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dem_error_study

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Process case studies on DEM uncertainty analysis at the Mont-Blanc massif and Northern Patagonian Icefield: Hugonnet et al. (2022).

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# dem_error_study

Code and results of [**Hugonnet et al. (2022), *Uncertainty analysis of digital elevation models by spatial inference from stable terrain***](https://doi.org/10.1109/jstars.2022.3188922). :globe_with_meridians: :mount_fuji:

The dataset is available at **[https://doi.org/10.5281/zenodo.7298913](https://doi.org/10.5281/zenodo.7298913)**.

Below a short guide to: perform the uncertainty analysis of your own DEMs, retrieve the case study datasets, reproduce the processing steps with the case studies, reproduce the figures and tables of the paper.

## Uncertainty analysis of your own data with xDEM

The methods of this study are implemented in [xDEM](https://github.com/GlacioHack/xdem), in routines that can be used to perform uncertainty analysis of your 
own DEM data.

There are **three basic examples** with:

- A single-line pipeline to [estimate an elevation error map](https://xdem.readthedocs.io/en/latest/basic_examples/plot_infer_heterosc.html#sphx-glr-basic-examples-plot-infer-heterosc-py),
- A single-line pipeline to [estimate the spatial correlation of errors](https://xdem.readthedocs.io/en/latest/basic_examples/plot_infer_spatial_correlation.html#sphx-glr-basic-examples-plot-infer-spatial-correlation-py),
- A single-line pipeline to [propagate elevation errors spatially](https://xdem.readthedocs.io/en/latest/basic_examples/plot_spatial_error_propagation.html#sphx-glr-basic-examples-plot-spatial-error-propagation-py).

Additionally, there are **three advanced examples** with:

- The detailed steps to [estimate and model the heteroscedasticity of elevation errors](https://xdem.readthedocs.io/en/latest/advanced_examples/plot_heterosc_estimation_modelling.html#sphx-glr-advanced-examples-plot-heterosc-estimation-modelling-py),
- The detailed steps to [estimate and model the spatial correlation of elevation errors](https://xdem.readthedocs.io/en/latest/advanced_examples/plot_variogram_estimation_modelling.html#sphx-glr-advanced-examples-plot-variogram-estimation-modelling-py),
- The detailed steps to [standardize elevation differences to use stable terrain as a proxy](https://xdem.readthedocs.io/en/latest/advanced_examples/plot_standardization.html#sphx-glr-advanced-examples-plot-standardization-py).

The full documentation of xDEM can be found at [https://xdem.readthedocs.io/](https://xdem.readthedocs.io/).

***Note at the date of 07.11.22:** xDEM is still in development (version 0.0.7), and its documentation in 
construction. Compared to version 0.0.6 used in this repository, several changes were added to xDEM late 2022, 
including:*

- *Construction of error pipelines that combines all steps,*
- *Addition of existing gallery examples and streamlining of existing ones,*
- *Minor fixes and improvements of routines.*

## Retrieve the case study datasets

The dataset consists of:
1. **Nearly-simultaneous Pléiades–SPOT-6 elevation differences at the Mont-Blanc massif, the Pléiades DEM used as a 
   reference for alignment and deriving terrain attributes, the SPOT-6 DEM** (.tif, 
   *~1 GB*) at 
   5 m posting and the **forest mask derived from ESA CCI** (.shp, *~5 kB*) available at 
   [https://doi.org/10.5281/zenodo.7298913](https://doi.org/10.5281/zenodo.7298913).
2. **Nearly-simultaneous ASTER–SPOT-5 elevation differences at the Northern Patagonian Icefield, the ASTER DEM 
   used as a reference, the quality of stereo-correlation out of MicMac, and the SPOT-5 DEM** at 30 m 
   posting (.tif, *~150 MB*) available at [https://doi.org/10.5281/zenodo.7298913](https://doi.org/10.5281/zenodo.7298913).

## Reproduce the processing steps with the case studies

### Setup environment

Most scripts rely on the code assembled in the package [xDEM](https://github.com/GlacioHack/xdem) which in turns relies on [SciKit-GStat](https://github.com/mmaelicke/scikit-gstat).
Some routines also rely on [GSTools](https://github.com/GeoStat-Framework/GSTools). You can rapidly 
install a working environment containing those packages and their dependencies with the 
*environment.yml* file, located at the root of the repository, using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html):

```sh
conda env create -f environment.yml
```

*Note: Due to continuous development changes, xDEM is set to v0.0.6 in the environment file to exactly reproduce the 
processing steps of the paper.*

### How to use

Scripts for reproducing the processing steps are located in *case_study_montblanc/* or *case_study_npi/*. Those are generally quite short as they use one-liner routines of xDEM.
Some computations (e.g., simulation of correlated field) are performed only in the *figures/* scripts. In the future, those might be integrated in xDEM.

While all scripts are commented, the details on the functions used are available through **[xDEM's documentation](https://xdem.readthedocs.io/)**,
 **[SciKit-GStat's documentation](https://mmaelicke.github.io/scikit-gstat/)** and **[GSTools's documentation](https://geostat-framework.readthedocs.io/projects/gstools/en/stable/)**.

## Reproduce the figures and tables of the paper

Scripts for reproducing the figures and tables are located in *figures/*. These scripts also depend on the environment 
file `environment.yml` as they rely on [Cartopy](https://scitools.org.uk/cartopy/docs/latest/) and 
[Seaborn](https://seaborn.pydata.org/) in addition to Matplotlib.  In some occasions, the figure scripts duplicate the 
processing steps done in *case_study_montblanc/* or *case_study_npi/* for plotting purposes (e.g., violin plots require 
the full distribution of samples, not only the binned estimates of dispersion).

For plotting figures of your own data, xDEM provides simpler plotting tools of binned data and variograms 
(see [example gallery](https://xdem.readthedocs.io/en/latest/basic_examples/index.html)).

Enjoy! :volcano:

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