ses 201 introductory remote sensing - learnlinelearnline.cdu.edu.au/units/ses201/downloads/env_2014...

ENV101, ENV202/502, ENV208/508: Field Trip

LITCHFIELD NATIONAL PARK FIELD TRIP

11th APRIL 2014

ENV 101 EARTH SYSTEMSENV 202/502 INTRODUCTORY REMOTE SENSING

ENV 208/508 APPLIED GIS

1. OVERVIEW

The aim of this field trip is to provide some field based skills that are important for anyone working with spatial data or the application of spatial data in environmental sciences. Being able to locate yourself in the real world and on remotely sensed imagery or maps is important to all spatial science students and professionals. In fact, being able to locate yourself in the real world and on maps is an important skill that everyone should have, particularly anyone who wants to spend time in the bush or fishing. Today we’ll be using a number of different instruments to collect data for a standard vegetation survey. Being comfortable with these skills and instruments is valuable for anyone in the field of environmental sciences.

Remote sensing and GIS and are not always about sitting in front of the computer. Field survey is an integral part of any mapping and monitoring program. It should also be enjoyable so please be safety conscious and consider your fellow students so everyone can have a good time.

1.1. Learning Outcomes

After participating in this field trip, students will be able to:1. Locate themselves on a satellite image and mapsheet by identifying features

in the environment that correspond to given images and maps;2. Understand the effects of spatial resolution on the amount of information and

detail that can be seen in an image, and in turn how this affects the degree of certainty to which you can locate yourself in the field;

3. Use a GPS to read co-ordinates and collect waypoints;4. Use a clinometer to measure tree heights; and5. Use a sighting tube or densitometer to estimate vegetation cover.

1.2. Preparation

Students are required to bring the following:

1. Clip-board with paper or exercise book2. Pens/pencils

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3. Ruler4. Calculator5. Food and drinks for snacks (NB: there will not be any opportunity to purchase

food or drink during the day, but lunch will be provided)6. Sunscreen7. Aerogard or similar8. Hat9. Change of clothes / shoes if the weather is wet

Students are advised to wear comfortable closed shoes (not thongs or sandals) and appropriate clothing including long pants.

Students will be supplied:1. GPS receiver2. Walkie talkie3. Whistle4. Compass 5. Clinometer6. Sighting tube7. Densiometer8. 1:100 000 Litchfield National Park Topographic Map9. Measuring tape (50 m)10. 2 x additional measuring tape (>2 m)11. Landsat 5 Thematic Mapper image12. GeoEye image13. MODIS image

1.3. Itinerary8.10 am Meet at Casuarina Campus - main entry bus/parking area

8.15 am Depart Casuarina Campus

8.45 am Pick up at Noonamah service station

10.30 am Arrive at education centre / orientation

10.45 am Map reading, spatial resolution, GPS

12.30 pm – 1.15pm Lunch

1.15 pm – 2.45pm Savannah site

4.30 pm Return CDU, Casuarina Campus

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2. 1ST STOP – EDUCATION CENTRE

2.1. Location and spatial resolution

Locate yourself on the imagery and maps that you have been given.

Turn on the GPS

Record a waypoint for your location

1. Complete the following table based on your observations from where you are standing, and comparing them to the topographic map sheet, Landsat, and MODIS sample image data sets

GPS / field observation

Topo Map Sheet

GeoEye Landsat 5 TM

MODIS

Record the coordinates of your positionHow accurately can you locate yourself (e.g. within 1m, 10m, 100m)?

Based on where you are standing, what landcover type would you assign to this location?

What is the smallest feature that you can identify?

Where directed, mark out a ground representation of a Landsat 5 TM 30x30m pixel.

2. What are the different landcover types / features within the ‘pixel’? List in order of dominance, and estimate the percentage coverage of each feature.

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3. Imagine how the pixel would look in an image. In a landcover map based on a Landsat 5 TM image, what landcover category would you assign to this ‘pixel’? What problems does the heterogeneity present when creating a map from remotely sensed data?

4. How do the spatial dimensions (spatial resolution and scene extent) affect your ability to understand patterns and processes in the environment?

5. (a) What projection, datum, and coordinate system/s have been used for your topographic map sheet? Comment on any differences between the reference systems in your hard copy datasets. What are the implications of any differences?

(b) What is the date of the production of the topographic mapsheet? Comment on the currency of this map and any implications of using it both in the field and with reference to the satellite data.

3. 2ND STOP – SAVANNAH SITE FIELD DATA COLLECTION

3.1. Vegetation SurveyCanopy cover is a way to measure vegetation biomass relatively quickly along a transect in the field. It is also often derived from satellite data, and can be monitored for changes over time. However, it does not take into account density of the understory vegetation. Here we will use a transect to record canopy cover and vegetation type so that variations in the area of interest can later be uploaded via the

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GPS into a GIS and mapped accordingly. This can later be compared with the available remotely sensed data

Diameter at breast height (DBH) is a standard measure used in expressing the diameter of a tree trunk. It is measured at approximately 1.3 m above the ground. The diameter can be measured using a set of callipers, or a tape that measures circumference but can be calibrated to diameter using the equation:

Diameter=Circumferenceπ

Foliage projective cover (FPC) gives an indication of the ground cover percentage that is covered by vegetation. It is a point sampling method that will be conducted here using a sighting tube with a crosshair where the user simply determines what has been ‘hit’ by the crosshair in the vertical projection (foliage, branch, or sky).

A schematic plan of the transect sampling is shown below.

100m

-2m 0m +2m

x

x

y

Approx areacovered until20 trees arerecordedtodetermine dbh, crownradii,and tree height

Distance from transectcentrelineDistance from transectstart

FPC transect liney

1. Record the GPS coordinate of the site at 0m2. Measure out the 100 m tape along the bearing given to you3. At 1 m intervals along the 100 m transect, observe the feature ‘hit’ by the

crosshair in the sighting tube (foliage, branch, or sky). 4. Place a tick in the appropriate box on the accompanying datasheet based on

what you see through the sighting tube at the cross hair. 5. Observe the feature directly below your measurement

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6. Place a tick in the appropriate box on the accompanying datasheet based on what is on the ground

7. Count the totals at the end of your transect. You should have a total number of 100 hits by the end of the transect (or 101 if you started at 0 m).

8. Starting at the beginning of the transect, determine the height, crown radii, and DBH of the first 20 living trees within 2 m of your transect that have a DBH > 5 cm (if you can put your fingers around the trunk)

9. For each tree that is measured, note its distance along the transect (i.e. 0 – 100 m), and it’s distance from the centreline (i.e. to the left of the transect will be between -2 and 0 m, to the right of the transect will be between 0 and 2 m)

4. RETURN TO CDUBefore you leave make sure that you have recorded on paper all the GPS readings you have taken and that you have made a note of the GPS number that you used.

Please make sure that you have returned all equipment.

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5. APPENDIX – MULTISPECTRAL IMAGE CAPTURE

Incident sunlight is known as Electromagnetic Radiation (EMR), and includes not only the light that we can see with our eyes (visible light), but also ultra violet (UV) light, infrared, gamma rays and radio waves to name a few radiation types (Figure 1).

Figure 1: The electromagnetic spectrum. Note the difference in wavelength size (gamma rays have a short wavelength, while radio waves have a long wavelength)

While we are only able to see a small amount of this overall radiation (visible), remote sensing instruments are able to ‘see’ or measure considerably more, particularly in the infrared region, allowing us to use them to provide information on features and processes on the earth’s surface. In essence, earth observation satellites record the amount of light reflected from or emitted by the earth’s surface.

Figure 2: Image acquisition process. Satellites record reflected light (EMR) in different wavelengths. These are stored as numbers within a grid of pixels. These data are transmitted to a receiving station, and then downloaded to computers, where an image can be put together.

Satellites have individual sensors that have been engineered to record specific wavelengths of EMR that are related to known features of interest. For example, it is known that vegetation absorbs red light for photosynthesis. Healthy vegetation also

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reflects a lot of Near Infra Red (NIR) because of the interaction of this type of light with the internal structure of leaves. Figure 3 shows a graph of the amount of light (%) reflected by three different features. The small amount of light that we can actually see is shown by the rainbow colour bar at the bottom of the graph – some satellite sensors can clearly tell us a lot more about these features!

Wavelength (um)

Ref

lect

ance

NIR MIR

Dry VegetationHealthy Vegetation

Bare GroundHealthy Vegetation 2

Figure 3: Spectral Profiles of healthy vegetation, dry vegetation, and bare ground

Remotely sensed images are essentially loads and loads of numbers. Just as you can take a photo with your digital camera, a sensor is doing pretty much the same thing. If you take a photo of a dying tree, you might notice that it is a brown colour, while a healthy tree is green. The concept is no different when using earth observation satellites with the exception that they can record wavelengths of light other than visible.

The only difficulty with satellites being able to ‘see’ more wavelengths of light than we can, is that we still need to be able to visualise the data that they capture! So as remote sensing scientists, we display these other wavelengths as blue, green, or red, as these are the colours that we can see. This is often difficult to grasp when you are first introduced to the concept. For example, Figure 4a shows a satellite image of Darwin, displayed exactly as we would see it – i.e. this is true colour. The colour that is displayed in the image is just as our eyes would perceive these features if we were looking down on them from above. Figure 4b shows data from the same satellite acquired at the same time, but displaying different wavelengths of data. Here vegetation looks extremely bright and green, not because trees are green, but because I have displayed the image using NIR coloured as green. A lot of the features within this image are clearer and easier to distinguish from their surrounds because of the extra information that the satellite can ‘see’.

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A B


Figure 4: (A) Landsat TM true colour image of Darwin; (B) False colour composite image of Darwin showing Mid Infrared, Near Infrared, and blue displayed as RGB.

When interpreting these images, it is important to know which wavelength is used and how it is displayed. This is demonstrated graphically below in Figure 5, with reference to Figure 4. To use these two figures together, you can see that something that is red in the true colour image is actually reflecting red light as acquired by the satellite. However something that is red in the false colour image is actually reflecting MIR light. This image gives us no information on visible red light at all!

Band 1 - Blue

Band 2 - Green

Band 3 - Red

Band 4 - NIR

Band 5 - MIR

Band 6 - MIR

Satellite Acquisition Computer Display

BlueBlue

GreenGreen

RedRed

A

Band 1 - Blue

Band 2 - Green

Band 3 - Red

Band 4 - NIR

Band 5 - MIR

Band 6 - MIR

Satellite Acquisition Computer Display

BlueBlue

GreenGreen

RedRed

B

Figure 5: Band combinations and display of (A) true colour image; and (B) False colour composite (as in Figure 4)

However, when you look at each of these images, you’ll notice that not everything is exactly blue, green, or red, but there are various shades and mixes. Blue, green, and red are known as the primary colours, while yellow, magenta, and cyan are the secondary colours. They mix in the following manner:

Blue + Red = MagentaRed + Green = YellowGreen + Blue = CyanBlue + Green + Red = White (Figure 6)

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Figure 6: Primary and Secondary colours and their mixes

Generally you will be able to see a lot more detail in your digital photo than that acquired from a satellite. However if you zoom in to the photo on your computer, you will notice at some point that it is made up of tiny squares. These are called pixels (picture elements). A satellite image is also made up of pixels, though these will represent a much larger area on the surface of the earth. The pixel size of GeoEye, for example is 2.4m; Landsat TM is 30m; MODIS is 1km. The effect of this should be apparent when you look at each of the images compared to each other, and compared to what you can see on the ground.

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ses 201 introductory remote sensing - learnlinelearnline.cdu.edu.au/units/ses201/downloads/env_2014...

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