Fall colors

Fall colors
Trees in Simpson Plaza, UW Campus - Oct 11, 2013 (photo: Ramesh Sivanpillai)

Friday, November 22, 2013

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.

Phenology of a young Oak tree

--- Katy-Jane Angwin, Thoa K. Pham, Peter C. Solin

Fall in Wyoming is highly variable, often with extreme temperature fluctuations and snowstorms. One may wonder how any plant is able to survive in such variable daily conditions. Oak trees (Quercus Spp.) can, therefore, be presumed to be particularly resilient in order to survive in Wyoming, to withstand temperatures in a state where not so long ago most people struggled to survive the winters.

Figure 1. Oak tree located at
N 41º 18.821’ and W 105º 34.837’.
This picture was taken on October 5, 2013.
Our experiment tracked an oak tree undergoing phenological changes from fall to winter. From September 19 to October 23, 2013, every other day we randomly collected two leaves from the oak tree located in Prexy’s pasture at the University of Wyoming (Figure 1).

Using a spectrophotometer, we measured the leaf reflectance across 11 visible and near-infrared wavelengths of light and calculated the Normalized Difference Vegetation Index (NDVI) to assess change over time. NDVI is an index that measures the difference in the absorption and reflection of light across red and near-infrared wavelengths. Healthy leaf absorbs most of blue and red light and reflects green and the near-infrared (NIR) light. Unhealthy leaf reflects more visible light and less NIR. This difference provides important information about plant physiological conditions. In general, normal end-of-season senescence result in NDVI decrease.

Because plants respond to cumulative temperature changes, not just daily events, we incorporated the minimum temperatures for each day in our research and gathered daily temperature from the National Climatic Data Center. During this experiment, there were three winter snowstorms where temperature dipped to below -6º C (20º F).

Figure 2. NDVI values for oak leaves collected across a 5 week time period from Prexy’s Pasture in Laramie, Wyoming, in response to winter storm events. This figure also shows minimum temperature data for Laramie, Wyoming, for the same 5 week time period. Regression equations and trendlines are presented.
We hypothesized that the relative NDVI values of the oak tree (Figure 2) would decrease as the temperature decreased.  Historical data has shown that NDVI values peak in the summer and trough in the winter. Our results showed distinct decreases in NDVI values, influenced by an increase in the red reflectance and a decrease in NIR values, coinciding with decreasing temperatures. Overall, our data shows the red reflectance values increase while the NIR regions of the spectrum decrease, which indicates the oak tree is entering the end-of-season state and is consistent with the decrease in NDVI. Trend lines also showed that the NDVI values of oak leaves would continue to decrease as colder winter weather moved into the area, which confirms our hypothesis.

Fall Changes in a Wyoming Maple Tree

-- Elijiah J. Attebury, John R. Copeland, Mary L. Harris, Elissa M. Paranto

What is the timing of senescence in 2013 for a maple tree on the University of Wyoming campus?  Can the change in spectral reflectance signature from one maple tree be characterized by a decline in the NDVI over time as leaves from the tree senescence?  Our study attempted to answer these questions.

Figure 1: Progression of senescence of a maple tree located near the Wyoming Union on the
University of Wyoming campus, from (a) September 24 (Julian Date 267),  (b) October 3 (JD 278),
(c) October 8 (JD 281), & (d) October 15( JD 288), 2013.
Senescence is the process of biological aging of deciduous leaves through time at the cellular level resulting in leaf deterioration (Figure 1). Whereas, NDVI is a vegetation index calculated using intensity of reflectance of visible red light and a band of infrared reflectance which is usually high in vigorous vegetation: NDVI= ((near infrared reflectance- red reflectance) / (near infrared reflectance + red reflectance)) x 100.

We sampled several leaves on each sampling date and tore them into pieces. We then mixed the pieces together and measured their reflectance with an Alta 2 hand-held spectrometer at 11 wavelengths ranging from 470 to 940 nm. Through sampling the reflectance of this one tree over a period of 43 days, we found that the NDVI rose at first to a maximum, and then proceeded to decline, until the leaves were brown and falling from the tree (Figure 2).

Figure 2: Graph of NDVI vs Time for a maple tree on the University of Wyoming campus 2013.
The trend line shows that NDVI declined with time as the fall season progressed.
The decline of NDVI is consistent with the progression of the senescence of a deciduous tree.  More detailed research, in another year, with daily measurements of reflectance and ambient weather conditions, might help define both natural, uninterrupted senescence and also the interrupting effects of early fall frosts on the phenological procession of this maple tree.

Thursday, November 21, 2013

Impact of pedestrian traffic on UW Prexy's Pasture

-- James Burford, Shay Horton, Thad Reger, Julia Vold

Do you ever wonder how much damage you are doing by walking on the grass instead of a path? Our experiment showcased how much effect humans actually had on the grass in Prexy’s pasture (Figure below).  We decided that testing the phenology of both severely disturbed and minimally disturbed grass areas would be an ideal experiment.

In testing our samples we collected and measured spectral reflectance of both the severely disturbed and minimally disturbed grass samples. We were able to take 8 sets of data from September 23rd, 2013 to October 17th, 2013 from this data we were able to infer that minimally disturbed grass does not lose its vigor as fast as severely disturbed grass does. Our hypothesis was confirmed in that minimally disturbed grass displayed higher spectral reflectance values than severely disturbed grass.

We made a graph based on spectral reflectance for each grass type as well as an NDVI graph for each (Figure 2). NDVI stands for Normalized Difference Vegetation Index and is calculated using the following formula: (NIR - RED)/(NIR + RED). NIR and RED are the near infrared and red wavelength values as read by the spectrometer.

The minimally disturbed grass showed very little change in NDVI over time (Figure 2A), whereas the severely disturbed grass displayed decreasing values (Figure 2B) that coincided with the temperature changes seen throughout the experiment. We fitted trend lines through the NDVI values and the difference between severely disturbed and minimally disturbed grasses based on their NDVI values. The linear graphs of NDVI values are shown below. Based on these graphs we can see that the rate of NDVI decline was higher in severely disturbed grass in comparison to the minimally disturbed grass.

Figure 2: NDVI values of minimally disturbed (A) and severely disturbed (B) grasses in Prexy’s Pasture
A source of error that could have caused outliers in our data sets could be from dirt that clung to the roots of the samples that we took.  This project went smoothly, and was very informative to all of our group members. We saw results close to what we expected to when we began the project in late October.

Monitoring the Phenology of Quaking Aspen in Cheney Plaza at the University of Wyoming

-- Shane Black, John Buffkin, Chayton Owens, Brandie Skorcz

The notion that Aspen leaves change colors every year seems simple, but it can become complicated when observing an individual tree and expecting to see it change within a time frame. Using a reflectance spectrometer we measured the amount of a leaf reflectance in an Aspen tree in Cheney Plaza, in the visible and infrared region of the spectrum.

We expected to see changes in leaf colors over a period of about a month, starting on September 19 and running through October 18, 2013. We used the measured red (645 nm) and infrared (735-810 nm) reflectance and calculated Normalized Difference Vegetation Index (NDVI).  NDVI is computed by taking the near infrared value minus the visible wavelength value, then dividing this value by the near infrared plus the visible light value.

NDVI shows the difference in leaf reflectance in these regions of the spectrum. Healthy leaves have high NDVI values while stressed and dying leaves have low NDVI values. We expected to see a gradual decline in NDVI values which would indicate an increase in the amount of reflectance observed in the visible spectrum and a decrease in the infrared spectrum. Based on a trend line we fitted to the NDVI values we observed a drop from about 0.62 to about 0.5 which indicates a small change in reflectance (Figure 1).

Figure 1. NDVI values for a young aspen tree in
Chaney Plaza at the University of Wyoming in Laramie, Wyoming 2013.

We did not see major changes in this trees’ leaf color and the NDVI values we obtained shows a small decline. If we had continued this experiment for more than a few weeks or a month, then we would have noticed further decline in NDVI values. This study is a good example of how you can use remote sensing to study different aspects of phenology. 

Wednesday, November 20, 2013

Phenology of a young Aspen tree in Simpson Plaza

--- Jack M Brown, James R. Schaffarzick, Stuart D. Thrash,  Zachariah M. Tuthill

Quaking aspen (Populous tremuloides) are known for their leaf color changing events that take place in the mid to late fall from a deep green to yellow/orange, then falling off of the tree for the winter, which involves changing phenological processes.

These phenological events are quantified by measuring the amount of light reflected by the leaves in visible and infrared regions, and using that data to calculate Normalized Difference Vegetation Index (NDVI).  NDVI is a measure of the vigor of a plant. This study monitored the fall phenology of a young aspen tree in Simpson Plaza on the campus of the University of Wyoming (Figure 1).

Figure 1. Young aspen tree our group
monitored in Simpson Plaza on the
campus of the University of Wyoming
Observations were taken a total of 27 times from September 19 to October 26, 2013. Each observation included:  a) Collecting 3-4 leaves from the bottom branch of the tree, b) Cutting leaves up (excluding stems), c) Homogenizing leaves (mixing them up), and d) Measuring percent reflectance values with a handheld spectrometer.

Reflectance values as well as NDVI values changed as the fall wore on, but 3 early season winter storm events drastically changed the leaf color, which affected the phenological events. NDVI values initially rose (leaves were still green), then declined when temperatures dropped below freezing at night (leaves were changing color/dying), or after winter storms were present (Figure 2).

Figure 2. NDVI Values of young aspen tree measured from September 19 to October 26, 2013.
Trend line fitted through these points accounted for 82% of the variation in the dataset.

After monitoring an aspen tree on campus for over a month, we concluded that early season winter storm events can have a dramatic effect on leaf phenological events that normally take place during the pre-winter season. If a winter storm rolls in early, it can change the timing of these events, as well as damage and kill aspen leaves.

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. 

Fertilizer can extend the growing season of tomatoes grown in high tunnel

-- Jenna Meeks

Do you ever feel there just aren’t enough hours in the day? How about enough days in the growing season for your home-grown tomatoes? The use of fertilizer may extend the growing season for tomato plants grown in a high-tunnel, a greenhouse like structure heated by solar radiation (Figure 1).

High-tunnel (Photo: Jenna Meeks)
One way to measure the benefits of fertilizers on plant growth is by analyzing leaf reflectance data. Reflectance is determined using a specific instrument which can see more than the visible rays detected by human eyes. It also encompasses the invisible region and this is where cellular differences in plant leaves can be seen as variations in reflectance.

The normalized differential vegetation index (NDVI), calculated using two wavelengths, red in the visible region and infrared in the invisible region, is used as a proxy for plant vigor. Therefore, NDVI is a tool to determine if fertilized tomato plants are healthier than unfertilized plants as the normal growing season comes to an end.

I studied the effect of four fertilizer treatments: a) Control (no fertilizer); b) 200 pounds of nitrogen as NPK fertilizer; c) a substitution of 75% of the N requirement with compost and; and d) a substitution of 25% of the N requirement with compost. Reflectance values were recorded from each plant four times with an Alta II Reflectance Spectrometer and the data were analyzed in Microsoft Excel.

The control plot displayed a lower NDVI than the fertilizer treatments, suggesting fertilized plants maintain their health for a longer period than unfertilized plants (Figure 2). The treatments with compost showed higher vigor than the control plot for half of the sampling dates, yet the 200lb N treatment was consistently higher than the control. These analyses are helpful to growers as they develop their fertilizer plan and crop rotations in their high-tunnels.

Tuesday, November 19, 2013

Aspen leaf reflectance through 2013 fall Season

--- Jake M. Hogan, Amanda R. O'Donnell, Matthew B. Manore, Patrick T. Snead

Figure 1: The aspen tree our group studied is the
foremost tree.  This photograph was taken on
October 11, 2013. The leaves on this tree
remained relatively unchanged in color from
the leaves in this photograph.
As the fall season progress, leaf color of aspen (Populus tremuloides) and other deciduous trees begin to change. This is due to the changes in the pigments in the leaves.

The objective of this project was to track the change of aspen leaf color growing in Prexy’s Pasture at the University of Wyoming (Figure 1) from September 19 (Julian date: 262) to Oct. 24 (Julian date: 297), 2013.

When the study began the leaves were green because they reflecting high amounts of green light in the visible part of the spectrum, meaning that their chlorophyll was still active. Using a digital spectrometer, we measured leaf spectral reflectance in the visible (470-700 nm) and infrared (735-940 nm) regions of the spectrum.

The percent reflectance values in red (645 nm) and infrared (735-810 nm) measured each day was used to calculate the normalized difference vegetation index values of the leaves, which indicate changes in leaf’s red and infrared reflectance (Figure 2).

The leaf reflectance remained relatively the same for much of the study; however, as the study drew to a close the reflectance of green light began to decline slightly and the non-visible wavelengths were reflected more which demonstrated that changes were occurring within the tree (Figure 2).

Figure 2:  NDVI for aspen trees leafs in front of the Agriculture building at the
University of Wyoming in Laramie, Wyoming; data were collected from
September 19, 2013 through October 24, 2013.  NDVI values were
calculated using the near infrared and red reflectance values in the
following formula (NIR – Red) / (NIR + Red).

The colder day and night temperatures were beginning to affect the leaves resulting in higher infrared wavelength reflectance, especially on Oct. 16, 2013 (day 289), even though the leaf color remained relatively unchanged. The study showed the hardiness of the tree that this group studied and the water and nutrient availability that reduced stress on the tree. The tree remained relatively unchanged for most of the study period, through three winter storms and freezing night temperatures.