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Add headlines to all notebooks
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The first chapter contains notebooks that document the data retrieval process from the Copernicus Open Access Hub.
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It explains how to use [`sentinelsat`](https://github.com/sentinelsat/sentinelsat) library to interact with the API: How to specify what kind of data we are interested in with the aide of open map data from [OpenStreetMap](https://www.openstreetmap.org/) and [`geopandas`](https://geopandas.org/) and how to download it. It then goes on to show how to use [`rasterio`](https://rasterio.readthedocs.io/) and [`matplotlib`](https://matplotlib.org/stable/index.html) to work with the data.
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It explains how to use the [`sentinelsat`](https://github.com/sentinelsat/sentinelsat) library to interact with the HTTP API: How to specify what kind of data we are interested in with the aide of open map data from [OpenStreetMap](https://www.openstreetmap.org/) and [`geopandas`](https://geopandas.org/) and how to download it. It then goes on to show how to use [`rasterio`](https://rasterio.readthedocs.io/) and [`numpy`](https://numpy.org/) to read and process the downloaded data and [`matplotlib`](https://matplotlib.org/stable/index.html) to visualize it.
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The Sentinel-2 satellite captures light in visible and invisible parts of the spectrum. This can be used to derive different kinds of useful information about ground-level phenomena. This chapter will detail how to use it to create true-color images for different moments in time.
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The Sentinel-2 satellite captures light in visible and invisible parts of the spectrum. This can be used to derive different kinds of useful information about ground-level phenomena. The end of this chapter will detail how to use it to create true-color images for different moments in time.
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# When is Brandenburg Covered in its Entirety?\n",
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"# Large Area Coverage Analysis\n",
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"\n",
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"When trying to generate mosaics covering the entire federal state of Brandenburg it was discovered that even over relatively long time frames (i.e. four weeks) uncovered areas could be detected.\n",
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"This discovery stands in contrast with the claim that the revisit time in that given latitude would be approximately one week.\n",
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Small-Scale NDVI Calculation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"source": [
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"import folium\n",
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"import sentinel_helpers\n",
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"from pathlib import Path"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Let's search for a more detailed geometry this time that would be hard to find outside of OpenStreetMap:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"\n",
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"tempelhofer_feld = sentinel_helpers.search_osm('Tempelhofer Feld, Berlin, Germany').iloc[:1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# there's a warning about this being in WGS84 which we can ignore\n",
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"bbox = tempelhofer_feld.bounds.values[0]\n",
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"minx, miny, maxx, maxy = bbox"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Is the area we have the area we want?\n",
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"## Verify the Obtained Geometry\n",
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"\n",
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"We introduce the `folium` library which lets us plot an OpenStreetMap-based map in the background.\n",
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"This enables us a better preview of our shape, because it allows us to surrounding features such as streets or parks.\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"The finally helpful tip for clipping came from [this website](https://automating-gis-processes.github.io/CSC/notebooks/L5/clipping-raster.html)."
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"The finally helpful tip for clipping came from [this website](https://automating-gis-processes.github.io/CSC/notebooks/L5/clipping-raster.html).\n",
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"\n",
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"**TODO** Make sure that we are using the correct range here."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Multi-Threading Comparison\n",
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"# Multi-Threading Performance Benchmark\n",
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"\n",
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"This notebook contains a performance comparison of different methods to process the NDVI calculations.\n",
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"\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Dealing with cloud masks\n",
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"# Cloud Masks\n",
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"\n",
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"The products contain several raster and vector files that represent cloud masks or cloud propabilities.\n",
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"\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Naive Approach to Downloading\n",
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"\n",
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"- How much data would we need to download when covering Brandenburg in its entirety?"
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]
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},
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"import os\n",
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"from sentinel_helpers import search_osm\n",
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"from sentinelsat import SentinelAPI\n",
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"from tqdm.notebook import tqdm\n",
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"\n",
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"api = SentinelAPI(os.getenv('SCIHUB_USERNAME'), os.getenv('SCIHUB_PASSWORD'))\n",
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"api._tqdm = tqdm\n",
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"\n",
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"start_date = date(2018, 1, 1)\n",
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"end_date = date(2018, 12, 31)\n",
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"cloud_coverage = (0, 30)"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Forest Fire Subset Definition\n",
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"\n",
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"- Other approach\n",
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"- Instead of focusing on all fires in an entire year, we focus on several firest that were covered well\n",
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"- → Jueterbog / Luckenwalde\n",
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"# Comparison of dNBR for three wild fires in 2018 and 2019\n",
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"\n",
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"This notebook contains the calculations necessary for mapping the burn severity of the previously selected products.\n",
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"This burn severity is measured as the ${\\Delta}NBR$, which is defined as:\n",
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"This burn severity is measured as the Normalized Burn Ratio (NBR).\n",
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"The development of this burn ratio over time is described using the ${\\Delta}$NBR, which is defined as:\n",
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"\n",
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"$$\n",
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"{\\Delta}NBR = NBR_\\text{pre-fire} - NBR_\\text{post-fire}\n",
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"{\\Delta}\\text{NBR} = \\text{NBR}_\\text{pre-fire} - \\text{NBR}_\\text{post-fire}\n",
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"$$\n",
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"\n",
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"Instead of only calculating values by overlaying different bands in one product, the ${\\Delta}NBR$ adds a time dimension to determine changes that have occured in the time span between the two chosen products.\n",
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"Instead of only calculating values by overlaying different bands in one product, the ${\\Delta}$NBR adds a time dimension to determine changes that have occurred in the time span between the two chosen products.\n",
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"\n",
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"## Methodology\n",
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"\n",
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"The $NBR$ of the products downloaded in the previous notebook is calculated using the notebook [02 Spectral Indices](\"02 Spectral Indices.ipynb\").\n",
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"These $NBR$ values are plotted side-by-side to get a visual impression of the changes that may have occured between two given dates.\n",
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"The ${\\Delta}NBR$ is calculated and, if showing signs of burned areas, compared to data given by the responsible German ministries.\n",
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"The NBR of the products downloaded in the previous notebook is calculated using the notebook [02 Spectral Indices](\"02 Spectral Indices.ipynb\").\n",
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"These NBR values are plotted side-by-side to get a visual impression of the changes that may have occurred between two given dates.\n",
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"The ${\\Delta}$NBR is calculated and, if showing signs of burned areas, compared to data given by the responsible German ministries.\n",
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"\n",
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"## Calculcations\n",
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"### Setup"
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"source": [
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"#### NBR plots\n",
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"\n",
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"We plot the $NBR$ values for all dates side by side to get a first visual impression of changes that have occured."
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"We plot the NBR values for all dates side by side to get a first visual impression of changes that have occurred."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Red dots mark the location of the evacuated villages, the background shows the calculated $NBR$ values, where areas with a low $NBR$ value are brighter and areas with a high NBR value are darker."
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"Red dots mark the location of the evacuated villages, the background shows the calculated NBR values, where areas with a low NBR value are brighter and areas with a high NBR value are darker."
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]
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},
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{
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