{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Cube Cloud Statistics\n",
"\n",
"This notebook explores Landsat-7 and Landsat-8 Data Cubes and reports cloud statistics for selected regions within a cube. This is valuable information for performing analysis. For example, if there are extensive clouds for a season it may significantly impact the mosaic product or index values. Another example is that a user may want to find a single date when there are few clouds to assess land features."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Supress Warning \n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Load Data Cube Configuration\n",
"import datacube\n",
"dc = datacube.Datacube(app = 'my_app')\n",
"# Import Data Cube API\n",
"import utils.data_cube_utilities.data_access_api as dc_api \n",
"api = dc_api.DataAccessApi()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"## LS8 Caqueta\n",
"# Latitude: (0.000134747292617865, 1.077843593651382) \n",
"# Longitude: (-74.91935994831539, -73.30266193148462) \n",
"# '2013-04-13', '2018-03-26'\n",
"# Resolution: (-0.000269494585236, 0.000269494585236)\n",
"\n",
"## LS8 Vietnam\n",
"# Latitude: (10.513927001104687, 12.611133863411238) \n",
"# Longitude: (106.79005909290998, 108.91906631627438) \n",
"# '2014-01-14', '2016-12-21'\n",
"# Resolution: (-0.000269494585236, 0.000269494585236)\n",
"\n",
"## LS7 Caqueta\n",
"# Latitude: (0.000134747292617865, 1.077843593651382) \n",
"# Longitude: (-74.91935994831539, -73.30266193148462) \n",
"# '1999-08-21', '2018-03-25'\n",
"# Resolution: (-0.000269494585236, 0.000269494585236)\n",
"\n",
"## LS7 Lake Baringo\n",
"# Latitude: (0.4997747685, 0.7495947795) \n",
"# Longitude: (35.9742163305, 36.473586859499996) \n",
"# '2005-01-08', '2016-12-24'\n",
"# Resolution: (-0.000269493, 0.000269493)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# CHANGE HERE >>>>>>>>>>>>>>>>>\n",
"\n",
"# Select a Product and Platform\n",
"# product = ls7_collection1_AMA_ingest\n",
"# platform = \"LANDSAT_7\"\n",
"\n",
"product = 'ls8_collection1_AMA_ingest'\n",
"platform = \"LANDSAT_8\"\n",
"output_crs = 'EPSG:4326'\n",
"resolution = (-0.000269494585236, 0.000269494585236)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# CHANGE HERE >>>>>>>>>>>>>>>>>>\n",
"# Select an analysis region (Lat-Lon) within the extents listed above. \n",
"# HINT: Keep your region small (<0.5 deg square) to avoid memory overload issues\n",
"# Select a time period (Min-Max) within the extents listed above (Year-Month-Day)\n",
"# This region and time period will be used for the cloud assessment\n",
"\n",
"#Sub-region selection - e.g Vietnam\n",
"latitude = (11.3124, 10.9124)\n",
"longitude = (106.9170, 107.3170) \n",
"time_extents = ('2014-01-01', '2016-01-01')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The code below renders a map that can be used to view the region.\n",
"from utils.data_cube_utilities.dc_display_map import display_map\n",
"display_map(latitude,longitude)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cloud Coverage "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import datacube\n",
"from utils.data_cube_utilities.dc_mosaic import ls8_unpack_qa, ls7_unpack_qa \n",
"import numpy as np\n",
"from functools import partial \n",
"import pandas as pd\n",
"\n",
"def build_cloud_coverage_table_landsat(product = None,\n",
" platform = None,\n",
" latitude = None,\n",
" longitude = None,\n",
" time = None,\n",
" output_crs = None,\n",
" resolution = None,\n",
" dc = None):\n",
"\n",
" if product is None: raise Exception(\"product argument is required\")\n",
" if platform is None: raise Exception(\"platform argument is required\")\n",
" if latitude is None: raise Exception(\"latitude argument is required\")\n",
" if longitude is None: raise Exception(\"longitude argument is required\")\n",
" \n",
" def clean_mask(ds, unpacking_func, bands):\n",
" masks = [unpacking_func(ds, band) for band in bands]\n",
" return np.logical_or(*masks).values\n",
" \n",
" unpack_function = {\"LANDSAT_7\": ls7_unpack_qa,\n",
" \"LANDSAT_8\": ls8_unpack_qa}\n",
"\n",
" dc = datacube.Datacube(app = \"\")\n",
" \n",
" load_params = dict(product = product,\n",
" platform = platform,\n",
" latitude = latitude,\n",
" longitude = longitude,\n",
" output_crs = output_crs,\n",
" resolution = resolution,\n",
" time = time,\n",
" measurements = ['pixel_qa'])\n",
" \n",
" \n",
" if time is not None: \n",
" load_params[\"time\"] = time\n",
" \n",
" product = dc.load(**load_params)\n",
" \n",
" times = list(product.time.values)\n",
" scene_slice_list = list(map(lambda t: product.sel(time = t).pixel_qa, times))\n",
" \n",
" clean_mask_list = list(map(partial(clean_mask, unpacking_func = unpack_function[platform], bands = [\"clear\", \"water\"]), scene_slice_list))\n",
" clean_pixel_count_list = list(map(np.sum, clean_mask_list))\n",
" percentage_list = list(map(lambda x:np.sum(x)/(x.shape[0] * x.shape[1]), clean_mask_list))\n",
" \n",
" data = {\"times\": times,\n",
" \"clean_percentage\": percentage_list,\n",
" \"clean_count\": clean_pixel_count_list }\n",
" \n",
" return pd.DataFrame(data=data, columns = [\"times\", \"clean_percentage\", \"clean_count\"])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"coverage_table = build_cloud_coverage_table_landsat(product = product,\n",
" platform = platform,\n",
" latitude = latitude,\n",
" longitude = longitude, \n",
" output_crs = output_crs,\n",
" resolution = resolution,\n",
" time = time_extents)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [
{
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