Methods for creating 3D visualizations of forest inventory plots and co-registering stem maps with lidar data.
As this project develops, it will support 3D volumetric representations of trees with the ability to incorporate uncertainty/error into field-based measurements and additional rendering parameters.
In addition to the basic value of providing a stochastic 3D visualization option for forest inventory plots, this workflow is intended to support an optimization to co-register lidar-derived data (e.g., canopy surface model or alpha-hulls of trees) with stem-mapped forest inventory data from the field. This is intended to improve the alignment of field data with remotely-sensed data to support the use of field data as training data for predictive models that rely primarily upon remotely-sensed data such as lidar and high-resolution imagery.
The incorporation of prior beliefs about the distributions of 3D forest parameters (e.g., measurement error in location of plot center, distance of trees from plot center, etc.) will be applied in a Bayesian Inverse Modeling approach to identify the values and distributions of parameters which minimize divergence between simulated forest surfaces and lidar-derived surface(s). The outcome of this optimization will include probabilistic insights about the most likely locations of forest inventory plots within a lidar scene.
If you don't want to do any coding yourself and instead just want to view and execute the code we've already written using our Jupyter Notebooks, use this link:
Use the conda package manager to reproduce the computing environment we used in developing this repo. Get Anaconda or Miniconda to do so.
- Clone this repo onto your local machine:
git clone https://github.com/d-diaz/Lidar_Plot_Registration.git - Create a conda environment from the environment.yml file included in this repo:
conda env create --name forest3d --file environment.yml - Activate this new conda environment:
source activate forest3d(Linux, OSX) oractivate forest3d(Windows) - Start a Jupyter session:
jupyter notebook - In your web browser Jupyter session, navigate to the "notebooks" folder in this repo and open up one of the Jupyter Notebooks!
├── LICENSE <- Open Source AF
├── README.md <- You're reading it
│
├── data /
│ ├── raw <- Raw lidar and tree list data
│ ├── interim <- Intermediate data that has been transformed
│ └── processed <- Processed data sets used for modeling/analysis
│
├── notebooks / <- Jupyter notebooks.
│ ├── 01_Check Data Formats.ipynb <- For checking whether your treelist is formatted appropriately for modeling
│ ├── 02_Visualize a Single Tree in 3D.ipynb <- Interactive inspection of how a tree crown is modeled, with widgets
│ └── 03_Visualize a Forest Plot in 3D.ipynb <- Visualize a list of trees
│
├── forest3d / <- Source code for use in this project
│ ├── validate_data.py <- To validate conformance of data with our specs
│ ├── geometry.py <- To generate 3D geometric models
│ ├── optimize.py <- To co-register point clouds with geometric models
│ ├── visualize.py <- To create interactive 3D visualization
│ └── test / <- Scripts for unit testing
|
├── setup.py <- Source code installable as python package (`python setup.py develop`)
├── environment.yml <- Requirements file for reproducing the analysis environment using conda
| (`conda create --name forest3d --file environment.yml`)
│
├── docs / <- Documentation, generated using Sphinx and hosted at Read the Docs
├── references / <- Relevant references and other explanatory materials
├── reports / <- Generated analysis as HTML, PDF, PPT, etc.
│ └── figures / <- Graphics and figures used in reports
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├── .gitignore <- Stuff for git to ignore
└── .travis.yml <- Settings for running Travis CI
