LittervsLens: Evaluating LAI from Litter Traps and Hemispherical Photos Across View Zenith Angles and Leaf Fall Phases

Overview

Leaf Area Index (LAI) is a crucial parameter for modeling ecosystem productivity, climate interactions, and hydrological processes. This project compares Digital Hemispherical Photography (DHP)-derived LAI with Litter Trap (LT)-derived LAI in temperate deciduous forests to evaluate accuracy and spatial footprint alignment. We assess the best-matching View Zenith Angle (VZA) and apply the LXG clumping index to improve estimation accuracy. Our study finds that DHP is effective in estimating LAI for broadleaf forests but faces limitations during leaf fall, leading to overestimation.

📂 Repository Structure

LittervsLens/
│── data/              # Raw and processed data
│   ├── all/           # Digital Hemispherical Photography images, structured by LT
│   ├── hemi_photo_cropped/  #cropped DHP images for figures
│   ├── LT_data/      
│       ├── LT_raw/        # Leaf images from litter traps, structured by date
│       ├── LT_masked/     # Litter trap images with masked leaves
│
│── results/           # Processed data outputs
│   ├── DHP_processed_Hinge.csv  # CSV with DHP data using the Hinge method
│   ├── LAI_results_gapfrac.csv  #gapfrac data
│   ├── LAI_results_wood_removal_full.csv   #wood removal LAI data with joined LT data
│   ├── LAI_results_wood_removal.csv        #wood removal data LAI
│   ├── LAI_results.csv                     #Raw DHP results
│   ├── LT_results.csv                      #Raw LT results
│   
│── scripts/           # Code for data analysis and visualization
│   ├── data_processing  # processing scripts 
│   ├── figures  # scripts for figures creation
│
│── README.md          # Project documentation

Installation & Dependencies

Prerequisites

Ensure you have R (≥ 4.0) and Python installed along with necessary packages.

Install R Dependencies

install.packages(c("tidyverse", "ggplot2", "dplyr", "lme4", "hemispheR", "MuMIn", "patchwork", "RColorBrewer", "png", "jpeg", "ggforce", "tidyr", "patchwork", "segmented", "grid", "magick"))

Install Python Dependencies

pip install numpy cv2 os csv scipy

Usage

Run the scripts in the following order to reproduce the results:

# Run Python scripts
python scripts/leaf_area.py  # Compute leaf area from LT images

# Run R script
Rscript scripts/HemispheR_process.qmd  # Analyze DHP data using hemispheR
Rscript scripts/Data_manipulation_plotting.qmd # DHP data manipulation and plotting the results

Outputs will be saved in the results/ directory.

Results & Figures

Example visualization:

🤝 Citation & Contact

If you use this dataset or analysis, please cite:

Author(s), Year. Paper Title. Journal. DOI

For questions or collaborations: 📧 Email: simon.lotz@posteo.de