Main technical characteristics

The 3^rd LiDAR data coverage of Catalonia is published in sheets of 1 x 1 km, where all sheets have a minimum density of 8 points/m², and 65% of them have a density greater than 10 points/m².

Distribution of surveys by sheets

The distribution of the point cloud density coverages of each 1x1 km sheet can be consulted in the metadata.

The 3^rd LiDAR data coverage of Catalonia comes from flights made with the LiDAR sensor during the years 2021-2022 and 2023.        
 

Data density distribution

The data density with the flight year of each 1x1 km sheet can be consulted in the metadata.   

Work methodology

• What is a point cloud?  
  It is a set of points positioned in three-dimensional space based on the coordinates (X, Y, H) of a given reference and projection system. Each of these points has different associated characteristics that help us to get to know the territory, one of the main ones is the class, which refers to the element to which the point belongs, be it the ground, a building, the vegetation, etc.
  Based on this classification we obtain an accurate three-dimensional model of the different elements of the territory.
• How is the flight carried out?  
  LiDAR data capture for the 3^rd coverage of Catalonia has been done from a dual Lidar and photographic sensor on board an aircraft. The aircraft performs the necessary flight missions to capture LiDAR data and aerial images until it covers the entire Catalan territory.
  This coverage required 159 missions or flight days.
• What is the resulting product and how is it worked and edited?  
  The data captured by the dual Lidar and photographic sensor are processed in parallel. First the trajectory of the aircraft is processed and the georeference is obtained from the GPS/INS data collected during the flight. Afterwards, the LiDAR data is worked on on the one hand to obtain the cloud of points and on the other hand the aerial images to obtain an orthoimage.
  The product obtained is a georeferenced cloud of points that gives a morphological view of the territory, since each point represents an element of the terrain and is the starting point for generating different elevation models.

Point cloud classification

The LiDAR point cloud is distributed in LAS 1.4 format and classified according to the ASPRS standard. Each point is assigned a class that identifies the type of terrain feature it belongs to (ground, vegetation, buildings, water, etc.). This classification is one of the key characteristics of the product, as it enables analytical exploitation. The classification has been obtained using automated algorithms, complemented by targeted manual reviews and edits to correct significant errors.

• Main thematic classes, which describe the predominant elements of the territory: ground, vegetation (by height), buildings.
• Specific subclasses, which allow refining the representation of certain elements, such as terrain key points (model key points), points associated with complex structures (bridges, façades or above buildings), or points over water.
• Quality control classes, which identify anomalous or non‑representative points (low points, air points, or overlaps between flight passes).
• Provisional or unclassified classes, which include points that cannot be reliably assigned to a specific category (Default).

This classification scheme allows selective work with the point cloud and enables the generation of valuable derived products, such as digital terrain models, vegetation cover analysis, or identification of built elements.

In this regard, classification is an essential element for converting the point cloud into a structured database suitable for territorial planning applications, environmental analysis, and volumetric studies.

Below is the list of LiDAR point classes used, including the class code, name, and a brief description of each.

Example of LiDAR point cloud classification

LiDAR point cloud classification

Featured case studies

Morphological study: elevation and surface models

Elevation models are obtained from the classified point cloud, which, depending on the classes chosen, will describe the morphology of certain elements of the territory. Thus, a Digital Surface Model (DSM) can be generated from all the classes that will describe the territory with all its elements. Or a Digital Terrain Model (DTM) can be generated using only the Terrain class that will describe the relief. Combined models can also be generated to describe the elevations of the terrain with vegetation (DTMv) or with buildings (DTMe). These elevation models have multiple uses; an example would be the use of the DTM to create maps of flood zones.

Models (DSM and DTM) and image

Vegetation study 

The density of the LiDAR point cloud obtained in the 3rd coverage allows the morphology of the forest mass to be described in detail. From these data, different variables that characterize the vegetation can be calculated, such as the height of the trees or the fraction of covered capacity. The resolution achieved also makes it possible to analyze the scrub and the distribution and structure of the forest, key aspects for forest planning, biodiversity conservation and fire prevention.

As a specific use case, LiDAR data, combined with field work, allow the development of innovative methodologies to generate forest inventories adapted to the reality of the formations of Catalonia. This information makes it possible to obtain structural variables of the tree masses and apply them at different scales, thus contributing to the planning and monitoring of forest management within the current management instruments.

Detailed description of the forest

Volumetric studies

The careful modelling of the terrain morphology obtained from LiDAR data allows us to monitor changes in the territory, especially in changing areas such as waste dumps, quarries and controlled deposits. Volumetric studies of the material supplied or extracted can be carried out from the different covers.

Volumetric calculation