From 26917fe6d1e2ba4095ba6949c661779af3b11bb8 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Fri, 3 May 2024 07:48:02 +0000 Subject: [PATCH] differences for PR #117 --- 01-intro-raster-data.md | 62 +-- 02-intro-vector-data.md | 30 +- 03-crs.md | 20 +- 04-geo-landscape.md | 24 +- 07-vector-data-in-python.md | 482 +++++++++++------------- 08-crop-raster-data.md | 266 +++++-------- fig/E07/greece_administration_areas.png | Bin 0 -> 64055 bytes fig/E07/greece_highways.png | Bin 0 -> 26479 bytes fig/E07/rhodes_administration_areas.png | Bin 0 -> 17351 bytes fig/E07/rhodes_assets.png | Bin 0 -> 45400 bytes fig/E07/rhodes_builtup_buffer.png | Bin 0 -> 18396 bytes fig/E07/rhodes_highways.png | Bin 0 -> 80495 bytes fig/E07/rhodes_infra_highways.png | Bin 0 -> 46913 bytes fig/E08/dem.png | Bin 0 -> 98203 bytes fig/E08/dem_matched.png | Bin 0 -> 122237 bytes fig/E08/visual_clip.png | Bin 0 -> 77146 bytes fig/E08/visual_clip_box.png | Bin 0 -> 374262 bytes fig/E08/visual_large.png | Bin 0 -> 197716 bytes index.md | 30 +- md5sum.txt | 16 +- setup.md | 27 +- 21 files changed, 430 insertions(+), 527 deletions(-) create mode 100644 fig/E07/greece_administration_areas.png create mode 100644 fig/E07/greece_highways.png create mode 100644 fig/E07/rhodes_administration_areas.png create mode 100644 fig/E07/rhodes_assets.png create mode 100644 fig/E07/rhodes_builtup_buffer.png create mode 100644 fig/E07/rhodes_highways.png create mode 100644 fig/E07/rhodes_infra_highways.png create mode 100644 fig/E08/dem.png create mode 100644 fig/E08/dem_matched.png create mode 100644 fig/E08/visual_clip.png create mode 100644 fig/E08/visual_clip_box.png create mode 100644 fig/E08/visual_large.png diff --git a/01-intro-raster-data.md b/01-intro-raster-data.md index c56b9dd5..cbb3f709 100644 --- a/01-intro-raster-data.md +++ b/01-intro-raster-data.md @@ -18,46 +18,28 @@ exercises: 5 ## Introduction -This episode introduces the two primary types of geospatial -data: rasters and vectors. After briefly introducing these -data types, this episode focuses on raster data, describing -some major features and types of raster data. +This episode introduces the two primary types of data models that are used to digitally represent the earth's surface: raster and vector. After briefly introducing these data models, this episode focuses on the raster representation, describing some major features and types of raster data. This workshop will focus on how to work with both raster and vector data sets, therefore it is essential that we understand the basic structures of these types of data and the types of phenomena that they can represent. ## Data Structures: Raster and Vector -The two primary types of geospatial data are raster -and vector data. Raster data is stored as a grid of values which are rendered on a -map as pixels. Each pixel value represents an area on the Earth's surface. Vector data structures represent specific features on the -Earth's surface, and -assign attributes to those features. Vector data structures -will be discussed in more detail in [the next episode](02-intro-vector-data.md). - -This workshop will focus on how to work with both raster and vector -data sets, therefore it is essential that we understand the -basic structures of these types of data and the types of data -that they can be used to represent. - -### About Raster Data - -Raster data is any pixelated (or gridded) data where each pixel is associated -with a specific geographic location. The value of a pixel can be -continuous (e.g. elevation) or categorical (e.g. land use). If this sounds -familiar, it is because this data structure is very common: it's how -we represent any digital image. A geospatial raster is only different -from a digital photo in that it is accompanied by spatial information -that connects the data to a particular location. This includes the -raster's extent and cell size, the number of rows and columns, and -its coordinate reference system (or CRS). +The two primary data models that are used to represent the earth's surface digitally are the raster and vector. **Raster data** are stored as a grid of values which are rendered on the map as pixels—also known as cells—where each pixel—or cell—represents a value of the earth's surface. Examples of raster data are satellite images or aerial photographs. Data stored according to the **vector data** model are represented by points, lines, or polygons. Examples of vector representation are points of interest, buildings—often represented as building footprints—or roads. + +Representing phenomena as vector data allows you to add attribute information to them. For instance, a polygon of a house can contain multiple attributes containing information about the address like the street name, zip code, city, and number. More explanations about vector data will be discussed in the [next episode](02-intro-vector-data.md). + +When working with spatial information, you will experience that many phenomena can be represented as vector data and raster data. A house, for instance, can be represented by a set of cells in a raster having all the same value or by a polygon as vector containing attribute information (figure 1). It depends on the purpose for which the data is collected and intended to be used which data model it is stored in. But as a rule of thumb, you can apply that discrete phenomena like buildings, roads, trees, signs are represented as vector data, whereas continuous phenomena like temperature, wind speed, elevation are represented as raster data. Yet, one of the things a spatial data analyst often has to do is to transform data from vector to raster or the other way around. Keep in mind that this can cause problems in the data quality. + +### Raster Data + +Raster data are any pixelated (or gridded) data where each pixel has a value and is associated with a specific geographic location. The value of a pixel can be continuous (e.g., elevation, temperature) or categorical (e.g., land-use type). If this sounds familiar, it is because this data structure is very common: it's how we represent any digital image. A geospatial raster is only different from a digital photo in that it is accompanied by spatial information that connects the data to a particular location. This includes the raster's extent and cell size, the number of rows and columns, and its Coordinate Reference System (CRS), which will be explained in [episode 3](03-crs.md) of this workshop. ![Raster Concept (Source: National Ecological Observatory Network (NEON))](fig/E01/raster_concept.png){alt="raster concept"} Some examples of continuous rasters include: 1. Precipitation maps. -2. Maps of tree height derived from LiDAR data. -3. Elevation values for a region. +2. Elevation maps. -A map of elevation for Harvard Forest derived from the [NEON AOP LiDAR sensor](https://www.neonscience.org/data-collection/airborne-remote-sensing) +A map of elevation for *Harvard Forest* derived from the [NEON AOP LiDAR sensor](https://www.neonscience.org/data-collection/airborne-remote-sensing) is below. Elevation is represented as a continuous numeric variable in this map. The legend shows the continuous range of values in the data from around 300 to 420 meters. @@ -69,8 +51,7 @@ continuous value such as elevation or temperature. Some examples of classified maps include: 1. Landcover / land-use maps. -2. Tree height maps classified as short, medium, and tall trees. -3. Elevation maps classified as low, medium, and high elevation. +2. Elevation maps classified as low, medium, and high elevation. ![USA landcover classification](fig/E01/USA_landcover_classification.png){alt="USA landcover classification"} @@ -147,12 +128,12 @@ of changes in resolution. ### Raster Data Format for this Workshop Raster data can come in many different formats. For this workshop, we will use -the GeoTIFF format which has the extension `.tif`. A `.tif` file stores metadata -or attributes about the file as embedded `tif tags`. For instance, your camera +the GeoTIFF format which has the extension `.tif`, since this is one of the most common formats to be used. +A `.tif` file stores metadata or attributes about the file as embedded `tif tags`. For instance, your camera might store a tag that describes the make and model of the camera or the date the photo was taken when it saves a `.tif`. A GeoTIFF is a standard `.tif` image format with additional spatial (georeferencing) information embedded in the file -as tags. These tags should include the following raster metadata: +as tags. These tags include the following raster metadata: 1. Extent 2. Resolution @@ -174,14 +155,11 @@ from a GeoTIFF file. ### Multi-band Raster Data A raster can contain one or more bands. One type of multi-band raster -dataset that is familiar to many of us is a color -image. A basic color image consists of three bands: red, green, and blue. -Each +dataset that is familiar to many of us is a color image. A basic color +image often consists of three bands: red, green, and blue (RGB). Each band represents light reflected from the red, green or blue portions of -the -electromagnetic spectrum. The pixel brightness for each band, when -composited -creates the colors that we see in an image. +the electromagnetic spectrum. The pixel brightness for each band, when +composited creates the colors that we see in an image. ![RGB multi-band raster image (Source: National Ecological Observatory Network (NEON).)](fig/E01/RGBSTack_1.jpg){alt="multi-band raster"} diff --git a/02-intro-vector-data.md b/02-intro-vector-data.md index 0962201c..66a9f661 100644 --- a/02-intro-vector-data.md +++ b/02-intro-vector-data.md @@ -34,7 +34,9 @@ vertex that has a defined x, y location. * **Polygons:** A polygon consists of 3 or more vertices that are connected and closed. The outlines of survey plot boundaries, lakes, oceans, and states or -countries are often represented by polygons. +countries are often represented by polygons. Note, that polygons can also contain one +or multiple holes, for instance a plot boundary with a lake in it. These polygons are +considered *complex* or *donut* polygons. :::callout ## Data Tip @@ -56,8 +58,11 @@ are represented by which vector type. ::::solution ## Solution -State boundaries are polygons. The Fisher Tower location is -a point. There are no line features shown. +State boundaries are shown as polygons. The Fisher Tower location is +represented by a purple point. There are no line features shown. +Note, that at a different scale the Fischer Tower coudl also have been represented as a polygon. +Keep in mind that the purpose for which the dataset is created and aimed to be used for determines +which vector type it uses. :::: ::: @@ -66,14 +71,14 @@ Vector data has some important advantages: * The geometry itself contains information about what the dataset creator thought was important * The geometry structures hold information in themselves - why choose point over polygon, for instance? * Each geometry feature can carry multiple attributes instead of just one, e.g. a database of cities can have attributes for name, country, population, etc -* Data storage can be very efficient compared to rasters +* Data storage can, depending on the scale, be very efficient compared to rasters +* When working with network analysis, for instance to calculate the shortest route between A and B, topologically correct lines are essential. This is not possible through raster data. The downsides of vector data include: -* Potential loss of detail compared to raster -* Potential bias in datasets - what didn't get recorded? +* Potential bias in datasets - what didn't get recorded? Often vector data are interpreted datasets like topographical maps and have been collected by someone else, for another purpose. * Calculations involving multiple vector layers need to do math on the - geometry as well as the attributes, so can be slow compared to raster math. + geometry as well as the attributes, which potentially can be slow compared to raster calculations. Vector datasets are in use in many industries besides geospatial fields. For instance, computer graphics are largely vector-based, although the data @@ -85,8 +90,9 @@ their features to real-world locations. ## Vector Data Format for this Workshop Like raster data, vector data can also come in many different formats. For this -workshop, we will use the Shapefile format. A Shapefile format consists of multiple -files in the same directory, of which `.shp`, `.shx`, and `.dbf` files are mandatory. Other non-mandatory but very important files are `.prj` and `shp.xml` files. +workshop, we will use the GeoPackage format. GeoPackage is developed by the [Open Geospatial Consortium](https://www.ogc.org/) and is *is an open, standards-based, platform-independent, portable, self-describing, compact format for transferring geospatial information.* (source: [https://www.geopackage.org/](https://www.geopackage.org/) ) A GeoPackage file, **.gpkg**, is a single file that contains the geometries of features, their attributes and information about the CRS used. + +Another vector format that you will probably come accross quite often is a Shapefile. Although we will not be using that format in this workshop we do believe it is useful to understand how that format works. A Shapefile format consists of multiple files in the same directory, of which `.shp`, `.shx`, and `.dbf` files are mandatory. Other non-mandatory but very important files are `.prj` and `shp.xml` files. - The `.shp` file stores the feature geometry itself - `.shx` is a positional index of the feature geometry to allow quickly searching forwards and backwards the geographic coordinates of each vertex in the vector @@ -114,8 +120,8 @@ objects in a single shapefile. More about shapefiles can be found on [Wikipedia.](https://en.wikipedia.org/wiki/Shapefile) Shapefiles are often publicly -available from government services, such as [this page from the US Census Bureau][us-cb] or -[this one from Australia's Data.gov.au website](https://data.gov.au/data/dataset?res_format=SHP). +available from government services, such as [this page containing all administrative boundaries for countries in the world](https://gadm.org/download_country.html) or +[topographical vector data from Open Street Maps](https://download.geofabrik.de/). ::: :::callout @@ -131,9 +137,9 @@ effects of particular data manipulations are more predictable if you are confident that all of your input data has the same characteristics. ::: -[us-cb]: https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html :::keypoints - Vector data structures represent specific features on the Earth's surface along with attributes of those features. +- Vector data is often interpreted data and collected for a different purpose than you would want to use it for. - Vector objects are either points, lines, or polygons. ::: diff --git a/03-crs.md b/03-crs.md index fcdd91f2..8acbf27a 100644 --- a/03-crs.md +++ b/03-crs.md @@ -27,22 +27,22 @@ CRS (coordinate reference system) and SRS (spatial reference system) are synonym will use only CRS throughout this workshop. ::: -The CRS associated with a dataset tells your mapping software (for example Python) +The CRS associated with a dataset tells your mapping software where the raster is located in geographic space. It also tells the mapping software what method should be used to flatten or project the raster in geographic space. -![Maps of the United States in different projections (Source: opennews.org)](https://media.opennews.org/cache/06/37/0637aa2541b31f526ad44f7cb2db7b6c.jpg){alt="US difference projections"} - -The above image shows maps of the United States in different projections. Notice +The image below (figure 3.1) shows maps of the United States in different projections. Notice the differences in shape associated with each projection. These differences are a direct result of the calculations used to flatten the data onto a 2-dimensional map. +![Figure 3.1: Maps of the United States in different projections (Source: opennews.org)](https://media.opennews.org/cache/06/37/0637aa2541b31f526ad44f7cb2db7b6c.jpg){alt="US difference projections"} + There are lots of great resources that describe coordinate reference systems and projections in greater detail. For the purposes of this workshop, what is important to understand is that data from the same location but saved in -different projections will not line up in any GIS or other program. Thus, it's +different projections will not line up. Thus, it is important when working with spatial data to identify the coordinate reference system applied to the data and retain it throughout data processing and analysis. @@ -55,14 +55,14 @@ CRS information has three components: degrees) and defines the starting point (i.e. where is [0,0]?) so the angles reference a meaningful spot on the earth. Common global datums are WGS84 and NAD83. Datums can also be local - fit to a particular area of the globe, but -ill-fitting outside the area of intended use. In this workshop, we will use the -[WGS84 -datum](https://www.linz.govt.nz/data/geodetic-system/datums-projections-and-heights/geodetic-datums/world-geodetic-system-1984-wgs84). +ill-fitting outside the area of intended use For instance local Cadastre, Land Registry and Mapping Agencies require a high quality of their datasets, which can be obtained using a local system. In this workshop, we will use the +[WGS84 datum](https://www.linz.govt.nz/data/geodetic-system/datums-projections-and-heights/geodetic-datums/world-geodetic-system-1984-wgs84). The datum is often also refered to as the Geographical Coordinate System. * **Projection:** A mathematical transformation of the angular measurements on a round earth to a flat surface (i.e. paper or a computer screen). The units associated with a given projection are usually linear (feet, meters, etc.). In this workshop, we will see data in two different projections. +Note that the projection is also often refered to as Projected Coordinate System. * **Additional Parameters:** Additional parameters are often necessary to create the full coordinate reference system. One common additional parameter is a @@ -91,6 +91,8 @@ stem of the fruit. What other parameters could be included in this analogy? ## Which projection should I use? +A well know projection is the [Mercator projection](https://en.wikipedia.org/wiki/Mercator_projection) introduced by the Flamisch Cartographer Gerardus Mercator in the 16th Century. This so-called cilindrical projection, meaning that a virtual cilinder is place on the globe to flatten it, it relatively accurate near to the equator, but towards the poles blows things up see:[Cylindrical projection](https://gisgeography.com/cylindrical-projection/). The main advantage of the Mercator projection is that it is very suitable for navigation purpuses since it always north as *up* and south and as *down*, in the 17th century this projection was essential for sailors to navigate the oceans. + To decide if a projection is right for your data, answer these questions: * What is the area of minimal distortion? @@ -212,9 +214,11 @@ generated and maintained manually. * [Choosing the Right Map Projection.](https://source.opennews.org/en-US/learning/choosing-right-map-projection/) * [Video](https://www.youtube.com/embed/KUF_Ckv8HbE) highlighting how map projections can make continents seems proportionally larger or smaller than they actually are. +* [The True size](https://www.thetruesize.com/) An intuitive webmap that allows you to drag countries to another place in the webmercator projection. ::: :::keypoints - All geospatial datasets (raster and vector) are associated with a specific coordinate reference system. - A coordinate reference system includes datum, projection, and additional parameters specific to the dataset. +- All maps are distored because of the projection. ::: diff --git a/04-geo-landscape.md b/04-geo-landscape.md index 6d8fdc70..23483075 100644 --- a/04-geo-landscape.md +++ b/04-geo-landscape.md @@ -27,7 +27,7 @@ The [Open Source Geospatial Foundation (OSGEO)](https://www.osgeo.org/) supports * [QGIS](https://www.qgis.org/en/site/) is a professional GIS application that is built on top of and proud to be itself Free and Open Source Software (FOSS). QGIS is - written in Python, has a python console interface, and has several interfaces written in R including + written in Python and C++, has a python console interface, allows to develop plugins and has several interfaces written in R including [RQGIS](https://cran.r-project.org/package=RQGIS). * [GRASS GIS](https://grass.osgeo.org/), commonly referred to as GRASS (Geographic Resources Analysis Support System), is a FOSS-GIS software suite used for @@ -50,7 +50,9 @@ The [Open Source Geospatial Foundation (OSGEO)](https://www.osgeo.org/) supports visualisation options, and runs under Windows and Linux operating systems. Like GRASS GIS, it can also be installed and made accessible in QGIS3. * [PostGIS](https://postgis.net/) is a geospatial extension to the PostGreSQL - relational database. + relational database and is especially suited to work with large vector datasets. + * [GeoDMS](https://geodms.nl/) is a powerful Open sources GIS which allows for + fast calculations and calculations with large datasets. Furthermore it allows for complex scenario analyses. ### Commercial software @@ -63,7 +65,7 @@ The [Open Source Geospatial Foundation (OSGEO)](https://www.osgeo.org/) supports ArGIS Online which you host locally. ESRI welcomes development on their platforms through their [DevLabs](https://developers.arcgis.com/). ArcGIS software can be installed using - [Chef Cookbooks from Github](https://github.com/Esri/arcgis-cookbook). + [Chef Cookbooks from Github](https://github.com/Esri/arcgis-cookbook). In addition, ESRI offers the [arcpy python library](https://pro.arcgis.com/en/pro-app/3.1/arcpy/get-started/what-is-arcpy-.htm) as part of an ArcGIS pro licence allowing bring all operations from the ArcGIS pro GUI to the python ecosystem. * Pitney Bowes produce [MapInfo Professional](https://www.pitneybowes.com/us/location-intelligence/geographic-information-systems/mapinfo-pro.html), which was one of the earliest desktop GIS programs on the market. * [Hexagon Geospatial Power Portfolio](https://www.hexagongeospatial.com/products/products) @@ -76,7 +78,7 @@ The [Open Source Geospatial Foundation (OSGEO)](https://www.osgeo.org/) supports * [PANGEO](https://pangeo.io/) is a community organization dedicated to open and reproducible data science with python. They focus on the Pangeo software ecosystem for working with big data in the geosciences. - * Google has created [Google Earth Engine](https://earthengine.google.com/) which + * Google developed [Google Earth Engine](https://earthengine.google.com/) which combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences @@ -112,15 +114,15 @@ Benefits of using a GUI include: Downsides of using a GUI include: - - Low reproducibility - you can't record your actions and replay - - Most are not designed for batch-processing files + - Low reproducibility - you can record your actions and replay, but this is limited to the functionalities of the software + - Batch-processing is possible, but limited to the funstionalities of the software and hard to be integrated with other workflows - Limited ability to customise functions or write your own - Intimidating interface for new users - so many buttons! In scientific computing, the lack of reproducibility in point-and-click software has come to be viewed as a critical weakness. As such, scripted CLI-style workflows are -again becoming popular, which leads us to another approach to doing GIS — via a -programming language. This is the approach we will be using throughout this workshop. +becoming popular, which leads us to another approach to doing GIS — via a +programming language. Therefore this is the approach we will be using throughout this workshop. ## GIS in programming languages @@ -129,7 +131,7 @@ programming languages like Java and C++. However, the learning curve for these languages is steep and the effort required is excessive for users who only need a subset of their functionality. -Higher-level scripting languages like Python and R are easier to learn and use. Both +Higher-level scripting languages like Python and R are considered easier to learn and use. Both now have their own packages that wrap up those geospatial processing libraries and make them easy to access and use safely. A key example is the Java Topology Suite (JTS), which is implemented in C++ as GEOS. GEOS is accessible in Python via the `shapely` @@ -161,7 +163,7 @@ are other popular options for data science. Traditional GIS apps are also moving back towards providing a scripting environment for users, further blurring the CLI/GUI divide. ESRI have adopted Python into their -software, and QGIS is both Python and R-friendly. +software by introducing [arcpy](https://developers.arcgis.com/documentation/arcgis-add-ins-and-automation/arcpy/), and QGIS is both Python and R-friendly. ## GIS File Types @@ -174,8 +176,10 @@ raster file types. | File Type | Extensions | Description | | --------- | ---------- | ----------- | | Esri Shapefile | .SHP .DBF .SHX | The most common geospatial file type. This has become the industry standard. The three required files are: SHP is the feature geometry. SHX is the shape index position. DBF is the attribute data. | +| GeoPackage | .gpkg | As an alternative for a Shapfile. This open file format is gaining terrain and exists of one file containing all necessary attribute information. | | Geographic JavaScript Object Notation (GeoJSON) | .GEOJSON .JSON |Used for web-based mapping and uses JavaScript Object Notation to store the coordinates as text. | | Google Keyhole Markup Language (KML) | .KML .KMZ | KML stands for Keyhole Markup Language. This GIS format is XML-based and is primarily used for Google Earth. | +| GPX or GPS Exchange Format | .gpx | Is an XML schema designed as a common GPS data format for software applications. This format is often used for tracking activities e.g. hiking, cycling, running etc. | | OpenStreetMap | .OSM | OSM files are the native file for OpenStreetMap which had become the largest crowdsourcing GIS data project in the world. These files are a collection of vector features from crowd-sourced contributions from the open community. | ### Raster diff --git a/07-vector-data-in-python.md b/07-vector-data-in-python.md index d8ee7716..61b9eb9c 100644 --- a/07-vector-data-in-python.md +++ b/07-vector-data-in-python.md @@ -19,426 +19,390 @@ questions: ## Introduction -As discussed in [Episode 2: Introduction to Vector Data](02-intro-vector-data.md), vector data represents specific features on the Earth's surface using points, lines, and polygons. These geographic elements can then have one or more attributes assigned to them, such as 'name' and 'population' for a city, or crop type for a field. Vector data can be much smaller in (file) size than raster data, while being very rich in terms of the information captured. +In the preceding episodes, we have prepared, selected and downloaded raster data from before and after the wildfire event in the summer of 2023 on the Greek island of Rhodes. To evaluate the impact of this wildfire on the vital infrastructure and built-up areas we are going to create a subset of vector data representing these assets. In this episode you will learn how to extract vector data with specific characteristics like the type of attributes or their locations. The dataset that we will generate in this episode can lateron be confronted with scorched areas which we determine by analyzing the satellite images [Episode 9: Raster Calculations in Python](09-raster-calculations.md). -In this episode, we will be moving from working with raster data to working with vector data. We will use Python to open and plot point, line, and polygon vector data. In particular, we will make use of the [`geopandas`](https://geopandas.org/en/stable/) package to open, manipulate and write vector datasets. +We'll be examining vector datasets that represent the valuable assests of Rhodes. As mentioned in [Episode 2: Introduction to Vector Data](02-intro-vector-data.md), vector data uses points, lines, and polygons to depict specific features on the Earth's surface. These geographic elements can have one or more attributes, like 'name' and 'population' for a city. In this epidoe we'll be using two open data sources: the Database of Global Administrative Areas (GADM) dataset to generate a polygon for the island of Rhodes and and Open Street Map data for the vital infrastructure and valuable assets. -![](fig/E07/pandas_geopandas_relation.png){alt="Pandas and Geopandas"} +To handle the vector data in python we use the package [`geopandas`](https://geopandas.org/en/stable/). This package allows us to open, manipulate, and write vector dataset through python. -`geopandas` extends the popular `pandas` library for data analysis to geospatial applications. The main `pandas` objects (the `Series` and the `DataFrame`) are expanded to `geopandas` objects (`GeoSeries` and `GeoDataFrame`). This extension is implemented by including geometric types, represented in Python using the `shapely` library, and by providing dedicated methods for spatial operations (union, intersection, etc.). The relationship between `Series`, `DataFrame`, `GeoSeries` and `GeoDataFrame` can be briefly explained as follow: +![](fig/E07/pandas_geopandas_relation.png){alt="Pandas and Geopandas"} - - A `Series` is a one-dimensional array with axis, holding any data type (integers, strings, floating-point numbers, Python objects, etc.) - - A `DataFrame` is a two-dimensional labeled data structure with columns of potentially different types1. - - A `GeoSeries` is a `Series` object designed to store shapely geometry objects. - - A `GeoDataFrame` is an extened `pandas.DataFrame`, which has a column with geometry objects, and this column is a `GeoSeries`. +`geopandas` enhances the widely-used `pandas` library for data analysis by extending its functionality to geospatial applications. The primary `pandas` objects (`Series` and `DataFrame`) are extended to `geopandas` objects (`GeoSeries` and `GeoDataFrame`). This extension is achieved by incorporating geometric types, represented in Python using the `shapely` library, and by offering dedicated methods for spatial operations like `union`, `spatial joins` and `intersect`. In order to understand how geopandas works, it is good to provide a brief explanation of the relationship between `Series`, a `DataFrame`, `GeoSeries`, and a `GeoDataFrame`: -In later episodes, we will learn how to work with raster and vector data together and combine them into a single plot. +- A `Series` is a one-dimensional array with an axis that can hold any data type (integers, strings, floating-point numbers, Python objects, etc.) +- A `DataFrame` is a two-dimensional labeled data structure with columns that can potentially hold different types of data. +- A `GeoSeries` is a `Series` object designed to store shapely geometry objects. +- A `GeoDataFrame` is an extended `pandas.DataFrame` that includes a column with geometry objects, which is a `GeoSeries`. :::callout ## Introduce the Vector Data -In this episode, we will use the downloaded vector data in the `data` directory. Please refer to the [setup page](../learners/setup.md) on how to download the data. +In this episode, we will use the downloaded vector data from the `data` directory. Please refer to the [setup page](../learners/setup.md) on where to download the data. Note that we manipulated that data a little for the purposes of +this workshop. The link to the original source can be found on the [setup page](../learners/setup.md). ::: -## Import Vector Datasets +## Get the administration boundary of study area + +The first thing we want to do is to extract a polygon containing the boundary of the island of Rhodes from Greece. For this we will use the [GADM dataset](https://gadm.org/download_country.html) layer `ADM_ADM_3.gpkg` for Greece. For your convenience we saved a copy at: `data/data/gadm/ADM_ADM_3.gpkg` +We will use the `geopandas` package to load the file and use the `read_file` function [see](https://geopandas.org/en/stable/docs/user_guide/io.html). Note that geopandas is often abbreviated as gpd. + ```python import geopandas as gpd +gdf_greece = gpd.read_file('../data/gadm/ADM_ADM_3.gpkg') ``` - -We will use the `geopandas` package to load the crop field vector data we downloaded at: `data/brpgewaspercelen_definitief_2020_small.gpkg`. +We can print out the `gdf_greece`variable: ```python -fields = gpd.read_file("data/brpgewaspercelen_definitief_2020_small.gpkg") -fields +gdf_greece ``` +```output +GID_3 GID_0 COUNTRY GID_1 NAME_1 \ +0 GRC.1.1.1_1 GRC Greece GRC.1_1 Aegean +1 GRC.1.1.2_1 GRC Greece GRC.1_1 Aegean +2 GRC.1.1.3_1 GRC Greece GRC.1_1 Aegean +.. ... ... ... ... ... +324 GRC.8.2.24_1 GRC Greece GRC.8_1 Thessaly and Central Greece +325 GRC.8.2.25_1 GRC Greece GRC.8_1 Thessaly and Central Greece + NL_NAME_1 GID_2 NAME_2 NL_NAME_2 \ +0 Αιγαίο GRC.1.1_1 North Aegean Βόρειο Αιγαίο +1 Αιγαίο GRC.1.1_1 North Aegean Βόρειο Αιγαίο +2 Αιγαίο GRC.1.1_1 North Aegean Βόρειο Αιγαίο +.. ... ... ... ... +324 Θεσσαλία και Στερεά Ελλάδα GRC.8.2_1 Thessaly Θεσσαλία +325 Θεσσαλία και Στερεά Ελλάδα GRC.8.2_1 Thessaly Θεσσαλία +... +324 POLYGON ((22.81903 39.27344, 22.81884 39.27332... +325 POLYGON ((23.21375 39.36514, 23.21272 39.36469... -The data are read into the variable `fields` as a `GeoDataFrame`. This is an extened data format of `pandas.DataFrame`, with an extra column `geometry`. - -This file contains a relatively large number of crop field parcels. Directly loading a large file to memory can be slow. If the Area of Interest (AoI) is small, we can define a bounding box of the AoI, and only read the data within the extent of the bounding box. - -```python -# Define bounding box -xmin, xmax = (110_000, 140_000) -ymin, ymax = (470_000, 510_000) -bbox = (xmin, ymin, xmax, ymax) +[326 rows x 17 columns] ``` +The data are read into the variable fields as a `GeoDataFrame`. This is an extened data format of `pandas.DataFrame`, with an extra column `geometry`. To explore the dataframe you can call this variable just like a `pandas dataframe` by using functions like `.shape`, `.head` and `.tail` etc. -Using the `bbox` input argument, we can load only the spatial features intersecting the provided bounding box. +To visualize the polygons we can use the [`plot()`](https://geopandas.org/en/stable/docs/user_guide/mapping.html) function to the `GeoDataFrame` we have loaded `gdf_greece`: ```python -# Partially load data within the bounding box -fields = gpd.read_file("data/brpgewaspercelen_definitief_2020_small.gpkg", bbox=bbox) +gdf_greece.plot() ``` -:::callout -## How should I define my bounding box? -For simplicity, here we assume the **Coordinate Reference System (CRS)** and **extent** of the vector file are known (for instance they are provided in the dataset documentation). - -You can also define your bounding box with online coordinates visualization tools. For example, we can use the "Draw Rectangular Polygon" tool in [geojson.io](https://geojson.io/#map=8.62/52.45/4.96). +![](fig/E07/greece_administration_areas.png){alt="greece_administrations"} -Some Python tools, e.g. [`fiona`](https://fiona.readthedocs.io/en/latest/)(which is also the backend of `geopandas`), provide the file inspection functionality without the need to read the full data set into memory. An example can be found in [the documentation of fiona](https://fiona.readthedocs.io/en/latest/manual.html#format-drivers-crs-bounds-and-schema). -::: +If you want to interactively explore your data you can use the [`.explore`](https://geopandas.org/en/stable/docs/user_guide/interactive_mapping.html) function in geopandas: -And we can plot the overview by: ```python -fields.plot() +gdf_greece.explore() ``` +In this interactive map you can easily zoom in and out and hover over the polygons to see which attributes, stored in the rows of your GeoDataFrame, are related to each polygon. -![](fig/E07/fields.png){alt="Crop fields inside the AOI"} +Next, we'll focus on isolating the administrative area of Rhodes Island. Once you hover over the polygon of Rhodos (the relatively big island to the east) you will find out that the label `Rhodos` is stored in the `NAME_3` column of `gdf_greece`, where Rhodes Island is listed as `Rhodos`. Since our goal is to have a boundary of Rhodes, we'll now create a new variable that exclusively represents Rhodes Island. -## Vector Metadata & Attributes -When we read the vector dataset with Python (as our `fields` variable) it is loaded as a `GeoDataFrame` object. The `read_file()` function also automatically stores geospatial information about the data. We are particularly interested in describing the format, CRS, extent, and other components of the vector data, and the attributes which describe properties associated -with each vector object. +To select an item in our GeoDataFrame with a specific value is done the same way in which this is done in a pandas `DataFrame` using [`.loc`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html). -We will explore +```python +gdf_rhodes = gdf_greece.loc[gdf_greece['NAME_3']=='Rhodos'] +``` -1. **Object Type:** the class of the imported object. -2. **Coordinate Reference System (CRS):** the projection of the data. -3. **Extent:** the spatial extent (i.e. geographic area that the data covers). Note that the spatial extent for a vector dataset represents the combined extent for all spatial objects in the dataset. +And we can plot the overview by (or show it interactively using `.explore`): +```python +gdf_rhodes.plot() +``` -Each `GeoDataFrame` has a `"geometry"` column that contains geometries. In the case of our `fields` object, this geometry is represented by a `shapely.geometry.Polygon` object. `geopandas` uses the `shapely` library to represent polygons, lines, and points, so the types are inherited from `shapely`. +![](fig/E07/rhodes_administration_areas.png){alt="rhodes_administrations"} -We can view the metadata using the `.crs`, `.bounds` and `.type` attributes. First, let's view the -geometry type for our crop field dataset. To view the geometry type, we use the `pandas` method `.type` on the `GeoDataFrame` object, `fields`. +Now that we have the administrative area of Rhodes Island. We can use the `to_file()` function save this file for future use. ```python -fields.type +# Save the rhodes_boundary to gpkg +gdf_rhodes.to_file('rhodes.gpkg') ``` +## Get the vital infrastructure and built-up areas -```output -0 Polygon -1 Polygon -2 Polygon -3 Polygon -4 Polygon - ... -22026 Polygon -22027 Polygon -22028 Polygon -22029 Polygon -22030 Polygon -Length: 22031, dtype: object -``` - -To view the CRS metadata: +### Road data from Open Street Map (OSM) -```python -fields.crs -``` +Now that we have the boundary of our study area, we will make use this to select the main roads in our study area. We will make the following processing steps: +1. Select roads of study area +2. Select key infrastruture: 'primary', 'secondary', 'tertiary' +3. Create a 100m buffer around the rounds. This buffer will be regarded as the infrastructure region. (note that this buffer is arbitrary and can be changed afterwards if you want!) -```output - -Name: Amersfoort / RD New -Axis Info [cartesian]: -- X[east]: Easting (metre) -- Y[north]: Northing (metre) -Area of Use: -- name: Netherlands - onshore, including Waddenzee, Dutch Wadden Islands and 12-mile offshore coastal zone. -- bounds: (3.2, 50.75, 7.22, 53.7) -Coordinate Operation: -- name: RD New -- method: Oblique Stereographic -Datum: Amersfoort -- Ellipsoid: Bessel 1841 -- Prime Meridian: Greenwich -``` - -Our data is in the CRS **RD New**. The CRS is critical to -interpreting the object's extent values as it specifies units. To find -the extent of our dataset in the projected coordinates, we can use the `.total_bounds` attribute: +#### Step 1: Select roads of study area + +For this workshop, in particular to not have everyone downloading too much data, we created a subset of the [Openstreetmap](https://www.openstreetmap.org/) data we downloaded for Greece from [the Geofabrik](https://download.geofabrik.de/europe.html). This data comes in the form of a shapefile (see [episode 2](02-intro-vector-data.md)) from which we extracted all the roads for `Rhodes` and some surrounding islands. The data is stored in the osm folder as `osm_roads.gpkg`, but contains *all* the roads on the island (so also hiking paths, private roads etc.), whereas we in particular are interested in the key infrastructure which we consider to be roads classified as primary, secondary or tertiary roads. + +Let's load the file and plot it: ```python -fields.total_bounds +gdf_roads = gpd.read_file('../data/osm/osm_roads.gpkg') ``` +We can explore it using the same commands as above: -```output -array([109222.03325 , 469461.512625, 140295.122125, 510939.997875]) +```python +gdf_roads.plot() ``` -This array contains, in order, the values for minx, miny, maxx and maxy, for the overall dataset. The spatial extent of a GeoDataFrame represents the geographic "edge" or location that is the furthest north, south, east, and west. Thus, it represents the overall geographic coverage of the spatial object. +![](fig/E07/greece_highways.png){alt="greece_highways"} -We can convert these coordinates to a bounding box or acquire the index of the Dataframe to access the geometry. Either of these polygons can be used to clip rasters (more on that later). +As you may have noticed, loading and plotting `osm_roads.gpkg` takes a bit long. This is because the file contains all the roads of Rhodos and some surrounding islands as well. Since we are only interested in the roads on Rhodes Island. We will use the [`mask`](https://geopandas.org/en/stable/docs/user_guide/io.html) parameter of the `read_file()` function to load only the roads on Rhodes Island. +Now let us overwrite the GeoDataframe `gdf_roads` using the mask with the GeoDataFrame `gdf_rhodes` we created above. -## Further crop the dataset +```python +gdf_roads = gpd.read_file('../data/osm/osm_roads.gpkg', mask=gdf_rhodes) +``` -We might realize that the loaded dataset is still too large. If we want to refine our area of interest to an even smaller extent, without reloading the data, we can use the [`cx`](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.cx.html) indexer: +Now let us explore these roads using `.explore` (or `.plot`): - ```python - # A smaller bounding box in RD - xmin, xmax = (120_000, 135_000) - ymin, ymax = (485_000, 500_000) +```python +gdf_roads.explore() +``` - fields_cx = fields.cx[xmin:xmax, ymin:ymax] - ``` +![](fig/E07/rhodes_highways.png){alt="rhodes_highways"} -## Export data to file +#### Step 2: Select key infrastruture -We will save the cropped results to a shapefile (`.shp`) and use it later. The `to_file` function can be used for exportation: +As you will find out while exploring the roads dataset, information about the type of roads is stored in the `fclass` column. To get an overview of the different values that are present in the collumn `fclass` , we can use the [`unique()`](https://pandas.pydata.org/docs/reference/api/pandas.unique.html) function from pandas: ```python -fields_cx.to_file('fields_cropped.shp') +gdf_roads['fclass'].unique() ``` +```output +array(['residential', 'service', 'unclassified', 'footway', + 'track_grade4', 'primary', 'track', 'tertiary', 'track_grade3', + 'path', 'track_grade5', 'steps', 'secondary', 'primary_link', + 'track_grade2', 'track_grade1', 'pedestrian', 'tertiary_link', + 'secondary_link', 'living_street', 'cycleway'], dtype=object) +``` +It seems the variable `gdf_roads` contains all kind of hiking paths and footpaths as well. Since we are only interested in vital infrastructure, classified as "primary", "secondary" and "tertiary" roads, we need to make a subselection. -This will write it to disk (in this case, in 'shapefile' format), containing only the data from our cropped area. It can be read again at a later time using the `read_file()` method we have been using above. Note that this actually writes multiple files to disk (`fields_cropped.cpg`, `fields_cropped.dbf`, `fields_cropped.prj`, `fields_cropped.shp`, `fields_cropped.shx`). All these files should ideally be present in order to re-read the dataset later, although only the `.shp`, `.shx`, and `.dbf` files are mandatory (see the [Introduction to Vector Data](02-intro-vector-data.md) lesson for more information.) +Let us first create a list with the labels we want to select. +```python +key_infra_labels = ['primary', 'secondary', 'tertiary'] +``` -## Selecting spatial features +Now we are using this list make a subselection of the key infrastructure using pandas´ [`.isin` function](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.isin.html). -From now on, we will take in a point dataset `brogmwvolledigeset.zip`, which is the underground water monitoring wells. We will perform vector processing on this dataset, together with the cropped field polygons `fields_cropped.shp`. +```python +key_infra = gdf_roads.loc[gdf_roads['fclass'].isin(key_infra_labels)] +``` -Let's read the two datasets. +We can plot the key infrastructure : ```python -fields = gpd.read_file("fields_cropped.shp") -wells = gpd.read_file("data/brogmwvolledigeset.zip") +key_infra.plot() ``` +![](fig/E07/rhodes_infra_highways.png){alt="rhodes_infra_highways"} -And take a look at the wells: +#### Step 3: Create a 100m buffer around the key infrastructure -```python -wells.plot(markersize=0.1) -``` +Now that we selected the key infrastructure, we want to create a 100m buffer around them. This buffer will be regarded as the infrastructure region. -![](fig/E07/wells-nl.png){alt="all wells in the NL"} +As you might have notice, the numbers on the x and y axis of our plots represent Lon Lat coordinates, meaning that the data is not yet projected. The current data has a geographic coordinate system with measures in degrees but not meter. Creating a buffer of 100 meters is not possible. Therfore, in order to create a 100m buffer, we first need to project our data. In our case we decided to project the data as +WGS 84 / UTM zone 31N, with EPSG code 32631 ([see chapter 03 for more information about the CRS and EPSG codes](/episodes/03-crs.md). -The points represents all the wells over the Netherlands. Since the wells are in the lat/lon coordinates. To make it comparable with fields, we need to first transfer the CRS to the "RD New" projection: +To project our data we use [.to_crs](https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoDataFrame.to_crs.html). We first define a variable with the EPSG value (in our case 32631), which we then us in the to_crs function. ```python -wells = wells.to_crs(epsg=28992) +epsg_code = 32631 +key_infra_meters = key_infra.to_crs(epsg_code) ``` +Now that our data is projected, we can create a buffer. For this we make use of [geopandas´ .buffer function](https://geopandas.org/en/stable/docs/user_guide/geometric_manipulations.html#GeoSeries.buffer) : +```python +key_infra_meters_buffer = key_infra_meters.buffer(100) +key_infra_meters_buffer +``` -Now we would like to compare the wells with the cropped fields. We can select the wells within the fields using the `.clip` function: +```output +53 POLYGON ((2779295.383 4319805.295, 2779317.029... +54 POLYGON ((2779270.962 4319974.441, 2779272.393... +55 POLYGON ((2779172.341 4319578.062, 2779165.312... +84 POLYGON ((2779615.109 4319862.058, 2779665.519... +140 POLYGON ((2781330.698 4320046.538, 2781330.749... + ... +19020 POLYGON ((2780193.230 4337691.133, 2780184.279... +19021 POLYGON ((2780330.823 4337772.262, 2780324.966... +19022 POLYGON ((2780179.850 4337917.135, 2780188.871... +19024 POLYGON ((2780516.550 4339028.863, 2780519.340... +19032 POLYGON ((2780272.050 4338213.937, 2780274.519... +Length: 1386, dtype: geometry +``` + +Note that the type of the `key_infra_meters_buffer` is a `GeoSeries` and not a `GeoDataFrame`. This is because the `buffer()` function returns a `GeoSeries` object. You can check that by calling the type of the variable. ```python -wells_clip = wells.clip(fields) -wells_clip +type(key_infra_meters_buffer) ``` - ```output -bro_id delivery_accountable_party quality_regime ... -40744 GMW000000043703 27364178 IMBRO/A ... -38728 GMW000000045818 27364178 IMBRO/A ... -... ... ... ... ... -40174 GMW000000043963 27364178 IMBRO/A ... -19445 GMW000000024992 50200097 IMBRO/A ... -[79 rows x 40 columns] +geopandas.geoseries.GeoSeries ``` -After this selection, all the wells outside the fields are dropped. This takes a while to execute, because we are clipping a relatively large number of points with many polygons. - -If we do not want a precise clipping, but rather have the points in the neighborhood of the fields, we will need to create another polygon, which is slightly bigger than the coverage of the field. To do this, we can increase the size of the field polygons, to make them overlap with each other, and then merge the overlapping polygons together. +Now that we have a buffer, we can convert it back to the geographic coordinate system to keep the data consistent. Note that we are now using the crs information from the `key_infra`, instead of using the EPSG code directly (EPSG:4326): -We will first use the `buffer` function to increase field size with a given `distance`. The unit of the `distance` argument is the same as the CRS. Here we use a 50-meter buffer. Also notice that the `.buffer` function produces a `GeoSeries`, so to keep the other columns, we assign it to the `GeoDataFrame` as a geometry column. ```python -buffer = fields.buffer(50) -fields_buffer = fields.copy() -fields_buffer['geometry'] = buffer -fields_buffer.plot() +key_infra_buffer = key_infra_meters_buffer.to_crs(key_infra.crs) +key_infra_buffer ``` +```output +45 POLYGON ((27.72826 36.12409, 27.72839 36.12426... +58 POLYGON ((27.71666 36.11678, 27.71665 36.11678... +99 POLYGON ((27.75485 35.95242, 27.75493 35.95248... +100 POLYGON ((27.76737 35.95086, 27.76733 35.95086... +108 POLYGON ((27.76706 35.95199, 27.76702 35.95201... + ... +18876 POLYGON ((28.22855 36.41890, 28.22861 36.41899... +18877 POLYGON ((28.22819 36.41838, 28.22825 36.41845... +18878 POLYGON ((28.22865 36.41904, 28.22871 36.41912... +18879 POLYGON ((28.23026 36.41927, 28.23034 36.41921... +18880 POLYGON ((28.23020 36.41779, 28.23007 36.41745... +Length: 1369, dtype: geometry +``` -![](fig/E07/fields-buffer.png){alt="50m buffer around the fields"} +As you can see, the buffers created in `key_infra_buffer` have the `Polygon` geometry type. -To further simplify them, we can use the `dissolve` function to dissolve the buffers into one: +To double check the EPSG code of key_infra: ```python -fields_buffer_dissolve = fields_buffer.dissolve() -fields_buffer_dissolve +print(key_infra.crs) + ``` +```output +EPSG:4326 +``` -All the fields will be dissolved into one multi-polygon, which can be used to `clip` the wells. +Reprojecting and buffering our data is something that we are going to do multiple times during this episode. To avoid have to call the same functions multiple times it would make sense to create a function. Therefore, let us create a function in which we can add the buffer as a variable. ```python -wells_clip_buffer = wells.clip(fields_buffer_dissolve) -wells_clip_buffer.plot() +def buffer_crs(gdf, size, meter_crs=32631, target_crs=4326): + return gdf.to_crs(meter_crs).buffer(size).to_crs(target_crs) ``` +For example, we can use this function to create a 200m buffer around the infrastructure the key infrastructure by doing: -![](fig/E07/wells-in-buffer.png){alt="Wells within 50m buffer of fields"} - -In this way, we selected all wells within the 50m range of the fields. It is also significantly faster than the previous `clip` operation, since the number of polygons is much smaller after `dissolve`. - -:::challenge -## Exercise: clip fields within 500m from the wells -This time, we will do a selection the other way around. Can you clip the field polygons (stored in fields_cropped.shp) with the 500m buffer of the wells (stored in brogmwvolledigeset.zip)? Please visualize the results. - -- Hint 1: The file `brogmwvolledigeset.zip` is in CRS 4326. Don’t forget the CRS conversion. - -- Hint 2: `brogmwvolledigeset.zip` contains all the wells in the Netherlands, which means it might be too big for the `.buffer()` function. To improve the performance, first crop it with the bounding box of the fields. - -::::solution ```python -# Read in data -fields = gpd.read_file("fields_cropped.shp") -wells = gpd.read_file("data/brogmwvolledigeset.zip") - -# Crop points with bounding box -xmin, ymin, xmax, ymax = fields.total_bounds -wells = wells.to_crs(28992) -wells_cx = wells.cx[xmin-500:xmax+500, ymin-500:ymax+500] +key_infra_buffer_200 = buffer_crs(key_infra, 200) +``` -# Create buffer -wells_cx_500mbuffer = wells_cx.copy() -wells_cx_500mbuffer['geometry'] = wells_cx.buffer(500) +### Get built-up regions from Open Street Map (OSM) -# Clip -fields_clip_buffer = fields.clip(wells_cx_500mbuffer) -fields_clip_buffer.plot() -``` -![](fig/E07/fields-in-buffer-clip.png){alt="fields within 50m buffer of the wells, truncated"} +Now that we have a buffered dataset for the key infrastructure of Rhodes, our next step is to create a dataset with all the built-up areas. To do so we will use the land use data from OSM, which we prepared for you in the file `data/osm_landuse.gpkg`. This file includes the land use data for the entire Greece. We assume the built-up regions to be the union of three types of land use: "commercial", "industrial", and "residential". -:::: -::: +Note that for the simplicity of this course, we limit the built-up regions to these three types of land use. In reality, the built-up regions can be more complex also there is definately more high quality (e.g. local government). +Now it will be up to you to create a dataset with valueable assets. You should be able to complete this task by yourself with the knowledge you have gained from the previous steps and links to the documentation we provided. -## Spatially join the features +:::challenge +## Exercise: Get the built-up regions -In the exercise, we clipped the fields polygons with the 500m buffers of wells. The results from this clipping changed the shape of the polygons. If we would like to keep the original shape of the fields, one way is to use the `sjoin` function, which join two `GeoDataFrame`'s on the basis of their spatial relationship: +Create a `builtup_buffer` from the file `data/osm_landuse.gpkg` by the following steps: -```python -# Join fields and wells_cx_500mbuffer -fields_wells_buffer = fields.sjoin(wells_cx_500mbuffer) -print(fields_wells_buffer.shape) -``` +1. Load the land use data from `data/osm_landuse.gpkg` and mask it with the administrative boundary of Rhodes Island (`gdf_rhodes`). +2. Select the land use data for "commercial", "industrial", and "residential". +3. Create a 10m buffer around the land use data. +4. Visualize the results. +After completing the exercise, answer the following questions: -```output -(11420, 46) -``` +1. How many unique land use types are there in `landuse.gpkg`? +2. After selecting the three types of land use, how many entries (rows) are there in the results? +Hints: -This will result in a `GeodataFrame` of all possible combinations of polygons and well buffers intersecting each other. Since a polygon can fall into multiple buffers, there will be duplicated field indexes in the results. To select the fields which intersects the well buffers, we can first get the unique indexes, and use the `iloc` indexer to select: +- `data/osm_landuse.gpkg` contains the land use data for the entire Greece. Use the administrative boundary of Rhodes Island (`gdf_rhodes`) to select the land use data for Rhodes Island. +- The land use attribute is stored in the `fclass` column. +- Reuse `buffer_crs` function to create the buffer. +::::solution ```python -idx = fields_wells_buffer.index.unique() -fiedls_in_buffer = fields.iloc[idx] +# Read data with a mask of Rhodes +gdf_landuse = gpd.read_file('../data/osm/landuse.gpkg', mask=gdf_rhodes) -fiedls_in_buffer.plot() -``` +# Find number of unique landuse types +print(len(gdf_landuse['fclass'].unique())) +# Extract built-up regions +builtup_labels = ['commercial', 'industrial', 'residential'] +builtup = gdf_landuse.loc[gdf_landuse['fclass'].isin(builtup_labels)] -![](fig/E07/fields-in-buffer-sjoin.png){alt="Fields in 50m buffer of wells, not truncated"} +# Create 10m buffer around the built-up regions +builtup_buffer = buffer_crs(builtup, 10) -## Modify the geometry of a GeoDataFrame +# Get the number of entries +print(len(builtup_buffer)) -:::challenge -## Exercise: Investigate the waterway lines -Now we will take a deeper look at the Dutch waterway lines: `waterways_nl`. Let's load the file `status_vaarweg.zip`, and visualize it with the `plot()` function. Can you tell what is wrong with this vector file? +# Visualize the buffer +builtup_buffer.plot() +``` -::::solution -By plotting out the vector file, we can tell that the latitude and longitude of the file are flipped. -```python -waterways_nl = gpd.read_file('data/status_vaarweg.zip') -waterways_nl.plot() +```output +19 +1336 ``` -![](fig/E07/waterways-wrong.png){alt="waterways, rotated"} -:::: -::: +![](fig/E07/rhodes_builtup_buffer.png){alt="rhodes_builtup_buffer"} -:::callout -## Axis ordering -According to the standards, the axis ordering for a CRS should follow the definition provided by the competent authority. For the commonly used EPSG:4326 geographic coordinate system, the EPSG defines the ordering as first latitude then longitude. -However, in the GIS world, it is custom to work with coordinate tuples where the first component is aligned with the east/west direction and the second component is aligned with the north/south direction. -Multiple software packages thus implement this convention also when dealing with EPSG:4326. -As a result, one can encounter vector files that implement either convention - keep this in mind and always check your datasets! +:::: ::: -Sometimes we need to modify the `geometry` of a `GeoDataFrame`. For example, as we have seen in the previous exercise **Investigate the waterway lines**, the latitude and longitude are flipped in the vector data `waterways_nl`. This error needs to be fixed before performing further analysis. +## Merge the infrastructure regions and built-up regions +Now that we have the infrastructure regions and built-up regions, we can merge them into a single region. We would like to keep track of the type after merging, so we will add two new columns: `type` and `code` by converting the `GeoSeries` to `GeoDataFrame`. -Let's first take a look on what makes up the `geometry` column of `waterways_nl`: +First we convert the buffer around key infrastructure: ```python -waterways_nl['geometry'] +data = {'geometry': key_infra_buffer, 'type': 'infrastructure', 'code': 1} +gdf_infra = gpd.GeoDataFrame(data) ``` -```output -0 LINESTRING (52.41810 4.84060, 52.42070 4.84090... -1 LINESTRING (52.11910 4.67450, 52.11930 4.67340... -2 LINESTRING (52.10090 4.25730, 52.10390 4.25530... -3 LINESTRING (53.47250 6.84550, 53.47740 6.83840... -4 LINESTRING (52.32270 5.14300, 52.32100 5.14640... - ... -86 LINESTRING (51.49270 5.39100, 51.48050 5.39160... -87 LINESTRING (52.15900 5.38510, 52.16010 5.38340... -88 LINESTRING (51.97340 4.12420, 51.97110 4.12220... -89 LINESTRING (52.11910 4.67450, 52.11850 4.67430... -90 LINESTRING (51.88940 4.61900, 51.89040 4.61350... -Name: geometry, Length: 91, dtype: geometry -``` - - -Each row is a `LINESTRING` object. We can further zoom into one of the rows, for example, the third row: +Then we convert the built-up buffer: ```python -print(waterways_nl['geometry'][2]) -print(type(waterways_nl['geometry'][2])) -``` - -```output -LINESTRING (52.100900002 4.25730000099998, 52.1039 4.25529999999998, 52.111299999 4.24929999900002, 52.1274 4.23449999799999) - +data = {'geometry': builtup_buffer, 'type': 'builtup', 'code': 2} +gdf_builtup = gpd.GeoDataFrame(data) ``` - -As we can see in the output, the `LINESTRING` object contains a list of coordinates of the vertices. In our situation, we would like to find a way to flip the x and y of every coordinates set. A good way to look for the solution is to use the [documentation](https://shapely.readthedocs.io/en/stable/manual.html) of the `shapely` package, since we are seeking to modify the `LINESTRING` object. Here we are going to use the [`shapely.ops.transform`](https://shapely.readthedocs.io/en/stable/manual.html?highlight=shapely.ops.transform#shapely.ops.transform) function, which applies a self-defined function to all coordinates of a geometry. +After that, we can merge the two `GeoDataFrame` into one: ```python -import shapely - -# Define a function flipping the x and y coordinate values -def flip(geometry): - return shapely.ops.transform(lambda x, y: (y, x), geometry) - -# Apply this function to all coordinates and all lines -geom_corrected = waterways_nl['geometry'].apply(flip) +import pandas as pd +gdf_assets = pd.concat([gdf_infra, gdf_builtup]).reset_index(drop=True) ``` +In `gdf_assets`, we can distinguish the infrastructure regions and built-up regions by the `type` and `code` columns. We can plot the `gdf_assets` to visualize the merged regions. See the [geopandas documentation](https://geopandas.org/en/stable/docs/user_guide/mapping.html) on how to do this: -Then we can update the `geometry` column with the corrected geometry `geom_corrected`, and visualize it to check: ```python -# Update geometry -waterways_nl['geometry'] = geom_corrected - -# Visualization -waterways_nl.plot() +gdf_assets.plot(column='type', legend=True) ``` +![](fig/E07/rhodes_assets.png){alt="rhodes_assets"} + -![](fig/E07/waterways-corrected.png){alt="waterways, corrected"} +Finally, we can save the `gdf_assets` to a file for future use: -Now the waterways look good! We can save the vector data for later usage: ```python -# Update geometry -waterways_nl.to_file('waterways_nl_corrected.shp') +gdf_assets.to_file('assets.gpkg') ``` :::keypoints - Load spatial objects into Python with `geopandas.read_file()` function. - Spatial objects can be plotted directly with `GeoDataFrame`'s `.plot()` method. -- Crop spatial objects with `.cx[]` indexer. -- Convert CRS of spatial objects with `.to_crs()`. -- Select spatial features with `.clip()`. +- Convert CRS of spatial objects with `.to_crs()`. Note that this generates a `GeoSeries` object. - Create a buffer of spatial objects with `.buffer()`. -- Merge overlapping spatial objects with `.dissolve()`. -- Join spatial features spatially with `.sjoin()`. +- Merge spatial objects with `pd.concat()`. ::: diff --git a/08-crop-raster-data.md b/08-crop-raster-data.md index ed224d95..a89f4be2 100644 --- a/08-crop-raster-data.md +++ b/08-crop-raster-data.md @@ -1,7 +1,7 @@ --- title: "Crop raster data with rioxarray and geopandas" -teaching: 70 -exercises: 30 +teaching: 40 +exercises: 0 --- :::questions @@ -9,15 +9,15 @@ exercises: 30 ::: :::objectives +- Align the CRS of geopatial data. - Crop raster data with a bounding box. - Crop raster data with a polygon. -- Match two raster datasets in different CRS. ::: It is quite common that the raster data you have in hand is too large to process, or not all the pixels are relevant to your area of interest (AoI). In both situations, you should consider cropping your raster data before performing data analysis. -In this episode, we will introduce how to crop raster data into the desired area. We will use one Sentinel-2 image over Amsterdam as the example raster data, and introduce how to crop your data to different types of AoIs. +In this episode, we will introduce how to crop raster data into the desired area. We will use one Sentinel-2 image over Rhodes Island as the example raster data, and introduce how to crop your data to different types of AoIs. :::callout ## Introduce the Data @@ -36,259 +36,189 @@ We also use the cropped fields polygons `fields_cropped.shp`, which was generate ## Align the CRS of the raster and the vector data -We load a true color image using `pystac` and `rioxarray` and check the shape of the raster: +### Data loading + +First, we will load the search results we saved in the data access episode. ```python import pystac -import rioxarray -# Load image and inspect the shape -items = pystac.ItemCollection.from_file("search.json") -raster = rioxarray.open_rasterio(items[1].assets["visual"].href) # Select a true color image -print(raster.shape) +# Load the search results +items= pystac.ItemCollection.from_file("rhodes_sentinel-2.json") ``` +We will inspect the first item. -```output -(3, 10980, 10980) +```python +item = items[0] ``` - -This will perform a "lazy" loading of the image, i.e. the image will not be loaded into the memory until necessary, but we can still access some attributes, e.g. the shape of the image. - -The large size of the raster data makes it time and memory consuming to visualize in its entirety. Instead, we can fetch and plot the overviews of the raster. "Overviews" are precomputed lower resolution representations of a raster, stored in the same COG that contains the original raster. +As we have shown in the previous episodes, this search result contains the information of the URL of different channels, for example, `visual`: ```python -# Get the overview asset -raster_overview = rioxarray.open_rasterio(items[1].assets["visual"].href, overview_level=3) -print(raster_overview.shape) - -# Visualize it -raster_overview.plot.imshow(figsize=(8,8)) +visual_href = item.assets['visual'].href ``` -![](fig/E08/crop-raster-overview-raster-00.png){alt="Overview of the raster"} - -As we can see, the overview image is much smaller compared to the original true color image. - -To align the raster and vector data, we first check each coordinate system. For raster data, we use `pyproj.CRS`: +We can open this asset with `rioxarray`, and specify the overview level, since this is a Cloud-Optimized GeoTIFF (COG) file. Here we use `overview_level=1`: ```python -from pyproj import CRS - -# Check the coordinate system -CRS(raster.rio.crs) +import rioxarray +visual = rioxarray.open_rasterio(visual_href, overview_level=1) +visual ``` - ```output - -Name: WGS 84 / UTM zone 31N -Axis Info [cartesian]: -- [east]: Easting (metre) -- [north]: Northing (metre) -Area of Use: -- undefined -Coordinate Operation: -- name: UTM zone 31N -- method: Transverse Mercator -Datum: World Geodetic System 1984 -- Ellipsoid: WGS 84 -- Prime Meridian: Greenwich + +[22605075 values with dtype=uint8] +Coordinates: + * band (band) int64 1 2 3 + * x (x) float64 5e+05 5e+05 5.001e+05 ... 6.097e+05 6.098e+05 + * y (y) float64 4.1e+06 4.1e+06 4.1e+06 ... 3.99e+06 3.99e+06 + spatial_ref int64 0 +Attributes: + AREA_OR_POINT: Area + OVR_RESAMPLING_ALG: AVERAGE + _FillValue: 0 + scale_factor: 1.0 + add_offset: 0.0 ``` +As we introduced in the raster data introduction episode, this will perform a "lazy" loading of the image, i.e. the image will not be loaded into the memory until necessary. - -To open and check the coordinate system of vector data, we use `geopandas`: +Let's also load the assets file generated in the vector data episode: ```python import geopandas as gpd - -# Load the polygons of the crop fields -fields = gpd.read_file("fields_cropped.shp") - -# Check the coordinate system -fields.crs -``` - - -```output - -Name: Amersfoort / RD New -Axis Info [cartesian]: -- X[east]: Easting (metre) -- Y[north]: Northing (metre) -Area of Use: -- name: Netherlands - onshore, including Waddenzee, Dutch Wadden Islands and 12-mile offshore coastal zone. -- bounds: (3.2, 50.75, 7.22, 53.7) -Coordinate Operation: -- name: RD New -- method: Oblique Stereographic -Datum: Amersfoort -- Ellipsoid: Bessel 1841 -- Prime Meridian: Greenwich +assets = gpd.read_file('assets.gpkg') ``` +### Crop the raster with a bounding box - -As seen, the coordinate systems differ. To crop the raster using the shapefile, we first need to reproject one dataset to the other's CRS. Since `raster` is large, we will convert the CRS of `fields` to the CRS of `raster` to avoid loading the entire image: +The assets file contains contains the information of the vital infrastructure and built-up areas in Rhodes Island. The visual image, on the other hand, has a larger extent. We can check this by visualizing the raster image: ```python -fields = fields.to_crs(raster.rio.crs) +visual.plot.imshow() ``` +![](fig/E08/visual_large.png){alt="Large visual raster"} -## Crop raster data with a bounding box - -The `clip_box` function allows one to crop a raster by the -min/max of the x and y coordinates. Note that we are cropping the original image `raster` now, and not the overview image `raster_overview`. +Let's check the extent of the assets to find out its rough location in the raster image. +We can use the `total_bounds` attribute to get the bounding box: ```python -# Crop the raster with the bounding box -raster_clip_box = raster.rio.clip_box(*fields.total_bounds) -print(raster_clip_box.shape) +assets.total_bounds ``` ```output -(3, 1565, 1565) +array([27.7121001 , 35.87837949, 28.24591124, 36.45725024]) ``` +The bounding box is in the form of `[minx, miny, maxx, maxy]`. Comparing this values with the raster image, we can see the magnitude of the bounding box coordinates does not match the coordinates of the raster image. This is because the two datasets have different coordinate reference systems (CRS). Therefore, we need to align the CRS of the two datasets before cropping the raster image. -We successfully cropped the raster to a much smaller piece. We can visualize it now: +Considering the raster image has larger data volume than the vector data, we will reproject the vector data to the CRS of the raster data. We can use the `to_crs` method: ```python -raster_clip_box.plot.imshow(figsize=(8,8)) -``` -![](fig/E08/crop-raster-crop-by-bb-02.png){alt="Raster cropped by a bounding box"} +# Reproject +assets = assets.to_crs(visual.rio.crs) -This cropped image can be saved for later usage: -```python -raster_clip_box.rio.to_raster("raster_clip.tif") +# Check the new bounding box +assets.total_bounds ``` +```output +array([ 564058.0257114, 3970719.4080227, 611743.71498815, 4035358.56340039]) +``` -## Crop raster data with polygons - -We have a cropped image around the fields. To further analyze the fields, one may want to crop the image to the exact field boundaries. -This can be done with the `clip` function: +Now the bounding box coordinates are updated. We can use the `clip_box` function to crop the raster image to the bounding box of the vector data: ```python -raster_clip_fields = raster_clip_box.rio.clip(fields['geometry']) +# Crop the raster with the bounding box +visual_clipbox = visual.rio.clip_box(*assets.total_bounds) + +# Visualize the cropped image +visual_clipbox.plot.imshow() ``` +![](fig/E08/visual_clip_box.png){alt="Clip box results"} -And we can visualize the results: -```python -raster_clip_fields.plot.imshow(figsize=(8,8)) -``` -![](fig/E08/crop-raster-crop-fields.png){alt="Ratser cropped by field polygons"} +:::callout +## Code Tip +Cropping a raster with a bounding box is a quick way to reduce the size of the raster data. Since this operation is based on min/max coordinates, it is not as expensive as cropping with polygons, which requires more accurate overlay operations. +::: -:::challenge -## Exercise: crop raster data with a specific code -In the column "gewascode" (translated as "crop code") of `fields`, you can find the code representing the types of plants grown in each field. Can you: +### Crop the raster with a polygon -1. Select the fields with "gewascode" equal to `257`; -2. Crop the raster `raster_clip_box` with the selected fields; -3. Visualize the cropped image. +We can also crop the raster with a polygon. In this case, we will use the `clip` function. We will use the `geometry` column of the `assets` GeoDataFrame to specify the polygon: -::::solution ```python -mask = fields['gewascode']==257 -fields_gwascode = fields.where(mask) -fields_gwascode = fields_gwascode.dropna() -raster_clip_fields_gwascode = raster_clip_box.rio.clip(fields_gwascode['geometry']) -raster_clip_fields_gwascode.plot.imshow(figsize=(8,8)) -``` - -![](fig/E08/crop-raster-fields-gewascode.png){alt="Raster croped by fields with gewascode 257"} +# Crop the raster with the polygon +visual_clip = visual_clipbox.rio.clip(assets["geometry"]) -:::: -::: +# Visualize the cropped image +visual_clip.plot.imshow() +``` +![](fig/E08/visual_clip.png){alt="Clip results"} -## Crop raster data using `reproject_match()` function +### Match two rasters -So far we have learned how to crop raster images with vector data. We can also crop a raster with another raster data. In this section, we will demonstrate how to crop the `raster_clip_box` image using the `raster_clip_fields_gwascode` image. We will use the `reproject_match` function. As indicated by its name, it performs reprojection and clipping in one go. +Sometimes we need to match two rasters with different extents, resolutions, or CRS. The `reproject_match` function can be used for this purpose. We will demonstrate this by matching the cropped raster `visual_clip` with the Digital Elevation Model (DEM) of Rhodes Island `rhodes_dem.tif`. +First, let's load the DEM: -To demonstrate the reprojection, we will first reproject `raster_clip_fields_gwascode` to the RD CRS system, so it will be in a different CRS from `raster_clip_box`: ```python -# Reproject to RD to make the CRS different from the "raster" -raster_clip_fields_gwascode = raster_clip_fields_gwascode.rio.reproject("EPSG:28992") -CRS(raster_clip_fields_gwascode.rio.crs) +dem = rioxarray.open_rasterio('./data/dem/rhodes_dem.tif') ``` +And visualize it: -```output - -Name: Amersfoort / RD New -Axis Info [cartesian]: -- [east]: Easting (metre) -- [north]: Northing (metre) -Area of Use: -- undefined -Coordinate Operation: -- name: unnamed -- method: Oblique Stereographic -Datum: Amersfoort -- Ellipsoid: Bessel 1841 -- Prime Meridian: Greenwich +```python +dem.plot() ``` +![](fig/E08/dem.png){alt="DEM"} -And let's check again the CRS of `raster_clip_box`: +From the visualization, we can see the DEM has a different extent, resolution and CRS compared to the cropped visual image. We can also confirm this by checking the CRS of the two images: ```python -CRS(raster_clip_box.rio.crs) +print(dem.rio.crs) +print(visual_clip.rio.crs) ``` - ```output - -Name: WGS 84 / UTM zone 31N -Axis Info [cartesian]: -- [east]: Easting (metre) -- [north]: Northing (metre) -Area of Use: -- undefined -Coordinate Operation: -- name: UTM zone 31N -- method: Transverse Mercator -Datum: World Geodetic System 1984 -- Ellipsoid: WGS 84 -- Prime Meridian: Greenwich +EPSG:4326 +EPSG:32635 ``` - -Now the two images are in different coordinate systems. We can use `rioxarray.reproject_match()` function to crop `raster_clip_box` image. +We can use the `reproject_match` function to match the two rasters. One can choose to match the dem to the visual image or vice versa. Here we will match the DEM to the visual image: ```python -raster_reproject_match = raster_clip_box.rio.reproject_match(raster_clip_fields_gwascode) -raster_reproject_match.plot.imshow(figsize=(8,8)) +dem_matched = dem.rio.reproject_match(visual_clip) ``` - -![](fig/E08/reprojectmatch-big-to-small.png){alt="Reproject match big to small"} - -We can also use it to expand `raster_clip_fields_gwascode` to the extent of `raster_clip_box`: +And then visualize the matched DEM: ```python -raster_reproject_match = raster_clip_fields_gwascode.rio.reproject_match(raster_clip_box) -raster_reproject_match.plot.imshow(figsize=(8,8)) +dem_matched.plot() ``` -![](fig/E08/reprojectmatch-small-to-big.png){alt="Reproject match small to big"} +![](fig/E08/dem_matched.png){alt="Matched DEM"} -In one line `reproject_match` does a lot of helpful things: +As we can see, `reproject_match` does a lot of helpful things in one line of code: 1. It reprojects. -2. It matches the extent using `nodata` values or by clipping the data. -3. It sets `nodata` values. This means we can run calculations on those two images. +2. It matches the extent. +3. It matches the resolution. -:::callout +Finally, we can save the matched DEM for later use. We save it as a Cloud-Optimized GeoTIFF (COG) file: + +```python +dem_match.rio.to_raster('dem_rhodes_match.tif', driver='COG') +``` +:::callout ## Code Tip -As we saw before, there also exists a method called `reproject()`, which only reprojects one raster to another projection. If you want more control over how rasters are resampled, clipped, and/or reprojected, you can use the `reproject()` method and other `rioxarray` methods individually. +There is also a method in rioxarray: [`reproject()`](https://corteva.github.io/rioxarray/stable/rioxarray.html#rioxarray.raster_array.RasterArray.reproject), which only reprojects one raster to another projection. 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HcmV?d00001 diff --git a/index.md b/index.md index 02d54801..f2064622 100644 --- a/index.md +++ b/index.md @@ -2,19 +2,23 @@ site: sandpaper::sandpaper_site --- -Data Carpentry’s teaching is hands-on, so participants are encouraged to use -their own computers to ensure the proper setup of tools for an efficient -workflow. To most effectively use these materials, please make sure to download -the data and install everything before working through this lesson. - -The data used in this lesson includes optical satellite images from -[the Copernicus Sentinel-2 mission][sentinel-2] and public geographical datasets -from [the dedicated distribution platform of the Dutch government][pdok]. -These are real-world data sets that entail sufficient complexity to teach many -aspects of data analysis and management. They have been selected to allow students -to focus on the core ideas and skills being taught while offering the chance -to encounter common challenges with geospatial data. +## Data Carpentries +[Data Carpentry’s](https://datacarpentry.org/) teaching is hands-on. Participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow. + +## Geospatial Raster and Vector Data with Python +In this lesson you will learn how to work with geospatial data and how to process these with python. Python is one of the most popular programming languages for data science and analytics, with a large and steadily growing community in the field of Earth and Space Sciences. The lesson is meant for participants with a working basic knowledge of Python and allow them to to familiarize with the world of geospatial raster and vector data. (If you are unfamiliar to python we recommend you to follow [this course](https://swcarpentry.github.io/python-novice-inflammation/) or study [this book](https://greenteapress.com/thinkpython2/thinkpython2.pdf) ). In the *Introduction to Geospatial Raster and Vector Data with Python* lesson you will be introduced to a set of tools from the Python ecosystem and learn how these can be used to carry out geospatial data analysis tasks. In particular, you will learn to work with satellite images (i.e. [the Copernicus Sentinel-2 mission][sentinel-2] ) and open topographical geo-datasets (i.e. [OpenStreetmap][osm]). You will learn how these spatial datasets can be accessed, explored, manipulated and visualized using Python. + +## Case study - Wildfires +As a case study for this lesson we will focus on wildfires. According to the IPCC assessment report, the wildfire seasons are lengthening as a result of changes in temperature and increasing drought conditions [IPCC](https://www.ipcc.ch/report/ar6/wg2/about/frequently-asked-questions/keyfaq1/). To analyse the impact of these wildfires, we will focus on the wildfire that occured on the Greek island [Rhodes in the summer of 2023](https://news.sky.com/story/wildfires-on-rhodes-force-hundreds-of-holidaymakers-to-flee-their-hotels-12925583) which had a devastating effect and led to the evacuation of [19.000 people](https://en.wikipedia.org/wiki/2023_Greece_wildfires). In this lesson we are going analyse the effect of this disaster by estimating which built-up areas were affected by these wildfires. Furthermore, we will analyse which vegetation and land-use types have been affected the most by the wildfire in order to get an understanding of which areas are more vulnerable to wildfires. Finally we are going to estimate which locations would be most suitable for placing watchtowers in the region. The analysis that we set up provides insights in the effect of the wildfire and generates input for wildfire mitigation strategies. + +*Note, that the analyses presented in this lesson are developed for educational purposes. Therefore in some occasions the analysis steps have been simplified and assumptions have been made.* + +The data used in this lesson includes optical satellite images from [the Copernicus Sentinel-2 mission][sentinel-2] and topgraphical data from [OpenStreetmap (OSM)][osm]. These datasets are real-world open data sets that entail sufficient complexity to teach many aspects of data analysis and management. The datasets have been selected to allow participants to focus on the core ideas and skills being taught while offering the chance to encounter common challenges with geospatial data. Furthermore, we have selected datasets which are available anywhere on earth. + +During this lesson we will setup an analysis pipeline which identifies scorched areas based bands of satelite images collected after the disaster in july 2023. Next, we will analyse the vegetation type, by calculating the [NDVI index](https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index), that was present in these areas before the wildfire by looking at satellite images before the disaster and compare them with the scorched areas. To confront the effected built-up areas and most important roads, we will be using OSM vector data and compare that with the scorched areas identified above. Finally, we will use elevation data to perform viewshed analyses in order to locate the best locations for (hypothetical) watchtowers, which in theory would allow to identify a wildfire earlier, thus allowing to react more quickly. + +To most effectively use these materials, make sure to download the data and install everything before working through this lesson (this especially accounts for learners that follow this lesson in a workshop). [sentinel-2]: https://sentinel.esa.int/web/sentinel/missions/sentinel-2 -[pdok]: https://www.pdok.nl +[osm]: https://www.openstreetmap.org/#map=14/45.2935/18.7986 [workbench]: https://carpentries.github.io/sandpaper-docs diff --git a/md5sum.txt b/md5sum.txt index 48aa8fda..c15127b8 100644 --- a/md5sum.txt +++ b/md5sum.txt @@ -2,19 +2,19 @@ "CODE_OF_CONDUCT.md" "c93c83c630db2fe2462240bf72552548" "site/built/CODE_OF_CONDUCT.md" "2023-07-27" "LICENSE.md" "b24ebbb41b14ca25cf6b8216dda83e5f" "site/built/LICENSE.md" "2023-07-27" "config.yaml" "36ecf0d8c86d44ced0b1bd223bc22fe5" "site/built/config.yaml" "2024-02-26" -"index.md" "7a1a621a2439b80c06afc6bb6665fc40" "site/built/index.md" "2023-07-27" +"index.md" "40c4f08f4dbe10e0609df95a9249bd9c" "site/built/index.md" "2024-05-03" "links.md" "f70df760c54e918207d3fafb9f48e1bf" "site/built/links.md" "2023-07-27" -"episodes/01-intro-raster-data.md" "61745ee38a6c30a40eb66cfc323f176f" "site/built/01-intro-raster-data.md" "2023-07-27" -"episodes/02-intro-vector-data.md" "48082eb809ec7caa0c3005fd6cdd5dae" "site/built/02-intro-vector-data.md" "2023-07-27" -"episodes/03-crs.md" "85d370c723c2a6e8b39e853d25363aef" "site/built/03-crs.md" "2023-07-27" -"episodes/04-geo-landscape.md" "04937f2c15667adbe259b68e76ac0d46" "site/built/04-geo-landscape.md" "2023-07-27" +"episodes/01-intro-raster-data.md" "2e01c2c1a18fd506ec00300e5bfc9720" "site/built/01-intro-raster-data.md" "2024-05-03" +"episodes/02-intro-vector-data.md" "3bf0a19a7e0e3990a0f2ee02815d1147" "site/built/02-intro-vector-data.md" "2024-05-03" +"episodes/03-crs.md" "40e659ca7228c07f84c40b51b601dc55" "site/built/03-crs.md" "2024-05-03" +"episodes/04-geo-landscape.md" "09e93cefc450045e23f31f3f0d00c6e1" "site/built/04-geo-landscape.md" "2024-05-03" "episodes/05-access-data.md" "3f4586875eaa4c175f5ed7cd6730b6be" "site/built/05-access-data.md" "2023-09-12" "episodes/06-raster-intro.md" "686d165b48bea5469d3eb73f0f54a421" "site/built/06-raster-intro.md" "2023-08-23" -"episodes/07-vector-data-in-python.md" "01a7e0adfcc44f6a1ad5c8ede060473d" "site/built/07-vector-data-in-python.md" "2023-08-08" -"episodes/08-crop-raster-data.md" "87861d050911db9082000d4788140766" "site/built/08-crop-raster-data.md" "2023-07-27" +"episodes/07-vector-data-in-python.md" "980d24ebc36dc8bb0746711fc5820e9a" "site/built/07-vector-data-in-python.md" "2024-05-03" +"episodes/08-crop-raster-data.md" "80a2dd98da359d6feaebe73a939b14b3" "site/built/08-crop-raster-data.md" "2024-05-03" "episodes/09-raster-calculations.md" "1d10f506ff34c4f024929719efcb1717" "site/built/09-raster-calculations.md" "2023-07-27" "episodes/10-zonal-statistics.md" "90edeb980d0bc716becda6a750ccb2df" "site/built/10-zonal-statistics.md" "2023-07-27" "episodes/11-parallel-raster-computations.md" "abf3942aef90750228d659914a8fec70" "site/built/11-parallel-raster-computations.md" "2023-07-27" "instructors/instructor-notes.md" "1b1cbfc8fff44565421842208ccdab4f" "site/built/instructor-notes.md" "2023-07-27" -"learners/setup.md" "c8efe7f5a88db9f82d4c43820e8897ce" "site/built/setup.md" "2023-10-09" +"learners/setup.md" "57a2bc11894512886ad1c3fbf51970d6" "site/built/setup.md" "2024-05-03" "profiles/learner-profiles.md" "ab92fe3511bf951857b47c0ecbf2ab79" "site/built/learner-profiles.md" "2023-07-27" diff --git a/setup.md b/setup.md index 6858b442..b9573e51 100644 --- a/setup.md +++ b/setup.md @@ -6,10 +6,15 @@ title: Setup 1. Create a new directory on your Desktop called `geospatial-python`. 2. Within `geospatial-python`, create a directory called `data`. -3. Download the following files and save them to the just created `data` directory (**do not unzip the files**, we will read from them directly): - * [brpgewaspercelen_definitief_2020_small.gpkg](https://figshare.com/ndownloader/files/37729413) - * [brogmwvolledigeset.zip](https://figshare.com/ndownloader/files/37729416) - * [status_vaarweg.zip](https://figshare.com/ndownloader/files/37729419) +3. Download the data required for this lesson via [this link](https://figshare.com/ndownloader/articles/25721754/versions/3) (612MB). +4. Unzip downloaded files and save them to the just created `data` directory. + +Now you should have the following files in the `data` directory: + +- `sentinel-2` - This is a directory containing multiple bands of Sentinel-2 raster images over the island of Rhodes, on Aug 27, 2023. +- `dem/rhodes_dem.tif` - This is the Digital Elevation Model (DEM) of the island of Rhodes, retrieved from [Copernicus Digital Elevation Model (GLO-30 instance)](https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model) and modified for this course. +- `gadm/ADM_ADM_3.gpkg` - This is the administration boundaries of Rhodes, downloaded from [GADM](https://gadm.org/) and modified for this course. +- `osm/osm_landuse.gpkg` and `osm/osm_roads.gpkg` - They are landuse poylgons and roads polylines of Rhodes, downloaded from [Openstreetmaps](www.openstreetmaps.org) via [Geofabrik](http://www.geofabrik.de/data/download.html) and modified for this course. ## Software Setup @@ -121,6 +126,15 @@ command in your terminal (use the **Anaconda prompt** on **Windows**). 3. Create the Python environment for the workshop by running: + The main python libraries that are used in this lesson are: + - [geopandas](https://geopandas.org/en/stable/) + - [rioxarray](https://github.com/corteva/rioxarray) + - [xarray-spatial](https://xarray-spatial.org/) + - [dask](https://www.dask.org/) + - [pystac](https://pystac.readthedocs.io/en/stable/) + + In order to **not** have to install all these libraries and their dependencies seperately, a .yaml configuration file has been created which will install all of them automatically. (If you still want to install all these packages seperately you could use `pip`. However, the easiest way is just to execute the .yaml file run the following command: + ```bash mamba env create -n geospatial -f https://raw.githubusercontent.com/carpentries-incubator/geospatial-python/main/files/environment.yaml ``` @@ -137,7 +151,7 @@ command in your terminal (use the **Anaconda prompt** on **Windows**). # $ conda deactivate ``` -5. Activate the `geospatial` environment: +5. Now Activate the `geospatial` environment by running: ```bash conda activate geospatial @@ -216,5 +230,4 @@ If all the steps above completed successfully you are ready to follow along with [anaconda-windows]: https://www.anaconda.com/download/#windows [python]: https://python.org [video-mac]: https://www.youtube.com/watch?v=TcSAln46u9U -[video-windows]: https://www.youtube.com/watch?v=xxQ0mzZ8UvA - +[video-windows]: https://www.youtube.com/watch?v=xxQ0mzZ8UvA \ No newline at end of file

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