Analysis and treatment of LiDAR data


The analysis and treatment of LiDAR data refers to the whole automatic processes that allow the filtering of overlap points between passes, the identification and filtering of noise points and the automatic classification algorithms.

The classification methodology proposed is divided into three phases: the first consists in the segmentation of the LAS files according to a simplification of the SIOSE categories (The Spanish Land Use and Land Cover Information System), the second is based on the classification of the cloud of points and the third is based on the calculation of vegetation indexes as an improvement of the classification.

Operations before the classification processes:


Classification process:


  1. SEGMENTATION OF LiDAR FILES BASED ON SIOSE CATEGORIES

    SIOSE Land Occupation Information System of Spain, is a database of land cover in Spain at a scale of 1: 25.000.



    For a better adjustment of the necessary parameters in the land use classification process, we carry out a segmentation of the LiDAR files based on the following simplified SIOSE categories:

    • Forest (1)
    • Scrub, grass, meadow, arable crops and tree crops (2)
    • Urban (3)
    • Isolated houses (4)
    • Rocky areas (5)
    • Industrial (6)
    • Water (7)

    An FME process has been designed to change the simple and composite coverage provided by the SIOSE data model, to the simplified SIOSE categories, and to segment the LiDAR points cloud based on this new categorization.




    You can consult the details and the code of the process in the section Workbenchs > SIOSE zoning

  2. DETECTION AND CLASSIFICATION OF GROUND POINTS

    The points of the LAS files are classified automatically in ground points and non ground points, according to the classification algorithm implemented in the LAStools tool.

    The classification process is based on the progressive reconstruction of the land.

    A first approximation of the surface is established from some of the lower cloud points. From this first approach, a criterion is established (angle and distance) that, if complied with, allows an additional point to be added to the surface:

    lasground -i [directory][file-name].laz -odir [directory] -olaz -step [n] -stddev [n] -offset [n] -spike [n] -ignore_class 7 12

    Definition of parameters for the lasground command based on the simplified SIOSE categories:

    Clase Step Spike Offset Standard deviation
    Forest (1) 5 0.50 0.20 1
    Flat terrain (2) 10 1 0.05 1
    Urban city (3) 25 0.50 0.05 1
    Urban (town) (3) 10 1 0.05 1
    Isolated houses (4) 8 1 0.05 1
    Rocky areas (5) 10 1 0.05 1
    Industrial (6) 30 0.50 0.05 1


    Then the heights of the points are calculated with respect to the ground points:

    lasheight -i directory][file-name].laz -classify_below -0.5 7 -classify_above 100.0 7 -classify_between 0 0.5 3 -classify_between 0.5 3 4 -classify_between 3 100.0 5 -ignore_class 7 12 -store_in_user_data

    The points of the LAS files are classified automatically in points of 'low vegetation', 'medium vegetation', 'high vegetation' and 'constructions':

    lasclassify -i [directory][file-name].laz -odir [directory] -olaz -step [n] -planar [n] -ground_offset [n] -ignore_class 7 12


    Definition of parameters for the lasclassify command based on the simplified SIOSE categories:

    Clase Step Planar Ground Offset
    Bosque (1) 2 0.40 2
    Terreno llano (2) 3 0.10 2
    Urbana city (3) 2 0.10 2
    Urbana (town) (3) 2 0.10 2
    Casas aisladas (4) 2 0.10 2
    Roquedos (5) 3 0.10 2
    Industrial (6) 2 0.30 3


    These three processes are included for execution within a workbench, whose details and code can be consulted in the section Workbenchs > LiDAR Classification

  3. CALCULATION OF THE NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI), HOW TO IMPROVE THE CLASSIFICATION

    A Normalized Difference Vegetation Index (NDVI), is a combination of spectral bands captured by a sensor. It allows to discriminate covers that have a different reflective behavior between two or more bands of the electromagnetic spectrum.

    The NDVI is one of the most used when estimating the quantity, quality and development of the vegetation. It is calculated by means of visible and near infrared light reflected by the vegetation. The calculation of this index is done with reflectivities, but when it is not necessary to grant a physical value to the results, the digital levels are used directly.

    The following expression is used: 

    NDVI= (IRC-R))/(IRC+R)


    where: IRC is the near infrared response and R is the response in red.

    The NDVI values vary between -1 and 1. The following are the approximate values of the NDVI for the different types of covers, which will serve to establish the classification thresholds:

    NO VEGETATION

    Water Beach Roads Paths Buildings
    < 0 < 0.05 < 0.10 < 0.15 < 0.10

    VEGETATION

    Burned area Dunes Grass Wooded Golf course
    < 0.10 0.10 - 0.20 0.20 - 0.30 0.30 - 0.40 0.50

    The vegetation index has been used as an improvement of the classification obtained in the classification process.

    To do this, a series of classification conditions are defined as a decision tree that allows erroneously assigned classification values to be corrected.

    The implementation of the decision tree has been carried out in a project in FME.

    Example of condition: For urban points (6), wrongly classified.

    clasificación = 6 && ndvi > 0.2 && height >=0 && height < 0.5 It is reclassified in 'low vegetation' (3)

Subsequent operations to the classification processes: