Seasonal Vegetation Change Based on Elevation

Alaska2014-2016

Introduction

• Over the summer I had the opportunity to learn how geoscientists research and perform analysis of a targeted area.

• I chose to use the northeastern region of Canada and Alaska and how seasons dictate how the vegetation changes month to month.

• The work presented does not use an appropriate length of time to be able to use in a valid study. This presentation is only meant to show some of the GIS lessons I have built on since my studies at TSU.

• The following is a presentation I put together with the information I gathered from my research.

Background

• The seasons in Alaska:

• Due to the Earth’s rotation, regions that are above 60 degrees north have the highest duration of sunlight during the summer in the northern hemisphere and the lowest duration during the winter.

• Some regions of Alaska only receive about 14 inches of annual rain; however permafrost allows plants to grow lush during the growing season.

• Snow in Alaska helps regulate the climate temperature because of it’s high albedo.

Purpose

Due to the Earth’s rotation, regions that are above 60 degrees north have the highest duration of sunlight during the summer and the lowest daylight duration in the winter. Alaska is the most undeveloped state in the US and also has many factors that effect the amount of vegetation it contains, such as; snow, temperature, precipitation, duration of daylight and many other environmental factors. In recent years temperatures all over the world are abnormally fluctuating in temperature. Being isolated from many manmade environmental factors, Alaska is still effected by climate change. Most affected may be the glaciers, but the interior must also be studied to understand how the environment is adapting to the abnormal temperatures.

Data

• Summary below about sensor used to gather spatial data:• MODIS NDVI sensor

• 8 data composited data sets

• 250-meter spatial resolution

• Geographical bounds:70.6128481181, -180, 180, 50.0128481181

• Projection: Albers

• Datum: WGS84

Sources:

• http://nsidc.org/cryosphere/snow/climate.html• http://neo.sci.gsfc.nasa.gov/view.php?datasetId=MOD11C1_M_LSTDA&year=

2015• http://www.fs.fed.us/rm/ecoregions/products/map-ecoregions-united-states• http://earthexplorer.usgs.gov/• https://lta.cr.usgs.gov/emodis

Methodology

• Images are an average of NDVI values over a period of 8 days• Colors that plants reflect are created by different types of pigments that have

adapted to absorb certain wavelengths; the wavelengths they reflect translateto with the visible spectrum

• Colors reflected by plants can be converted to wavelength values, which canthen be quantitatively analyzed and interpreted using descriptive statistics

• NDVI is a ratio equated from using visible wavelengths and near infrared wavelengths.

• Each pixel in an image taken from a sensor is equated using the NDVI equation. The product for each pixel indicates the brightness value of a pixel for the new image(the NDVI image).

Methodology cont.

• Once the data was acquired it went through manual processing so that conclusive statistics could be gathered after analysis.• Downloaded data had a range of -2,000:10,000 so I multiplied each data set by a factor

of .0001(-.2:1)(or divide by 1000)

• Bare soil, snow or water can skew data so values from -.2 to .2 were set to null and background values set to No Data, since we only want to measure vegetation

• The final range of .2:1 and no values to skew results

• Next I used the raster calculator to discover differences between months and years; and also difference of means.

Difference in Vegetation Change2014-2015

• By using an analysis method called Image Differencing, you can detect what areas have positive or negative change from one year to the next during the same acquisition period.

• First, the imagery from each year must be averaged to form one image.

• Then using another tool in the program; take the difference of two images.• 2014-2015=(growth or decline)

• Areas that have a higher amount of change can signal geographical abnormalities in the area. These abnormalities could be further investigated to find the cause.• (i.e. forest fires, mining, logging, invasive species, unseasonably early warm

temperatures, urban growth or decline,…)

Legend

Growth

Decline

As apparent as it is that there more pixels indicating a decline in vegetation from 2014 to 2015, you cannot use two years of data to gather scientific results. A longer length of time is needed to gather reliable results.

This an elevation map I used with my analysis. It’s important to know where changes are happening relative to elevation.

Legend

High Elevation

Low Elevation

Feb., 9:18

Mar.

Apr.

May

June, 18:47

July

Aug.

Sep.

Oct.

0:00

2:24

4:48

7:12

9:36

12:00

14:24

16:48

19:12

21:36

Feb. Mar. Apr. May June July Aug. Sep. Oct.

Daylight Amounts 2014

Feb., 9:16

Mar.

Apr.

May

June

July

Aug.

Sep.

Oct.

0:00

2:24

4:48

7:12

9:36

12:00

14:24

16:48

19:12

21:36

Feb. Mar. Apr. May June July Aug. Sep. Oct.

Daylight Amounts 2015

Monthly Daylight Duration

• As you can see daylight duration stays the same year to year, the Earth’s rotation and tilt doesn’t change much.

• Although it’s important to know the amounts of daylight relative to the vegetation growth each month.

Digital Elevation-Alaska

July 2014

Feb., 9:18Mar.

Apr.May

June, 18:47

July, 17:44Aug.

Sep.

Oct.

0:00

4:48

9:36

14:24

19:12

0:00

Feb. Mar. Apr. May June July Aug. Sep. Oct.

Daylight Amounts 2014

July of has the highest amount of photosynthetic activity.

July 2015

Digital Elevation-Alaska

Feb.Mar.

Apr.May

JuneJuly

Aug.Sep.

Oct.

0:00

4:48

9:36

14:24

19:12

0:00

Feb. Mar. Apr. May June July Aug. Sep. Oct.

Daylight Amounts 2015

July, once again, has the highest amount of photosynthetic activity.

Monthly Image Differences

• Using the Image Differencing analysis method can also be helpful for finding the difference in green reflectance for each month.

• This is important because a researcher may need to narrow down what part of the year vegetation showed change.• The following slide show uses image differencing for each month to narrow

down results.

• Increasing temperatures can cause the growing season to shift; which in turn, effects the ecosystem. Most organisms rely on vegetation, the changes that come with shifting seasons come with consequences that need to be studied.

Digital Elevation-Alaska

AugustComparing each month year to year, August has the highest amount of variety of change.

A vast cluster of red pixels to the east indicates that this wide area saw less greenness during August 2014.

While green “hotspots” saw more growth.

Legend

Growth

Decline

Snow Factor

• One factor of vegetation growth and temperature is the amount of snow an area sees in a year. Especially in a region such as Alaska.

• Data was gathered for the previous years of 2014 and 2015; November 2013 and 2014.• November was used because this is when the snow fall is at it’s peak for the

following growth seasons of 2014 and 2015.

A majority of this area saw minimal change.

More snow in 2015

Same amount

Less snow in 2015

1

-1

Legend

Snow Difference

Temperature DifferenceFebruary

Greenness DifferenceFebruary

Red circles show where greenness and temperature coincide with each other.

Images here are from February, when plants begin to show greenness.

Yellow circles show where snow and temperature coincide with each other.

The pixels in the temperature image are lighter where temperatures were in 2015.

Using multiple datasets can show how different natural factors react to each other.

Conclusion: UTSA LSRG Lab Internship -Summer 2016

• Temperature, sunlight, elevation and precipitation all play roles in the phenological cycle of plants. We can study and measure all these factors to gain a better understanding of how and the earth is changing.

• Changes are apparent in my research but more data over a longer period of is needed. As from this study I’ve noticed that greenness peaks in July and areas of low elevation are the first to see photosynthesis activity in the growing season.

How greesnness change may effect ecosysyem• With temperatures rising causing earlier growth seasons, different

types of vegetation could see a growth or decline.

• This may cause plants high in nutritional value to be overgrown by more invasive species leaving herbivores and omnivores with less food sources.

• Or higher temperatures may cause plants with hardier root systems meant for extremely low temperatures to weaken leaving less support to hold soil.

• Permafrost could also see a decline leaving less soil structure for plants to grow.