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b/notebooks/01_classification.html @@ -875,12 +875,12 @@

Loading the Data

False Color Image

-

In addition to the regular RGB Image, we can swap any of the bands from the visible spectrum with any other bands. In this specific case the red band has been changed to the near infrared band. This allows us to see vegetated areas more clearly, since they now appear in a bright red color. This is due to the fact that plants absorb regular red light while reflecting near infrared light .

+

In addition to the regular RGB Image, we can swap any of the bands from the visible spectrum with any other bands. In this specific case the red band has been changed to the near infrared band. This allows us to see vegetated areas more clearly, since they now appear in a bright red color. This is due to the fact that plants absorb regular red light while reflecting near infrared light [NAS20].

diff --git a/notebooks/02_floodmapping.html b/notebooks/02_floodmapping.html index f064c26..cf1790c 100644 --- a/notebooks/02_floodmapping.html +++ b/notebooks/02_floodmapping.html @@ -60,7 +60,7 @@ - + @@ -409,8 +409,8 @@

Reverend Bayes updates our Belief in Flood Detection

From Backscattering to Flood Mapping

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This notebook explains how microwave (\(\sigma^0\)) backscattering can be used to map the extent of a flood. We replicate in this exercise the work of on the TU Wien Bayesian-based flood mapping algorithm.

-

In the following lines we create a map with EOmaps of the \(\sigma^0\) backscattering values.

+

This notebook explains how microwave (\(\sigma^0\)) backscattering can be used to map the extent of a flood. We replicate in this exercise the work of Bauer-Marschallinger et al. [BMCT+22] on the TU Wien Bayesian-based flood mapping algorithm.

+

In the following lines we create a map with EOmaps [Qua24] of the \(\sigma^0\) backscattering values.

@@ -599,7 +599,7 @@

Likelihoods -

+
@@ -626,7 +626,7 @@

Posteriors - +

@@ -651,7 +651,7 @@

Flood Classification
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+

diff --git a/notebooks/references.html b/notebooks/references.html index 88a3fe8..2c5fa3d 100644 --- a/notebooks/references.html +++ b/notebooks/references.html @@ -368,6 +368,26 @@

References

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+
+
+[BMCT+22] +

Bernhard Bauer-Marschallinger, Senmao Cao, Mark Edwin Tupas, Florian Roth, Claudio Navacchi, Thomas Melzer, Vahid Freeman, and Wolfgang Wagner. Satellite-Based Flood Mapping through Bayesian Inference from a Sentinel-1 SAR Datacube. Remote Sensing, 14(15):3673, January 2022. Number: 15 Publisher: Multidisciplinary Digital Publishing Institute. URL: https://www.mdpi.com/2072-4292/14/15/3673 (visited on 2024-06-03), doi:10.3390/rs14153673.

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+
+[NAS20] +

NASA. Earth observatory. 2020. URL: https://earthobservatory.nasa.gov/features/MeasuringVegetation/measuring_vegetation_2.php (visited on 2024-07-26).

+
+
+[Qua24] +

Raphael Quast. Eomaps: a python package to visualize and analyze geographical datasets. 2024. URL: https://doi.org/10.5281/zenodo.6459598, doi:10.5281/zenodo.6459598.

+
+
+[RHS+74] +

John Wilson Rouse, Rüdiger H Haas, John A Schell, Donald W Deering, and others. Monitoring vegetation systems in the great plains with erts. NASA Spec. Publ, 351(1):309, 1974.

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+