rindcalc
Raster Index Calculator
rind calc is a python package created to allow raster index calculation using Landsat-8 using gdal and numpy. Landsat bands are pulled directly from file downloaded from USGS containing all bands in landsat scene. Since rindcalc only requires the file in which Landsat-8 bands are contained instead of each individual band to be specified, it allows for easy, quick, and seamless index calculations from Landsat-8 imagery.
Indices: AWEIsh, AWEInsh, NDMI, MNDWI, NDVI, GNDVI, SAVI, NDBI, NDBaI, NBLI, EBBI, UI, NBRI
K means unsupervised classification module utilizes sci-kit learn's MiniBatchKMeans which provides significantly faster computation times than the standard K-means algorithm, but with slightly worse result [1]. 'No Data' values are populated with the median value of the array as the classification algorithm does not work with numpy arrays that contain 'nan' values.
Dependencies
- GDAL (v 3.0.0 or greater)
- numpy (v 1.0.0 or greater)
- sci-kit learn ( v (0.22.1 or greater))
Installation
Windows
pip install rindcalc
For Windows installation gdal wheels must be installed first.
Modules
Index Modules
- AWEIsh(landsat_dir, aweish_out)
- AWEInsh(landsat_dir, aweinsh_out)
- NDMI(landsat_dir, ndmi_out)
- MNDWI(landsat_dir, mndwi_out)
- NDVI(landsat_dir, ndvi_out)
- GNDVI(landsat_dir)
- SAVI(landsat_dir, soil_brightness, savi_out)
- NDBI(landsat_dir, ndbi_out)
- NDBaI(landsat_dir, ndbai_out)
- NBLI(landsat_dir, nbli_out)
- EBBI(landsat_dir, ebbi_out)
- UI(landsat_dir, ui_out )
- NBRI(landsat_dir, nbri_out)
landsat_dir = Landsat-8 folder that contains all bands
*_out = out file raster will be saved as
i.e. Landsat-8 folder structure:
.
|--LC08_L1TP_091086_20191222_20191223_01_RT Landsat Folder ex. #1
| |-- LC08_L1TP_091086_20191222_20191223_01_RT_B1.TIF
| |-- LC08_L1TP_091086_20191222_20191223_01_RT_B2.TIF
| |-- LC08_L1TP_091086_20191222_20191223_01_RT_B3.TIF
| |-- ...
|-- 2019_12_22 Landsat Folder ex. #2
| |-- LC08_L1TP_091086_20191222_20191223_01_RT_B1.TIF
| |-- LC08_L1TP_091086_20191222_20191223_01_RT_B2.TIF
| |-- LC08_L1TP_091086_20191222_20191223_01_RT_B3.TIF
| |-- ...
K Means Classification Module
- k_means(input_raster, out_raster, clusters, itr, batch_size)
clusters = Number of classes wanted
itr = Number of iterations to perform
batch_size = Size of mini batches
EX:
import rindcalc as rc
landsat_dir = 'C:/.../.../LC08_L1TP_091086_20191222_20191223_01_RT'
ndvi_out = 'C:/.../.../NDVI_1.tif'
rc.NDVI(landsat_dir, ndvi_out)
OR:
import rindcalc as rc
rc.NDVI(landsat_dir = 'C:/.../.../2019_12_22', ndvi_out = 'C:/.../.../NDVI_2.tif')
KMEANS EX:
import rindcalc as rc
input_raster = 'C:/.../.../NDVI.tif'
out_raster = 'C:/.../.../NDVI_K.TIF'
clusters = 2
itr = 10
batch_size = 50
rc.k_means(input_raster, out_raster, clusters, itr, batch_size)
Landsat-8 Bands
| Band Number | Name | µm | Resolution |
|---|---|---|---|
| 1 | Coastal/Aerosal | 0.433–0.453 | 30 m |
| 2 | Blue | 0.450–0.515 | 30 m |
| 3 | Green | 0.525–0.600 | 30 m |
| 4 | Red | 0.630–0.680 | 30 m |
| 5 | NIR | 0.845–0.885 | 30 m |
| 6 | SWIR 1 | 1.560–1.660 | 30 m |
| 7 | SWIR 2 | 2.100–2.300 | 30 m |
| 8 | Panchromatic | 0.500–0.680 | 15 m |
| 9 | Cirrus | 1.360–1.390 | 30 m |
| 10 | TIR 1 | 10.6-11.2 | 100 m |
| 11 | TIR 2 | 11.5-12.5 | 100 m |
Indices
Water
-
AWEIsh = ((Blue + 2.5 * Green - 1.5 * (NIR + SWIR1) - 0.25 * SWIR2)) / (Blue + Green + NIR + SWIR1 + SWIR2)
-
AWEInsh = ((4 * (green_band - swir1_band) - (0.25 * nir_band + 2.75 * swir1_band)) / (green_band + swir1_band + nir_band))
-
NDWI = ((nir_band - swir1_band) / (nir_band + swir1_band))
-
MNDWI = ((Green - SWIR1) / (Green + SWIR1))
Moisture
- NDMI = ((NIR - SWIR1) / (NIR + SWIR1))
Vegetation
-
NDVI = ((NIR - Red) / (NIR + Red))
-
Green NDVI (GNDVI) = ((nir_band - green_band) / (nir_band + green_band))
-
SAVI = ((NIR - Red) / (NIR + Red + L)) x (1 + L)
- L = Soil Brightness Factor
-
MSAVI2 = (((2 * nir_band + 1) - (np.sqrt(((2 * nir_band + 1)**2) - 8 * (nir_band - red_band)))) / 2)
Urban/Landscape
-
NDBI = (SWIR1 - NIR) / (SWIR1 + NIR)
-
NDBaI = ((SWIR1 - TIR) / (SWIR1 + TIR))
-
NBLI = ((Red - TIR) / (Red + TIR))
-
EBBI = ((swir1_band - nir_band) / (10 * (np.sqrt(swir1_band + tir_band))))
-
UI = ((swir2_band - nir_band) / (swir2_band + nir_band))
Fire
- NBRI = ((nir_band - swir2_band) / (nir_band + swir2_band))
1 maintainer
Release files for rindcalc 1.1.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| rindcalc-1.1.1.tar.gz | 8.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| rindcalc-1.1.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 35.9 kB
Release files / rindcalc-1.1.1.tar.gz
| Download URL | rindcalc-1.1.1.tar.gz |
|---|---|
| Size | 8.7 kB |
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Release files / rindcalc-1.1.1-py3-none-any.whl
| Download URL | rindcalc-1.1.1-py3-none-any.whl |
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| Tags | Python 3 |
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