Changes in sensecense rates of cottonwood trees under different light conditions

--- Amanda Pennino, Wyatt Good and Tyler Therkildson

In this study, spectral reflectance of leaves from two cottonwood trees (Populus deltoides) grown in full sun and shade conditions were used to measure the rate of senescence, or leaf drop, between trees. Light reflectance was measured using an ALTA II Spectrometer on 3 three leaves for each tree twice a week between September 15 and October 18. Light reflectance was then converted to average reflectance values and then used in a normalized vegetation index (NDVI) to assess for leaf greenness.

Changes in the NDVI values of cottonwood tree (orange - exposed to sun, blue - shaded) leaves

It was found that the tree growing in full sun portrayed lower NDVI values overall compared to the shade tree, insinuating that it’s chlorophyll content breakdown and senescence rate was quicker than the tree in shade. Additional studies should take place over a longer period and to an improved number of trees to further validate these results and to explore similar trends in other deciduous trees.

Monitoring phenology of White oak and Eastern cottonwood trees using remote sensing

-- Tealand Stende; Jesse Shenefelt, Amanda Lee, Paul Ratigan, Abdelaziz Nilahyane

As the fall season progresses and the temperature decreases, leaf color of deciduous trees begins to change as a result of changes in the pigments of the leaves. Using the ALTA II reflectance spectrometer, the reflectance values were measured and the Normalized Difference Vegetation Index (NDVI) was calculated for the Eastern Cottonwood (Populus deltoides) and the White oak (Quercus alba). Tree leaves wee collected at the University of Wyoming Campus in order to monitor and understand on a local scale how these trees respond to climate changes during the fall season, and then draw a general picture comparing both trees in terms of phenological events.

The results revealed that the decline in NDVI is consistent with the progression of the senescence of both trees, but different trends regarding change in leaf color and the leaf-out timing were apparent. The NDVI values for the oak tree were associated with the quadratic model with an R**2 = 74%, while the NDVI values for the cottonwood tree lowed a linear trend (R**2 = 73%).

Invisible Changes

-- Ryan W, Huxtable, McKenna J. Pieper, Ian P. Walker

The objective of this project was to monitor the phenology of a young cottonwood tree (Populus deltoides) as the autumn season progressed into winter. We collected leaves from the tree from September 19, 2013 to October 24, 2013 and measured the change in leaf reflectance in 11 spectral regions using a spectrometer.

Cottonwood tree at Sullivan Plaza at University of Wyo.

We used the Normalized Difference Vegetation Index (NDVI), which quantifies how healthy the vegetation is based on the amount of light reflected in the red (visible to human eyes) and infrared (invisible to human eyes), to analyze trends. This tree is growing in Sullivan plaza, located on the University of Wyoming campus, just outside the Education Building (Figure 1).

Since this tree and the surrounding trees are regularly irrigated (water stress is common in arid regions such as Laramie), we concluded that the conditions are favorable for healthy tree growth. We started our observations on September 19, 2013 while all the leaves on the tree were still green.

Collection of data from an individual leaf happened in two phases; as a single sampling unit, and after cutting the leaf into pieces. We wanted to quantify whether sample size and leaf orientation (facing up or down) influences the reflectance reading.

We hypothesized as leaves change their color from green to yellow, their reflectance values and NDVI plots would show corresponding changes as well. During the course of the experiment, there were 3 winter storm events but the leaf color did not change from green to yellow as expected.

Although we were not able to visibly see change in leaf color, the NDVI plot shows that the amount of light reflected by the leaf in the red and near infrared regions was changing (Figure 2).

Figure 2: NDVI values of solid (squares) and pieces (diamond) of leaves from a cottonwood tree. Trend
line fitted for solid leaf NDVI values accounted for 85% of the variation, while the trend line fitted
for leaf pieces NDVI accounted for 65% of the variation.

The figure also shows the difference in leaf reflectance from the two different phases. Both plots show a decreasing trend in NDVI, but the solid leaf measurements shows a more stronger trend (R² = 84%). This means we can use this chart to estimate the NDVI values at a given time of year. We even feel the plot shows when the snowstorm hit, October 4, 2013, and how it affected the rest of our experiment. As the plot shows, the solid leaf had a linear decline in NDVI before the above date, afterwards the points become more randomized.

Fall reflectance values of a Cottonwood tree

--- Ryan C. Lermon, Jaramie R. McLean, Travis N.J. Moody, Marie K. Stiles

What if you could find the best place to view the fall leaf colors without having to drive to several locations to find that best one? Remote sensing is creating the ability to do just that with the use of Normalized Difference Vegetation Index (NDVI). NDVI is calculated based on the amount of light reflected by the leaves in the near Infrared (NIR) and red regions. The experiment performed used a cottonwood tree that is located between the Education Building and the Half Acre Gym on the University of Wyoming campus (Figure 1).

Figure 1: Cottonwood tree (close to the light pole)
monitored by our team. 

Leaf collection and reflectance measurement began prior to the onset of a fall temperature event (freezing) to trigger stress and change in leaf reflectance on September 19, 2013. Leaf reflectance values were collected every other day from the onset until the leaves were brown and falling off the tree.

The hypothesis is that reflectance values of the red and NIR will change and the NDVI (NIR - Red) / (NIR + Red) calculations will enable the ability to identify areas of stress or the optimum color viewing time of leaves in the fall.

We now know that most of the stress inducing factors are the lack of water and the slow decrease of the fall season temperatures. Leaves begin to change from a bright and dark green to a yellowish green, to a yellowish brown and finally the brown color we all know as the leaves fall on the ground. The data collected and analyzed supports this hypothesis (Figure 2).

Figure 2 : Plot of NDVI values we collected over the month of the project.
The trend line decreases slowly over time and starts to show the difference
in reflectance for the cottonwood tree located north of Half Acre Gym,
and south of the Education Building.

We learned how a cottonwood leaves start to change reflectance and the values we measured across ten wavelengths can be used to determine stress or even color viewing times. We now have a better understanding of the effects that these stressors create on plants.

The graph (Figure 2) showing the NDVI values and the trend line associated with the values (R² = 56%) helps to clarify that the trend line for the cottonwood tree starts high of reflectance and then slowly starts to trend down. This is common among trees and plants coming into the fall months. It shows how the leaves turn from the color green to yellow and then brown as they started to fall off.