Hands-on course to GIS and Remote Sensing with GRASS GIS

The hands-on GRASS GIS course at ITC – University of Twente on November 3rd, 2017 was a great success. The course, organized by ITC and OSGeo.nl, offered a very nice introduction to GRASS GIS by Veronica Andreo and a guided tour about working with GRASS GIS by Sajid Pareeth.

KLdistribution

As part of the course, we also developed three modules with hands-on exercises on different topics related to raster time series processing, remote sensing images processing and spatial interpolation in GRASS GIS.

All the course materials are available online, so check them out and enjoy 🙂

 

 

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K-fold cross validation in GRASS GIS

A common technique to estimate the accuracy of a predictive model is k-fold cross-validation. In k-fold cross-validation, the original sample is randomly partitioned into a number of sub-samples with an approximately equal number of records. Of these sub-samples, a single sub-sample is retained as the validation data for testing the model, and the remaining sub-samples are combined to be used as training data. The cross-validation process is then repeated as many times as there are sub-samples, with each of the sub-samples used exactly once as the validation data (Table 1).

table_1
Table 1. Illustration of data partitioning in a 4-fold cross-validation, with training data used to train the model, and test data to validate the model.

The k evaluation results can then be averaged (or otherwise combined) to produce a single estimation. The advantage of this method is that all observations are used for both training and validation, and each observation is used for validation exactly once.

Functions for modelling and machine learning in e.g., R and Python’s Scikit-learn often contain build-in cross-validation routines. But it is also fairly easy to build such a routine yourself. This tutorial shows how one can easily build a k-fold cross-validation routine in GRASS GIS, e.g., to evaluate the predictive performance of two interpolation techniques, the inverse Distance Weighting and bilinear spline interpolation.

elevation_v1
Figure 1. A) Elevation map of North Carolina. B) Elevation estimation based on inverse distance weighting interpolation of the elevation at 150 random sample points. C) Residue map with the differences between A and B. D) Relative differences between A and B, computed as (A-B)/A. Map C and D are overlaid with the 150 sample locations.

This tutorial is available on https://tutorials.ecodiv.earth.

Plotting GRASS data in Python

GRASS GIS offers some useful but basic plotting options for raster data. However, for plotting of data in attribute tables and for more advanced graphs, we need to use other software tools. In this tutorial I explore some of the possibilities offered by Pandas plot() and how we can further tune plots using matplotlib / pyplot library.

map_municipals
Map of the municipals in Wake County, North Carolina, and for each municipal the distribution of distances to the nearest school (data source: North Carolina sample data set).

 

GRASS and Pandas – from attribute table to pandas dataframe

Introduction

In this post I show how to import an attribute table of a vector layer in a GRASS GIS database into a Pandas data frame. Pandas stands for Python Data Analysis Library which provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language. For people familiar with R, the Pandas data frame is an object similar to the R data frame. They are a lot like the most common way in which spreadsheets are used, with the data presented in rectangular form with columns holding variables and rows holding observations. An important characteristic is that the data frame, like a spreadsheet, can hold different types of data in different columns: numbers, character data, dates and so on. Continue reading “GRASS and Pandas – from attribute table to pandas dataframe”

Terrain attribute selection in environmental studies

Exploring species-environment relationships is important for amongst others habitat mapping, biogeographical classification, conservation, and management. And it has become easier with (i) the advance of a wide range of tools, including many open source tools, and (ii) availability of more relevant data sources. For example, there are many tools with which it is relatively easy to create a wide range of derived terrain variables using digital elevation (DEM) or bathymetric (DBM) models. However, the ease of use of many of these tools, especially when used by non-experts, may lead to the selection of arbitrary or sub-optimal set of variables. In addition, derived variables will often be highly correlated (Lecours et al. 2017).

Continue reading “Terrain attribute selection in environmental studies”