LISEM

https://lisemmodel.com/docs/
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### Install LISEM Python Package

Source: https://lisemmodel.com/docs/usingpython

Install the LISEM Python package using pip. Ensure all required dependencies are met.

```bash
pip install lisem_..._.whl
```

--------------------------------

### Recursive Fibonacci Function Example

Source: https://lisemmodel.com/docs/usingscripting

A C-style function definition example, specifically a recursive implementation of the Fibonacci sequence. Demonstrates function return types and arguments.

```angelscript
int fibonacci(int n)
{
	if(n <= 1)
	{
		return n;
	}
	else
	{
		return fibonacci(n-1) + fibonacci(n-2);
	}
}
```

--------------------------------

### Initiate Automated Calibration

Source: https://lisemmodel.com/docs/calibration

Starts the automated calibration process using gradient descent. Provide initial parameter values, positivity constraints, a custom error function, and step sizes.

```lisemscript
OptimizeCustom({1.0,1.0},{true,true},@Calibrate,0.01,0.05);
```

--------------------------------

### Converting Raster to Vector

Source: https://lisemmodel.com/docs/warping

Convert a raster dataset into a vector dataset. This example vectorizes a channel width raster, using the 'width' attribute.

```csharp
rivers.shp = Vectorize(channelwidth.map,"width");
```

--------------------------------

### Conditional If/Else Statement

Source: https://lisemmodel.com/docs

A basic example of an if/else statement in LISEM scripting. The code within the 'if' block executes if the condition is true; otherwise, the 'else' block executes.

```angelscript
void main()
{
	if(true)
	{
		Print("This is printed")
	}else
	{
		Print("This is not printed")
	}
}
```

--------------------------------

### Download Soil Classification Data

Source: https://lisemmodel.com/docs/webdata

Downloads soil classification data from SOILGRIDS using a WCS service with a GET request. The downloaded GeoTIFF is converted to a map object.

```lisemscript
soilclass.tif = ToMap(Download("http://maps.isric.org/mapserv?map=/map/wrb.map&SERVICE=WCS&VERSION=1.0.0&REQUEST=GetCoverage&coverage=MostProbable&CRS=EPSG:3978&BBOX=-2200000,-712631,3072800,3840000&WIDTH=3199&HEIGHT=2833&FORMAT=GTiff"));
```

--------------------------------

### Recursive Fibonacci Function

Source: https://lisemmodel.com/docs

An example of a recursive function definition in LISEM scripting, calculating the nth Fibonacci number. Functions follow a C-style format.

```angelscript
int fibonacci(int n)
{
	if(n <= 1)
	{
		return n;
	}else
	{
		return fibonacci(n-1) + fibonacci(n-2);
	}
}
```

--------------------------------

### Cutting Raster Subsets

Source: https://lisemmodel.com/docs/warping

Extract a subset of a raster dataset using either a defined Region or by getting the region from a shapefile.

```csharp
section_custom.tif = RasterCut(dem.tif,Region(0,1.0,0.0,1.0));
section.tif = RasterCut(dem.tif,GetRegion(shape.shp));
```

--------------------------------

### Setting CRS for Raster Layers

Source: https://lisemmodel.com/docs/warping

Set the Coordinate Reference System (CRS) for a raster layer by getting it from another file, using WGS84 UTM, or an EPSG code.

```csharp
test.tif = SetCRS(tes.tif,GetCRS(test2.tif)); //get CRS from other file
test.tif = SetCRS(tes.tif,GeoProjectionFromWGS84UTM(30,true)); //use WGS84 UTM 30 N
test.tif = SetCRS(tes.tif,GeoProjectionFromEPSG(4326)); //use WGS84 (EPSG code 4326)
```

--------------------------------

### Custom Calibration Error Function

Source: https://lisemmodel.com/docs/calibration

Defines a custom function to calculate the error for the calibration process. This example runs the LISEM model, loads predicted and actual data, and returns an error value based on MapContinuousCohensKappa.

```lisemscript
double Calibrate(array<double> params)
{
	RunModel("run/Couli_AutoCalib.run","Internal Friction Angle_cal_mult="+ToString(params[0])+"|" + "Cohesion Bottom_cal_mult="+ToString(params[1]));

	Map impact = LoadMap("res/safetyfactor.map");
	Map inventory = 1.0-LoadMap("maps/inventory.tif");
	double errorval = MapContinuousCohensKappa(inventory,impact,1.0);

	Print("Running model with ifa= " + ToString(params[0]) +"& coh= " + ToString(params[1]) + " score = " + ToString(1.0-errorval));

	return (1.0-errorval);
}
```

--------------------------------

### Logistic Binary Classification for Susceptibility

Source: https://lisemmodel.com/docs/classification

Performs a logistic binary classification, for example, for landslide susceptibility, using normalized slope, DEM, and PGA maps.

```plaintext
MathExpression expr = MapLogisticRegression({MapNormalize(slope.map),MapNormalize(dem.map),MapNormalize(pga.map)},training.map);
Print(expr.GetExpression());
```

--------------------------------

### Basic Script Structure

Source: https://lisemmodel.com/docs

A simple script demonstrating basic map calculations and function calls. Scripts must include a void main() function as the entry point.

```angelscript
void main()
{
	a.tif = b.tif + 2.0;
	slope.tif = Slope(dem.tif);
	angle.tif = Sin(Atan(Slope(dem.tif)));
	
	a.map = MapMaximum(angle.tif)
}
```

--------------------------------

### Simulate Time Steps for Water Flow

Source: https://lisemmodel.com/docs/dynamicmodels

Sets up a loop to simulate water flow over a specified period with defined time steps. It prints a message every minute of simulated time.

```csharp
void main()
{
	Map dem = dem.tif;
	
	//we will simulate 1 hour, with timesteps of 6 seconds.
	int n_steps = 60 *60/6; //nunmber of timesteps
	float t = 0.0; //time in seconds
	float dt = 6.0; //timestep in seconds
	
	for(int i = 0; i < n_steps; i++)
	{
		t = t + 6;
		
		//message the user if we are finished another minute
		if( floor(t)%60 == 0)
		{
			Print("We are now at minute : " + ToString(t/60));
		}
	}
}
```

--------------------------------

### Simple Water-Flow Simulation using FlowDynamic

Source: https://lisemmodel.com/docs/flooding

This script demonstrates a basic flood simulation using the FlowDynamic function. It requires elevation, initial water height, and Manning's N maps. The simulation runs for a specified number of timesteps, storing flow height and velocity periodically.

```void
void main()
{
    
    Map dem = dem.map; 
    Map whinitial = initialwaterheight.map;
    Map n= dem * 0.0 + 0.1; //homogeneous mannings n of 0.1
    
    Print("Create Initial Properties");
    
	//a list with {height, x-velocity, y-velocity}
    array<Map> flowprops = {whinitial,whinitial * 0.0,whinitial*0.0};
    
    Print("Add Layers");
    UILayer l = AddViewLayer( whinitial, "elevation",true);
    for(int i = 0; i < 5000; i++)
    {
        flowprops = FlowDynamic(dem, n,flowprops[0],flowprops[1],flowprops[2],0.1);
        
        ReplaceViewLayer(l, flowprops[0]);
		
		//every 50 timesteps, store the flow height and velocity
        if( i % 50== 0)
        {
            whelev.tif = dem+flowprops[0];
            whvel.tif = sqrt(flowprops[2] *flowprops[2]  + flowprops[1] *flowprops[1]) ;
        
        }
        Print("Timestep : " + ToString(i));
        
    } 
}
```

--------------------------------

### Scripting with File Loading and Loops

Source: https://lisemmodel.com/docs

This script shows how to load maps from files (relative and absolute paths) and process them within a loop. It also includes comments and defines a custom function.

```angelscript
//this is a comment
//here the main() function is defined
//the entry point
void main()
{
	a.map = b.map;
	Map a = a.map;

              //Load… functions take a path relative to the working directory
	Map b = LoadMap("b.map")

              //The Load.. functions have a variant that takes an absolute path
	Shapes s = LoadShapesAbsPath("C:/s.shp")

	//this loop runs for 100 iterations
	for(int i = 0; i < 100; i++)
	{
        b = AddToB(b);
    }
}

//define another function call AddToB, it takes 1 argument of type Map
Map AddToB(Map b)
{
	b += c.map;
	return b;
}
```

--------------------------------

### Main Function for Calibration

Source: https://lisemmodel.com/docs/calibration

The main function that initiates the calibration process by calling OptimizeCustom with specific parameters.

```lisemscript
void main()
{
OptimizeCustom({1.0,1.0},{true,true},@Calibrate,0.01,0.05);
}
```

--------------------------------

### Simulate Rainfall and Water Flow

Source: https://lisemmodel.com/docs/dynamicmodels

Simulates rainfall for the first half hour and applies a dynamic flow approximation using the FlowDynamic function. Results are saved to 'h_final.tif'.

```csharp
void main()
{
	Map dem = dem.tif;
	
	//use the elevation model to create an empty map of identical size.
	//this will be the water height, flow velocity in x and y direction
	array<Map> flow = {dem * 0.0,dem * 0.0,dem * 0.0};
	
	
	//we will simulate 1 hour, with timesteps of 6 seconds.
	int n_steps = 60 *60/6; //nunmber of timesteps
	float t = 0.0; //time in seconds
	float dt = 6.0; //timestep in seconds
	for(int i = 0; i < n_steps; i++)
	{
		t = t + dt;
		
		if(t < 30*60)
		{
			//convert to meter/hour
			flow[0] += 50 * dt * 0.001 / (60*60);
		}
        
        //make a timestep of 6 second with the dynamic flow approximation
        //the resutls overwrites the old data
        flow = FlowDynamic(dem,dem*0.0 + 0.1,flow[0],flow[1],flow[2],6,0.2);
		
		//message the user if we are finished another minute
		if( floor(t)%60 == 0)
		{
			Print("We are now at minute : " + ToString(t/60));
		}
        
        //save the model results
	    h_final.tif = flow[0];
    
	}
	
}
```

--------------------------------

### Define a Class in AngelScript

Source: https://lisemmodel.com/docs

Demonstrates the C-style syntax for defining a class with member variables and methods. Local variables can shadow class properties, requiring explicit access using 'this'.

```angelscript
// The class declaration
class MyClass
{
// A class method
void DoSomething()
{
  // The class properties can be accessed directly
  a *= 2;
  // The declaration of a local variable may hide class properties
  int b = 42;
  // In this case the class property have to be accessed explicitly
  this.b = b;
}
// Class properties
int a;
int b;
}

```

--------------------------------

### Calibrate LISEM Model Parameters

Source: https://lisemmodel.com/docs/lisemcalibration

This script calibrates LISEM simulations for cohesion and internal friction angle. It defines an error function and uses OptimizeCustom to find optimal parameters. Ensure the run file and map paths are correct.

```C++
//this function is the error function. It takes a list of parameters (2) and returns the model error 
double Calibrate(array<double> params)
{
	//run the LISEM model, by calling a run file within the working directory.
	//The second argument is a string containing the options is the following format: "name=value|name2=value2"
	//the names can be found within the run-files. Add _cal_mult to a name to get the calibration multiplier.
	//Here, we set the calibration multiplier for internal friction angle and cohesion.
	
	RunModel("run/Couli_AutoCalib.run","Internal Friction Angle_cal_mult="+ToString(params[0])+"|" + "Cohesion Bottom_cal_mult="+ToString(params[1]));


	//load the impact and inventory maps
	Map impact = LoadMap("res/safetyfactor.map");
	Map inventory = 1.0-LoadMap("maps/inventory.tif");
	
	//calculate error value through Cohens Kappa
	double errorval = MapContinuousCohensKappa(inventory,impact,1.0);

	//print info to output window
	Print("Running model with ifa= " + ToString(params[0]) +"& coh= " + ToString(params[1]) + " score = " + ToString(1.0-errorval));

	//actual error is 1.0- Cohens Kappa
	return (1.0-errorval);
}


void main()
{
	//Optimize custom will minimize the error for a function
	//the function Calibrate is defined above
	//we start with two parameters of value 1.0 that must be positive.
	//finite step size = 0.01, gradient step size  = 0.05
	//gradient step size might need to be lowered for stable convergence.
	OptimizeCustom({1.0,1.0},{true,true},@Calibrate,0.01,0.05);
}

```

--------------------------------

### Apply Window Average Filter (60)

Source: https://lisemmodel.com/docs/filters

Applies a window average filter with a specified window size (60) to a dataset for smoothing.

```lisem
dem_lidar_av.map = WindowAverage(dem_lidar.map,60)
```

--------------------------------

### Basic Conditional Logic in a Script

Source: https://lisemmodel.com/docs/usingscripting

Demonstrates the use of if/else statements for conditional execution within a LISEM script. The Print function outputs messages.

```angelscript
void main()
{
	if(true)
	{
		Print("This is printed")
	}
}

```

--------------------------------

### Visualize Water Flow Simulation

Source: https://lisemmodel.com/docs/dynamicmodels

Adds a hillshade background and a dynamic water height layer to the map viewer. The water height layer is updated in each time step during the simulation.

```csharp
void main()
{
	Map dem = dem.tif;
	
	//use the elevation model to create an empty map of identical size.
	//this will be the water height, flow velocity in x and y direction
	array<Map> flow = {dem * 0.0,dem * 0.0,dem * 0.0};
	
    //add view layers
    AddViewLayer(HillShade(dem),"hillshade");
    UILayer hlay = AddViewLayer(flow[0],"flow height (m)");
	
	//we will simulate 1 hour, with timesteps of 6 seconds.
	int n_steps = 60 *60/6; //nunmber of timesteps
	float t = 0.0; //time in seconds
	float dt = 6.0; //timestep in seconds
	for(int i = 0; i < n_steps; i++)
	{
		t = t + dt;
		
		if(t < 30*60)
		{
			//convert to meter/hour
			flow[0] += 50 * dt * 0.001 / (60*60);
		}
        
        //make a timestep of 6 second with the dynamic flow approximation
        //the resutls overwrites the old data
        flow = FlowDynamic(dem,dem*0.0 + 0.1,flow[0],flow[1],flow[2],6,0.2);
		
		//message the user if we are finished another minute
		if( floor(t)%60 == 0)
		{
			Print("We are now at minute : " + ToString(t/60));
		}
        
        //update map viewer
        ReplaceViewLayer(hlay,flow[0]);
        
        //save the model results
	    h_final.tif = flow[0];
    
	}
	
}
```

--------------------------------

### Loading a List of Maps from a GeoTIFF

Source: https://lisemmodel.com/docs

Demonstrates how to load multiple map bands from a GeoTIFF file into an Array of Maps. The Array type is templated, specifying the type of data it holds.

```angelscript
Array<Map> bands = LoadMapBandList(spectral.tif)
```

--------------------------------

### Create Empty Map for Training Data

Source: https://lisemmodel.com/docs/classification

Initializes an empty map with the same dimensions as the input raster, intended for storing training data for supervised classification.

```plaintext
training.tif = result.tif[0] * 0.0;
```

--------------------------------

### Benchmark GPU vs CPU Raster Calculation

Source: https://lisemmodel.com/docs/gpu

This snippet benchmarks a simple GPU raster calculation against its CPU equivalent. It demonstrates uploading data to the GPU, performing operations, and downloading results. Note that data transfer between CPU and GPU can impact performance.

```C++
void main()
{
	//first create a temporary empty gpu map
	//this will initialize all the gpu-side code, but this is done only once 
	GpuMap temp = GpuMap();
    
	//now we do the benchmark
    GPUMap m = ToGPU(dem_lidar_5ml.map);
    double t = GetAppTime();
    GPUMap m2 = pow(sqrt(sqrt(m)),2.0);
    Print("GPU TIME " + ToString(GetAppTime() - t));
    
    test.map = ToCPU(m2);
   
    Map mc = dem_lidar_5ml.map;
    t = GetAppTime();
    Map mc2 = sqrt(sqrt(mc));
    Print("CPU TIME " + ToString(GetAppTime() - t));

}
```

--------------------------------

### Create Multi-band Raster File

Source: https://lisemmodel.com/docs/datamanipulation

Combines multiple raster layers into a single multi-band raster file.

```angelscript
layers.tif = {dem.map,slope.map,WindowAverage(flux.map,100))
```

--------------------------------

### Execute Geo-spatial Algorithms

Source: https://lisemmodel.com/docs/datamanipulation

Applies advanced geo-spatial algorithms like flux accumulation using input maps.

```angelscript
flux.map = Accuflux(ldd.map,Slope.map)
```

--------------------------------

### Estimate Channel Width and Depth

Source: https://lisemmodel.com/docs/elevationdata

Estimates channel width and depth using global relationships based on flow accumulation, converting cell size to kilometers.

```python
channelwidth.tif = 9.68*(accuflux.tif/1e6)**0.32
channeldepth.tif = 11.3*(accuflux.tif/1e6)**0.083
```

--------------------------------

### Calculate 2D Flow Accumulation

Source: https://lisemmodel.com/docs/elevationdata

Calculates 2D flow accumulation, potentially considering different flow dynamics.

```python
accuflux2d.tif = Accuflux2D(dem.map,dem.map * 0.0 + 1.0,dem.map * 0.0,0.3,1.0)
```

--------------------------------

### Load and Convert Map Data

Source: https://lisemmodel.com/docs/datamanipulation

Loads a map in one format and saves it to another, like ASC to GeoTIFF.

```angelscript
dem.tif = dem.asc
```

--------------------------------

### Map Operations with Advanced Operators

Source: https://lisemmodel.com/docs

Illustrates the use of advanced operators for map manipulation, including bitwise AND (&), right shift (>>), and the custom equality operator (.eq).

```angelscript
Conditional.tif = MapIf( (a.map >> b.map) & (c.tif.eq(d.tif)), iftrue.tif, iffalse.tif)
```

--------------------------------

### Apply Machine Learning Model for Elevation Data

Source: https://lisemmodel.com/docs/downloads

Use this code to apply a machine learning model to an elevation file for creating high-resolution elevation data. Replace 'model.onnx' with the desired model file.

```python
hr.tif = ApplyMLModel({lr.tif},"model.onnx",32,32,{0,3,1,2});
```

--------------------------------

### Highlight Filled Depressions

Source: https://lisemmodel.com/docs/elevationdata

Calculates the difference between the original DEM and the depression-filled DEM to highlight changes.

```python
dif.tif = dem_fix.tif - dem.tif
```

--------------------------------

### Download OpenStreetMap Data via POST

Source: https://lisemmodel.com/docs/webdata

Downloads OpenStreetMap data using a POST request to the Overpass API. The downloaded XML data is processed and saved to a file. Ensure WaitForAllDownloads() is called to complete the download.

```lisemscript
void main()
{
	DownloadFilePost("http://overpass-api.de/api/interpreter",OSMExtentToXMLPost(Region(0.0,1.0,0.0,1.0),"osm.osm"));
	WaitForAllDownloads();
}
```

--------------------------------

### Prepare Sentinel-2 Bands for Classification

Source: https://lisemmodel.com/docs/classification

This script downloads Sentinel-2 bands, cuts out a specific region, and warps them to a uniform resolution for use in classification.

```plaintext
void main()
{
    //get the desired bands in a list
    array<string> names = GetItemsContaining(GetFilesInDir(),{"B01","B02","B03","B04","B05","B06","B07","B08"});
    
	//set the desired area and create a mask by cutting the second band (which is 10 meter resolution)
	Region area = Region(301575,334651,5.72216e+06,5.75079e+06);
    Map mask = RasterCut(LoadMap(names[1]),area);
	
	//make a map array, and put each band cut and warped to the right resolution (10 meters)
    array<Map> bands;
    for(int i = 0; i < names.length(); i++)
    {
        bands.Append(RasterWarp(mask,RasterCut(LoadMap(names[i]),area)));
    }
	
	//Store result in multi-band tif file
    result.tif = bands;
}
```

--------------------------------

### Calculate Slope using LISEM Script Tool

Source: https://lisemmodel.com/docs/gettingstarted

This command calculates the slope from a Digital Elevation Model (DEM) file and saves the result to a new GeoTIFF file. Ensure the input DEM file is in the current working directory.

```plaintext
slope.tif = Slope(dem.tif)
```

--------------------------------

### Run Support-Vector-Machine Classification

Source: https://lisemmodel.com/docs/classification

Performs a supervised classification using the Support Vector Machine algorithm on the prepared multi-band raster data and training data.

```plaintext
classes.tif = SupervisedClassification(result.tif, training.tif);
```

--------------------------------

### Estimate Channel Width

Source: https://lisemmodel.com/docs/hydrology

Estimates channel width using a logarithmic relationship based on accuflux. This formula is derived from empirical observations.

```LISEM Script
channelwidth.tif = 9.68*(accuflux.tif/1e6)**0.32
```

--------------------------------

### Calculate Drainage Network with Advanced Parameters

Source: https://lisemmodel.com/docs/studysite

Calculates the drainage network with advanced parameters to control catchment merging, size, and handling of missing values. Useful for refining watershed delineation.

```python
ldd.tif = DrainageNetwork(dem.tif,1e6,1e6,1e6,1e6, true)
```

--------------------------------

### Load Elevation Model

Source: https://lisemmodel.com/docs/dynamicmodels

Loads a Digital Elevation Model (DEM) from a GeoTIFF file into the script.

```csharp
void main()
{
	Map dem = dem.tif;
}
```

--------------------------------

### Generate Drainage Network (LDD)

Source: https://lisemmodel.com/docs/elevationdata

Creates a Local Drainage Direction (LDD) map from a DEM, indicating the flow direction for each cell.

```python
ldd.tif = DrainageNetwork(dem.tif,1e4,1e4,1e4,1e4)
```

--------------------------------

### Create Channel Mask from Accuflux Threshold

Source: https://lisemmodel.com/docs/hydrology

Generates a channel mask by applying a threshold to the calculated accuflux. Cells exceeding the threshold are identified as channel cells.

```LISEM Script
channelmask.map = MapIf(accuflux.map >> 5000,1.0,0.0);
```

--------------------------------

### Generate Hillshade from DEM

Source: https://lisemmodel.com/docs/elevationdata

Creates a hillshade representation of the terrain from a DEM, useful for visualization.

```python
hillshade.tif = HillShade(dem.tif)
```

--------------------------------

### Converting Vector to Raster

Source: https://lisemmodel.com/docs/warping

Convert a vector dataset to a raster dataset. This can be done by rasterizing an attribute value or by burning a specified value.

```csharp
channelwidth.tif = Rasterize(target.map,rivers.shp,"width"); //put attribute value of "width"
channelwidth_burn.tif = Rasterize(target.map,rivers.shp,"",true,1.0); //burn value 1.0
```

--------------------------------

### Apply Collapsed Operators to Files

Source: https://lisemmodel.com/docs/datamanipulation

Applies arithmetic operations directly to the data within a raster file, modifying it in place.

```angelscript
test.tif += 100
```

```angelscript
test.tif += dem.map
```

--------------------------------

### Identify Channel Locations

Source: https://lisemmodel.com/docs/elevationdata

Selects potential channel locations by applying a threshold to the flow accumulation data.

```python
channel.tif = MapIf(accuflux.tif >> 1000.0,1.0,0.0)
```

--------------------------------

### Apply Window Average Filter

Source: https://lisemmodel.com/docs/filters

Blurs and smoothens raster data by applying a window average. Adjust the window size (e.g., 100) for more or less smoothing.

```lisem
dem_lidar_wa.map = WindowAverage(dem_lidar.map,100);
```

--------------------------------

### AccuFlux2D Soil Depth Model

Source: https://lisemmodel.com/docs/soils

An advanced soil depth model using AccuFlux2D for better spatial patterns, especially with custom accuflux maps. Suitable for larger channel networks.

```map
soildepthn.map = AccufluxSoil(dem.map,AccuFlux2D(dem.map,dem.map * 0.0 + 1.0,dem.map * 0.0 + 1.0,500,0.2,1.0),dem.map * 0.0 + 0.001,0.0000001,2,500,0.2);
```

--------------------------------

### Telea Inpainting

Source: https://lisemmodel.com/docs/interpolation

Uses the Telea inpainting algorithm to fill in data gaps. This method is suitable for larger gaps and considers the surrounding data to fill missing values.

```plaintext
inpaint_telea.tif = InpaintTelea(gap.tif,6)
```

--------------------------------

### Generate Contour Lines from DEM

Source: https://lisemmodel.com/docs/elevationdata

Creates contour lines from a DEM at a specified interval.

```python
contour.shp = RasterContour(dem.tif,50.0)
```

--------------------------------

### Identify Pits and Catchments

Source: https://lisemmodel.com/docs/studysite

Identifies pits and defines catchments based on a drainage network. This is crucial for segmenting the study area into distinct hydrological units.

```python
pit.tif = Pit(ldd.tif);
catchments.tif = Catchments(ldd.tif,pit.tif);
```

--------------------------------

### Fix Local Depressions in DEM

Source: https://lisemmodel.com/docs/elevationdata

Corrects local depressions in a DEM by applying pit filling, considering diagonal connections.

```python
dem_fix.tif = DrainageNetworkDEM(dem.tif,1e4,1e4,1e4,1e4)
```

--------------------------------

### Steady State Soil Depth Model

Source: https://lisemmodel.com/docs/soils

Calculates soil depth using a steady-state model based on flow accumulation. Requires calibration for reliable results. May have issues with large channel networks.

```map
soildepth.map = SteadyStateSoil(dem.map,dem.map * 0.0 + 0.1, dem.map * 0.0 + 0.2,100,0.2,1.0);
```

--------------------------------

### Approximate Distance Transform with Spread

Source: https://lisemmodel.com/docs/distances

Estimates the lowest friction value from each cell to a source cell. Requires a source mask and friction values.

```plaintext
channeldistance.map = Spread(channelmask.map,0.0,1.0)
```

--------------------------------

### Nearest Neighbor Interpolation

Source: https://lisemmodel.com/docs/interpolation

Fills in values based on the nearest neighbors. Useful for quick resampling but can result in blocky outputs.

```plaintext
nearest.tif = ResampleNearest(dem.tif,4)
```

--------------------------------

### Reduce Map to Single Value

Source: https://lisemmodel.com/docs/datamanipulation

Calculates a single summary value from a map, such as the average, and prints it to the output window.

```angelscript
Print(MapAverageRed(dem.map))
```

--------------------------------

### Estimate Channel Depth

Source: https://lisemmodel.com/docs/hydrology

Estimates channel depth using a logarithmic relationship based on accuflux. This formula complements the channel width estimation.

```LISEM Script
channeldepth.tif = 11.3*(accuflux.tif/1e6)**0.083
```

--------------------------------

### Sparse Interpolation

Source: https://lisemmodel.com/docs/interpolation

Prepares data for other interpolation methods by creating a sparse representation. This can be a precursor for more complex algorithms.

```plaintext
sparse.tif = ResampleSparse(dem.tif,4)
```

--------------------------------

### Infinite Slope Model Calculation

Source: https://lisemmodel.com/docs/slopefailure

This snippet calculates the Factor of Safety (FoS) using the Infinite Slope model. It requires maps for cohesion, internal friction angle, soil density, and digital elevation model (DEM) data. Ensure all necessary maps are loaded before execution.

```python
FoS.map = (coh.map + (thetai.map * 1000.0 + thetas.map * soildensity.map)* tan(ifa.map) * sin(atan(slope(dem.map))) * cos(atan(slope(dem.map))))/((thetai.map * 1000.0 - thetas.map * soildensity.map)* cos(atan(slope(dem.map)))* cos(atan(slope(dem.map))));
```

--------------------------------

### Logical Operators for Shapefiles

Source: https://lisemmodel.com/docs/datamanipulation

Uses logical operators to find intersections or unions between shapefiles, like finding where rivers and roads cross.

```angelscript
bridges.shp = rivers.shp | roads.shp
```

--------------------------------

### Bicubic Interpolation

Source: https://lisemmodel.com/docs/interpolation

Uses bicubic interpolation, considering a 4x4 neighborhood and second derivatives. This generally provides smoother and more accurate results than bilinear interpolation.

```plaintext
cubic.tif = ResampleCubic(dem.tif,4)
```

--------------------------------

### Create Custom Edge-Preserving Filter

Source: https://lisemmodel.com/docs/filters

Linearly blends a window-averaged result with the original data based on slope to preserve edges. The blending factor is controlled by the slope.

```lisem
dem_lidar_wa2.map = min(1.0,max(0.0,1.0-Slope(dem_lidar.map)))*WindowAverage(dem_lidar.map,50) + min(1.0,max(0.0,Slope(dem_lidar.map)))*dem_lidar.map;
```

--------------------------------

### Calculate Drainage Network

Source: https://lisemmodel.com/docs/studysite

Calculates the drainage network from a Digital Elevation Model (DEM). This is a fundamental step for watershed analysis.

```python
ldd.tif = DrainageNetwork(dem.tif);
```

--------------------------------

### Navier-Stokes Inpainting

Source: https://lisemmodel.com/docs/interpolation

Applies the Navier-Stokes inpainting technique to fill data gaps. Similar to Telea, it uses surrounding data but may produce different results, especially regarding derivatives.

```plaintext
inpaint_ns.tif = InpaintNS(gap.tif,6)
```

--------------------------------

### Extract Catchment Region and Cut DEM

Source: https://lisemmodel.com/docs/studysite

Extracts a specific catchment's spatial extent and cuts the Digital Elevation Model (DEM) to this region. This allows for focused analysis of individual catchments.

```python
Region r = ClassExtent(catchments.tif, 56);
dem_cut.tif = RasterCut(dem.tif,r);
```

--------------------------------

### Apply Deconvolution Filter

Source: https://lisemmodel.com/docs/filters

Attempts to reverse the effect of a previous smoothing operation (like averaging) by applying a deconvolution filter. Parameters control the filter strength and iterations.

```lisem
dem_lidar_deconvolve.map = RasterDeconvolve(dem_lidar.map,10,0.2)
```

--------------------------------

### Distributing Channel Width with Spread

Source: https://lisemmodel.com/docs/distances

Distributes river width over neighboring cells using the Spread function when the width exceeds cell size. This involves conversions due to the nature of the spread function.

```plaintext
channelwidth_new.map = max(0.0,min(-Spread(MapIf(channelwidth.map >> 0.0,1.0,0.0),-MapIf(channelwidth.map << 10.0, channelwidth.map,10.0+ (channelwidth.map-10)/2),1.0),10))
```

--------------------------------

### Apply Frost Noise Filter

Source: https://lisemmodel.com/docs/filters

Removes sparse errors or noise from a raster dataset using the Frost noise filter. Parameters control the neighborhood size and iteration count.

```lisem
dem_lidar_frost.map = NoiseFilterFrost(dem_lidar_noise.map,3,1)
```

--------------------------------

### Machine Learning Super-Resolution Interpolation

Source: https://lisemmodel.com/docs/interpolation

Applies a deep neural network model for super-resolution, significantly improving interpolation quality and preserving visual detail. The model can be applied repeatedly, but may introduce artifacts.

```plaintext
sr.tif = ApplyMLModel({dem.tif},"DahalModel.onnx",32,32,{0,3,1,2},true)
```

```plaintext
sr2.tif = ApplyMLModel({sr.tif},"DahalModel.onnx",32,32,{0,3,1,2},true)
```

--------------------------------

### Mean-Shift Segmentation

Source: https://lisemmodel.com/docs/segmentation

Applies mean-shift segmentation to a raster image. Use this to group pixels with similar characteristics into distinct segments. Parameters control the search window size and the minimum segment size.

```python
segmentation.tif = SegmentizeMS(sentinel.tif,100,500,20,200)
```

--------------------------------

### Calculate Accuflux

Source: https://lisemmodel.com/docs/hydrology

Calculates the accuflux based on the drainage network (LDD). This is a precursor to identifying channel cells.

```LISEM Script
accuflux.map = Accuflux(ldd.map,1.0);
```

--------------------------------

### Saxton Pedotransfer Functions

Source: https://lisemmodel.com/docs/soils

Provides Saxton et al. (2006) pedotransfer functions to estimate soil properties like saturated hydraulic conductivity (ksat), soil water retention (suction, field capacity, porosity), and parameters A and B.

```map
ksat.tif = SaxtonKSat(sand.map,clay.map,organic.map,gravel.map);
```

```map
sucton.tif = SaxtonSuction(sand.map,clay.map,organic.map,saturation.map,gravel.map);
```

```map
thetar.tif = SaxtonFieldCapcity(sand.map,clay.map,organic.map,gravel.map);
```

```map
thetas.tif = SaxtonPorosity(sand.map,clay.map,organic.map,gravel.map);
```

```map
A.tif = SaxtonA(sand.map,clay.map,organic.map,gravel.map);
```

```map
B.tif = SaxtonB(sand.map,clay.map,organic.map,gravel.map);
```

--------------------------------

### Calculate Confusion Matrix

Source: https://lisemmodel.com/docs/classification

Generates a confusion matrix to compare two classified maps, useful for evaluating classification accuracy or comparing different methods.

```plaintext
confusionmatrix.tbl = MapConfusionMatrix(classes.tif,classes_unsuper.tif);
```

--------------------------------

### Bilinear Interpolation

Source: https://lisemmodel.com/docs/interpolation

Performs bilinear interpolation, which considers a 2x2 neighborhood. This method maintains data derivatives better than nearest neighbor.

```plaintext
linear.tif = ResampleLinear(dem.tif,4)
```

--------------------------------

### Calculate Error using MapContinuousCohensKappa

Source: https://lisemmodel.com/docs/calibration

Calculates the error value using the MapContinuousCohensKappa function, which is a continuous adaptation of the Cohens Kappa performance metric.

```lisemscript
double errorval = MapContinuousCohensKappa(inventory,impact,1.0);
```

--------------------------------

### Calculate Flow Accumulation

Source: https://lisemmodel.com/docs/elevationdata

Computes the accumulated flow into each cell based on a drainage network map.

```python
accuflux.tif = Accuflux(ldd.tif,1.0)
```

--------------------------------

### Add Layer to Map Viewer

Source: https://lisemmodel.com/docs/datamanipulation

Adds a specified raster layer to the LISEM map viewer.

```angelscript
AddViewLayer(dem.asc)
```

--------------------------------

### Calculate Cohens Kappa Error for Landslide Runout

Source: https://lisemmodel.com/docs/lisemcalibration

Use this function to calculate the Cohens Kappa metric for comparing binary landslide impact predictions against observed data. This method corrects for biases present in simpler accuracy calculations.

```plaintext
double errorval = MapContinuousCohensKappa(inventory.map,impact.map,1.0);
Print("Error is: " + ToString(errorval));
```

--------------------------------

### Warping Raster to a Target Raster

Source: https://lisemmodel.com/docs/warping

Warp a raster dataset directly to match the extent, CRS, and cell sizes of an existing target raster file.

```csharp
warped.tif = RasterWarp(target.tif,GeoProjectionFromWGS84UTM(30,true));
```

--------------------------------

### Perform Unsupervised Classification

Source: https://lisemmodel.com/docs/classification

Conducts an unsupervised classification by specifying the desired number of classes. This method can help clip value ranges and remove outliers.

```plaintext
classes_unsuper.tif = UnSupervisedClassification(result.tif, 5);
```

--------------------------------

### Access Specific Raster Band

Source: https://lisemmodel.com/docs/datamanipulation

Selects and uses a specific band from a multi-band raster file for calculations.

```angelscript
layer.map = layers.tif[0] + layer.tif[1]
```

--------------------------------

### Estimate Soil Depth Map

Source: https://lisemmodel.com/docs/hydrology

Uses the AccufluxSoil function to estimate the soil depth map based on a digital elevation model (DEM). This function requires several parameters related to flow accumulation and soil properties.

```python
soildepth.map = AccufluxSoil(dem.map,AccuFlux2D(dem.map,dem.map * 0.0 + 1.0,dem.map * 0.0 + 1.0,500,0.2,1.0),dem.map * 0.0 + 0.001,0.0000001,2,500,0.2);
```

--------------------------------

### Estimating Travel Effort with Varying Friction

Source: https://lisemmodel.com/docs/distances

Estimates travel effort or times by considering spatially varying friction, such as roads and slopes. The friction is lower on roads and higher on non-roads and steeper slopes.

```plaintext
traveleffort.map = Spread(coast.map,0.0,MapIf(roadwidth.map >> 1,1 + slope.map,2 + 5 * slope.map))
```

--------------------------------

### Compound Map Calculations

Source: https://lisemmodel.com/docs/datamanipulation

Performs complex calculations involving multiple maps and mathematical functions. Ensure all rasters have the same dimensions.

```angelscript
calc.tif = dem.tif + slope.map^2.0 + sin(atan(sqrt(dem.map)))
```

--------------------------------

### Detect Linear Features

Source: https://lisemmodel.com/docs/datamanipulation

Identifies linear features in terrain data, such as rivers or roads, and saves them as a shapefile.

```angelscript
lines.shp = LineDetect(Slope(dem.map))
```

--------------------------------

### Euclidean Distance Transform

Source: https://lisemmodel.com/docs/distances

Calculates the actual Euclidean distance to the closest source cell. Requires an inverted input mask.

```plaintext
channeldistance.map = DistanceTransform(~channelmask.map)
```

--------------------------------

### Kriging Interpolation

Source: https://lisemmodel.com/docs/interpolation

Provides a statistically sound estimate of the most likely value using Kriging. Note that this method tends to under-estimate variability in the dataset.

```plaintext
kriging.tif = Kriging(sparse.tif,10)
```
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