Script a Habitat Suitability Map#


This exercise guides through the creation of rasters (osgeo.gdal.Dataset), the usage of georeferences, raster array calculations, as well as the conversion of a raster to a polygon shapefile and modifications to the shapefile’s Attribute Table to calculate usable (physical) habitat area. For this purpose, a Raster class is written, which enables mathematical operations between its instances through the implementation of magic methods. The exercise also shows how flusstools can be used to leverage complex challenges with just a few lines of code.


Python libraries: NumPy, Pandas, OSGeo and GDAL Including ogr and osr, geopandas, alphashape, shapely, and JSON. Understand how Object Orientation and Classes work as well as Geospatial Python. To visualize results, make sure to install QGIS and do the QGIS Tutorial.

Get ready by cloning the exercise repository:

git clone

Sacramento suckers in the South Yuba River (source: Sebastian Schwindt @hydroinformatics on YouTube).

What is Habitat Suitability?#

Fish and other aquatic species rest, orient, and reproduce in a fluvial environment that represents their physical habitat. Throughout their different life stages, different fish have specific physical habitat preferences which are defined, for instance, as a function of water depth, flow velocity, and grain size of the riverbed. The so-called Habitat Suitability Index \(HSI\) can be calculated for hydraulic (water depth or flow velocity) and morphological (e.g., grain size or cover in the form of large wood) parameters individually to describe the quality of physical habitat for a fish and at a specific life stage. The figure below shows exemplary \(HSI\) curves for the fry, juvenile, and adult life stages of rainbow trout as a function of water depth. The \(HSI\) curves look different in every river and should be established individually by an aquatic ecologist.

HSI curves examples trout

Fig. 61 Habitat Suitability Index \(HSI\) curves for the fry, juvenile, and adult life stages of rainbow trout in a cobble-bed river. Take care: HSI curves look different in any river and need to be established by an aquatic ecologist.#

The \(HSI\) concept also accounts for the so-called cover habitat in the form of the cover Habitat Suitability Index \(HSI_{cov}\). Cover habitat is the result of local turbulence caused by roughness elements such as wood, boulders, or bridge piers. However, in this exercise, we will only deal with hydraulic habitat characteristics (not cover habitat).

Adult trout swimming in cover habitat created by a bridge pier in the upper Neckar River.

The combination of multiple \(HSI\) values (e.g., water depth-related \(HSI_{h}\), flow velocity-related \(HSI_{u}\), grain size-related \(HSI_{d}\), and/or cover \(HSI_{cov}\)) results in the combined Habitat Suitability Index \(cHSI\). There are various calculation methods for combining different \(HSI_{par}\) values into one \(cHSI\) value, where the geometric mean and the product are the most widely used deterministic combination methods:

combined Habitat Suitability Index (cHSI) calculation methods

  • Geometric mean: \(cHSI = (\prod_{par} HSI_{par})^{1/n}\)
    For instance, the combination of the water depth-related \(HSI_{h}\) and flow velocity-related \(HSI_{u}\) with the geometric mean method is: \(cHSI = (HSI_{h} \cdot HSI_{u})^{1/2}\)

  • Product: \(cHSI = \prod_{par} HSI_{par}\)
    For example, the combination of the water depth-related \(HSI_{h}\) and flow velocity-related \(HSI_{u}\) with the product method is: \(cHSI = (HSI_{h} \cdot HSI_{u})\)

  • Fuzzy rule sets [NSW13]

Therefore, if the pixel-based \(HSI\) values for water depth and flow velocity are known from a two-dimensional (2d) hydrodynamic model, then for each pixel the \(cHSI\) value can be calculated either as the product or geometric mean of the single-parameter \(HSI_{par}\) rasters.

This habitat assessment concept was introduced by Bovee [Bov86] and Stalnaker et al. [SLH+95] (direct download). However, these authors built their usable (physical) habitat assessment based on one-dimensional (1d) numerical models that were commonly used in the last millennium. Today, 2d numerical models are the state-of-the-art to determine physical habitat geospatially explicit based on pixel-based \(cHSI\) values. There are two different options for calculating the usable habitat area (\(UHA\)) based on pixel-based \(cHSI\) values (and even more options can be found in the scientific literature).

Calculate Usable Habitat Area (UHA)

  1. Use a threshold value above which a pixel is classified as a usable habitat.

    • Typical values for the threshold value \(cHSI_{crit}\) are between 0.4 (tolerant) and 0.75 (strict).

    • The usable habitat area \(UHA\) results from the pixel size \(px_{a}\) (e.g., in m\(^2\)) multiplied by the number of pixels (\(px\)) where \(cHSI > cHSI_{crit}\):
      \(UHA = px_{a} \cdot \sum px_{i}(cHSI_{px_{i}} > cHSI_{crit})\)

  2. Multiply the pixel \(cHSI\) value with the pixel size.

    • The pixel area is weighted by its habitat quality expressed by the \(cHSI\) value:
      \(UHA = px_{a} \cdot \sum cHSI_{px_{i}}\)

Weighted Usable Area (WUA) vs. UHA

Some authors (e.g., Yao et al. [YBR18], Tuhtan et al. [TNW12]) incorrectly refer to this weighting as Weighted Usable Area (WUA), which conflicts with the original definition of WUA [Bov86].

The threshold method is preferable over the weighting method because the \(HSI\) has the unit of Index and is therefore not dimensionless. As a result, unrealistic units of Index areas (e.g., Index-m\(^2\)) are created in the weighting method, which is also introducing non-measurable uncertainty.

An alternative to the deterministic calculation of the \(HSI\) and \(cHSI\) values of a pixel is a fuzzy logic approach [NSW13]. In the fuzzy logic approach, pixels are classified, for instance, as low, medium, or high habitat quality as a function of the associated water depth or flow velocity using categorical (low, medium, or high), expert assessment-based \(HSI\) curves. The \(cHSI\) value results from the center of gravity of superimposed membership functions of considered parameters (e.g., water depth and flow velocity).

Sustainable river management involves the challenge of designing an aquatic habitat for target fish species at different life stages. The concept of usable physical habitat area represents a powerful tool to leverage the assessment of the ecological integrity of river management and engineering measures. For example, by calculating the usable habitat area before and after the implementation of measures, valuable conclusions can be drawn about the ecological integrity of restoration efforts.

This exercise demonstrates the use of 2d hydrodynamic modeling results to algorithmically evaluate usable habitat area based on the calculation of geospatially explicit \(cHSI\) values.

Available Data and Code Structure#

The following flow chart illustrates the provided code and data. Functions, methods, and files to be created in this exercise are highlighted in bold, italic, YELLOW-ish font.

HSI curves examples trout

Fig. 62 Structure of the provided template file tree and their relations.#

The provided QGIS project file visualize_with_QGIS.qgz helps to verify input raster datasets and results.

Two-dimensional (2d) Hydrodynamic Modelling (Folder: BASEMENT)#

This exercise uses (hydraulic) flow velocity and water depth rasters (GeoTIFFs) produced with the ETH Zurich*s BASEMENT software. Read more about hydrodynamic modeling with BASEMENT in the BASEMENT chapter. The hydraulic rasters were produced with the BASEMENT developer’s example data from the Flaz River in Switzerland (read more on their website). The water depth water_depth.tif and flow velocity flow_velocity.tif rasters are provided for this exercise in the folder /basement/.

Habitat Suitability Index HSI Curves (folder: habitat)#

The /habitat/ folder in the exercise repository contains \(HSI\) curves in the form of an xlsx workbook (trout.xlsx) and in the form of a JSON file (trout.json). Both files contain the same data for rainbow trout of a hypothetical cobble-bed river and this exercise only uses the JSON file (the workbook serves for visual verification only).


GEO_UTILS (Folder: geo_utils)

A couple of gdal-based functions for processing rasters and shapefiles were introduced in the lecture. This exercise re-uses some of these functions, which are available in the geo-processing code repository specifically for this ebook. The repository contains a functional block from flusstools.geotools (in the geo_utils folder) that enables to better understand and modify the behavior of flusstools. Even though already provided in this exercise, make sure that the geo_utils repository is well implemented in the exercise directory (i.e., geo_utils scripts are stored in a folder tree like this: Exercise-geco\geo_utils\). The \geo_utils\ folder corresponds to the geo-utils\geo_utils\ directory when you clone the repository (alternatively use from flusstools import geotools as geo).

Make sure that in the \geo_utils\ file, the nan_value is defined as 0.0 (nan_value = 0.0).


The code in this exercise uses a file where all necessary libraries and global variables are loaded centrally.

# This is

import os
import logging
import random
import shutil
import string
import json

import numpy as np
import pandas as pd

import geo_utils as geo # alternative: from flusstools import geotools as geo

cache_folder = os.path.abspath("") + "\\__cache__\\"
par_dict = {"velocity": "u",
            "depth": "h",
            "grain_size": "d"}
nan_value = 0.0


At this point in the course, it is assumed that students are familiar with object orientation and especially with writing functions. Therefore, many basic functions for this exercise are already provided with the script (alphabetically ordered list):

  • cache is a wrapper for parent functions to enforce that intermediate geospatial datasets (e.g., the intermediate product of a sum of rasters) are stored in a temporary cache folder that is deleted after the script ran.

  • check_cache verifies if the cache folder defined in already exists. The function is automatically called by the cache wrapper.

  • create_random_string(length) generates unique file names for temporary (cached) datasets, where length is an integer value that determines the number of characters of the random string to be created.

  • interpolate_from_list(x_values, y_values, xi_values) linearly interpolates \(y_{i}\) values from two sorted lists containing paired x and y values for a List of given \(x_{i}\) values (returns a numpy.array of the same length as xi_values). If one of the \(x_{i}\) values is beyond the value range of x_values, the function appends the nan_value defined in to the results array.

  • interpolate_y(x1, x2, y1, y2, xi) is called by the interpolate_from_list function for paired lower and upper x1-y1 and x2-y2 floats of the x_values and y_values Lists (returns a float number corresponding to the linearly interpolated yi value of the xi-yi pair between x1-y1 and x2-y2). If xi is not numeric, or if the interpolation results in a ZeroDivisionError, the function returns the nan_value defined in

  • log_actions(fun) wraps a function (fun), where actions should be written to a logfile. Logging is started with the start_logging function (see below) and logging is stopped with logging.shutdown().

  • read_json opens a JSON file and returns it as Python object. In this exercise, this function will be used to open the /habitat/trout.json file. The \(HSI\) values can then be assessed from the JSON object, for example:

trout = read_json("PATH/" + "trout.json")

>>> 0.0198
  • remove_directory(directory) removes a directory (string argument). Be careful, this function aggressively removes the directory and all its contents with little chance of data recovery.

  • start_logging() starts logging to a logfile (logfile.log) and the Python console at the logging.DEBUG level.


The parent Raster class is stored in the script, where magic methods, a pseudo private _make_raster, and a save method will be created in this exercise. The HSIRaster class in the script is a child of the Raster class. In this exercise, we will only look at how this child class is structured and what it produces (i.e., no modifications are necessary).


The two scripts and represent the focal point of this exercise and make use of the provided data and Python scripts. Therefore, only the basic framework functions and imports are pre-existing in these two template scripts.

Create and Combine HSI Rasters#

Complete the __init__ Method of the Raster Class (

The script imports the functions and libraries loaded in the script, and therefore, also the script. For this reason, the NumPy and Pandas libraries are already available (as np and pd, respectively), and the geo_utils package is already imported as geo (import geo_utils as geo in

The Raster class will load any GeoTIFF file name as a geo-referenced array object that can be used with mathematical operators. First, we will complement the __init__ method by a (extract from the file_name argument), as well as georeferences and array datasets:

    # __init__(...) of Raster class in = file_name.split("/")[-1].split("\\")[-1].split(".")[0]

If the provided file_name does not exist, the __init__ method creates a new raster with the file_name (this behavior is already implemented in the if not os.path.exists(file_name) statement. Next, load the osgeo.gdal.dataset, the np.array, and the geo_transformation of the raster. For this purpose, use the raster2array function from this eBook, which is also implemented in the exercise’s geo_utils (geo) package:

    # __init__(...) of Raster class in
        self.dataset, self.array, self.geo_transformation = geo.raster2array(file_name, band_number=band)

To identify the EPSG number (Authority code) of a raster, retrieve the spatial reference system (SRS) of the raster. Also for this purpose we have already developed a function in the lecture with the get_srs from the theory section on reprojection. Load the SRS and the EPSG number using the get_srs function with the following two lines of code in the __init__ method:

    # __init__(...) of Raster class in
        self.srs = geo.get_srs(self.dataset)
        self.epsg = int(self.srs.GetAuthorityCode(None)

The __init__ method of the Raster class is complete.

Complete Magic Methods of the Raster Class (

To enable mathematical operations between multiple instances of the Raster class, implement Overloading and Magic Methods that tell the class what to do when two Raster instances are for example added (+ sign), multiplied (* sign), or subtracted (- sign). For instance, implementing the magic methods __truediv__ (for using the / operator), __mul__ (for using the * operator), and __pow__ (for using the ** operator) will enable the usage of Raster instances like this:

# example for Raster instances, when operators are defined through magic methods

# load GeoTIFF rasters from file directory
velocity = Raster("/usr/geodata/u.tif")
depth = Raster("/usr/geodata/h.tif")

# calculate the Froude number using operators defined with magic methods
Froude = velocity / (depth * 9.81) ** 0.5

# save the new raster"/usr/geodata/froude.tif")

The Raster class template already contains one exemplary magic method to enable division (__truediv__):

    # Raster class in
    def __truediv__(self, constant_or_raster):
            self.array = np.divide(self.array, constant_or_raster.array)
        except AttributeError:
            self.array /= constant_or_raster
        return self._make_raster("div")

Here is what the __truediv__ method does:

  • The input argument constant_or_raster can be another Raster instance that has an array attribute or a numeric constant (e.g., 9.81).

  • The method tries to invoke the array attribute of constant_or_raster.

    • If constant_or_raster is a raster object, then invoking contant_or_raster.array is successful. In this case self.array is overwritten with the element-wise division of the array by contant_or_raster.array. The element-wise division builds on NumPys built-in function np.divide, which is a computationally efficient wrapper of C/C++ code (much faster than a Python loop over array elements).

    • If constant_or_raster is a numeric value, then invoking contant_or_raster.array results in an AttributeError and the __truediv__ method falls in the except AttributeError statement, where self.array is simply divided by constant_or_raster.

  • The method returns the result of the pseudo private method self._make_raster("div") ([recall PEP 8 Code Style and Conventions, which corresponds to a new Raster instance of the actual Raster instance divided by constant_or_raster. The new Raster instance is a temporary GeoTIFF file in the cache folder (recall the cache function). This is how the pseudo-private method _make_raster(self, file_marker) looks like:

Make Raster function

def _make_raster(self, file_marker):
    f_ending = "__{0}{1}__.tif".format(file_marker, create_random_string(4)
    geo.create_raster(cache_folder + + f_ending, self.array, epsg=self.epsg,
    return Raster(cache_folder + + f_ending)

This function:

  • Uses the string-type argument file_marker to add it to the file name of the base GeoTIFF along with a random, four characters-long string (recall the create_random_string). file_marker is unique for every implemented operator. For the __truediv__ method use file_marker="div". Thus, the temporary GeoTIFF file name is defined as cache_folder + + f_ending (e.g. "C:\Excercise-geco\__cache__\velocity__divhjev__.tif").

  • Applies the Create a Raster (Array to Raster) function from geo_utils to write the temporary GeoTIFF to the __cache__ folder with the original raster’s spatial reference system.

  • returns a new Raster instance of the temporary, cached GeoTIFF file.

When using the _make_raster method, add the following magic methods to the Raster class (function placeholders are already present in the template):

  • __add__ (+ operator):

            self.array += constant_or_raster.array
        except AttributeError:
            self.array += constant_or_raster
        return self._make_raster("add")
  • __mul__ (* operator):

            self.array = np.multiply(self.array, constant_or_raster.array)
        except AttributeError:
            self.array *= constant_or_raster
        return self._make_raster("mul")
  • __pow__ (** operator):

            self.array = np.power(self.array, constant_or_raster.array)
        except AttributeError:
            self.array **= constant_or_raster
        return self._make_raster("pow")
  • __sub__ (- operator):

            self.array -= constant_or_raster.array
        except AttributeError:
            self.array -= constant_or_raster
        return self._make_raster("sub")

The last item to complete in the Raster class is the built-in save method that receives a file_name (string) argument defining the directory and save-as name of the Raster instance:

        save_status = geo.create_raster(file_name, self.array, epsg=self.epsg, nan_val=0.0, geo_info=self.geo_transformation)
        return save_status

Why do we need the save_status variable? First, it states if saving the raster was successful (save_status=0), and second, this information could be used to delete the raster from the __cache__ folder and flush the memory (feel free to do so for speeding up the code).

Write HSI and cHSI Raster Creation Script#

The provided script already contains required package imports, an if __name__ == '__main__' stand-alone statement as well as the void main, get_hsi_curve, get_hsi_raster, and combine_hsi_rasters functions:

cHSI Template script

from fun import *
from raster_hsi import HSIRaster, Raster
from time import perf_counter

def combine_hsi_rasters(raster_list, method="geometric_mean"):

def get_hsi_curve(json_file, life_stage, parameters):

def get_hsi_raster(tif_dir, hsi_curve):

def main():

if __name__ == '__main__':
    # define global variables for the main() function
    parameters = ["velocity", "depth"]
    life_stage = "juvenile"
    fish_file = os.path.abspath("") + "\\habitat\\trout.json"
    tifs = {"velocity": os.path.abspath("") + "\\basement\\flow_velocity.tif",
            "depth": os.path.abspath("") + "\\basement\\water_depth.tif"}
    hsi_output_dir = os.path.abspath("") + "\\habitat\\"

    # run code and evaluate performance
    t0 = perf_counter()
    t1 = perf_counter()
    print("Time elapsed: " + str(t1 - t0)

The if __name__ == '__main__' statement contains a time counter (perf_counter) that prompts how long the script takes to run (typically between 3 to 6 seconds). Make sure that

  • the parameters list contains "velocity" and "depth" (as per the par_dict in the script),

  • the file paths are defined correctly, and

  • a life stage is defined (i.e., either "fry", "juvenile", "adult", or "spawning" as per the /habitat/fish.xlsx workbook).

The following paragraphs show step by step how to load the \(HSI\) curves from the JSON file (get_hsi_curve), apply them to the flow_velocity and water_depth rasters (get_hsi_raster), and combine the resulting \(HSI\) rasters into \(cHSI\) rasters (combine_hsi_rasters).

The get_hsi_curve function will load the \(HSI\) curve from the JSON file (/habitat/trout.json) in a dictionary for the two parameters "velocity" and "depth". Thus, the goal is to create a curve_data dictionary that contains one Pandas DataFrame object for all parameters (i.e., velocity and depth). For example, curve_data["velocity"]["u"] will be a Pandas Series of velocity entries (in m/s) that corresponds to curve_data["velocity"]["HSI"], which is a Pandas Series of \(HSI\) values. Similarly, curve_data["depth"]["h"] is a Pandas Series of depth entries (in meters) that corresponds to curve_data["depth"]["HSI"], which is a Pandas Series of \(HSI\) values (corresponds to the curves shown in the HSI graphs above). To extract the desired information from the JSON file, get_hsi_curve takes three arguments (json_file, life_stage, and parameters) in order to:

  • Get the information stored in the JSON file with the read_json function (see above).

  • Instantiate a void curve_data Dictionary that will contain the Pandas DataFrames for "velocity" and "depth".

  • Run a loop over the (two) parameters ("velocity" and "depth"), in which it:

    • Creates a void par_pairs List for storing pairs of parameter (par) - \(HSI\) values as nested lists.

    • Iterates through the length of provided curve data, where valid data pairs (e.g., [u_value, HSI_value]) are appended to the par_pairs List. This iteration is what actually creates the nested List.

    • Converts the final par_pairs list to a Pandas DataFrame that it adds to the curve_data Dictionary.

  • return the curve_data Dictionary with its Pandas DataFrames.

def get_hsi_curve(json_file, life_stage, parameters):
    # read the JSON file with fun.read_json
    file_info = read_json(json_file)
    # instantiate output dictionary
    curve_data = {}
    # iterate through parameter list (e.g., ["velocity", "depth"])
    for par in parameters:
        # create a void list to store pairs of parameter-HSI values as nested lists
        par_pairs = []
        # iterate through the length of parameter-HSI curves in the JSON file
        for i in range(0, file_info[par][life_stage].__len__():
            # if the parameter is not empty (i.e., __len__ > 0), append the parameter-HSI (e.g., [u_value, HSI_value]) pair as nested list
            if str(file_info[par][life_stage][i]["HSI"]).__len__() > 0:
                    # only append data pairs if both parameter and HSI are numeric (floats)
                except ValueError:
                    logging.warning("Invalid HSI curve entry for {0} in parameter {1}.".format(life_stage, par)
        # add the nested parameter pair list as pandas DataFrame to the curve_data dictionary
        curve_data.update({par: pd.DataFrame(par_pairs, columns=[par_dict[par], "HSI"])})
    return curve_data

In the main function, call get_hsi_curves to get the \(HSI\) curves as a Dictionary. In addition, implement the cache and the log_actions wrappers (recall the descriptions of provided functions) for the main function:



def main():
    # get HSI curves as pandas DataFrames nested in a dictionary
    hsi_curve = get_hsi_curve(fish_file, life_stage=life_stage, parameters=parameters)


With the provided HSIRaster ( class, the \(HSI\) rasters can be conveniently created in the get_hsi_raster function. Before using the HSIRaster class, make sure to understand how it works. The HSIRaster class inherits from the Raster class and initiates its parent class in its __init__ method through Raster.__init__(self, file_name=file_name, band=band, raster_array=raster_array, geo_info=geo_info). Then, the class calls its make_hsi method, which takes an \(HSI\) curve (nested List) of two equal List pairs (List of parameters and List of \(HSI\) values) as argument. The make_hsi method:

  • Extracts parameter values (e.g., depth or velocity) from the first element of the nested hsi_curves List, and \(HSI\) values from the second element of the nested hsi_curves List.

  • Uses NumPys built-in np.nditer function, which iterates through NumPy arrays with high computational efficiency (read more about nditer).

    • The nditer loop passes the par_values as x_values List argument and the hsi_values as y_values List arguments to the interpolate_from_list function (recall the function descriptions above).

    • The array values (i.e., flow velocity or water depth) correspond to the xi_values List argument of the interpolate_from_list function.

    • The interpolate_from_list function then identifies for each element of the xi_values List the closest elements (\(x_{i}\) values) in the x_values List and the corresponding positions in the y_values List.

    • The interpolate_from_list function passes the identified values to the interpolate_y function, which then linearly interpolates the corresponding yi value (i.e., an \(HSI\) value).

    • Thus, the flow velocity or water depths in self.array are row-wise (row-by-row) replaced by \(HSI\) values.

  • returns a Raster instance using the pseudo-private _make_raster method (recall its contents).

from raster import *

class HSIRaster(Raster):
    def __init__(self, file_name, hsi_curve, band=1, raster_array=None, geo_info=False):
        Raster.__init__(self, file_name=file_name, band=band, raster_array=raster_array, geo_info=geo_info)

    def make_hsi(self, hsi_curve):
        par_values = hsi_curve[0]
        hsi_values = hsi_curve[1]
            with np.nditer(self.array, flags=["external_loop"], op_flags=["readwrite"]) as it:
                for x in it:
                    x[...] = interpolate_from_list(par_values, hsi_values, x)
        except AttributeError:
            print("WARNING: np.array is one-dimensional.")
        return self._make_raster("hsi")

Modify the get_hsi_rasters function to directly return a HSIRaster object:



def get_hsi_raster(tif_dir, hsi_curve):
    return HSIRaster(tif_dir, hsi_curve)

The get_hsi_raster function requires two arguments, which it must receive from the main function. For this reason, iterate over the parameters List in the main function and extract the corresponding raster directories from the tifs Dictionary (recall the variable definition in the standalone statement). In addition, save the Raster objects returned by the get_hsi_raster function in another Dictionary (eco_rasters) to combine them in the next step into a \(cHSI\) raster.



def main():
    # get HSI curves as pandas DataFrames nested in a dictionary
    hsi_curve = get_hsi_curve(fish_file, life_stage=life_stage, parameters=parameters)

        # create HSI rasters for all parameters considered and store the Raster objects in a dictionary
    eco_rasters = {}
    for par in parameters:
        hsi_par_curve = [list(hsi_curve[par][par_dict[par]]),
        eco_rasters.update({par: get_hsi_raster(tif_dir=tifs[par], hsi_curve=hsi_par_curve)})
        eco_rasters[par].save(hsi_output_dir + "hsi_%s.tif" % par)

Of course, one can also loop over the parameters List directly in the get_hsi_raster function.

Test if the code works

This is a good moment to test if the code works. Run and verify that the two GeoTIFF files (/habitat/hsi_velocity.tif and /habitat/hsi_depth.tif) are created correctly. QGIS visualizes the GeoTIFF-products and the activated Identify Features button in QGIS enables to check if the linearly interpolated \(HSI\) values agree with the \(HSI\) curves in the provided workbook (/habitat/trout.xlsx). Thus, load both GeoTIFF pairs in QGIS: /habitat/hsi_velocity.tif + /basement/flow_velocity.tif and /habitat/hsi_depth.tif + /basement/water_depth.tif.

Next, we come to the reason why we had to define magic methods for the Raster class: combine the \(HSI\) rasters using both combination formulae presented above (recall the product and geometric mean formulae), where "geometric_mean" should be used by default. The combine_hsi_rasters function accepts two arguments (a List of Raster objects corresponding to \(HSI\) rasters and the method to use as string).

If the method corresponds to the default value "geometric_mean", then the power to be applied to the product of the Raster List is calculated from the nth root, where n corresponds to the number of Raster objects in the raster_list. Otherwise (e.g., method="product"), the power is exactly 1.0.

The combine_hsi_rasters function initially creates an empty \(cHSI\) Raster in the cache_folder, with each cell having the value 1.0 (filled through np.ones). In a loop over the Raster elements of the raster_list, the function multiplies each \(HSI\) raster with the \(cHSI\) raster.

Finally, the function returns the product of all \(HSI\) rasters to the power of the previously determined power value.

def combine_hsi_rasters(raster_list, method="geometric_mean"):
    if method is "geometric_mean":
        power = 1.0 / float(raster_list.__len__()
        # supposedly method is "product"
        power = 1.0

    chsi_raster = Raster(cache_folder + "chsi_start.tif",
    for ras in raster_list:
        chsi_raster = chsi_raster * ras

    return chsi_raster ** power

To finish the script, implement the call to the combine_hsi_rasters function in the main function and save the result as \(cHSI\) GeoTIFF raster in the /habitat/ folder:



def main():

    for par in parameters:
        hsi_par_curve = [list(hsi_curve[par][par_dict[par]]),
        eco_rasters.update({par: get_hsi_raster(tif_dir=tifs[par], hsi_curve=hsi_par_curve)})
        eco_rasters[par].save(hsi_output_dir + "hsi_%s.tif" % par)

    # get and save chsi raster
    chsi_raster = combine_hsi_rasters(raster_list=list(eco_rasters.values(),
                                      method="geometric_mean") + "chsi.tif")

Run the HSI and cHSI Code#

A successful run of the script should look like this (in PyCharm):

run execute script calculation combined habitat suitability index raster map

Fig. 63 A Windows Python console running the above-created scripts.#

Plotted in QGIS, the \(cHSI\) GeoTIFF raster should look like this:

chsi calculation

Fig. 64 The cHSI raster plotted in QGIS, where poor physical habitat quality (cHSI close to 0.0) is colored in red and high physical habitat quality (cHSI close to 1.0) is colored in green.#

Result Interpretation#

The presentation of the \(cHSI\) raster shows that preferred habitat areas for juvenile trout exist only close to the banks. Also, numerical artifacts of the triangular mesh used by BASEMENT are visible. Therefore, the question arises whether the calculated flow velocities and water depths, and in consequence also the \(cHSI\) values, close to the banks can be considered representative.

Calculate Usable Habitat Area UHA#

Write the Code#

The \(cHSI\) rasters enable the calculation of the available usable habitat area. The previous section featured examples using the fish species trout and its juvenile life stage, for which we will determine here the usable habitat area \(UHA\) (in m\(^2\)) using a \(cHSI\) threshold value (rather than the pixel area weighting approach). So we follow the threshold formula described above, using a threshold value of \(cHSI_{crit} = 0.4\). Thus, every pixel that has a \(cHSI\) value of 0.4 or greater counts as usable habitat area.

From a technical point of view, this part of the exercise is about converting a raster into a polygon shapefile as well as accessing and modifying the Attribute Table of the shapefile.

Similar to the creation of the \(cHSI\) raster, there is a template script available for this part of the exercise, called, which contains package and module imports, an if __name__ == '__main__' stand-alone statement, as well as the void main and calculate_habitat_area functions. The template script looks like this:

# this is (template)
from fun import *
from raster import Raster

def calculate_habitat_area(layer, epsg):

def main():

if __name__ == '__main__':
    chsi_raster_name = os.path.abspath("") + "\\habitat\\chsi.tif"
    chsi_threshold = 0.4


In the if __name__ == '__main__' statement, make sure that the global variable chsi_raster_name corresponds to the directory of the \(cHSI\) raster created in the previous section. The other global variable (chsi_threshold) corresponds to the \(cHSI_{crit}\) value of 0.4 that we will use with the threshold formula.

In the main function, start with loading the \(cHSI\) raster (chsi_raster) as a Raster object. Then, access the NumPy array of the \(cHSI\) raster and compare it with chsi_threshold using NumPys built-in greater_equal function. np.greater_equal takes an array as first argument and a second argument, which is the condition that can be a numeric variable or another NumPy array. Then, np.greater_equal checks if the elements of the first array are greater than or equal to the second argument. In the case of the second argument being an array, this is an element-wise \(\geq\) comparison. The result of np.greater_equal is a Boolean array (True where the greater-or-equal condition is fulfilled and False otherwise). However, to create an osgeo.gdal.Dataset object from the result of np.greater_equal, we need a numeric array. For this reason, multiply the result of np.greater_equal by 1.0 and assign it as a new NumPy array of zeros (False) and ones (True) to a variable named habitat_pixels (see the code block below).

With the habitat_pixels array and the georeference of chsi_raster, create a new integer GeoTIFF raster with the create_raster function (also available in flusstools.geotools); here, use geo.create_raster. In the following code block the new raster is saved in the /habitat/ folder of the exercise as habitat-pixels.tif.


def main():
    # open the chsi raster
    chsi_raster = Raster(chsi_ras_name)
    # extract pixels where the physical habitat quality is higher than the user threshold value
    habitat_pixels = np.greater_equal(chsi_raster.array, chsi_threshold_value) * 1
    # write the habitat pixels to a binary array (0 -> no habitat, 1 -> usable habitat)
    geo.create_raster(os.path.abspath("") + "\\habitat\\habitat-pixels.tif",

In the next step, convert the habitat pixel raster into a polygon shapefile and save it in the /habitat/ folder as habitat-area.shp. The conversion of a raster into a polygon shapefile requires that the raster contains only integer values, which is the case in the habitat pixel raster (only zeros and ones - recall Raster to Polygon). Use the raster2polygon function in the geo_utils folder (package) to create the new polygon shapefile, specify habitat-pixels.tif as raster_file_name to be converted, and /habitat/habitat-area.shp as output file name. The geo.raster2polygon function returns an osgeo.ogr.DataSource object and we can pass its layer including the information of the EPSG authority code (from chsi_raster) directly to the not-yet-written calculate_habitat_area() function:


def main():
    ... (create habitat pixels raster)

    # convert the raster with usable pixels to polygon (must be an integer raster!)
    tar_shp_file_name = os.path.abspath("") + "\\habitat\\habitat-area.shp"
    habitat_polygons = geo.raster2polygon(os.path.abspath("") + "\\habitat\\habitat-pixels.tif",

    # calculate the habitat area (will be written to the attribute table)
    calculate_habitat_area(habitat_polygons.GetLayer(), chsi_raster.epsg)

For the calculate_habitat_area() function to produce what its name promises, we need to populate this function as well. For this purpose, use the epsg integer argument to identify the unit system of the shapefile.


def calculate_habitat_area(layer, epsg):
    # retrieve units
    srs = geo.osr.SpatialReference()
    area_unit = "square %s" % str(srs.GetLinearUnitsName()


In practice, many mistakes are made due to the incorrect use of area units, which is often not obvious at first because of the size of geospatial data (several gigabytes). There are many units of length and area (meters, feet, acre, hectare, km\(^2\)) and a difference of an order of magnitude is sometimes only noticed when a critical reviewer or a local expert becomes suspicious. In the application shown here, we use the information of the length units only to output the total area with a correct reference to the area units (m\(^2\)) on the console, but in practice, this information can save a career.

To determine the habitat area, the area of each polygon must be calculated. For this purpose, add a new field to the layer in the Attribute Table, name it "area", and assign a geo.ogr.OFTReal (numeric) data type (recall how to create a field an data types). Then, create a void List called poly_size, in which we will write the area of all polygons that have a field value of 1. To access the individual polygons (features) of the layer, iterate through all features using a for loop, which:

  • Extracts the polygon of every feature using polygon = feature.GetGeometryRef()

  • Appends the polygon’s area size to the poly_size List if the field "value" of the polygon (at position 0: feature.GetField(0)) is 1 (True).

  • Writes the polygon’s area size to the Attribute Table with feature.SetField("area", polygon.GetArea().

  • Saves the changes (calculated area) to the shapefile layer with layer.SetFeature(feature).

C/C++ efficiency in Python

Looping through an attribute table is computationally expensive in Python. If a shapefile has many elements (points, lines, polygons), this loop can last for hours, days, or even weeks. Therefore, it can be useful to convert a shapefile into a raster and perform calculations using NumPys computationally efficient built-in functions (C/C++ wrappers), which are many times faster. A particular problem is the processing of large lidar datasets (several million points), where it may be necessary to use other software (read more at

The last information needed after the for loop is the total area of the "value"=1 polygons, which we get by writing the sum of the poly_size List to the console. Therefore, the second and last part of the calculate_habitat_area function looks like this:

def calculate_habitat_area(layer, epsg):

    ... (extract unit system information)

    # add area field
    layer.CreateField(geo.ogr.FieldDefn("area", geo.ogr.OFTReal)

    # create list to store polygon sizes
    poly_size = []

    # iterate through geometries (polygon features) of the layer
    for feature in layer:
        # retrieve polygon geometry
        polygon = feature.GetGeometryRef()
        # add polygon size if field "value" is one (determined by chsi_treshold)
        if int(feature.GetField(0):
        # write area to area field
        feature.SetField("area", polygon.GetArea()
        # add the feature modifications to the layer

    # calculate and print habitat area
    print("The total habitat area is {0} {1}.".format(str(sum(poly_size), area_unit)



To calculate other geometry attributes than the polygon area (e.g., envelope extents, derive a convex hull, or get the length of lines), refer to the Calculate Geometric Attributes section and use those functions in lieu of polygon.GetArea().

Run the Usable Habitat Area Calculation Code#

A successful run of the script should look like this (in PyCharm):

calculate usable habitat area Python gdal

Fig. 65 Successful run of the script.#

Plotted in QGIS, the habitat-area shapefile looks like this (use Categorized symbology):

calculate usable habitat area Python gdal map raster QGIS

Fig. 66 The habitat-area shapefile plotted in QGIS with Categorized symbology, where the usable habitat area \(UHA\) (\(cHSI >\) 0.4) is delineated by the hatched purple patches and their dashed outlines.#

Result Interpretation#

The \(UHA\) of the analyzed river section represents a very small share of the total wetted area, which can be interpreted as an ecologically poor status of the river. However, a glance at a map and the simulation files of the Flaz example of BASEMENT suggests that at a discharge of 50 m\(^3\)/s, a flood situation can be assumed. As during floods, there are generally higher flow velocities, which are out-of-favor of juvenile fish, the small usable habitat area is finally not surprising.


Remember that the here presented habitat assessment assumes that fish prefer regions with high \(cHSI\) values and that rivers with a high proportion of areas with high \(cHSI\) values are ecologically particularly valuable. This approach represents an assessment of the physical habitat state with limited information on the functional habitat state.


HOMEWORK 1: Rewrite the magic methods of the Raster class by using def __METHOD__(self, other: Raster) -> Raster: instead of def __METHOD__(self, constant_or_raster): and the _make_raster method.

HOMEWORK 2: Abandon (delete) the geo_utils folder and replace the import  geo_utils as geo with import flusstools.geotools as geo