width=720, clim=(0,1)).opts(title=\"NDVI Image\")" @@ -238,37 +263,66 @@ "source": [ "### Calculate the Normalized-Difference Water Index (NDWI)\n", "\n", - "https://en.wikipedia.org/wiki/Normalized_difference_water_index" + "We can also calculate a range of other useful indices using the same basic normalized difference vegetation index approach. For example, if we want to investigate the variation in plant / surface water content we can use an SVI such as the Normalized-Difference Water Index [(NDWI)](https://en.wikipedia.org/wiki/Normalized_difference_water_index). Like NDVI, the NDWI uses two different bands to enhance the detection of a plant property, in this case water content, and does that by using a band centered in the NIR and another band centered in the shortwave infrared that is sensitive to plant water content using the following equation: NDWI = (NIR – SWIR)/(NIR + SWIR).\n", + "\n", + "Because it has the same basic structure as NDVI we can still use the same **normalized_diff()** function but instead replace the default bands with 2200nm and 864nm" ] }, { "cell_type": "code", "execution_count": null, - "id": "c7b165d2-8dae-4c9c-bfe1-fca4ef90ca94", + "id": "9aa99cea-623a-4ba9-b16b-c1869a0b8f2f", "metadata": {}, "outputs": [], "source": [ - "ds_geo['wavelengths'].data" + "ndwi = normalized_diff(input_xarray = ds_geo, band1=2200, band2=864)" ] }, { "cell_type": "code", "execution_count": null, - "id": "9aa99cea-623a-4ba9-b16b-c1869a0b8f2f", + "id": "46e2a566-ec92-4824-abb5-142629fab27d", "metadata": {}, "outputs": [], "source": [ - "ndwi = normalized_diff(input_xarray = ds_geo, band1=2200, band2=864)" + "ndwi.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', aspect = 'equal', frame_width=720, clim=(0,1)).opts(title=\"NDWI Image\")" + ] + }, + { + "cell_type": "markdown", + "id": "30f33351-d608-46ac-9da3-4d5cd600c0bf", + "metadata": {}, + "source": [ + "### Calculate red-edge NDVI\n", + "\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "46e2a566-ec92-4824-abb5-142629fab27d", + "id": "e1960c24-1d22-4229-b102-fcf9f0955067", "metadata": {}, "outputs": [], "source": [ - "ndwi.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', aspect = 'equal', frame_width=720, clim=(0,1)).opts(title=\"NDVI Image\")" + "rendvi = normalized_diff(input_xarray = ds_geo, band1=705, band2=750)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "64f6c1e1-006a-493d-91c0-c16d16237aed", + "metadata": {}, + "outputs": [], + "source": [ + "rendvi.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', aspect = 'equal', frame_width=720, clim=(0,1)).opts(title=\"reNDVI Image\")" + ] + }, + { + "cell_type": "markdown", + "id": "0ef27072-9f83-4589-aa9f-1b26270cf578", + "metadata": {}, + "source": [ + "### Calculate the Photochemical Reflectance Index" ] }, { @@ -278,7 +332,7 @@ "metadata": {}, "outputs": [], "source": [ - "pri_image = pri2(input_xarray = ds_geo, band1=531, band2=570)" + "pri_image = pri(input_xarray = ds_geo, band1=531, band2=570, scaled=True)" ] }, { @@ -288,23 +342,43 @@ "metadata": {}, "outputs": [], "source": [ - "pri_image.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', aspect = 'equal', frame_width=720, clim=(0,0.6)).opts(title=\"PRI Image\")" + "pri_image.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', aspect = 'equal', frame_width=720, clim=(0.44,0.48)).opts(title=\"PRI Image\")" + ] + }, + { + "cell_type": "markdown", + "id": "ba4f24fb-7332-4a4a-92a1-4afeda129fef", + "metadata": {}, + "source": [ + "### Calculate a simple ratio index\n", + "\n", + "Let's now calculate another common set of indices, called \"simple ratio\" indices. In this case, we will calculate what is known as the \"Water Band Index\" (WBI)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8519cd8e-8539-4b5f-9cb2-231fe59498ea", + "metadata": {}, + "outputs": [], + "source": [ + "wbi = simple_ratio(input_xarray = ds_geo, band1=900, band2=970)" ] }, { "cell_type": "code", "execution_count": null, - "id": "a71e48a1-cbce-4113-83e1-2e50172faee6", + "id": "60893cdb-d2da-43e3-b58a-4fba6a55bddb", "metadata": {}, "outputs": [], "source": [ - "pri_image.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', aspect = 'equal', frame_width=720).opts(title=\"PRI Image\")" + "wbi.hvplot.image(cmap='viridis', geo=True, tiles='ESRI', aspect = 'equal', frame_width=720, clim=(0.8,1.2)).opts(title=\"WBI Image\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "38eceb8e-7de7-4a66-ae91-a627b81a9ffd", + "id": "0bd6dc7d-f6c1-4791-94a6-a7338b04a113", "metadata": {}, "outputs": [], "source": [] diff --git a/tools/functions/__pycache__/spectral_index.cpython-310.pyc b/tools/functions/__pycache__/spectral_index.cpython-310.pyc index b50231792209c4d5865c99d069308c5dde4fa513..9ab46350ebdfc861bc16a23e3669f89292f88815 100644 GIT binary patch delta 568 zcmZ8cy-FlO5bo~j`C*)$-96A<Pld(<aYGSKFx9|76ubrDlB3-kbx!w(o>?#$*+jVL zf@Kn4!BBh)UqIMIFf-BzFu1DT4de>ytNObBzP_#hX~z9ZC1hwHYbU2N8pq4)tH>Y* z0nQEo$)E~81ekHC!5l=G38?&Ft@{1mh8(p!lBQ?oV<f(Vp6wzO>^Cboa1ejRwX{sk zm|MugZ8E|JTVmH<;ec}?j4C{k4F;}#^YD_Vzd4MB3ax#fX6kg<>*Q&_r*i%9CSSys zEe*8M-EEb1^ko9mpz`xX^@sUjn5%(FZJWVW%#g@-`RuFxi8q}vbMzh|OdRYr6`~em z%H|Q4&@<|Yzr?)80}*j4Y~}S4x4zzt4_Em5xT~s8AyK_sxTxOI7v+;YUdz(%;4)E0 zTj^;Jk}H*V^;x1$bcTxiJ<?Zh@}r0E-W~UihnHGj`R;!miOl>bXcA5L{j&SENLfJ2 zsxc2p)gqFEmM|Qn5eO~E)F`hbw6U;2q%kd2DW@OWu}_y~|1U>DJtR4y2%GWpKNsS1 A+yDRo delta 281 zcmZpdm?6lQ&&$ij00dWFCZ+{(OyrZ1lK}EkI8&HXm|Ga57*e=W*izVmEXEX$U<OU@ zjX{Pi(#gyqEl|J?q*;L28HkH*CYCEGGuN<4Fw_7^aRzCITGkrY6lQUT6c!+xt%eO~ z2y5c+iR;2QJF+fjnrz3-H(8oPp}vS4sJ(~>NZevBD9ViD1`<Z``K2WVr6onYAUQC> z2U5TSl9L9qia}226X0VMVB}$BVJs2_a{V-!iWq@(5fhLAvIRhFK?s{4#0DusG7)0z Q<k#Fn93WqCG4V+N0LYs#1ONa4 diff --git a/tools/functions/spectral_index.py b/tools/functions/spectral_index.py index e2fa9b6..79422ea 100644 --- a/tools/functions/spectral_index.py +++ b/tools/functions/spectral_index.py @@ -24,7 +24,7 @@ def normalized_diff(input_xarray, band1=650, band2=850): return(ndiff_image) -def pri2(input_xarray, band1=531, band2=570): +def pri(input_xarray, band1=531, band2=570, scaled=True): """ This function takes an input orthorectified EMIT xarray image and calculates a Photochemical Reflectance Index (PRI) image based on the selected bands. This function outputs the PRI2, which is the PRI scaled on a 0-1 scale @@ -38,12 +38,45 @@ def pri2(input_xarray, band1=531, band2=570): param: band1 the EMIT band number to use for band 1 param: band2 the EMIT band number to use for band 2 + + param: scaled scale the output pri """ reflb1 = input_xarray.sel(wavelengths=band1, method='nearest') reflb2 = input_xarray.sel(wavelengths=band2, method='nearest') pri = (reflb1-reflb2)/(reflb1+reflb2) - pri2_output = (pri+1)/2 - return(pri2_output) + + if scaled == True: + pri = (pri+1)/2 + + return(pri) + + +def simple_ratio(input_xarray, band1=900, band2=970): + """ + This function takes an input orthorectified EMIT xarray image and calculates a simple ration index image based on + the selected bands. The assumption is the input image is an EMIT image in xarray + format prepared using the emit_xarray() function from emit_tools. + + Function parameters: + param: input_xarray is the input EMIT xarray image currently located in memory; at present this will not accept + the full path to a file that hasn't been loaded into memory yet (TODO) + + param: band1 the EMIT band number to use for band 1 + + param: band2 the EMIT band number to use for band 2 + """ + + reflb1 = input_xarray.sel(wavelengths=band1, method='nearest') + reflb2 = input_xarray.sel(wavelengths=band2, method='nearest') + sr = reflb1/reflb2 + + return(sr) + + + + + + \ No newline at end of file