UCLDEPARTMENTOFGEOGRAPHY

Examination Questions & Model Answers

(Academic year 2015-2016)

PLEASE PREPARE YOUR QUESTIONS AND ANSWERS BY USING THE FOLLOWING GUIDELINES; 1. Use Times New Roman 12 2. Enter the Module Code and Title 3. Please do not switch between text/fonts 4. Ensure all technical terms are correct 5. Each question must have the marks it’s worth shown 6. Show marks as e.g. [50 marks] not 50% or 50% of marks 7. Each question should be out of 100 marks 8. Run spell checker 9. If you are setting more than one question then please submit ONE file only 10. Please indicate the 2nd Marker 11. Please indicate if there any special instructions for e.g. if a question is compulsory Module Code:

GEOGG141

Module Title:

Principles and Practice of Remote Sensing

Contributor:

M. Disney 2nd Marker

P. Lewis

Special Instructions Question No:

1

i) Describe the importance of the Planck blackbody energy distribution to remote sensing. You

should make clear the key features of the distribution, using figures where appropriate. [50%] ii) Show that around 84% of the total power of a blackbody at 5770K lies in the visible-SWIR region

i.e. l = 400-2500 nm (0.4 to 2.5 µm). [30%] iii) Describe TWO other implications of the sun’s blackbody energy distribution. [20%]

You may assume s, the Stefan-Boltzmann constant = 5.7×10-8 Wm-2K-4 and Wien’s displacement constant, k = 2897 µmK. Values of the integral of the Planck energy distribution from 0 to l as a function of lT, F0èλ, for blackbodies at various temperatures T are as follows: lT (µmK ×103) F0èλ(lT) 2 0.067 3 0.273

UCLDEPARTMENTOFGEOGRAPHY4 0.481 5 0.634 6 0.738 8 0.856 10 0.914 12 0.945 14 0.963 16 0.974

Model Answer:

i) Ideally would draw like this making clear it’s a log-log scale. Could draw linear-log also but needs to make clear scales again. Figure must be labelled correctly and the key shape must be right (same for both lines); peak must be at about 0.5 um for Sun (6000k) and about 10um for Earth (300K), with total going from 0.1 to over 100 for Sun, and 3 to < 100 for Earth. Three points from Planck are: allows us to predict shape so we known energy between any two wavelengths so we know signal for EO measurement; integral gives us total energy output of a BB which is Stefan-Boltzman Law – good answer gives the Planck equation and SB (M = sT4); differential gives us the wavelength at which emittance is a maximum, lmax which is Wien’s displacement law i.e. lmax = k/T where k = 2897 µmK. In log-log space lmax increases inversely with reducing T in linear fashion as in diagram.

ii) Need total energy from 0.4-2.5 um, so do for 0 to 0.4 and then 0 to 2.5 and subtract first. For 0-0.4, lT is 0.4 × 5770 = 2.308 µmK ×103 so interpolate between 2 and 3 i.e. (2.308-2) / (3-2) × (0.273-0.067) + 0.067 = 0.130. For 2.5, lT is 2.5 × 5770 = 14.4 µmK ×103 so interpolate between 14 & 16 i.e. (14.4-14)/(16-14) × (0.974-0.963) + 0.963 = 0.965. So final answer is difference i.e. 0.965 – 0.130 = 0.835 = 84%.

iii) Could mention: absorption features of chlorophyll and photosynthesis – pigments with absorption in the visible (red, blue, less in green); sensitivity of mammalian eyes to visible part of the spectrum; absorption and warming of Earth, re-radiation in thermal part of the spectrum; rapid drop-off of distribution in UV and ozone.

UCLDEPARTMENTOFGEOGRAPHY

UCLDEPARTMENTOFGEOGRAPHY Module Code:

GEOGG141

Module Title:

Principles and Practice of Remote Sensing

Contributor:

M. Disney 2nd Marker

P. Lewis

Special Instructions Question No:

2

EITHER i) Outline the advantages of LIDAR measurements for estimating forest canopy properties, using

examples and clearly defining the particular forest properties you discuss. [70%] ii) Describe the impact of footprint size and terrain on a full-waveform lidar return that might be

expected over a forest canopy. [30%] Model Answer:

i) First needs to distinguish between terrestrial, airborne and spaceborne (e.g. ICeSAT and GEDI), difference of spatial coverage and footprint sizes in the latter two. Key properties include: direct measurement of canopy height and vertical structure (amount and arrangement of stuff); crown size and shape (TLS, ALS); indirect, biomass from volume (TLS); biomass from allometry (ALS, spaceborne); stem density (all); terrain under canopy (all); structure eg vertical crown profiles can tell us about gap fraction (LAI and interception) and forest age and type (all). A good answer should divide into the various categories (TLS, ALS, space) and also discriminate between discrete return and waveform systems, and even phase-based/photon counting. Limits of coverage to airborne; spaceborne difficult - non-existent following demise of IceSAT/GLAS, but we will have IceSAT II, GEDI. Excellent answer should provide reference to literature of eg forestry applications, limitations of height and coverage and difficulty of defining what height we mean.

ii) A good answer might show a vertical canopy profile of this sort (e.g. for small footprint << canopy dimeter):

b) Small-footprint (<1m).

Key points are: a) pick up shape of canopy and hard ground return, slower increase in return due to canopy shape and prominent ground; waveform should vaguely correspond to crown shape. Difference w large footprint (>> canopy size) is that we get an aggregate ‘area’ response, not per tree.

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So what does canopy height mean in this sense? The other issue is the amount of energy required to trigger a return, so possible greater underestimation of height. For terrain, need to show the impact in the waveform i.e. returns from one part of the footprint before another, which we can misinterpret as canopy height variation. Less important for smaller footprint. For large footprint, any terrain over a few degrees may swamp canopy height information. A good answer would show this in a figure if possible, and possibly also how we might at least partially deal witht it via accurate terrain modelling with a DEM. But still limited.

UCLDEPARTMENTOFGEOGRAPHY Module Code:

GEOGG141

Module Title:

Principles and Practice of Remote Sensing

Contributor:

M. Disney 2nd Marker

P. Lewis

Special Instructions Question No:

3

i) What are the key advantages and disadvantages of RADAR observations over optical. You should include figures and examples where appropriate. [40%]

ii) The RADAR equation can be stated in terms of received power, 𝑃" at a RADAR antenna as:

𝑃" =𝑃$𝐺&𝜆&𝜎4𝜋 +𝑅-

Define the various terms in the equation and outline the various physical principles that determine the form of the equation. [40%]

iii) Define what is meant by SAR interferometry, giving one example applications of it in practice. [20%] Model Answer:

i) Advantages: all-weather; night/day operation; penetration of atmosphere due to v high transmission in microwave; info. indep. of optical eg on soil moisture, roughness or biomass due to penetration of dense canopies at eg P band; correlation of backscatter and biomass (to higher biomass range than for optical) detection of objects with strong angular features; altimetry; surface roughness; ability to use phase info for interferometry. Disadvantages: requirement for power (weight, cost); coherent scattering (interference, speckle); difficulty of interpreting values particularly for interaction with vegetation; problem of building RT models due to coherence. Other things considered in both cases IF sensible/justifiable.

ii) Terms are: 𝑃$= transmitter power (W); G is gain (a good answer would mention this assumes transmitting and receiving antennae same i.e. bi-static G2); 𝜆 =wavelength (m); 𝜎 =radar scattering cross section; R = range (m). Form arises via considering power output by transmitted, spreading outward per unit solid angle (area of a sphere, in both out and return directions hence 4𝜋 +𝑅-. The scattering cross section arises out of considering the projected area of the target in both the incident and scattered directions as well as the scattering properties of the surface (i.e. gain of target) in both directions.

iii) Has to define SAR and interferometry as use of phase difference to resolve distances. Needs to cover requirement for 2 or more observations either by repeat pass or large antennae. Good answer would then mention coherence information and phase unwrapping to produce coherence images and then interferograms. Applications include: DEM generation; small topographic variations i.e. deformation, subsidence, volcanoes; ice sheet dynamics; even forest height. A v. good answer would refer to a specific example rather than general area (eg refer to sensor, date, location, literature etc).

UCLDEPARTMENTOFGEOGRAPHY Module Code:

GEOGG141

Module Title:

Principles and Practice of Remote Sensing

Contributor:

M. Disney 2nd Marker

P. Lewis

Special Instructions Question No:

4

i) Derive an expression relating the orbital period T, of a satellite in a stable orbit, to its altitude

above the Earth’s surface, h. Your answer should show all working for any derivations required and you should define all terms. [30%]

ii) Use this expression to determine the altitude of a satellite with an orbital period of 100 minutes. [15%]

iii) If the instrument swath is 200km, derive the repeat time for this instrument at the equator. [20%] iv) Give ONE advantage and ONE disadvantage of a near polar satellite orbit for environmental

applications. [20%] v) Why is the altitude of a low-earth orbit sensor typically not stable over time? [15%]

You may assume ME, the Earth’s mass, to be 5.983x1024kg; the universal gravitational constant G to be 6.67x10-11 Nm2kg2; and the radius of the Earth to be 6.38x106m. Model Answer:

i) In stable orbit the gravitational force Fg = GMEms/RsE2 = centripetal force Fg = msvs

2/RsE where G is universal gravitational constant (6.67x10-11 Nm2kg2); ME is Earth mass (5.983x1024kg); ms is satellite mass (unknown) and RsE is distance from Earth centre to satellite i.e. 6.38x106 + h where h is satellite altitude; vs is linear speed of satellite (=wsRsE where ws is the satellite angular velocity, rad s-1 = 2p/T for orbital period T). So for stable orbit GME/(RsE+h)3 = (2p/T)2.

ii) Rearrange so h = [T2GME/4p2]1/3 - Rs, where T = 100x60 = 6 x103 s, so h = 759 km. iii) Repeat time is time taken to return to the same sub-satellite point on the Earth’s surface i.e. number of

orbits to cover circumf of Earth = 2p*6.38x106 and we cover 200km per orbit, so require 2p*6.38x106/200 = 200.4 orbits, and at 100 mins this is ~2004 mins = 13.9 days.

iv) Altitude varies for two reasons – first, wobble up and down due to varying gravitational field strength of Earth due to variations in density; second, due to gradual degradation of orbit due to drag of atmosphere. Good answer would mention atmospheric drag varies with solar activity & how to deal with (boost or crash).

UCLDEPARTMENTOFGEOGRAPHY Module Code:

GEOGG141

Module Title:

Principles and Practice of Remote Sensing

Contributor:

M. Disney 2nd Marker

P. Lewis

Special Instructions Question No:

5

Question a) Define the following terms and briefly detail their relevance to remote sensing:

i) Lambertian and specular reflectance [20 marks] ii) Whiskbroom and pushbrom scanners [20 marks] iii) Atmospheric window [20 marks] iv) Resolution AND sampling (e.g. angular, spatial) [20 marks] v) BRDF and albedo, and their relationship [20 marks]

Model Answer:

i) Lambertian: surface which reflects isotropically i.e. equally in all directions. Used as a first approximation for many surface reflectance and radiative transfer modelling assumptions as makes things simple because of lack of angular dependence AND integral over 2pi = pi. So intensity varies with cosine of solar zenith. But few surfaces actually Lambertian. Give examples of Lambertian surfaces. A very good answer might even detail why Specular: opposite of Lambertian, mirror-like and all radiation reflected in a single direction 180 deg in azimuth from source, at same zenith angle. Water surfaces are primarily specular.

ii) Whiskbroom: moving mirror either rotates fully, or oscillates across the track i.e. perpendicular to direction of instrument travel. Examples are: Landsat TM, MSS, MODIS. One way to design an opto-mechanical scanning system to allow instrument IFOV to be scanned across the surface to build up spatial coverage. Pushbroom: linear array of detectors scanning along in a single line in direction of travel. Requires splitting of incoming light into wavelengths either by prism, filter etc. so sometimes multiple single lines (MISR) for different wavelengths.

iii) Atmospheric window – part of the EM spectrum where the Earth’s atmosphere is transparent to radiation. Good answer would give examples of windows in the visible & SWIR, & microwave (RADAR). Best answer would indicate that opacity of atmosphere between windows is due to absorption by gases eg O2, O3, CO2 and water vapour. For surface remote sensing we have to look in the atmospheric windows as only region where signal can escape to be measured. A really good answer would give figure showing some of key absorption features –even approximately.

iv) Resolution: the smallest unit of something that can be resolved/measured (angular or spatial). A full answer would distinguish this from sampling i.e. the spacing between measurements of a given resolution, or frequency, density of measurements (in time, space/angle). Eg for time we are talking about shutter speed effectively for resolution – not the same as sampling. A really good answer would possibly use e.g. repeat time taken for an orbiting sensor to return to the same point on the Earth’s surface. A good answer would illustrate this with eg a 700km polar orbiting

UCLDEPARTMENTOFGEOGRAPHY

satellite, orbital period of ~100 mins, 14 orbits per day and the rotation of the Earth so would take 16 days to return to same point.

v) BRDF and albedo and their relationship: BRDF: Bi-Directional Reflectance Distribution Function of area dA defined as: ratio of incremental radiance, dLe, leaving surface through an infinitesimal solid angle in direction W(qv, fv), to incremental irradiance, dEi, from illumination direction W’(qi, fi) [sr-1]. In practice this is defined over finite area and lam as well i.e. units are [sr-1um-1]. Albedo: the ratio of total outgoing flux to incoming irradiance i.e. fraction 0-1. Not an inherent property of surface as dependent on diffuse/direct illumination and hence atmospheric state. Albedo is the BRDF integrated over all viewing and illumination angles i.e. so can integrate over incoming diffuse (hemispherical) and direct and outgoing same. EG MODIS algorithm approximates albedo as relative proportion of ‘white sky’ (bihemispherical) and ‘black sky’ (directional hemispherical) albedo, dependent on proportion of direct to diffuse illumination.

UCLDEPARTMENTOFGEOGRAPHY Module Code:

GEOGG141

Module Title:

Principles and Practice of Remote Sensing

Contributor:

M. Disney 2nd Marker

P. Lewis

Special Instructions Question No:

6

Question EITHER Describe how national and international space agencies are addressing the requirements for so-called Essential Climate Variables (ECVs) for monitoring and modelling climate. You should give examples of ECVs, outline the key requirements for their provision, as well as detailing the choices of platform, instrument and spatial, temporal resolutions that have been developed to provide them. [100%] OR You are asked to provide an outline proposal for a new remote sensing system to quantify climate AND anthropogenic (man-made) impacts on forest are and type in the boreal regions (northern hemisphere forests, Eurasia, Canada, N. America, Siberia). It is key that the system be able to observe a given region more than once during a given year in order to monitor intra-annual changes. Your proposal should carefully consider the mission requirements, and describe the resulting trade-offs that may be required in sensor type(s), wavelength(s), spatial resolution and orbit. [100%] Model Answer: EITHER: Clearly NASA CEOS/EOS and ESA Sentinel programs are the most obvious examples. In the first instance describe the Terra and Aqua platforms, instruments, orbits and give some examples of products: albedo, LAI and fAPAR might be ok; SST, LST, and related eg fire land cover, atmospheric CO2, NOX. Lots of others too. MUST mention consistency and continuity over 10+ years hence consistency with existing data records. Need to see instrument names and properties for at least some of these. For Sentinel programme give examples of the various sensors and their properties. Mention integration of geostationary for high temporal coverage in gap between climate and short-term NWP. Critically, needs to say what aspects are key eg the paired/multiple platforms for spatial/temporal coverage; spatial scales (10s m for Sentinel 2, 100s for S3) etc. Synergy of multiple complementary platforms. RADAR and optical is important also. Good answer would cover these and also how data can be combined from both and/or integrated with longer term datasets e.g. Landsat, AVHRR. OR: Scope for many types of system. A competent answer would initially consider the specific issue of what are the key requirements special about the required temporal and spatial scales – typically once/twice a year perhaps, and at the 100 m to km scale – needs to cover N Hemisphere. Doesn’t need to be global BUT needs to cover large areas so could go for several smaller, faster platforms. Climate-relevant studies implies must have continuity of a mission for eg a decade or more (phenology, growing season length etc); anthropogenic means repeat time likely to be sub-annual. Anthrop impacts might include deforestation but also afforestation

UCLDEPARTMENTOFGEOGRAPHYfor a really good answer! As well as fires – major impact, so need either burned area from reflectance and/or active fires (thermal), and ideally fuel-related info so water-sensitive bands. Not limited to current systems and in fact should move beyond this. MUST consider the constraints – the NH requirement to higher latitudes means clouds and season length will be important and probably the controlling factors. As a result, optical will be limited by cloud so either need several platforms (see Sentinels), or even better, optical + RADAR instrument eg a C or X-band RADAR, correlated with moisture and biomass (see ALOS PALSAR). We also need to get change, and not just loss, so perhaps a higher resolution optical + RADAR system or even a constellation. Wavelengths would need to be eg optical vis-NIR for veg properties. Doesn’t require spectral detail, more temporal. A good answer would highlight what’s currently available and so make clear why the proposed system would be better. Clearly, the Sentinels should provide a template for this, so a good answer might follow and augment this with eg a LIDAR – entirely acceptable to describe the Sentinel programme (eg 1 + 2 + 3) and suggest this is adequate if backed up with evidence. Should show evidence of the literature and reading on the subject for any half-decent answer.

UCLDEPARTMENTOFGEOGRAPHY Module Code:

GEOGG141

Module Title:

Principles and Practice of Remote Sensing

Contributor:

M. Disney 2nd Marker

P. Lewis

Special Instructions Question No:

7

i) Describe the key pre-processing stages that are generally applied to Earth Observation data in order to transform raw DN values to at-sensor radiance, describing any models and/or ancillary data that may be required. [70%]

ii) Describe THREE types of scattering that occur within the Earth’s atmosphere. You should make clear any wavelength and directional dependence of each scattering type. [30%]

i) Radiometric calibration of instrument properties; radiometric correction (dealing with non-linear detectors, striping, loss etc); geometric correction; atmospheric correction. Either empirical method such as empirical line correction which requires identifying v dark (eg clear deep water, dense veg) and bright stable targets, dust/desert then assuming L = gain * DN + offset, where offset is atmospheric path radiance. Requires a priori knowledge of ground, and assumes Lambertian surface (no angular effects), large homogeneous areas (ignores adjacency effects) and stability i.e. stays same over time. Also is per-band i.e. assumes same scattering across whole image. So, ok for narrow swath instruments (eg IKONOS, Landsat) but not for wider swath airborne or moderate res. satellite. Another method would be using full radiative transfer model of the atmosphere such as MODTRAN or 6S. Big advantage is it does the job properly accounting for gaseous absorption, aerosols etc. and can be done per pixel. Down side is slow(er) and requires, ideally, info. on atmospheric properties (aerosol optical depth, ozone and water concentrations, types of aerosol etc). The 6S model is used for operational MODIS products. A third method would be to use multi-angle data as per MISR to actually include the atmosphere in any retrieval process as you get multiple path length estimates and so can solve fo the atmospheric radiance. Radiometric calibration - account for sensor response and non-linearities, either done pre-flight and then with checks over time against stable targets, and/or using on-board calibration eg via blackbody heaters; geometric correction – accounting for sensor and surface movement by camera modelling and resampling. Good answers would provide a little bit of detail on each method chosen.

ii) 10 marks for each. Rayleigh scattering: (particles << λ) due to dust, soot or some gaseous components (N2, O2). Very strongly inversely wavelength dependent (1/λ4). Some directional dependence, function of scatter number density and distance. Mie scattering: (particles approx. same size as λ), e.g. dust, pollen, water vapour. Strongly directional (backscattering), affects longer λ than Rayleigh, BUT weak dependence on λ, mostly in the lower portions of the atmosphere where larger particles more abundant, dominates when cloud conditions are overcast i.e. large amount of water vapour (mist, cloud, fog) results in almost totally diffuse illumination. Non-selective: (particles >> λ) e.g. water droplets and larger dust particles; all λ affected about equally (hence name), results in fog, mist, clouds etc. appearing white = equal scattering of red, green and blue λs.