Latest Score:

3.6/5

in 2018

score trend is flat over time
Weight: 15%

The length of the growing season varies from year to year based on spring and fall temperatures. Increases in the length of growing season can benefit some species by providing extra time for growth and reproduction, but can cause harm to others by increasing heat, water, and metabolic stress1. Ideally, a healthy forest would have a growing season that remains relatively consistent. Here, we quantify the growing season as the number of days between spring leaf emergence and fall leaf senescence using MODIS satellite images2. A high score means that the growing season length is not shorter or longer than the long-term average.

1Rustad, L., Campbell, J., Dukes, J.S., Huntington, T., Lambert, K.F., Mohan, J. and Rodenhouse, N., 2012. Changing climate, changing forests: The impacts of climate change on forests of the northeastern United States and eastern Canada.

The length of the growing season can vary from year to year based on spring and fall temperatures. Here, we quantify the growing season as the number of days between spring leaf emergence and fall leaf senescence based on changes in vegetation indices derived from MODIS satellite imagery averaged across all forested pixels. Because abnormally short and abnormally long growing seasons can increase stress and risk forested ecosystems, the current year is scored as the magnitude of deviation from the long-term mean. Increases in the length of growing season can benefit some species by providing extra time for growth and reproduction, but can cause harm to others by increasing heat, water, and metabolic stress1 and increasing susceptibility to extreme weather events (e.g. late frost). Similarly, decreases in the growing season can limit seasonal growth and carbohydrate storage, with additional impacts on the phenology of insects and migratory species that rely on the forested ecosystem.

1Rustad, L., Campbell, J., Dukes, J.S., Huntington, T., Lambert, K.F., Mohan, J. and Rodenhouse, N., 2012. Changing climate, changing forests: The impacts of climate change on forests of the northeastern United States and eastern Canada.
Interpretation provided by:

Jennifer Pontius. Research Ecologist, US Forest Service; Principal Investigator, Forest Ecosystem Monitoring Cooperative (2020)

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The score is calculated using a target value and the historical range of the the entire long-term dataset. The higher the score, the closer this year's value is to the target.

Once the score is computed for each year, the trend in scores over time is calculated. If the trend is significantly positive or negative, the long-term trend is marked as increasing or decreasing respectively.

Component Description
Scored as

Distance between minimum and maximum (scaled 1-5)

Target value

Long-term mean

Directionality of scores

No change from the long-term mean is better.

Minimum value used in scoring

Data minimum - 10% of range

Maximum value used in scoring

Data maximum + 10% of range

Using MODIS remotely sensed phenology products, we selected the Growing Season Duration (DUR) dataset as an assessment of the total length of the functional growing season1. We clipped imagery to the state of Vermont boundary2, and masked to limit only those pixels identified as forested (Deciduous (41), Evergreen (42), and Mixed (43), and Woody Wetlands (90) cover pixels), according to 2011 National Land Cover Dataset3. In each MODIS image, we excluded values of-1000 (unknown) and 1000 (water), then calculated the mean pixel value per image. We also computed the mean for each pixel over all image years and then computed the mean across pixels to establish the long-term mean of the dataset. We set the data target as the long-term mean. The current year is scored for where it falls between the target and the upper scoring bounds (maximum value in the dataset plus 10% of the range) or the lower scoring bounds (minimum value in the dataset minus 10% of the range), scaled to be between 1 and 5.

1MODIS Phenology Metrics. 2018. https://modis.gsfc.nasa.gov/. Imagery available at: https://earthexplorer.usgs.gov/
2 US Census. 2016. US Boundaries. Available at: https://www.census.gov/geo/maps-data/data/cbf/cbf_state.html
3 US Geological Society. 2018. National Land Cover Dataset. Available at: https://www.mrlc.gov/finddata.php

STRESSORS INDICATORS