{"id":11944,"date":"2023-08-30T10:00:00","date_gmt":"2023-08-30T08:00:00","guid":{"rendered":"https:\/\/www.sigterritoires.fr\/?p=11944"},"modified":"2023-08-30T09:13:11","modified_gmt":"2023-08-30T07:13:11","slug":"qgis-for-lidar-digital-feature-model-dem-with-open-lidar-tools","status":"publish","type":"post","link":"https:\/\/www.sigterritoires.fr\/index.php\/en\/qgis-for-lidar-digital-feature-model-dem-with-open-lidar-tools\/","title":{"rendered":"QGis for LIDAR: digital feature model (DEM) with Open Lidar Tools."},"content":{"rendered":"\n

In this article you’ll find out how to create a Digital Feature Model in just a few clicks and visualize the results, thanks to the Open Lidar Toolbox plugin.<\/p>\n\n\n\n

Tutorial HD LIDAR data processing with QGis<\/h2>\n\n\n\n
1- LIDAR data in QGis 3.32<\/a><\/p>\n\n\n\n
2- Download LIDAR HD data from IGN and load it into QGis<\/a><\/p>\n\n\n\n
3- Tools for LIDAR data in QGis 3.32<\/a><\/p>\n\n\n\n
4-Colorize a point cloud from an orthophoto<\/a><\/p>\n\n\n\n
5-Colorize from an image with LAStools<\/a><\/p>\n\n\n\n
6- Digital surface model (DSM) with CloudCompare and LASto<\/a>ols<\/p>\n\n\n\n
7- digital terrain model (DTM) with CloudCompare<\/a><\/p>\n\n\n\n
8- Creating a Digital Terrain Model with LAStools<\/a><\/p>\n\n\n\n

9- Creating a Digital Feature Model with Open Lidar Toolbox.<\/h2>\n\n\n\n
In the previous chapters we saw how to create a Digital Surface Model<\/a> (DSM) and a Digital Terrain Model (DTM). The third type of digital model is the Digital Feature Model (DFM). All three are grouped together in what are known as Digital Elevation Models.<\/p>\n\n\n\n
$\"les$ <\/a><\/figure>\n\n\n\n
The digital terrain model is simply the bare surface of the land, excluding all other features such as buildings and vegetation.<\/p>\n\n\n\n
The digital surface model takes into account everything on the ground (buildings and vegetation).<\/p>\n\n\n\n
The digital feature model takes into account the land surface and buildings, excluding vegetation.<\/p>\n\n\n\n
Although the three models produce different results, and the processes for obtaining them differ, the fact remains that most tools and techniques can be adapted to all three. For example, when interpolating the point cloud to obtain the resulting raster, in the case of DTM, only the ‘ground’ class will be retained for interpolation, while for DEM ‘ground’ and ‘buildings’ will be retained, and for DSM no class will be excluded.<\/p>\n\n\n\n
All of which is to say that, although we’ll be tackling the creation of Digital Feature Models here, with techniques developed primarily for archaeology, there’s nothing to stop you adapting and using them for DTMs and DSMs.<\/p>\n\n\n\n
OPEN LIDAR TOOLBOX<\/h2>\n\n\n\n
The integration of topographic data from airborne LiDAR has become a fundamental element in archaeological prospecting efforts. Faced with the need to develop a rigorous and transparent data processing methodology, the Open Lidar Tools toolbox proposes a processing flow specifically adapted to archaeological point clouds, with the aim of producing optimized products for various applications. The proposed workflow improves the classification of points on the ground as well as those inside structures.<\/p>\n\n\n\n
The major innovation in this processing flow lies in the approach to interpolating data in the form of raster grids, by introducing a hybrid interpolation method. This method combines inverse distance weighting (IDW) with a triangulated irregular network (TIN) and linear interpolation. Solutions for enhanced visualization have also been integrated. The plug-in for QGIS enables the entire processing flow to be executed in a single step, and the tool requires no specialized skills other than general familiarity with the QGIS environment.<\/p>\n\n\n\n
The aim of this pipeline and tool is to facilitate the processing of airborne LiDAR data specific to archaeology, but not only.<\/p>\n\n\n\n
A word of caution<\/h3>\n\n\n\n
The Open Lidar Tools plug-in uses GRASS processing. If you have installed version 3.32 with the installer, check that the Grass tab appears in the processing panel.<\/p>\n\n\n\n
$\"outils$ <\/a><\/figure>\n\n\n\n
If not, once you’ve checked that the necessary plugins are activated, you’ll need to install OSGeo4W version 3.32 for Grass calls to work.<\/p>\n\n\n\n
Installing Open Lidar Toolbox<\/h3>\n\n\n\n
Install the Open LiDAR Toolbox plugin for QGIS (in QGIS go to : Extensions-> Install\/Manage extensions ->installed extensions -> all -> Open LiDAR Toolbox -> Install Plugin<\/em><\/strong>).<\/p>\n\n\n\n
$\"installation$ <\/a><\/figure>\n\n\n\n
Open Lidar Toolbox requires three other plug-ins to be installed:<\/p>\n\n\n\n
\n
Lastools<\/a><\/li>\n\n\n\n
WhiteboxTools<\/li>\n\n\n\n
Relief Visualisation Toolbox<\/li>\n<\/ul>\n\n\n\n
For the first two, we have already seen how to install them in the previous chapters. For Relief Visualisation Toolbox, simply add it from the menu Extensions-> Install\/Manage extensions -> installed extensions -> all -> Relief Visualisation Toolbox -> Install Plugin<\/em><\/strong>.<\/p>\n\n\n\n
Open Lidar Toolbox modules<\/h2>\n\n\n\n
At the top of the plugin’s processing menu, you’ll find a module called ONE. This processing is a pipeline, linking all the LIDAR data processing modules in the plugin.<\/p>\n\n\n\n
$\"sch\u00e9ma$ <\/a><\/figure>\n\n\n\n
Point cloud processing is based on averaging enhanced by sequential processing. First, buildings are classified with parameters optimized for building detection. Next, the entire point cloud without buildings is reprocessed with parameters optimized for ground point detection, favoring the preservation of archaeological features over the removal of vegetation. An additional step, targeting low-density data, is to classify all unclassified points that lie within \u00b1 0.2 m of ground points as ground points. Vegetation points are classified according to the ASPR scheme. However, due to software limitations and pipeline streamlining, medium and tall vegetation are combined in class 5.<\/p>\n\n\n\n
All you need to do is specify the LIDAR point file to be processed, whether it is classified or not, the coordinate system, the digital feature model pitch and the name for the classified point cloud file.<\/p>\n\n\n\n
$\"dilaogue$ <\/a><\/figure>\n\n\n\n
You give a LIDAR point cloud as input, classified or not, and you obtain:<\/p>\n\n\n\n
\n
two rasters with point densities of low vegetation and terrain<\/li>\n\n\n\n
one raster with the digital feature model and one with the associated confidence levels<\/li>\n\n\n\n
the classified point cloud<\/li>\n\n\n\n
five visualization rasters, using different techniques, of the DEM<\/li>\n<\/ul>\n\n\n\n
$\"sorties$ <\/a><\/figure>\n\n\n\n
Here’s an example based on an unclassified point cloud from French IGN<\/p>\n\n\n\n
$\"nuage$ <\/a><\/figure>\n\n\n\n
The first output of the One Step Processing pipeline is the classified point cloud:<\/p>\n\n\n\n
$\"classement$ <\/a><\/figure>\n\n\n\n
The outputs with low vegetation and terrain point densities, as well as DEM confidence levels, are used by the interpolation module. We’ll see how useful they are later.<\/p>\n\n\n\n
We now have our digital feature model:<\/p>\n\n\n\n
$\"mod\u00e8le$ <\/a><\/figure>\n\n\n\n
Sky View Factor (SVF) visualization<\/h3>\n\n\n\n
The Sky-View Factor (SVF) is a tool used in the field of geomatics, particularly in topographic analysis and landscape visualization. The Sky-View Factor (SVF) is an index that measures the portion of sky visible from a given point on the ground, as a function of the surrounding topography. This index can be used to characterize the visibility of the sky from a specific location, which can have important implications in various fields such as climatology, ecology, urban planning, etc.<\/p>\n\n\n\n
SVF is calculated by simulating the line of sight from a given point to the sky, and measuring the proportion of this line of sight that is obstructed by surrounding landscape features such as buildings, trees and terrain relief. The higher the SVF, the clearer the view to the sky.<\/p>\n\n\n\n
$\"visualisation$ <\/a><\/figure>\n\n\n\n
Openness visualization<\/h3>\n\n\n\n
Openness\u00a0\u00bb relief visualization is a tool used in geomatics to analyze and visualize the topography of a landscape in terms of visibility and exposure to different directions. Aperture measures the ability of a point on the ground to see and be seen from different directions.<\/p>\n\n\n\n
Aperture is calculated by measuring the angle between the terrain normal at a given point and the directions towards which exposure is to be assessed. In other words, aperture indicates how easily a point can \u00ab\u00a0see\u00a0\u00bb or be \u00ab\u00a0seen\u00a0\u00bb from surrounding areas.<\/p>\n\n\n\n
$\"visualisation$ <\/a><\/figure>\n\n\n\n
Visualization for archaeological topography (VAT)<\/h3>\n\n\n\n
Visualization for archaeological topography (VAT) combines hill shading (or hill shading in three directions), slope, positive aperture and sky view factor with predetermined calculation and blending parameters for \u00ab\u00a0normal\u00a0\u00bb and very flat terrain. The visualization methods selected are complementary and the blending modes specific because they amplify the particular characteristics.<\/p>\n\n\n\n
$\"visualisation$ <\/a><\/figure>\n\n\n\n
DME (Difference from mean elevation) visualization<\/h3>\n\n\n\n
Difference from Mean Elevation (DME) visualization is a technique used to visually represent variations in terrain elevation in relation to a reference mean elevation. This method is often used in geomatics and topographic analysis to highlight areas where the terrain shows significant deviations from an expected mean elevation.<\/p>\n\n\n\n
Here’s how DME visualization works:<\/p>\n\n\n\n
\n
Calculating the Average Elevation: First, an average elevation is calculated for the entire study area. This can be done by taking the average of the elevation values of all terrain points in the area.<\/li>\n\n\n\n
Difference calculation: Next, for each terrain point, the difference between the point’s actual elevation and the calculated average elevation is determined. This difference is often expressed in terms of meters or other units of measurement appropriate to the elevation.<\/li>\n\n\n\n
Map coloring: The calculated differences are then represented visually using a color scale. Areas where elevation is above average can be colored with warm hues (such as red) to indicate higher elevations, while areas below average can be colored with cool hues (such as blue) to indicate lower elevations.<\/li>\n\n\n\n
DME Map creation: By applying this coloring to the entire terrain, a DME map is created. This map allows you to quickly visualize areas where the terrain has unusually high or low elevations compared to the expected average elevation.<\/li>\n<\/ol>\n\n\n\n
DME visualization can be useful for detecting unusual topographical features, such as mounds, depressions or other interesting geological formations. It can also help identify areas where phenomena such as erosion or sedimentation have altered the expected topographic profile<\/a>.<\/p>\n\n\n\n
$\"visualisation$ <\/a><\/figure>\n\n\n\n
Hillshade visualization<\/h3>\n\n\n\n
Analytical shading is calculated in several directions, evenly distributed between 0\u00b0 and 360\u00b0. The 0\u00b0 is always in band 1, followed by clockwise azimuths, e.g. 45\u00b0 in band 2, 90\u00b0 in band 3 \u2026 315\u00b0 in band 8, for a calculation in 8 directions. The 8-bit image is the result of a calculation in three directions, separated by 60\u00b0 (315\u00b0 in the red band, 15\u00b0 in the green band, 75\u00b0 in the blue band).<\/p>\n\n\n\n
$\"visualisation$ <\/a><\/figure>\n\n\n\n
Other modules available in Open Lidar Toolbox<\/h2>\n\n\n\n
The individual steps of the ONE Step Processing module are available as independent modules.<\/p>\n\n\n\n
$\"les$ <\/a><\/figure>\n\n\n\n
Classify LAS\/LAZ<\/h3>\n\n\n\n
The difference between this classification tool and the other toolboxes we have seen in previous chapters is the sequence of three steps:<\/p>\n\n\n\n
\n
First, the buildings are classified using parameters optimized for building detection.<\/li>\n\n\n\n
Next, the entire point cloud without buildings is reprocessed with parameters optimized for ground point detection<\/li>\n\n\n\n
finally, all unclassified points within \u00b1 0.2 m of ground points are classified as ground points.<\/li>\n<\/ol>\n\n\n\n
Vegetation points are classified according to the ASPR scheme, but medium and tall vegetation are combined in class 5.<\/p>\n\n\n\n
CREATE DFM<\/h3>\n\n\n\n
This tool is a pipeline, just like One Step Processing. If you check the box indicating that the input point cloud is already classified, it executes the One Step Processing pipeline except for the first part, classification, and the last, visualization.<\/p>\n\n\n\n
If you leave this box unchecked, it runs One Step Processing except for the last part, visualization.<\/p>\n\n\n\n
Visualizations (from DFM)<\/h3>\n\n\n\n
For any digital model in raster format, you can use this module to directly create the 5 visualizations used in the Open LIdar Toolbox plugin, without having to use the Raster visualization Toolbox.<\/p>\n\n\n\n
The other modules<\/h3>\n\n\n\n
Create base data, DFM confidence map<\/em><\/strong> and Hybrid interpolation<\/em><\/strong> are a set of tools for interpolating a LIDAR point cloud to create a digital raster model.<\/p>\n\n\n\n
While in the pipeline they are applied to the creation of a digital feature model, they are equally useful for creating digital terrain and surface models.<\/p>\n\n\n\n
The major problem with this step in all digital models is choosing the most appropriate interpolation method for the data available. Open Lidar Toolbox offers a hybrid method, i.e. one that uses two different interpolation methods depending on the region of the point cloud, selecting the most accurate method according to a series of criteria (confidence map).<\/p>\n\n\n\n
This deserves a chapter of its own, and we’ll be doing just that in the following article.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this article you’ll find out how to create a Digital Feature Model in just a few clicks and visualize the results, thanks to the Open Lidar Toolbox plugin. Tutorial HD LIDAR data processing with QGis…<\/p>\n","protected":false},"author":1,"featured_media":11951,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"give_campaign_id":0,"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"sfsi_plus_gutenberg_text_before_share":"","sfsi_plus_gutenberg_show_text_before_share":"","sfsi_plus_gutenberg_icon_type":"","sfsi_plus_gutenberg_icon_alignemt":"","sfsi_plus_gutenburg_max_per_row":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[2422,1377],"tags":[2665,2663,2090,2667,1758,2661,1386],"class_list":["post-11944","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-lidar-en","category-qgis-en","tag-dfm","tag-digital-feature-model","tag-lidar-en","tag-open-lidar-toolbox-en","tag-plugin-en","tag-point-cloud","tag-qgis-en"],"aioseo_notices":[],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/www.sigterritoires.fr\/wp-content\/uploads\/2023\/08\/svf_one_open_lidar_toolboximg.png?fit=1773%2C1771&ssl=1","jetpack_shortlink":"https:\/\/wp.me\/p6XU0A-36E","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/posts\/11944","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/comments?post=11944"}],"version-history":[{"count":0,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/posts\/11944\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/media\/11951"}],"wp:attachment":[{"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/media?parent=11944"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/categories?post=11944"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.sigterritoires.fr\/index.php\/wp-json\/wp\/v2\/tags?post=11944"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}

Visualizations (from DFM)<\/h3>\n\n\n\nFor any digital model in raster format, you can use this module to directly create the 5 visualizations used in the Open LIdar Toolbox plugin, without having to use the Raster visualization Toolbox.<\/p>\n\n\n\n

Visualizations (from DFM)<\/h3>\n\n\n\n
For any digital model in raster format, you can use this module to directly create the 5 visualizations used in the Open LIdar Toolbox plugin, without having to use the Raster visualization Toolbox.<\/p>\n\n\n\n