mirror of
https://github.com/heyarne/earth-observation-for-journalism.git
synced 2026-05-06 19:13:40 +02:00
Merge branch 'main' of github.com:heyarne/remote-sensing-for-journalism into main
This commit is contained in:
commit
b54636eb2f
4 changed files with 10 additions and 7 deletions
|
|
@ -14,6 +14,9 @@ The registration form can be found at https://scihub.copernicus.eu/dhus/.
|
||||||
## Target Audience
|
## Target Audience
|
||||||
|
|
||||||
These notebooks assume Python knowledge as well as familiarity with common Python data processing tools like the `pandas` library.
|
These notebooks assume Python knowledge as well as familiarity with common Python data processing tools like the `pandas` library.
|
||||||
|
The topic is approached primarily from a computer science perspective, i.e. not an aeronautical, not a geophysical, or any other one.
|
||||||
|
As a consequence the focus will be how different tasks can be implemented.
|
||||||
|
Many considerations behind a particular action or processing step can only be briefly touched.
|
||||||
|
|
||||||
## Obtaining and Running the Code
|
## Obtaining and Running the Code
|
||||||
|
|
||||||
|
|
|
||||||
|
|
@ -974,12 +974,12 @@
|
||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"Downloaded 0.00GB\n"
|
"Downloaded 3.78GB\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"downloaded_bytes_total = sum(p['downloaded_bytes'] for p in downloads.values())\n",
|
"downloaded_bytes_total = sum(p['size'] for p in downloads.values())\n",
|
||||||
"print(f'Downloaded {(downloaded_bytes_total / 1024 ** 3):.2f}GB')"
|
"print(f'Downloaded {(downloaded_bytes_total / 1024 ** 3):.2f}GB')"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
|
|
|
||||||
|
|
@ -1,11 +1,11 @@
|
||||||
# Overview
|
# Overview
|
||||||
|
|
||||||
Following the explorations about how spectral indices can be calculated, this chapter aims to calculate burn areas for different forest fires in Brandenburg and Mecklenburg-Vorpommern in 2018 and 2019.
|
Following the previous chapters' exploration of spectral indices, this chapter aims to calculate burn areas for different forest fires in Brandenburg and Mecklenburg-Vorpommern in 2018 and 2019.
|
||||||
|
|
||||||
While there are Copernicus services that provide forest fire observation services, a lot of the fires during the active wild fire season in Germany are not covered by them.
|
While there are Copernicus services that provide forest fire observation services, a lot of the fires during the active wild fire season in Germany are not covered by them.
|
||||||
Nevertheless these fires have lasting impacts in their ecosystems and are a contributor to CO2 emissions, which serves as the motivation to see if additional information can be gained by looking closely at the Sentinel-2 data.
|
Nevertheless these fires have lasting impacts in their ecosystems and are a contributor to CO2 emissions, which serves as the motivation to see if additional information can be gained by looking closely at the Sentinel-2 data.
|
||||||
|
|
||||||
After an evaluation of the feasibility of a detailed look at the entire area of Brandenburg for an entire year this chapter defines three forest fires of interest for which media coverage during the active fire as well as government reports at a later point provide sufficient information.
|
After an evaluation of the feasibility of a detailed look at the entire area of Brandenburg for an entire year this chapter defines three forest fires for which media coverage during the active fire and government reports at a later point provide sufficient information.
|
||||||
It goes on to analyze the Copernicus data to see if media coverage during the active fire could have been improved by incorporating Sentinel-2 data.
|
It goes on to analyze the Copernicus data to see if media coverage during the active fire could have been improved by incorporating Sentinel-2 data.
|
||||||
|
|
||||||
For this purpose the Delta Normalized Burn Ratio (dNBR) is introduced, which is the combines the Normalized Burn Ratio (NBR) of an observation before and after the fire to provide an indicator for change in burn severity.
|
For this purpose the Delta Normalized Burn Ratio (dNBR) is introduced, which is an index combining the Normalized Burn Ratio (NBR) of an observation before and after the fire to provide an indicator for change in burn severity in a given area.
|
||||||
|
|
|
||||||
|
|
@ -77,7 +77,7 @@
|
||||||
"cell_type": "markdown",
|
"cell_type": "markdown",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"source": [
|
"source": [
|
||||||
"The `postdam_mittelmark_geom` is a collection of three points:"
|
"The `treuenbrietzen_geom` is a collection of three points:"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
|
|
@ -196,7 +196,7 @@
|
||||||
],
|
],
|
||||||
"source": [
|
"source": [
|
||||||
"treuenbrietzen_geom = gpd.read_file(product_path / 'evacuated_2018.json')\n",
|
"treuenbrietzen_geom = gpd.read_file(product_path / 'evacuated_2018.json')\n",
|
||||||
"# geodataframe_on_map(postdam_mittelmark_geom)\n",
|
"# geodataframe_on_map(treuenbrietzen_geom)\n",
|
||||||
"treuenbrietzen_geom"
|
"treuenbrietzen_geom"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue