Task 2 | Integration of Municipal Tree points and Laser-detected Tree Crown polygons

This repository provides a workflow for preparing municipal tree data for i-Tree Eco analysis and extrapolating the results to full the study area extent, using the lidar-segmented tree crowns and auxiliary GIS datasets.

Code is provided for the following tasks:

  1. i-Tree Eco Data Preparation: preparing an input dataset for i-Tree Eco analysis by supplementing existing municipal tree inventories with crown geometry from the ALS data and auxiliary spatial datasets following the workflow by Cimburova and Barton (2020).

  2. i-Tree Eco Extrapolation: extrapolating the outputs from i-Tree Eco analysis to all trees in the study area.

The repository is applied on the Norwegian municipalities: Bærum, Bodø, Kristiansand and Oslo.

Workflow | i-Tree Eco Data Preparation

Detailed description of the workflow is provided in the project note (in prep).

  1. Prepare Data entry point: prepare_data.py tasks: (i) load the lidar-segmented tree crown polygons from the ALS data per neighbourhood (ii) load the in situ tree stems from the municipal tree inventory (iii) clean the in situ tree stems - manual municipality-specific cleaning tasks (see REF) - automatic cleaning tasks: - set standard field design - translate tree species - ensure that each tree stem contains: stem_id, dbh, height, crown_diameter (iv) group tree stem points by neighbourhood

  2. Join the in situ tree stems with the lidar-segmented tree crowns entry point: join_data.py tasks: (i) classify the geometrical relationship (ii) split lidar-segmented tree crowns that overlap with multiple tree stems (iii) model the crown geometry of tree stems that do not overlap with lidar-segmented trees (iv) quality control wether each crown polygon is assigned to a single tree stem (v) join the in situ tree stems with the lidar-segmented tree crowns

    Geometrical Relations:

    • Case 1: one polygon contains one point (1:1), simple join.

    • Case 2: one polygon contains more than one point (1:n), split crown with voronoi tesselation.

    • Case 3: a point is not overlapped by any polygon (0:1), model tree crown using oslo formula.

    • Case 4: a polygon does not contain any point (1:0), not used to train i-tree eco/dataset for extrapolation.

  3. Compute tree attributes and auxillary attributes

    entry point: compute_attributes.py tasks: (i) compute tree crown attributes (all trees in thes study area) - overlay attributes (pollution zone, neighbourhood code) - crown_id (based on neighbourhood code and objectid) - tree height, crown area (ii) compute tree stem attributes (in-situ trees) - overlay attributes (e.g. pollution zone, neighbourhood code, land use) - tree attributes (e.g. dbh, height, crown diameter) - join crown attributes (e.g. crown_id, crown area, crown volume, crown shape) - building related attributes (e.g. building distance, building direction) - crown condition (e.g. crown light exposure)

    IMPORTANT NOTES: do not run building related attr. and crown condition attr. within pipeline. Run them separatly and cosely check the results.

    STEP 3 NEEDS CLEANING, BUILDING CROWN CONDITION SUPER SLOW (e.g. +12h runtime)

Workflow | i-Tree Eco Extrapolation

Detailed description of the workflow is provided in the project note.

References

  • Cimburova, Z., & Barton, D. N. (2020). The potential of geospatial analysis and Bayesian networks to enable i-Tree Eco assessment of existing tree inventories. Urban Forestry & Urban Greening, 55, 126801. https://doi.org/10.1016/j.ufug.2020.126801

Acknowledgments

This repository is part of the project:

TREKRONER Prosjektet | Trærs betydning for klimatilpasning, karbonbinding, økosystemtjenester og biologisk mangfold.

This repository uses code adapted fromt the repository i-Tree-Eco by Cimburova, Z. 2022, this repository is licensed under the GNU General Public License (GPL).