GEOG272 Final
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Feb 20, 2024
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1 Name____Joseph Massaro________01/17/2024______
GEOG 272 Introduction to Earth observing science Final Exam There are three sections to this examination. The exam has 100 points total. Section I contains 16 single choice questions where you must answer each question. Each question is worth 2 points. This section counts for 32 points total. Even if you are unsure of an answer, it is better to guess than to leave it blank. Section II contains 12 short-answer questions. Each question is worth 4 points. This section counts for 48 points total. You can use lists, diagrams, and/or equations to answer these questions, but lists should be clear, diagrams must include a brief written explanation (like a figure caption), and the important relationships in an equation must be briefly explained. Section III contains 2 long-answer questions worth 10 points each. Typically these questions should be answered with at least a paragraph. Detailed lists may be ok depending on what the question asks. Diagrams may be used, but they must be well labeled and include a written explanation. Good Luck!!
2 Section I – Single Choice 16 questions - 2 points each (32 points total) Please put your answers here: 1. D 2. C 3. A 4. B 5. A 6. A 7. C 8. A 9. D 10. D 11. D 12. C 13. B 14. B 15. B 16. C \ \ \ \ 1. Which of the following is NOT true of vegetation indices? a.
indices can indicate vegetation growth stages b.
indices can indicate plant productivity (biomass) c.
indices can be directly linked to biophysical parameters (e.g. leaf area index) d.
indices alone can be used to determine the cause of a disturbance 2. The spatial resolution of the MODIS sensor is best described as which of the following? a)
Very high resolution b)
High resolution c)
Moderate resolution d)
Coarse resolution 3. The order of EM regions, from shortest to longest wavelengths, is: a)
visible, near infrared, microwave b)
near infrared, visible, microwave c)
microwave, visible, near infrared d)
visible, microwave, near infrared 4. Radiation is defined as:
a)
Physically moving the molecules/atoms from one place to another b)
Energy emitted into space by any object above 0 Kelvin c)
Transfer of energy through collisions of atoms or molecules 5. Chlorophyll, cell structure, and moisture content in plants absorb/reflect electromagnetic energy in _________, __________, and ________ wavelengths respectively. a)
0.4 to 0.7 μm, 0.7 to 1.3 μm, 1.3 to 3.0 μm b)
0.7 to 1.3 μm, 0.4 to 0.7 μm, 1.3 to 3.0 μm c)
1.3 to 3.0 μm, 3 to 14 μm, 1 mm to 1 m d)
3 to 14 μm, 1 mm to 1 m, 0.2 to 0.4 μm 6. Which of the following is not a property of geostationary satellites/sensors?
3 a)
Narrow swath width b)
High temporal resolution c)
Orbital altitude greater than polar orbiting systems d)
Remains focused on a set location on the Earth’s surface 7. The three additive primary colors include:
a. Red, Blue, Yellow b. Cyan, Magenta, Yellow c. Red, Green, Blue d. Black, Gray, White 8. An increase in moisture content typically has which effect on the spectral profile of vegetation and soil? a)
Decreased shortwave infrared reflectance b)
Increased shortwave infrared reflectance c)
Increased shortwave infrared reflectance for vegetation, decreased shortwave infrared reflectance for soil d)
No effect on the spectral profiles of vegetation and soil 9. Mie scattering in the atmosphere is the result of electromagnetic energy interactions with a. Very large particles b. Clouds c. Molecules that are much smaller than the wavelength of the electromagnetic radiation d. Particles in the atmosphere that are approximately equal in size to the wavelength of the electromagnetic radiation 10. The most sunlight is emitted between:
a. 8-14 μm b. 1mm-1m c. 0.1-0.2 μm d. 400 – 700 nm 11.
With a Lambertian surface, the apparent brightness of an object will be: a. Brighter when viewed in the same direction as illumination b. Brighter when viewed in the opposite direction of illumination c. Completely dark d. The same regardless of view angle 12. Which of the following statements is not true of a radar system? a)
Active radar can penetrate clouds b)
Radar can provide information on surface roughness, moisture content, and dielectric properties of the surface
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4 c)
Radar illumination angle depends on the sun d)
Radar can image large areas 13. Which of the following scenarios would likely see the smallest impact on NDVI? a)
Deforestation in the Amazon River basin b)
Dust storm in Southwest United States c)
Winter wheat harvest in Ukraine d)
Wildfire in an African savanna 14. Which of the following is the physical variable detected by the sensor?
a. irradiance b. radiance c. reflectance d. radiant flux 15. Which of the following laws describes the wavelength of maximum emissions for a blackbody? a)
Stefan-Boltzmann Law b)
Wien’s Displacement Law c)
Kirchoff’s Radiation Law d)
Wheaton’s Law 16. Which will have the highest NDVI? a.
soil b.
water c.
deciduous forest d.
snow
5 Section II – Short -Answer Questions Answer all questions.
12 questions – 4 points each (48 points total).
1. What is the difference between specular and diffuse reflection
? Spectral reflection occurs when the surface is smooth in comparison to the wavelength. Spectral reflection will redirect almost all or all of the radiation in a single direction. This is observed on smooth surfaces like mirrors, and the reflection light mirrors the angle of incidence. The reflection is clear with sharp highlights. Diffuse reflection occurs on uneven surfaces such as paper, cloth, or unpolished wood. In this reflection, the surface is rough compared to the wavelength and the energy of the reflection is scattered in all directions when they strike the surface. This results in a dispersed and less defined reflection without distinct highlights. 2. The below chart is a reflectance curve of vegetation. Please add reflectance curves of typical soil and water on the chart (2 points). Then explain the major differences among the three types of surfaces (vegetation, soil and water, 2 points). The yellow curve should be BRIGHT SOIL, the Brown reflectance curve is DARK SOIL, and the light blue reflectance curve is WATER. Vegetation reflection involves the effects of leaf chlorophyll and water content on leaf reflectance. In the vegetation, a higher chlorophyll content leads to increased absorption and reduced reflectance in the visible range. On the leaves, a higher water content results in more absorption and less reflectance in the shortwave infrared (SWIR) range.
6 Soil reflectance is influenced by mineral composition and soil moisture. Increased soil moisture tends to decrease reflectance, resulting in parallel curves in soil reflectance spectra. Organic matter content also plays a role with higher levels of organic matter leading to decreased reflectance. The soil texture caused by the portion of clay, silt, and sand, affects the soil's ability to retain moisture but its roughness. Dry soils have higher reflectance, and as water content increases the reflectance overall decreases across all wavelengths. Liquid water has low reflectance and clear water has the highest compared to dirty water. In infrared wavelengths water has high absorbance and almost no reflectance. Moving/turbid water has high reflectance in the visible range. The reflectance of snow is influenced by the composition and type, where fresh snow has the highest reflectance, followed by glacier ice and dirty glacier ice. Sow has high reflectance overall near 1 in the visible range and decreases in the near infrared range. As snow ages the reflectance also decreases. 3. What are the differences between true color and false color images (2 points)? What are some of the advantages of false color images (2 points)?
False color images use non-visible bands of the electromagnetic spectrum, such as infrared, red, and green. In these images, colors are different from the natural hues. So, this can be seen when vegetation may show up in varying shades of red. The assignment of different spectral bands to the red, green, and blue channels creates many a variety of false color combinations. False color images can be used in and are appropriate for many different jobs like finding specific features like vegetation health, moisture content, or geological formations. Advantages of false color images include an increase of discrimination, especially in vegetation and land cover analysis. Infrared bands within false color images can be used to monitor vegetation health and distinguish between healthy or unhealthy vegetation. Also, false color images can help with mineral exploration by finding minerals and geological formations based on their spectral signatures. False color images help with land cover classification by providing extra information for distinguishing between different land cover types. 4. Describe unsupervised classification (as compared to supervised classification, 2 points), its major advantages and disadvantages (1 point each). Unsupervised classification involves the clustering of image data into natural spectral groupings present in the scene, and then determining the land cover identity of these clusters by comparing the classified image data to ground reference data. An analyst requests the computer to examine the image and extract spectrally distinct clusters. The algorithm clusters data to find inherent classes, resulting in a spectral class map. The analyst labels clusters and may group some clusters together to create an informational class map. In the clustering stage, methods include K-means clustering, Iterative Self-Organizing Data Analysis (ISODATA), and Fuzzy Classification. So, Unsupervised classification involves running a clustering algorithm, identifying classes, editing, or evaluating signatures, and then evaluating the classification.
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7 On the other hand, supervised classification requires you to select training fields and then edit or evaluate the signatures and perhaps go back to select new field or make sure your selections are accurate. Then you classify the image and evaluate your classification. Unlike unsupervised classification, supervised classification involves more of the active involvement of an image analyst who guides the process by specifying numerical descriptors for different land cover types. The analyst must choose areas on the ground and digitizes polygons within them. The algorithm then identifies signatures, such as mean, variance, and covariance, and classifies all pixels and then can make a class map. The process involves determining possible classes of features. During the training stage, the training data must be substantial, Training areas should be strategically located throughout the image, and each area should exhibit a unimodal frequency distribution for each feature. In the classification stage, the analyst selects an appropriate classification algorithm. The accuracy of supervised classification relies heavily on the quality of training data, making it a preferable option when dealing with high-quality datasets. It is good at applications such as identifying damaged vegetation in agricultural areas or classifying regions based on specific species. Also, an advantage to supervised and disadvantage to unsupervised is that supervised will offer more direct control. But an advantage to unsupervised is that they demand less effort and human expertise, especially when targeting the more precise land cover classes. Unsupervised classification is more fitting in situations where training data is lacking or when exploring new areas. This method automatically identifies patterns without predefined classes, proving advantageous for detecting subtle patterns or concealed structures often overlooked in supervised approaches. So, although unsupervised classification may have lower precision than manual predictions, another advantage is that it eliminates the need for manually determining the number of classes. 5. How does the particle size of soil influence its reflectance (4 points)? The reflection of soil is definitely influenced by its particle size. If the particles are small, like clay, the soil becomes smoother and reflects more light, making it shinier. So, Reflectance increases with a decrease in particle size. Different-sized particles absorb and scatter light in their own ways. Bigger particles absorb less and scatter more, making the soil less reflective, while smaller particles, like clay, absorb more and scatter less, making the soil more reflective. Also, the kind of minerals in the soil can change how it reflects light. The size of the particles also affects how much water the soil can keep, and this influences how much light it reflects. 6. When describing electromagnetic radiation, what are wavelength and frequency? (2 points) What is their relationship? (2 points)
Wavelength is the distance between peaks in a wave and is measured in a form of meters, like nanometers (nm) or micrometers (µm). Frequency means the number of oscillations or cycles occurring in a wave within a unit of time and is measured in hertz (Hz).1 Hz corresponds to one cycle per second. The relationship is inversely proportional meaning that an increase in wavelength results in a decrease in frequency. So shorter wavelengths align with higher frequencies, and the longer wavelengths correspond to lower frequencies. The different areas of
8 the electromagnetic spectrum are defined by distinct wavelengths and frequencies. And the types of instruments we discussed are designed to capture specific bands within this spectrum to obtain info. 7. Briefly describe Rayleigh Scattering, Mie Scattering, and Non-selective Scattering (1 point each). Why is scattering important for remote sensing (1 point)? Rayleigh scattering, or clear atmosphere scattering, occurs when the size of particles is smaller than the wavelength of electromagnetic (EM) radiation. This is the dominant scattering process observed at high altitudes in the atmosphere, to approximately 9–10 km. Also, Rayleigh scattering is wavelength-dependent, so its effects vary based on wavelength, Rayleigh Scattering scatters light by small particles or gas molecules, favoring shorter wavelengths and contributing to the blue color of the sky. Mie scattering deals with larger atmospheric particles such as dust, pollen, smoke, and water droplets. The particles scatter colors uniformly, but absorb the blue and green, giving a reddish color to the sky. Mie happens when the particles involved are comparable in size to visible light. Mie scattering is more complicated in its wavelength dependence than Rayleigh. The effects of Mie scattering on electromagnetic radiation are influenced by the specific wavelength of the light involved, adding more complexity. Nonselective scattering happens when the size of particles is significantly larger than the wavelength of the electromagnetic radiation. The particles, often water droplets and ice crystals found in fog banks and clouds, have substantial size. “Nonselective" means that the scattering is not dependent on wavelength. So we see nonselective scattering results in a whitish or grayish haze, as the visible wavelengths are scattered equally, causing a uniform appearance Scattering is the redirection of electromagnetic (EM) energy by atmospheric particles or molecules, and is affected by factors like particle size, concentration, EM wavelength, and atmosphere depth. This is especially important for remote sensing because it affects the quality of data you are using, and the different types of scattering can show different atmospheric conditions. Also, the wavelength dependence of scattering can help you find facts about particles like size or material they are made of or the kind of aerosols they are, clouds, and atmospheric constituents in remote sensing applications. This makes it important for remotes sensing because it influences light interactions with the atmosphere and surfaces, further affecting the data collected by sensors. 8. What are the four resolutions of remote sensing? Describe each one.
These are Spectral, Spatial, Radiometric, and Temporal. Spectral resolution is the location, number, and bandwidth of the EM spectrum a specific sensor can see. A higher spectral resolution gives you more detailed analysis of the surface changes or features based on different wavelengths.
9 Radiometric resolution is the levels of intensity that can be divided into several levels by the remote sensor within a specific band. This is how a system recognizes changes in the intensity of electromagnetic radiation. The higher radiometric resolution lets you see finer differences in brightness or color, and this can help you distinguish between objects that have small differences. Temporal resolution is how frequently the data can be obtained, depending on orbit, field of view, and the number of satellites. It describes how often a location is seen or scanned by a system. The higher temporal resolution gives us more frequent updates of changes over time. Spatial resolution is how big the pixels are and how close you can discriminate objects, whether pixels are 100m, 30m. 10m. 5m. 1m, or 30 cm. This refers to the level of detail in the image and the size of the smallest visual feature. Higher spatial resolution images show smaller objects and more detail. 9. What is the difference between the simple ratio vegetation index and the normalized difference vegetation index? Give the equation for each index (2 points), indicate the maximum value of each index (1 point) and one limitation of NDVI (1 point).
The simple ratio divides the Near infrared band over the Red band. If you have a larger SR value this means healthy vegetation, and lower values mean soil, or water. There is not necessarily a maximum value for the simple ratio, and it is considered unbounded. The simple ratio is good at eliminating irradiance and transmittance. The equation is 𝑆𝑅
= 𝑁𝐼𝑅
/ 𝑅𝑒𝑑
A limitation occurs if the red band is zero, because in the simple ratio you would have to divide by zero. Which is not possible. Normalized Difference Vegetation Index (NDVI) exploits reflectance in NIR and red spectral ranges. A difference is that NDVI will normalize the difference between the NIR and red bands to reduce the effects irradiance variation. The NDVI has a maximum value of 1 and all values will be between -1 and 1 Areas with no vegetation or nothing growing should have a value of 0,a0 and if land is very dry the value can be less than zero. NDVI can also help lessen the disturbances or noise from topography changes or the sun. An equation is: 𝑁𝐷𝑉𝐼
= (
𝑁𝐼𝑅
– 𝑅𝑒𝑑)
/ (
𝑁𝐼𝑅
+ 𝑅𝑒𝑑) A limitation is that there can be saturation at higher vegetation biomass of the vegetation fraction. Also, the effects of clouds and other atmospheric effects may still slightly alter the data and the values calculated, and certain conditions can limit the number of multispectral bands that be used to capture an area. 10. Explain geostationary and polar orbits (2 points). What are some advantages and disadvantages of each (2 points)?
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10 A polar orbit is a type of satellite orbit around the Earth in a north-to-south direction. This makes it have a circular path. This type of orbit lets us image and scan all latitudes and lets data acquisition happen at the same local time. Polar orbits typically have altitudes ranging from 700 to 800 km. It can take around 100 minutes to complete one orbit. And the Earth rotates 25 degrees or so during this time. During the day, there are around 14.5 orbits. The lower orbit gives it a higher spatial resolution. A disadvantage is that they cannot continually scan a single location as well as geostationary can. A geostationary orbit involves a satellite rotating at the same speed as the Earth, positioned at high altitude over 35,000km. This orbit allows the satellite to keep viewing the same territory. Also, geostationary satellites can obtain data every 5-15 minutes. This is much higher altitude and orbit so, a disadvantage is that the spatial resolution of the data is not as great, but the temporal resolution is better with data acquired so fast. Since geostationary satellites match the rate of the earth, the temporal resolution is much finer. This is good for monitoring severe weather and tracking severe storms. 11. Why the sky is blue in a clear day (2 points) and it appears reddish in sunrise and sunset (2 points)? As sunlight hits the earth in the atmosphere gases and particles cause it to scatter in a lot of directions. Blue light has shorter and smaller waves, so it is very prone to scattering, creating the typical blue sky. During the day, air molecules scatter blue light from the sun more than other colors. During sunrise and sunset, sunlight travels a greater distance, leading to the scattering of blue components and least scattered light which is red results in the red color in the sky. 12. Five different things can happen to electromagnetic energy as it passes through the atmosphere. Identify, discuss, and diagram. These should be Refraction, reflection, absorption, transmittance, and scattering. Scattering refers to the redirection of electromagnetic (EM) energy by particles suspended in the atmosphere or large molecules of atmospheric gases. It has 3 types which are Rayleigh scattering, particle size is significantly smaller than the wavelength of EM radiation, Mie scattering, when particle size is roughly equal to the wavelength of EM radiation, and Nonselective scattering, when particle size is much larger than the wavelength, causing the scattering of all wavelengths in a roughly equal manner. Refraction involves the bending of light rays at the interface between two light-
transmitting media.
11 Absorption is when radiation transmitted and scattered by the atmosphere happens when the atmosphere impedes or significantly reduces the transmission of radiation or its energy. The absorbed energy is then reradiated at longer wavelengths. Ozone, Carbon dioxide, and Water vapor take up most solar radiation. Reflection is the process where an electromagnetic wave interacts with a surface and bounces back. This results in a change of direction of a wave when it hits a surface and the angle of incidence of the wave should be equal to the angle of reflection. Transmittance is when some of the radiation penetrates to the surface of the earth or water. Through somewhat transparent surfaces, radiation can go through, slightly diminished. So, this is how much not initially absorbed and gets through to the earth’s surface. INCIDENT EM RADIATION INCIDENT EM RADIATION Reflection Scattering Refraction Absorption Transmittance Surface of the Earth Section III – Long-Answer Questions 2 questions – 10 points each (20 points total)
12 1. Discuss the differences between optical and radar remote sensing systems. Consider the wavelengths each system uses, what each system measures, the limitations of each system, etc. Optical remote sensing systems operate across the visible, near-infrared, and infrared spectrum, capturing high-resolution images based on reflected or emitted light. They measure the electromagnetic radiation within the optical part of the EM spectrum. The sensors designed for optical remote sensing capture the reflected sunlight or emitted radiation emanating from the Earth's surface within these wavelength bands. They are good at providing detailed visual information, facilitating the interpretation of landscapes. A major limitation for optical sensors is the sensitivity to cloud cover and reliance on daylight conditions, making them hard to use or impossible to use during bad weather or nighttime. Also, they are limited compared to RADAR in that they cannot penetrate surfaces like vegetation or soil. Radio Detection and Ranging remote sensing systems function with microwave frequencies and can be used in all-weather. Radar is an active system that generates, transmits, and captures a signal. Radar systems have high spatial resolution and can be used to help optical systems. Radar measures the ratio between a received electrical field signal divided by the field incident of that location and calculates a Backscatter coefficient. The values measured include Amplitude, intensity, and Phase. Radar is influenced by parameters such as wavelength, polarization, and incidence angle when emitting EM waves. Synthetic Aperture Radar (SAR) is a form of radar that uses antenna over an area to get high spatial resolution with methods like spotlight or Stripmap. Radar signals can penetrate through clouds, vegetation, and darkness, allowing radar systems to measure surface topography and terrain. They can measure the structure and density of vegetation, helping monitor forests and agricultural areas. The send pulses through a transmitter and the target reflects the pulses which are they captured by a receiver. They use the time delay between signal transmission and reception to gather information on surface topography and vegetation structure. Radar is also useful because it can penetrate soil and vegetation to find data or features hidden from optical sensors. Also, they can notice the very small changes in surface roughness or erosion/man-made effects. A limitation is that even though they can operate in various conditions, radar images can lack the very fine visual detail that you get in optical data. Another limitation is that RADAR cannot easily penetrate water so accurately scanning underwater areas or seabeds is not possible.
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13 2. Design a complete remote sensing project for desertification in the Iberian Peninsula (Spain and Portugal). Consider your answer in terms of potential users and their requirements, sensor resolutions, mapping techniques, etc. Monitoring Desertification in the Iberian Peninsula Using Remote Sensing Desertification is an environmental challenge that needs up to date monitoring and effective management. A well-rounded remote sensing project designed to assess and monitor desertification in the Iberian Peninsula, (Spain and Portugal) can use remote sensing technologies to offer insights sustainable land use practices and managing the environment. Users would include Government agencies, especially those in the Environmental and Agricultural Departments. They would play a big role in addressing desertification. The project could try to meet their needs by showing them an overview of desertification trends over time. This would mean the project would use high-resolution optical and radar sensors for detailed land cover and vegetation analysis. The techniques used would be those finding land cover classification, vegetation health and change, and soil moisture analysis. What governments want is usable data and facts to back up the eventual informed decision-making that they plan to do to address the desertification issue. Further users include Universities, researchers, and academic institutions who are looking to examine an in-depth analysis of high-resolution sensor data. For them, a detailed spectral analysis would include the calculations of spectral indices, vegetation indices including simple ratio and NDVI as well as others. The changes in vegetation and vegetation stress can be translated from raw data and to a graphical representation through analysis. Patterns and themes of certain areas including land use, previous land cover, soil moisture and temperature indicate the factors that are contributing to desertification. This is something that the researchers will need to create effective mitigation strategies. Also, the local community and non-governmental organizations will also value and want easily understandable and accessible information. The sensor resolution of the medium scale and optical sensors will be simpler to access and understand and help provide a regional context. User friendly thematic maps can be made with the sensor data to show desertification risk areas. For the project to be useful for everybody in the local communities the mapping should be classified with a limited number of classes and simplified borders to reduce complexity and confusion. The main results and most affected desertification areas should be highlighted to help simplify. The project could use a variety of sensor data from different sensing systems. The optical sensors used could be one of the Landsat or Sentinel-2 for land cover classification and change detection. The radar sensors could be Sentinel-1 to monitor bad weather, or dust storms as well as finding soil moisture. According to eh sensors data the expected and usual issues like atmospheric interference, radiometric calibration, and geometric correction would be addressed in the preprocessing phase to help with accuracy. In this project, when analyzing the image, we will use land cover classification. This helps identify areas prone to desertification. If we use supervised classification, we will need training data of land that we know for a fact is already under desertification. We may need to select various classes with small differences in their intensity of desertification. This would mean
14 compiling training data of land areas that are somewhat showing desertification, substantial desertification, and extreme desertification. Also, we could have classes representing land that is vegetative but not healthy, perhaps on its way to a desertification state. We could then use this data to classify the entirety of the Iberian Peninsula to visualize all areas that fall under these classes and see how much is truly becoming or already desert. The vegetation indexes of all areas, even desert ones can be useful to compare with measures like soil moisture or soil temperature to see if there is correlation between these and desertification areas. The mapping part of the project is very important and several thematic maps that highlight desertification risk areas will be generated in a range of resolutions and scales of all parts of the Peninsula. Again, this is important because different users will want different types of maps with varying detail. Along with the maps and presentation of raw data, there should be a very comprehensive report that explains the desertification trends and possible contributing factors. Trends could be established by also using previous emote sensing data from prior studies or data acquired from sensors in decades past. This would allow us to compare areas that have become more desert-like or areas that have improved and recovered from desertification. A comparison of trends could also be used to determine what prior methods were used that were successful in mitigation desertification. This data, or some of it at least, could be published publicly for awareness and publicity. The timeline of the project should have enough time for a complete and large level of data acquisition, and we should not rush the pre-processing stages. After the project tis finalized, there should also be continued monitoring that is consistent in method which can updates the trends originally found and help make sure that the information stays relevant and accurate because incorporation of new data is always beneficial for project like this.