FLApy 2.0.0

Forest Light availability heterogeneity Analysis in Python

Forest Light Analyzer Python package: FLApy

General description

Forest Light Analyzer python package (FLApy) is a python package for assessing light availability（LAv） condition and analysing Light availability Heterogeneity at any observers within forest using Airborne Laser Scanning data and analysis the change or heterogeneity of LAv over spatial scale. At the same time, FLApy can also be used to analysing the Light Availability Heterogeneity (LAH) of forest, and calculate a series of indicators to describe the 3D LAH of forest at different spatial scales.

Demo Data

The demo Data can be found in the demo folder.

Getting started

FLApy is recommended to be installed in a virtual environment (Python version: 3.8.6). GDAL(http://www.gdal.org/) and C++ 14.0 are required to be installed before installing FLApy.

Installation

pip install FLApy      # Install the package

Usage

The FLApy workflow is as follows:

A simple example can be found in the Simple guidance.

Import FLApy

import FLApy as fp

Read data

In general, the point cloud data is required, and the DSM, DEM and DTM are optional. The FLApy package can read the point cloud data in the LAS format. And the DSM and DTM can be produced automatically from the point cloud data when the SFL is generated. Yet, the third-party tools are recommended to produce these raster data. Especially, the study area is large, and the point cloud data is too big, the DSM, DEM and DTM can be produced by using the lastools. Besides, if the study area locates in a mountainous area, the DEM is recommended to be provided.

site = fp.DataManagement.StudyFieldLattice()
site.read_LasData('/your/path/to/point.las')    

generate A Study-Field Lattice (SFL)

SFL is a data container for storing the information of each voxel in the study area. And all data processing and analysis in FLApy are based on the SFL. gen_SFL is a function to generate the SFL. It needs a study area extent determined by [min_X, max_X, min_Y, max_Y]. The resolution is the size of each voxel in the SFL. The unit is meter.

site.gen_SFL([xmin, xmax, ymin, ymax], resolution=1)

Compute the Light Availability (LA) at voxels

The LAv is calculated at each voxel in the SFL. The LAv is calculated by the LAcalculator class. The voxel-traverse method is used to calculate the LAv within SFL if the observation type is default. A cutting-edge parallel computing method is used to calculate the LAv within SFL, it can save a lot of time if the study area is large. The actual processing time depends on the number of CPU cores.

siteLA = fp.LAcalculator.LAcalculator(site)
siteLA.computeBatch(siteLA)

3D Light Availability Heterogeneity (LAH) analysis

The LAH is calculated by the LAH_calculator class. The field is the LA field to be calculated. If the path of saving is not provided, the LAH will be saved in the same path of the SFL.

siteLAH = fp.LAHanalysis.LAH_calculator(siteLA)
results = siteLAH.com_allLAH()
print(results)

Visualization

The visualize module is used to visualize the data in the SFL. The field is the field to be visualized. The module contains the following functions:

• vis_3Dpoint is used to visualize the 3D point cloud data.
• vis_Raster is used to visualize the raster data.
• vis_Figures is used to visualize the summary of the LAH analysis across the four spatial scales.

fp.Visualization.vis_Figures(siteLAH)

LAH indicators

All support for calculation 3D-LAH indicator system.

Indicators	Scale	Abbreviation
Average	Voxel	AVE_Vox
Standard_deviation	Voxel	STD_Vox
Coefficient_of_variation	Voxel	CV_Vox
Range	Voxel	RAN_Vox
Spatial_autocorrelation	Voxel	SAC_Vox
Diversity	Voxel	DIV_Vox
Gini_coefficient	Voxel	GINI_Vox
Light_attenuation_rate	Vertical	LAR_Ver
Height_of_inflection_point	Vertical	HIP_Ver
Relative_height_of_inflection_point	Vertical	HIPr_Ver
Convex_hull_area	Vertical	ACH_Ver
Average	Horizontal	AVE_Hor
Standard_deviation	Horizontal	STD_Hor
Height of max Standard deviation	Horizontal	STDmh_Hor
Relative height of max Standard deviation	Horizontal	STDmhr_Hor
Coefficient_of_variation	Horizontal	CV_Hor
Range	Horizontal	RAN_Hor
Spatial_autocorrelation	Horizontal	SAC_Hor
Diversity	Horizontal	DIV_Hor
Gini_coefficient	Horizontal	GINI_Hor
Hot_volume	3D_Cluster	HVOL_3D
Cold_volume	3D_Cluster	CVOL_3D
Relative_hot_volume	3D_Cluster	HVOLr_3D
Relative_cold_volume	3D_Cluster	CVOLr_3D
Volume_ratio_of_hot_to_cold	3D_Cluster	VRH2C_3D
Largest_hot_volume	3D_Cluster	LHV_3D
Largest_cold_volume	3D_Cluster	LCV_3D
Hot_abundance	3D_Cluster	HAB_3D
Cold_abundance	3D_Cluster	CAB_3D
Hot_volume_average	3D_Cluster	HVA_3D
Cold_volume_average	3D_Cluster	CVA_3D
Hot_cohesion	3D_Cluster	HCO_3D
Cold_cohesion	3D_Cluster	CCO_3D
Hotspot related circumscribing sphere	3D_Cluster	HCC_3D
Coldspot related circumscribing sphere	3D_Cluster	CCC_3D
Hot_shape_index	3D_Cluster	HSI_3D
Cold_shape_index	3D_Cluster	CSI_3D

Notation

The FLApy package is developed by Bin Wang ([e-mail])

Authors

Bin Wang^{1, 2}, Cameron Proctor², Zhiliang Yao^{3, 4}, Ninglv Li¹, Qifei Chen¹, Wenjun Liu¹, Suhui Ma¹, Chuanbao Jing¹, Zhaoyu Zhou¹, Weihong Liu¹, Yufeng Ma¹, Zimu Wang¹, Zhiming Zhang¹, Luxiang Lin^{3, 5}

1. School of Ecology and Environmental Sciences, Yunnan University, Kunming, 650500, China
2. School of the Environment, University of Windsor, Windsor, N9B 3P4, Canada
3. CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Kunming, China
4. University of Chinese Academy of Sciences, Beijing, China
5. National Forest Ecosystem Research Station at Xishuangbanna, Mengla, Yunnan, China

Author mail: Bin Wang ([e-mail]); Zhiming Zhang ([e-mail])

Acknowledgements

This research is supported by the National Natural Science Foundation of China (32260291), The Second Tibetan Plateau Scientific Expedition and Research (STEP) program (2019QZKK0308), and the Joint Fund of the National Natural Science Foundation of China-Yunnan Province (U1902203). In addition, it has received strong support from The Project for Talent and Platform of Science and Technology in Yunnan Province Science and Technology Department (202205AM070005), the Major Program for Basic Research Project of Yunnan Province (202101BC070002) and the Key Research and Development Program of Yunnan Province (No. 202303AC100009). We also thank the Ailao Mountain Nature Reserve Ecological Station for field work support, and the Southeast Asian Biodiversity Institute (151C53KYSB20200019) for laboratory platforms. Besides, we acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2022-04861). Finally, we would like to express our sincere thanks to Prof. Jiajia Liu of Fudan University, Prof. Hans De Boeck of the University of Antwerp, and Dr. Suhui Ma and Dr. Chuanbao Jing of Yunnan University for their valuable suggestions and guidance on the writing of this work.

Authors' contributions

• Bin Wang, Zhiming Zhang, and Luxiang Lin conceived of the idea and contributed to the concept, and Bin Wang co-designed the package with Cameron Proctor.
• The Python package was developed by Bin Wang and tested by Zhiliang Yao, Qifei Chen and Ninglv Li.
• Zhiliang Yao and Ninglv Li worked together to create the simulated forest.
• Weihong Liu and Zhaoyu Zhou conducted the ground forest species inventory.
• The field UAV data were collected by Zhaoyu Zhou, Qifei Chen, Yufeng Ma, and Zimu Wang in collaboration.
• Bin Wang, Qifei Chen, and Yufeng Ma, Wenjun Liu created the annotations and the user manual for FLApy and are responsible for building and maintaining the GitHub repository.
• Bin Wang drafted the manuscript, and Bin Wang drew all the graphs in the manuscript.
• Cameron Proctor, Zhiliang Yao, Ninglv Li, Suhui Ma, Chuanbao Jing and Zhiming Zhang provided advice for two cases.
• Zhiming Zhang, Cameron Proctor, Zhiliang Yao, Wenjun Liu, Suhui Ma, Chuanbao Jing, and Luxiang Lin reviewed and commented on the manuscript.
• All authors read and accepted the final version of the manuscript.

References to third-party packages

Pyvista: https://github.com/pyvista/pyvista

• Sullivan and Kaszynski, (2019). PyVista: 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK). Journal of Open Source Software, 4(37), 1450, https://doi.org/10.21105/joss.01450

numpy: https://numpy.org/

• Harris, C.R., Millman, K.J., van der Walt, S.J. et al., (2020). Array programming with NumPy. Nature 585, 357–362. https://doi.org/10.1038/s41586-020-2649-2

xarray: https://github.com/pydata/xarray

• Hoyer, S. & Hamman, J., (2017). xarray: N-D labeled Arrays and Datasets in Python. Journal of Open Research Software. 5(1), p.10. DOI: http://doi.org/10.5334/jors.148

open3d: http://www.open3d.org/

• Qian-Yi Zhou and Jaesik Park and Vladlen Koltun, (2018). Open3D: A Modern Library for 3D Data Processing. arXiv e-prints, arXiv:1801.09847.

laspy: https://github.com/laspy/laspy/tree/master

PVGeo: https://pvgeo.org/index.html

• Sullivan et al., (2019). PVGeo: an open-source Python package for geoscientific visualization in VTK and ParaView. Journal of Open Source Software, 4(38), 1451, https://doi.org/10.21105/joss.01451

scipy: https://www.scipy.org/

• Virtanen, P., Gommers, R., Oliphant, T.E. et al., (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Meth 17, 261–272. https://doi.org/10.1038/s41592-019-0686-2

tqdm: https://github.com/tqdm/tqdm

• Casper da Costa-Luis, (2019). tqdm: A Fast, Extensible Progress Bar for Python and CLI. Journal of Open Source Software, 4(40), 1467, https://doi.org/10.21105/joss.01467

matplotlib: https://matplotlib.org/

• Hunter, J.D., (2007). Matplotlib: A 2D graphics environment. Computing In Science & Engineering, 9(3), pp.90-95. https://doi.org/10.1109/MCSE.2007.55

SALib: https://salib.readthedocs.io/en/latest/

• Herman, J. and Usher, W., (2017). SALib: An open-source Python library for Sensitivity Analysis. Journal of Open Source Software, 2(9), p.97. DOI: http://doi.org/10.21105/joss.00097

scikit-learn: https://scikit-learn.org/stable/

• Pedregosa, F., Varoquaux, G., Gramfort, A. et al., (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, pp.2825-2830.

joblib: https://joblib.readthedocs.io/en/latest/

p_tqdm: https://github.com/swansonk14/p_tqdm

naturalneighbor: https://github.com/innolitics/natural-neighbor-interpolation

• Park, S.W., Linsen, L., Kreylos, O., Owens, J.D. and Hamann, B., (2006). Discrete Sibson interpolation. IEEE Transactions on Visualization and Computer Graphics, 12(2), pp.243-253. https://doi.org/10.1109/TVCG.2006.27

pandas: https://pandas.pydata.org/

• McKinney, W., (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference (Vol. 445, pp. 51-56).

miniball: https://github.com/marmakoide/miniball

Citation

In submission.