Dataset Card for Phenology-Normal-Hawaii

Dataset Details

This dataset provides time series of vegetation color indices collected from the PUUM site, designed to support fine-grained phenological analysis. It includes three types of GCC (Green Chromatic Coordinate) and RCC (Red Chromatic Coordinate) curves:

• Raw curves: Original time series extracted from masked individual crowns without additional processing.
• Brightest-pixel curves: Curves filtered using K-means clustering to retain only the brightest pixels, helping to reduce noise from shadows and background variation (specifically for Koa trees).
• Smoothed curves: Curves further processed with a discrete wavelet transform to denoise and smooth short-term fluctuations, preserving major seasonal trends.

All curve data are stored in CSV files, with each file containing a time series for a specific individual and curve type. The CSV format ensures easy loading and integration into common data analysis workflows.

Each curve tracks vegetation color changes at the level of individual tree crowns across multiple seasons. The dataset supports research into individual phenology patterns, intra-site variability, and ecological modeling that requires high-temporal-resolution vegetation signals.

The three curve types allow flexible usage depending on the desired balance between fidelity to raw data and robustness to noise.

Supported Tasks

The dataset supports time-series forecasting.

Dataset Structure

/dataset/
    timeseries/
        EB_0001/
            RAW/
                NEON.D20.PUUM.DP1.00033_DB_0001_roistats.csv
                NEON.D20.PUUM.DP1.00033_DB_0001_1day.csv
                NEON.D20.PUUM.DP1.00033_DB_0001_3day.csv
            KMEANS/
                NEON.D20.PUUM.DP1.00033_EB_0001_roistats.csv
                NEON.D20.PUUM.DP1.00033_EB_0001_1day.csv
                NEON.D20.PUUM.DP1.00033_EB_0001_3day.csv
            DWT/
                NEON.D20.PUUM_EB_0001_trend_summary.csv
        ...
        EB_0004/
            RAW/
                NEON.D20.PUUM.DP1.00033_DB_0001_roistats.csv
                NEON.D20.PUUM.DP1.00033_DB_0001_1day.csv
                NEON.D20.PUUM.DP1.00033_DB_0001_3day.csv
            DWT/
                NEON.D20.PUUM_EB_0001_trend_summary.csv
        ...

Data Instances

All the files are time-series of GCC or RCC, under the folder of corresponding individual masks. The RAW folder contains time-series acquired by simply applying the basic mask. The KMEANS folder contains time-series calculated with pixels of high brightness. And the DWT folder contains time-series after discrete wavelet transform.

Data Fields

roistats.csv

Column Name	Description
date	Date when the image was taken (YYYY-MM-DD)
local_std_time	Local standard time when the image was captured
doy	Day of year (1–365/366) corresponding to the image date
filename	Name of the image file
solar_elev	Solar elevation angle at the time of image capture (degrees)
exposure	Exposure setting used for the image
awbflag	Automatic white balance flag indicating image color adjustment status
mask_index	Index identifying the masked region of interest within the image
gcc	Green chromatic coordinate (proportion of green in total brightness)
rcc	Red chromatic coordinate (proportion of red in total brightness)
r_mean	Mean red channel value of selected pixels
r_std	Standard deviation of red channel values
r_5_qtl	5th percentile of red channel values
r_10_qtl	10th percentile of red channel values
r_25_qtl	25th percentile (first quartile) of red channel values
r_50_qtl	50th percentile (median) of red channel values
r_75_qtl	75th percentile (third quartile) of red channel values
r_90_qtl	90th percentile of red channel values
r_95_qtl	95th percentile of red channel values
g_mean	Mean green channel value of selected pixels
g_std	Standard deviation of green channel values
g_5_qtl	5th percentile of green channel values
g_10_qtl	10th percentile of green channel values
g_25_qtl	25th percentile (first quartile) of green channel values
g_50_qtl	50th percentile (median) of green channel values
g_75_qtl	75th percentile (third quartile) of green channel values
g_90_qtl	90th percentile of green channel values
g_95_qtl	95th percentile of green channel values
b_mean	Mean blue channel value of selected pixels
b_std	Standard deviation of blue channel values
b_5_qtl	5th percentile of blue channel values
b_10_qtl	10th percentile of blue channel values
b_25_qtl	25th percentile (first quartile) of blue channel values
b_50_qtl	50th percentile (median) of blue channel values
b_75_qtl	75th percentile (third quartile) of blue channel values
b_90_qtl	90th percentile of blue channel values
b_95_qtl	95th percentile of blue channel values
r_g_correl	Correlation coefficient between red and green channel values
g_b_correl	Correlation coefficient between green and blue channel values
b_r_correl	Correlation coefficient between blue and red channel values

1day/3day.csv

Column Name	Description
date	Date of observation (YYYY-MM-DD)
year	Year of observation
doy	Day of year (1–365/366) corresponding to the observation date
image_count	Number of images taken on that date
midday_filename	Filename of the midday image
midday_r	Mean red channel value of the midday image
midday_g	Mean green channel value of the midday image
midday_b	Mean blue channel value of the midday image
midday_gcc	Green chromatic coordinate (GCC) calculated from the midday image
midday_rcc	Red chromatic coordinate (RCC) calculated from the midday image
r_mean	Mean red channel value across all daily images
r_std	Standard deviation of red channel values across daily images
g_mean	Mean green channel value across all daily images
g_std	Standard deviation of green channel values across daily images
b_mean	Mean blue channel value across all daily images
b_std	Standard deviation of blue channel values across daily images
gcc_mean	Mean green chromatic coordinate (GCC) across daily images
gcc_std	Standard deviation of GCC values across daily images
gcc_50	50th percentile (median) of GCC values
gcc_75	75th percentile of GCC values
gcc_90	90th percentile of GCC values
rcc_mean	Mean red chromatic coordinate (RCC) across daily images
rcc_std	Standard deviation of RCC values across daily images
rcc_50	50th percentile (median) of RCC values
rcc_75	75th percentile of RCC values
rcc_90	90th percentile of RCC values
max_solar_elev	Maximum solar elevation angle recorded during the day
snow_flag	Flag indicating presence of snow (1 if snow detected, 0 otherwise)
outlierflag_gcc_mean	Outlier flag for the daily mean GCC value
outlierflag_gcc_50	Outlier flag for the 50th percentile GCC value
outlierflag_gcc_75	Outlier flag for the 75th percentile GCC value
outlierflag_gcc_90	Outlier flag for the 90th percentile GCC value

trend_summary.csv

Column Name	Description
img_date	Date of observation (YYYY-MM-DD)
A4D4	The 4th-level approximation and detail coefficients obtained from the discrete wavelet transform
A8D8	The 8th-level approximation and detail coefficients obtained from the discrete wavelet transform

Dataset Creation

Curation Rationale

The dataset contains individual GCC and RCC curves for the PUUM site. It was created to enable detailed analysis of vegetation phenology at the level of individual tree crowns, rather than relying solely on site-level averages. By extracting and tracking GCC (Green Chromatic Coordinate) and RCC (Red Chromatic Coordinate) values for specific regions over time, the dataset provides fine-grained insights into seasonal patterns, canopy development, and potential variability among individuals.

Source Data

PhenoCam images in PUUM site were downloaded. This data product is developed upon the proposed pipeline to generate phenological time-series based on the images.

To produce this dataset, people first provide manual labels of individual masks (see below for more details). We use the images from 10am to 2pm every day for best lighting conditions. The raw time-series are generated by directly applying the mask to the image. Then we conduct K-means clustering on the individual pixels of every image. As the flowers of Koa trees are of white color, we only keep the pixels with the largest brightness to perform GCC calculation. And finally, we apply discrete wavelet transform to get the smoothed time-series.

Annotations

We provide manual annotations of individual masks. Each mask identifies one tree individual. Based on the acquired masks, we can perform phenological analysis for different individuals.

Annotation process

The annotation is conducted through an interactive interface created with matplotlib. Users are able to draw the contour for the individual of interest. Then the mask will be automatically saved based on the contour. The interface script can be seen in script.

Who are the annotators?

Faye Xie

Personal and Sensitive Information

N/A

Considerations for Using the Data

Please cite this dataset if you use it.

Bias, Risks, and Limitations

The dataset is biased towards the individuals close to the PhenoCam. While the observed individuals within one species demonstrate similar phenological timing, the conclusion doesnt't apply to the other individuals or other sites in the island.

Recommendations

We don't recommend using the conclusion drawn from this dataset to infer the other sites in Hawaii.

Licensing Information

This product is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This means the material can be freely shared, copied, redistributed, adapted, and built upon for any purpose, including commercial use, as long as appropriate credit is given to the original source. Users must provide attribution, indicate if changes were made, and link to the license. No additional restrictions beyond those stated in the license terms may be applied.

Citation

BibTeX:

@misc{phenology_normal_hawaii
    author = {Jianyang Gu and Faye Xie and Colby Stakun-Pickering},
    title = {Hawaii PUUM Individual Phenology Dataset},
    year = {2025},
    url = {https://huggingface.co/datasets/imageomics/phenology_normal_hawaii},
    publisher = {Hugging Face}
}

Please also cite the PhenoCam images:

@misc{DP1.00033.001/provisional,
  url = {https://data.neonscience.org/data-products/DP1.00033.001},
  author = {{National Ecological Observatory Network (NEON)}},
  language = {en},
  title = {Phenology images (DP1.00033.001)},
  publisher = {National Ecological Observatory Network (NEON)},
  year = {2025}
}

Acknowledgements

This work was supported by the Imageomics Institute, which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Data used in this research were provided by the PhenoCam Network, which has been supported by the National Science Foundation, the Long-Term Agroecosystem Research (LTAR) network which is supported by the United States Department of Agriculture (USDA), the U.S. Department of Energy, the U.S. Geological Survey, the Northeastern States Research Cooperative, and the USA National Phenology Network. We thank the PhenoCam Network collaborators, including site PIs and technicians, for publicly sharing the data that were used in this paper.

Dataset Card Authors

Jianyang Gu

Dataset Card Contact

Jianyang Gu (gu.1220[at]osu[dot]edu)