How to improve the outputs of dense vegetation areas?

Trees, forest and dense vegetation areas have specific properties that require special care. When processing such datasets, 3 type of issues might occur:


Initial Processing: Low number of calibrated images

Pix4Dmapper relies on visual similarities between overlapping images to reconstruct the model. Trees and dense vegetation, due to their complex geometry (thousands of branches and leaves), often appear very different between overlapping images. Therefore, it is more difficult to find enough similarities between overlapping images and, if the overlap is not enough, it leads to a low number of calibrated images.

To ensure to have a high number of calibrated images it is recommended to:

  • Process with 1/2 Keypoints Image Scale: This processing option can lead to higher number of calibrated images in case the default option (original keypoint image scale) does not calibrate all the images. 
  • Increase the overlap between the images to at least 85% frontal overlap and 60% side overlap.
  • Increase the flight altitude: When flying higher, the images suffer less from perspective distortions and it is easier to detect visual similarities between overlapping images in vegetation areas.

For more details about the flight plan: 202557459.

 

Point Cloud Densification: Areas without 3D points generated

Even when all the images are calibrated, it is possible that no additional 3D points are computed at areas with trees and vegetation during step 2. Point Cloud and Mesh. In order to improve the Point Cloud in such areas it is recommended to:

  • Increase the overlap between the images to at least 85% frontal overlap and 60% side overlap.
  • Increase the flight altitude: When flying higher, the images suffer less from perspective distortions and it is easier to detect visual similarities between overlapping images in vegetation areas.
  • Set the Image Scale option for the Point Cloud to 1/4 or 1/8 if the dataset consists only of vegetation areas. If the dataset has roads or buildings as well, set the Image scale option to 1/2 and use the Multiscale option: 202557799.


Orthomosaic: Visual deformation and artifacts

The Orthomosaic is generated from the DSM and it suffers from artifacts at areas where the 3D model is not good. As trees have an infinite amount of details (trunk, branches, leaves) they can only be approximately modeled. This approximation creates visual distortions and artifacts when generating the Orthomosaic. In order to remove the distortions it is recommended to:

  • Improve the DSM by:
    • Computing a more accurate Point Cloud by setting the Image Scale option for the Point Cloud to 1/4 or 1/8 if the dataset consists only of vegetation area. If the dataset has roads or buildings as well, set the Image Scale option to 1/2 and use the Multiscale option: 202557799.
    • Removing the noise of the Point Cloud: 202560499.
    • Using the Triangulation method instead of the Inverse Distance Weighting (IDW) algorithm to generate the DSM. The IDW method is the default processing option but it is more adequate for buildings than for tree or plant cover. For more information: 202557769.
  • Improve directly the orthomosaic by:
    • Using the Mosaic Editor: Replace areas with distortions by images generated using a planar projection instead of an ortho projection. For more information about how to use the Mosaic Editor: 202558709.
       
      Important: Planar projections may not preserve distances and should be used only to improve the visual aspect of the orthomosaic and not to generate orthomosaics that will be used for measuring purposes.
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