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2 Pro Tips for Your Next Airline Flight

Sat, 22 Feb 2014 19:56:48 +0000

These tips were learned first-hand by not making use of them, unfortunately. I will from now on.

Pro Tip 1: Book each leg as its own itinerary

This one might seem like a bad idea at first, but that’s just because you’ve been lucky so far. Let me demonstrate. Assume you have a flight Montreal –> Toronto –> Rome –> etc. Now let’s say you miss the first leg (a minor 70-minute hop) either by accident or deliberately. That’s not far-fetched; maybe you decided to make the drive with a friend the day before instead of blast along at 800 km/h and hope for the best.

Most likely, the airline will inform you that your remaining legs have all been cancelled due to your “no-show” for the first one. You can argue and plead, but their policy is most likely exactly that. Poof goes your holiday. If you had booked each leg separately, they would have been safely treated individually. I admit that you may pay extra by not bundling the entire trip into a single itinerary, but I for one view that as a small insurance against any larger sort of catastrophe.

Google “first leg no show flight cancel” to find many, many sad people who figured this out too late, just as I did.

Pro Tip 2: When late, never give up on a flight

Simply put, keep trying to make a flight even after you think it is a lost cause. In my case, the flight was supposed to depart at 9:55, and I got to the terminal (because of a late previous leg) at 10:00. I gave up, and went to rebook the flight with the ticket agent. As I left them, thankfully with a flight safely booked for the following day, my phone buzzed. An email from the airline arrived saying the flight had been delayed 15 minutes. I shrugged and wished I had at least tried to make it. A few minutes later another email arrived, announcing a second 15-minute delay. And only a few more minutes passed before it was delayed yet another 15!

If I had tried at all, I could have made that flight, barring the chance that security would not have let me. But I’ll never know now.

Use OS X for Conveniently Remembering Developer Info

Fri, 22 Jul 2011 07:21:30 +0000

Software developers, whether for desktop platforms, mobile devices, or web services, inherently need to use a lot of pieces of information as part of their daily routine.

These scraps might include IP addresses, server names, configuration strings, and code snippets.

My typical approach used to be to store all of that in plain text files, since I can easily search for them using Spotlight, and it’s easy to create/edit them. The downside is that each time I need one, I need to open the text file, highlight the text, copy it, switch back to the application where I will use it, and paste the text. Sometimes I need multiple things from one text file, and sometimes I need multiple things from multiple files. It’s hard on the wrist! :P

Et voila! Une solution! Since version 10.6 (Snow Leopard), Mac OS X has had a global text substitution feature. It can be found at its System Preferences pane, found in the Language & Text section under the Text tab. It allows text to be dynamically replaced in the vast majority of programs, even non-Apple ones such as Skype, although it might have to be enabled from the program’s Edit->Substitutions menu.

Now, an entry could be added to the system pref pane mentioned above to map bob to 192.168.0.15. From then on, wherever text substitutions are enabled, you can save yourself the extra keystrokes. The downside is it doesn’t work in some important applications like Firefox for whatever reason.

Note that if you need something like this but on a much larger scale, you should check out other third-party solutions such as TextExpander. Not only will they be much more powerful, their settings will be easier to modify than Apple makes theirs.

Let me know if and how you use Mac OS X’s text substitution feature in the comments. Mention any alternative products you use, too!

Carleton University GEOM4003 (Remote Sensing) Study Guide

Mon, 18 Apr 2011 04:05:17 +0000

Carleton University
GEOM4003 - Remote Sensing of the Environment
Final exam from Winter 2011
Duration: 2 hours
10 known questions + 1 unknown

Notes:

These are the potential exam questions supplied by the professor in the Winter 2011 term, and the answers were put together by students in that class as a study guide for the final.
I am not responsible for the mark any person gets on a future GEOM4003 exam.
These Q/A are from a GEOM4003 final exam given in a semester in 2011; I do not guarantee that any of them will be on a future version of any exam.
I do not guarantee the accuracy of these Q/A.
These potential questions were divided among six students and then compiled. The following answers may not be correct or complete.

Question 1 (6 marks): Solar radiation is scattered by the atmosphere. Identify and explain the three types of scattering processes that occur.

Answer: Scattering is the unpredictable diffusion of radiation by particles in the atmosphere. There are three types:

Rayleigh scattering: Atmospheric particle size less than the wavelength of EMR. Shortest wavelengths are scattered.
Mie scattering: Atmospheric particle size is equal to the wavelength of EMR. Longer wavelengths are scattered than by Rayleigh scattering.
Non-selective scattering: Atmospheric particle size is greater than the wavelength of EMR. Scatters EMR of all wavelengths, thus is non selective.

Question 2 (2 marks): State and describe the equation that describes the Earth’s shortwave energy balance (the terms you should use are incident energy, absorption, transmittance, and reflectance) The total amount of incident energy is equal to the total amount of reflected + total amount of absorbed + total amount of transmitted energy.

Question 3 (3 marks): What are atmospheric windows and why are they important to remote sensing?

Answer: Wavelengths where absorption does not occur are called atmospheric windows. These are important to remote sensing because they allow us to accurately measure surface characteristics within given wavelengths of EMR. Furthermore, users of remotely sensed imagery can target their analyses around these wavelengths and in turn produce high quality remotely sensed imagery products which characterize certain terrain for a given objective.

Question 4 (4 marks): Remote sensors measure the radiance of a ground target. Define the term radiance (giving the units of measurement) and explain why remote measurements of radiance only partially capture ground reflectance characteristics.

Answer: Sensors measure radiance: The amount of EMR reflected or emitted per unit source area per unit solid angle (Wm²sr^-1). Remote measurements of radiance only partially capture ground reflectance characteristics because at sensor radiance also detects radiance from other non-target sources known as path radiance. These sources include atmospheric reflection and scattering as well as reflectance from other non target sources.

Question 5 (6 marks): Define the terms path radiance, target radiance and at-sensor radiance. Use a diagram to illustrate your answer.

Answer:

At-sensor radiance: The total incoming radiation which arrives at the sensor which includes both target and path radiance.
Target radiance: The radiance which is coming directly from the target area of interest which is being measured.
Path radiance: Radiances from non target sources which can include atmospheric reflection and scattering, and reflectance from other non target sources.

Question 6 (2 marks): Define the terms surface reflectance and albedo, and give their units of measurement.

Answer: Albedo is the ratio of reflected radiation from a surface to incident radiation upon the surface; i.e. surface albedo refers to the fraction of incoming EMR that is reflected by the ground. Surface reflectance is dictated by the surface roughness compared to the wavelength of EMR. There are two primary types of surface reflectance:

Diffuse: When wavelength is less than variations in surface height or surface particle sizes, reflection from surface is diffuse.
Specular: Smoother surfaces are specular or near-specular reflectors.

Question 7 (3 marks): What is the bidirectional reflectance distribution function (BRDF) and how do we characterize it?

Answer: The bidirectional reflectance distribution function gives the target reflectance as a function of sun and view geometry. The BRDF can be characterized by taking multi-angular observations of a surface.

Question 8 (2 marks): Describe two ways / mechanisms to characterize the BRDF of a surface.

Answer: The bidirectional reflectance distribution function (BRDF) is characterized through multi-angular observations, whereby the reflectance of a surface is measured from all angles to produced a 3D model of its reflectance. One method of accomplishing this is through the use of a ground based goniometer, as in Figure 1 below. A second method is through the use of multiple-view airborne or satellite data, such as that provided by the MISR satellite.

Question 9 (7 marks): On a single graph, draw hypothetical spectral reflectance curves for healthy and unhealthy vegetation. Identify and explain the leaf physiological properties that lead to differences in these curves. Your diagram should include wavelengths stretching from the blue to the short-wave infrared. Make sure the B, G, R, NIR and SWIR wavebands are labeled on your x axis.

Answer: As seen in the figure below, we can see that, overall, unhealthy grass reflects much more across all wavelengths than healthy grass, except in the NIR, in which healthy grass reflects slightly more. Also, unhealthy grass features a trend of steadily increasing reflectance as measurements move from blue to SWIR, whereas healthy grass has many variations in reflectance levels. For example, reflectance off healthy grass increases from blue to green, then falls from green to red, before increasing sharply in NIR and falling again in SWIR. The higher absorption of blue and red wavelengths in healthy grass is a result of the chlorophyll content of the grass, which absorbs more blue and red radiation than green in the process of photosynthesis. Since unhealthy grass has lost its chlorophyll, it absorbs more green radiation and reflects more highly in the red. Differences in SWIR reflectance are a result of the water content of the grass. Healthy grass contains more water in its mesophyll layer, which absorbs SWIR radiation. Unhealthy grass, has dried out, and as such SWIR is reflected much more highly.

Question 10 (3 marks): What are the main plant physical characteristics that influence leaf reflectance in visible wavelengths?

Answer: The main physical characteristics that influence leaf reflectance in visible wavelengths are the pigment levels contained within the leaf. Chlorophyll A and be absorb blue and red wavelengths. Chlorophyll A and B are found in the plant’s mesophyll layer. Chlorophyll A and B do not absorb as much at green wavelengths as red and blue, which is why leaves appear to be green. When leaves are under stress or senesce, chlorophyll pigments begin to disappear, and reflectance in the red and blue wavelengths increases relative to green, so the leaves change colour.

Question 11 (1 marks): Plants reflect highly in the green portion of the electromagnetic spectrum. True or false? Justify your answer in one or two sentences.

Answer: False. Plants do not reflect highly in the green wavelength, they just reflect more green compared to blue and red, the other wavelengths in the visible spectrum.

Question 12 (3 marks): What are the main characteristics that influence plant canopy reflectance in visible wavelengths?

Answer: There are many factors that influence the reflectance of plant canopies in visible wavelengths. Some of these factors are measurement-dependent, and others are ecosystem-dependent. Measurement-dependent properties include sun angle, sensor geometry, spectral sensitivity of the sensor, and sensor instantaneous field of view (IFOV). Ecosystem-dependent properties include crown shape and diameter, trunk density and diameter, leaf area and angle, understory area and angle, and soil texture moisture and colour.

Question 13 (3 marks): What are spectral vegetation indices and why are they useful? Give an example.

Answer: Spectral vegetation indices are mathematical algorithms that attempt to enhance vegetation signals in satellite data while minimizing background noise. They attempt to correct for various sources of error such as atmospheric conditions, sun and view angle, topographic slope and soil variation. We use vegetation indices to construct accurate models of biophysical conditions. The presence of background noise can often lead an analyst to a false conclusion about vegetation conditions. Therefore, it is critical that vegetation data be corrected through the use of a vegetation index. An example of a vegetation index is the Normalized Difference Vegetation Index (NDVI).

Question 14 (5 marks): What are the ideal properties of a “good” vegetation index? Do indices always meet these properties? Give an example to illustrate your point.

Answer: A good vegetation index should maximize sensitivity to plant biophysical parameters over a wide range of vegetation conditions, normalized effects of sun and view angle or atmosphere for consistent spatio-temporal comparisons, normalize effects of canopy background variation such as slope, soil variation and woody material, and be coupled to a specific measureable biophysical parameter such as biomass or water content. VIs do not always meet these properties. An example is the SAVI index, which fails to accurately normalize the effects of view angle.

Question 15 (6 marks): Identify and briefly explain the factors that influence the spectral characteristics of natural lake water bodies. Compare these factors with those of deep ocean water.

Answer: Natural lakes often contain low-mineral water, and can have near-ideal water reflectance curves, especially if they are surrounded by wind barriers and thus have relatively smooth surfaces. However, the spectral characteristics depend in part on the depth of the water, which if shallow can allow radiation to reflect off the bottom. This is in contrast to deep ocean water on all three levels. Open ocean water is deep, has zero wind barriers, and has high mineral content.

Question 16 (5 marks): The total at-sensor radiance over a water body is the sum of four sources of EMR. One of these sources is the subsurface volumetric radiance of the water. (a) List and describe the other three sources of EMR that contribute to at-sensor radiance over water; and (b) Specify the equation you would use to calculate the subsurface volumetric radiance (Hint: the other terms in the equation are the total at-sensor radiance and the three radiances you identified in (a)).

Answer: Not answered.

Question 17 (5 marks): On a single graph, draw hypothetical spectral reflectance curves for snow and clouds. Your diagram should include wavelengths stretching from the blue to the short-wave infrared. Using your diagram, explain what wavelengths are more suitable for distinguishing between snow and cloud. Make sure the B, G, R, NIR and SWIR wavebands are labeled on your x axis.

Answer:

Question 18 (3 marks): On a single graph, draw hypothetical spectral reflectance curves for dry and wet soil. Your diagram should include wavelengths stretching from the blue to the short-wave infrared. Make sure the B, G, R, NIR and SWIR wavebands are labeled on your x axis.

Answer:

Question 19 (6 marks): Explain the term “signal-to-noise ratio” and explain why it is important. List the factors that influence the signal-to-noise ratio recorded by an optical sensor at a given time.

Answer: A signal-to-noise ratio is a measure of how much of a signal has been corrupted by noise (i.e. the higher the ratio the cleaner the signal). In the case of an optical sensor, it refers to how much of each pixel value was generated by incoming radiation versus how much was caused by effects including:

Fixed pattern noise
- Physical aberrations of digital sensor chip
- Long exposure
- Low ISO speed
Random noise
- Short exposure
- High ISO speed

Question 20 (Y marks): Explain why sensors measuring in broad wavebands can make spectral measurements at finer spatial resolutions compared to sensors measuring in narrow wavebands.

Answer: Not answered.

Question 21 (3 marks): What is the main difference between multispectral and hyperspectral remote sensing? How do spectral curves derived from these two remote sensing types differ?

Answer: Multispectral sensors measure a few very narrow wavebands, whereas hyperspectral processing takes into account many wavebands, totaling a very wide swath of spectrum.

Question 22 (4 marks): Line or column striping is a problem often encountered in multispectral remote sensing. Describe two methods for correcting line or column striping.

Answer: Multispectral remote sensing is defined as the collection of reflected, emitted, or back-scattered energy from an object or area of interest in multiple bands (regions) of the electromagnetic spectrum. One problem that may occur is line or column stripping. This effect could be removed by local average and/or histogram normalization methods.

Local averaging – pixels in the defective line are replaced with an average of values of the neighbouring pixels in adjacent lines just above and below. In other words replace bad pixels with values based upon the average of adjacent pixels not influenced by striping; this approach is founded upon the notion that the missing value is probably quite similar to the pixels that are nearby.

Histogram normalization – data from all lines are accumulated at intervals of n-lines, the histogram for defective defectors displays an average different from the others. This strategy is to replace bad pixels with new values based upon the mean and standard deviation of the band in question, upon statistics developed for each detector assuming that it overall statistics for the mission data must resemble those from the good detectors.

Question 23 (3 marks): Is it necessary to undertake atmospheric correction when undertaking an image classification using single-date imagery? Explain your answer.

Answer: No, it is not necessary to perform atmospheric correction on a single date of remotely sensed data. This is because atmospherically correcting a single date of imagery is often equivalent to subtracting a constant from all pixels in a spectral band, suggesting that as long as the training data from the image to be classified have the same relative scale, atmospheric correction has little effect on classification accuracy.

Question 24 (6 marks): Describe two methods of undertaking atmospheric correction on a satellite image.

Answer: There are several ways to atmospherically correct remotely sensed data. Two major types of atmospheric correction are absolute atmospheric correction and relative atmospheric correction.

The general goal of absolute radiometric correction is to turn the digital brightness values (or DN) recorded by a remote sensing system into scaled surface reflectance values. These values can then be compared or used in conjunction with scaled surface reflectance values obtained anywhere else on the planet.

Radiative transfer models – Realistic estimates effects of atmospheric scattering and absorption on satellite imagery. User can incorporate ground radiometer measurements from in situ data (single spectrum enhancement).
Empirical line calibration forces image data to match in situ spectral data obtained at same time and date as image.

Relative atmospheric correction is often used when the information for the absolute atmospheric correction is not available. For a single-image, normalize using histogram adjustment. If histograms are shifted to left so that zero values appear in data, effect of atmospheric scatter can be minimized but does not correct for atmospheric attenuation (absorption).

For multi-data image, normalize using regression where a base image is selected and other images are transformed to radiometric scale of this image. Regression coefficient applied to each pixel in other dates to normalize images.

Question 25 (6 marks): List and describe the three types of interpolation used to correct the geometric distortion in satellite images.

Answer: Geometric distortions are an error on an image and are classified into internal distortion resulting from the geometry of the sensor, and external distortions resulting from the altitude of the sensor or the shape of the object. There are three methods of geometric correction (interpolation) as mentioned below:

Systematic correction – When the geometric reference data or the geometry of sensor are given or measured, the geometric distortion can be theoretically or systematically avoided. For example, the geometry of a lens camera is given by the collinearity equation with calibrated focal length parameters of lens distortions. The tangent correction for an optical mechanical scanner is a type of system correction.
Non-systematic correction – Polynomials to transform from a geographic coordinate system to an image coordinate system, or vice versa, will be determined with given coordinates of ground control points using the least square method. The accuracy depends on the order of the polynomials, and the number and distribution of ground control points.
Combine method – First the systematic correction is applied, then the residual errors will be reduced using lower order polynomials. Usually the goal of geometric correction is to obtain an error within plus or minus one pixel of its true position.

Question 26 (9 marks): We often express the accuracy of a classification in terms of (a) user accuracy, and (b) producer accuracy.

Explain these two terms.
Is it possible for a classification to have a high producer accuracy, but a low user accuracy? Explain your answer.
Is it possible for a classification to have a high user accuracy, but a low producer accuracy? Explain your answer.

Answer:

User’s Accuracy – The probability that a pixel classified as a particular habitat on the image is actually that habitat. Accuracy from the point of view of a map user (not a map maker). How often is the type the map says should be there really there? Can be computed (and reported) for each thematic class. User accuracy = 100 – commission error. User’s accuracies are computed by dividing the number of correctly classified pixels in each category by the total number of pixels that were classified in that category (the row total).
Producer’s Accuracy – map accuracy from the point of view of the map maker (producer). The probability that a pixel in a given habitat category will have been classified correctly on the image. How often are real features on the ground correctly shown on the map? Can be computed (and reported) for each thematic class. Producer accuracy = 100 – omission error. Producer’s accuracies result from dividing the number of correctly classified pixels in each category (the column total). It is calculated by dividing the number of correct pixels for a class by the actual number of ground truth pixels for that class.
Yes, it is possible for a classification to have a high producer accuracy, but a low user accuracy. What this would indicate is that the analyst did a good job of training the classifier using the calibration information available, but the resultant classification was poor, based on information from the validation pixels. Yes, it is possible for a classification to have a high user accuracy, but a low producer accuracy. You can have high user’s accuracy when the error of commission is low but also have low producer’s accuracy when you have higher error of omission.

Question 27 (4 marks): You have been given red and near-infrared spectral Landsat TM bands for a study region. You plan to (a) use the image bands to create an NDVI image for the study area; (b) define a regression relationship between pixel NDVI values to measurements of biomass taken in the field; and (c) apply this relationship to all pixels in the image to create a map of biomass for the study area. Do you need to do an atmospheric correction on your red and near-infrared image bands? Explain your answer.

Answer:

NDVI is a commonly used vegetation index that is calculated: NDVI = (NIR- red)/(NIR + red), where NDVI = Normalized Difference Vegetation Index, NIR = Near Infrared, as the NDVI measure has been found to make it easier to differentiate between vegetation types and has also been useful in biomass estimation.
NDVI is a unitless measure with a positive correlation to vegetation amount or health. A nonlinear trendline may result from the saturation of NDVI values levels of high biomass. At levels of high biomass, the sensor is unable to pick up further variations in the NIR, leading to the nonlinear trend. This nonlinear trend has been observed often over forested environments. Another explanation is that the NDVI – biomass relationship is actually a linear relationship, but only appears to be nonlinear because the full range of biomass values have not been sampled (i.e. if we had sampled over a wider range of biomass values, then the relationship would be linear).
???

Question 28 (3 marks): What are the advantages of using histogram-equalized contrast stretches over simple linear contrast stretches?

Answer: Histogram equalization modeling techniques provide a sophisticated method for modifying the dynamic range and contrast of an image by altering that image such that its intensity histogram has a desired shape. Unlike contrast stretching, histogram modeling operators may employ non-linear and non-monotonic transfer functions to map between pixel intensity values in the input and output images. Histogram equalization employs a monotonic, non-linear mapping which re-assigns the intensity values of pixels in the input image such that the output image contains a uniform distribution of intensities (i.e. a flat histogram). This technique is used in image comparison processes (because it is effective in detail enhancement) and in the correction of non-linear effects introduced by, say, a digitizer or display system. It maximizes the information content of the image.

Question 29 (4 marks): Explain the concepts of low-frequency and high-frequency filtering in the spatial domain.

Answer: Low spatial frequencies occur when pixel values change gradually. The filter emphasizes the low frequency. Simple filter: use pixel brightness to make a new brightness filter based on means. “Denoise”, blur more severe as window size increases. High spatial frequencies occur when pixel values change rapidly. The filter emphasizes the high frequency. These filters usually applied to enhance local variations. Can be applied with weighted windows or by subtracting LFF from twice original pixel value.

Question 30 (2 marks): What is the main difference between the Sobel Edge Detector filter and other filters?

Answer: The Sobel Edge Detector filter simultaneously uses two 3x3 filters, is non-linear edge detection, and a pixel is considered an edge if it exceeds some user-specified threshold. The Sobel Edge Detector filter is used to create edge maps, where white lines (edges) appear on a black background. Furthermore, the Sobel Edge Detector calculates the gradient of the image, giving the direction of largest increase from light to dark and rate of change.

Question 31 (6 marks): Explain the concept of Principal Components Analysis (PCA) and explain why it is useful in remote sensing.

Answer: PCA is a methodology to reduce the number of redundant variables into Principal Components (main ones). Reduces information in n-band data set into fewer bands while maintaining as much information as possible. Principle Components can be used in place of original data. This method is great at simplifying data for modeling purposes (biophysical lab).

Question 32 (4 marks): Explain the concept of image fusion / pan-sharpening. Describe one technique for undertaking image fusion / pan-sharpening.

Answer: Image fusion combines many images to add more information than available in one image. It can occur at the pixel, feature or decision processing level. Pan sharpening transforms coarse spatial resolution multi-spectral to fine spatial resolution colour by fusing with fine spatial resolution (black and white) the Brovey Method limited to 3 bands, significant colour distortion.

Question 33 (4 marks): When undertaking a supervised classification, what steps should one take to make sure that a training data set most accurately represents the spectral variation present within each information class.

Answer: Ensure training areas are:

cover enough pixels to encompass total variation (minimum 100 pixels)
5-10 training fields through image
smaller training fields capture variability better compared to a few large ones but must be large enough to accurately estimate spectral properties. Too large though and may have undesirable variation
should be located through image, careful about edges to avoid transitional regions
ensure data within training classes are uniform and non bimodal. If the class is not spectrally uniform, maybe better training with 2 classes and then merged after. Afterwards, inspect frequency histograms to note bimodal tendencies or skewness. Split training classes if necessary.

Question 34 (2 marks): Explain how the analysis of training class histograms can improve the definition of training classes for a supervised classification.

Answer: Training class histograms show frequency distribution of spectral data for a given classification. Also allows bimodaly to be identified, data split into subclasses if necessary.

Question 35 (8 marks): Describe how the following supervised classifications work: (a) Box classifier; (b) Minimum distance to means classifier; (c) Nearest neighbour classifier; and (d) Maximum likelihood classifier.

Answer:

based upon range of pixel values from training area. Class boundary defined by box around class mean in spectral space (one band vs another) (usually ±1SD. Pixel is classified based upon which box it falls in. Classified if unknown if not in any box. Fast but problems if class ranges overlap
uses mean pixel value in training classes. A pixel is classified by computing the distance between the value and each of class averages. Classified to nearest class mean. Unknown if beyond some predefined distance. Fast, but insensitive to different quantities of variance in spectral response, not used when spectral classes are similar and high variance.
uses value of nearest pixel in training data (and distance to pixel), pixel is classified based on training class which is closest in spectral distance. Nearest-neighbour, k- nearest neighbour and k-nearest-neighbour distance weighted. Slow because distance calculations between pixel and all pixels in training data. Not used when training data is not well separated in spectral space.
assumes training data and classes are normally distributed. Each class has a probability density function that says the probability of a pixel belonging to a class. Pixel is assigned to most likely class, or unknown if below some threshold. Slow!! Not always effective.

Question 36 (1 marks): Maximum likelihood classifiers always produce better classifications than any other classifier. True or False?

Answer: False. It is computationally intensive and does not always produce superiour results.

Question 37 (3 marks): List three advantages and three disadvantages of supervised classification.

Answer: Advantages of Supervised Classification:

Analyst defines informational classes for a specific purpose within study region.
Allows comparison to other classifications of same or neighboring regions.
Classification is tied to specific areas of known identity (training areas).
Analyst may be able to detect serious errors by examining training data.

Disadvantages of Supervised Classification:

Analyst imposes a classification structure upon the data.
Training data is defined on informational classes and not spectral classes.
Training data may not be representative of entire image conditions.

Question 38 (2 marks): What is fuzzy classification, and what is its main advantage over traditional “crisp logic” classification?

Answer: Fuzzy supervised classification allows “mixed pixels” to be partial members of many classes. E.g. Pixel is 0.3 = water; 0.7 = forest. Crisp logic assigns one thematic class to a pixel, ensuring many pixels will be in error to some degree.

Question 39 (2 marks): Why do we often undertake post-classification smoothing after we generate an image classification?.

Answer: Traditional per-pixel classifiers may lead to “salt and pepper” appearance. It is often necessary to smooth the classified output to show only the dominant classification.

Question 40 (5 marks): List and briefly explain the five remote sensing considerations when doing a multi-temporal change detection analysis.

Answer: Remote Sensing Considerations when using multiple image dates:

Temporal resolution; approximately the same time of day, same anniversary dates, etc. Eliminates duirnal and seasonal sun angle affects that can confuse the interpretation of multi-date imagery.
Look angle; influence of look angle on surface reflectance can be significant (different effects of forward scatter and back-scatter).
Spatial resolution; Spatial resolution is determined by the features to be observed. Imagery should be registered to common map projection with high accuracy.
Spectral resolution; Spectral resolution of sensor used should be sufficient to sense reflectances in spectral regions of interest. The same sensor should be used to collect data on multiple dates.
Radiometric resolution; Output from sensors reach the analyst as a set of digital numbers (DN). Each value is recorded as a series of binary digits (bits; 8 bits = 1 byte). Range of brightness values in an image is given by the number of bits available. Should thus keep constant when using multiple image dates.

Question 41 (4 marks): What are the differences between image differencing and image ratioing techniques for undertaking change detection analysis? Under what circumstances would you choose to use the image differencing technique? Under what circumstances would you choose to use the image ratioing technique?

Answer: Difference is absolute vs relative change. Small absolute changes can be important because they may indicate large relative changes on the landscape. Image differencing subtracts a first-date image from a second date image pixel-by-pixel. A pixel that has not changed yields a value of 0. Image ratioing ratios a first-date image to a second-date image pixel-by-pixel. A pixel that has not changed yields a value of 1. Use image differencing for identifying regions of change and no-change. Data such as vegetation indices (e.g. NDVI: Vegetation Index Differencing). Use image ratioing for reducing impacts of sun angle, shadow and topography. Cannot be used to define thresholds.

Question 42 (3 marks): List three advantages that microwave remote sensing has over optical remote sensing methods.

Answer:

Can penetrate the atmosphere under most conditions, including cloud, allowing all-weather remote sensing.
Give different view of terrain compared to visible and thermal imaging.
May penetrate vegetation, sand, and surface layers of snow.
Has own illumination, and the angle of illumination can be controlled.
Enables resolution to be independent of distance to the object (SAR).
May operate simultaneously in several wavelengths (frequencies), having multi-frequency potential.
Can produce overlapping images for stereoscopic viewing and RADARgrammetry.
Supports interferometric operation using two antennas for 3-D mapping.

Question 43 (4 marks): Explain the differences between (a) ground-range, and (b) slant-range in microwave remote sensing. Will two objects 100m apart in the slant range be 100m apart in the ground range?

Answer:

Ground-range geometry: Proper (x,y) position relative to one another.
Slant-range geometry: Uncorrected images displayed as slant-range, the straight-line distance between RADAR and the target. Can be converted to ground range using Pythagorean theorem.
No? but the slant-range will be closer to the ground-range distance as the distance from the sensor increases.

Question 44 (6 marks): What are three types of geometric distortion that occur in microwave imagery? Explain each distortion.

Answer: Relief displacement in RADAR images is in direction towards Radar antenna.

Foreshortening: When terrain that sloped towards RADAR appears compressed. Difficult to correct. Influenced by objects height (higher object= greater foreshortening), depression angle (greater depression = greater foreshortening), location of objects across track range (objects near range are foreshortened more that in far range).
Layover: Is an extreme case of foreshortening. Occurs when the incident angle is small the foreslope. Causes summits of tall features to lay over their bases. Cannot be corrected even if surface topography is known. Care must be taken when interpreting areas where layover exists (mountainous).
Shadow: Obscure important information, no information is recorded in shadowed areas. Features casting shadows in far range may have backslope illumination in near range. Shadow can provide important information on landscape features.

Question 45 (4 marks): What surface environmental factors affect the strength of RADAR back-scatter?

Answer:

Surface roughness characteristics
Sensor properties (λ, depression angle, polarization)
Surface electrical characteristics
Vegetation characteristics
Surface moisture characteristics

Getting an American Pilot Certificate Using a Canadian PPL

Tue, 15 Mar 2011 22:45:58 +0000

At one point I was in New Mexico and wanted to have a bit of flying there to see what it’s like. Of course, though, I couldn’t fly an American aircraft with just a Canadian PPL. Well I’ve done some research on the FAA website and come up with two options for getting my hands on an American PPL. One is a license and the other is a certificate, but you will have to decide which is better suited to your needs.

The first is called a “conversion”, and means that the States gives the holder of a Canadian PPL a full & unrestricted American PPL. It’s quite the process, though, and includes an extensive written test. In addition, all recency of experience requirements for the American PPL must be met before any flight is made using the converted license. Details can be found at gleim.com.

Submit a Verification of Authenticity request form to the FAA
Receive written notification acknowledging verification of the information
Pass a written knowledge test with at least 70%
Travel to an FSDO, present all documents, and obtain a converted license

The second, more friendly, option is termed a “certificate issued on the basis of a foreign license.” Specifically, the States trusts the Canadian license enough to allow a northern pilot to fly American planes - as long as that pilot meets all conditions required of them by their PPL. The process to get one of these is slightly less rigorous than for conversion. Details can be found at the FAA website.

Mail or fax a Verification of Authenticity form to the FAA and wait 45-90 days
Visit an FSDO and obtain a paper certificate
Get a final certificate in the mail

So the long story is I am going to go for the latter option, the certificate. I’ve downloaded the form and just have to fax it in then wait for a response. I will update this article once I’ve reached that stage. Hopefully it’ll be spring by that time and I’ll be able to take my motorcycle for a ride across the border to pick up the paper copy!

UPDATE (March 16, 2011): I have received a letter from the States saying that my license has been verified by Transport Canada, and now I am waiting for a phone call to schedule a time to drive to the Albany NY FSDO to pick up the paper copy of my new certificate!

UPDATE (August 15, 2011): After several emails with a rep at the Albany FSDO, I was given an appointment today to come sign for the new certificate. The trip there was a nice 6 hour drive in good weather. There was one form that had to filled out with my personal information as well as my flying hours. Note that they (Americans) want the hours broken down differently than what is typically kept in a Canadian logbook. For example, their “solo” time is literally “solo” - no passengers are allowed for it to count. Anyhow, after everything was filled out exactly as required, I was handed a newly printed temporary certificate. I’ll post one more update when I get the final, credit-card-style license in the mail!

Flight Time and Ratings Summary

Mon, 07 Feb 2011 19:22:28 +0000

Breakdown of all my flight time into PIC, dual, instrument, cross-country + more. Also a list of licenses and ratings I hold.

Licenses and Ratings:

Name	Date Effective
Glider Pilot License	August 2006
Private Pilot License	November 2009
Night Rating	April 2014

Aircraft Flown:

Aircraft Name	Time on Type
Cessna 150M	~80 hours
Diamond Katana DA20	~45 hours
Schweizer SGS-232-A	~12 hours
Piper Warrior PA-28	~5 hours
Cessna 172	~4 hours
Grob G103	~1 hour
Grob 115C	~1 hour

Time Breakdown:

Single-Engine Aircraft	Hours
Day Dual	82.6
Day PIC*	49.4
Night Dual	11.0
Night PIC	6.2
Total	149.2

Cross-Country	Hours
Dual	6.3
PIC	7.5
Night	2.6
Total	16.4

Instrument	Hours
Actual	0.0
Hood	10.0
Total	10.0

Gliding	Hours
Dual	4.8
PIC	8.6
Total	13.4

* PIC stands for Pilot-in-command, and refers to the person in control of an aircraft.

First Thoughts on the Diamond Katana DA20

Thu, 09 Dec 2010 22:28:52 +0000

The Diamond Katana DA20 A1 is a light trainer and comparable to the Cessna 150, but the similarities stop there. The Katana has a low-wing, tandem-seat, carbon-fiber/plastic composite airframe, and has numerous advantages including a constant speed prop, control sticks (think fighters), and a greater usable payload.

There are two trainer variants of this aircraft, the DA20 A1 and the DA20 C1. Diamond began producing the A1 in 1994 as an improvement to the DV20, which in turn was a development of the HK36R motorglider. Flight schools found the 80HP powerplant to be lacking, and Diamond responded with the C1 variant in 1998. The C1 crams a 125HP engine into the same frame as the A1, and gives the aircraft an extra 34 knots of cruise airspeed.

I flew an A1 for the first time last week at OAS, and although the weather was not perfect and false horizons made things tricky, the plane definitely gets my approval. One thing to pay attention to, coming from the 150, is a much smaller and lower engine cowling, giving a mistaken belief that the plane is in a nose-down attitude, and causing the pilot to pitch up. I did exactly this several times, and only caught the mistake because the VSI showed an unexpected 300 ft/min climb.

The constant speed prop was something I was apprehensive about simply because of the extra layer of complexity. In practice, though, the correct prop RPM is a simple function of the phase of flight: full fine for takeoff/landing/airwork, and a coarser setting for cruise. The end result is a very quiet cruise, with the prop’s 1900-2000 RPM hiding the fact that the engine is spinning at 2400. Cruise is also made better by the beautiful overhead canopy which significantly improves visibility.

The plane features auto-leaning dual carburettors, and it is nice to not worry about the mixture. Unfortunately, from what I saw on this flight it seems that the system is not perfect, and the engine required carb heat to reduce roughness at a certain power setting.

Finally, the stick control is very nice compared to the yoke found in the Cessna 150, and reduces muscle fatigue due to its proximity to the pilot. All in all, the Katana DA20 made my flight very enjoyable, and I feel confident about picking it up quickly.

Weekend Hike in the Frontenac Provincial Park

Sat, 16 Oct 2010 02:08:18 +0000

Frontenac Provincial Park is a 52-square-kilometre region of the Frontenac Axis, the southern arm of the Canadian Shield. Established in 1974, it offers 48 campsites accessible by either well-maintained trails or canoe. Just slightly over two hours south-west of Ottawa, the park has a mixture of trails with a wide range of difficulty.

Intro: The Park, Weather, and GPS
Planned Route
The Hike: Scenery, Terrain, and Wildlife
GPS Track Log: Successes and Mistakes

Intro: The Park, Weather, and GPS

I was recently invited along on a weekend hiking trip through the Frontenac area with three other adults. In our case we got to the park around noon on Friday, April 30, and made it back to our car by mid-afternoon on Sunday, May 2. The weather was forecast to be mostly rain, but instead we were given clear skies and warm temperatures. A thunderstorm drenched us for an hour on Saturday night, but we had already made camp and it didn’t affect us.

This was my first time to the Park, and I wanted to use my BlackBerry’s GPS to record our track so I could review it on a computer once I got home. I wrote a short program in Java that polled the GPS receiver every so often, and saved its coordinates to a log file, along with its current altitude and speed. See the attached Google Earth KMZ file for the final result - at the very least the view is interesting, and it is easily useful for anyone planning a similar route.

Planned Route

As I mentioned above, we arrived at the Park’s main office at noon the first day, and paid our entrance fees. As of our trip each adult cost $11 per night, but for current pricing please visit the Park’s website. Campsites are not available for pre-registration, and are run on a first-come-first-served basis. Our relatively early arrival compared to a typical weekend meant that we got our choice of sites.

We chose our route the day of, based on the previous experience of a person in our group. This is what we agreed on:

Day/Leg 1: hike to Campsite #1 (~9 km)
Day/Leg 2: hike to Campsite #13 (~10 km)
Day/Leg 3: hike back to car (~10 km)

The Hike: Scenery, Terrain, and Wildlife

Leaving our car at Arab Lake Parking Lot, we set out towards Campsite #1. The first leg was the toughest, often cutting directly across contour lines on the map. Getting halfway along the first leg took about two thirds of the time we spent hiking that day. After reaching a certain cliff by a large lake (possibly at 44.50370, -76.51545 but I can’t be sure), the rest of the trail becomes much simpler. The slopes die off into a series of grassy fields that make for easy hiking, albeit without shade. We reached camp at 4:30pm, after a total of exactly four hours. There were numerous black flies that made for an uncomfortable evening, but they disappeared at dusk. Side note: we were advised that it would take five hours to arrive at camp. Side side note: we were an adult-only group, so your mileage will vary.

If you don’t mind me adding another paragraph in here, I’d like to mention the campsites themselves. Every one we saw (#1, #3, #5, #13) was well maintained and provided sandy, elevated, 14-square-foot tent bases. There is also a fireplace with a grill at each site, as well as a latrine.

At 9:15 the next morning (Saturday) we strapped on our packs and headed out of camp towards the main trail that would lead us to #13. Unfortunately, we all missed the sign and, being unsure how far back the entrance to the trail was, sped 600 meters the wrong way before turning around. Lesson learned (or so we thought): read every sign carefully.

This portion of the trail is filled with beautiful scenery, including waterfalls. At one point the trail winds along the top of an elevated ridge between two lakes. There are beaver dams that hold massive amounts of water back as we walk along, several feet under the water level. Trillium flowers can be seen occasionally as well as small animals such as chipmunks. In fact, as we descended one hill, one of us nearly put his hand on a black rat snake that was lying in a tree.

After about four hours of hiking we reached Campsite #5, which includes a sandy beach on the north-east end of Big Salmon Lake. We had less than 2.5 kilometres left, but there was a threat of a thunderstorm so we continued on after refilling our water bottles. With just 1.3 klicks to go, we reached the turnoff for Campsite #13. It would seem we did not learn our lesson from the same morning, because we glossed over the sign and once again went the wrong way. Fortunately this time we realized our mistake after only 400 meters, and found our way to camp. Total time en-route that day, excluding mistakes, was five hours.

Campsite #13, being 1.3 kilometres off the main trail, would seem rather out of the way, but actually both the trail and the site itself make up for the distance. We were also favoured by an absence of bugs - perhaps they were frightened off by the impressive thunderstorm that rolled through just after we made camp.

After making a late start to our third day, Sunday, we retraced our errant steps from the evening before, but this time knowing they were in the right direction. Between two and three kilometres out, we met with an old logging road that let us move quickly. The road eventually reverted to a trail, but one that was gentler than the trails from the previous days. We quickly made Campsite #3 after just three hours, and ate lunch by the southern end of Big Salmon Lake. Despite the time of year, a couple of us went swimming. Honestly, “swimming” is the word for it, as the pictures prove!

An hour later we were back at the car, turning our cell phones on again, and shedding our walking sticks. Fifteen minutes later our legs were doing nothing yet we were moving North along Bedford Road, and the trip was behind us.

GPS Track Log: Successes and Mistakes

When we first set out from our car, I told my BlackBerry to record our location every ten minutes to save battery. Unfortunately, that interval is long enough for the GPS chip to “cool down” between updates, causing sporadic logging. The upshot is that that first leg was recorded using only two points, which is pretty horrible. I replaced that leg with a simple straight line when I manipulated the data at home.

That problem did not happen for the rest of the trip, since I discovered that polling the GPS takes virtually zero battery, and I could afford to set the refresh interval at five seconds. Five seconds is so short that the resulting high-resolution log is fun to look at.

You will notice a file below entitled Frontenac_Hiking_Spring_2010.kmz. It is a Google Earth file containing our track, campsites, photos, and a narrated tour. It is important to note that it does not internally contain any large files, for instance the the photos and the tour audio. Those are hosted on this website, which means that in order to get the full experience you will need an active Internet connection.

Cheers!

Download the GPS log in KMZ format: Frontenac_Hiking_Spring_2010.kmz (100 KB)

How to Get Tether Working with a Mac and a Fido BlackBerry Bold 9000

Sat, 16 Oct 2010 01:54:55 +0000

This is the configuration that worked for me. Please note that I was unable to get anything working when using the USB method.

BlackBerry Model: Bold 9000
BlackBerry OS: 5.0
Computer OS: Mac OS X 10.6.3 Snow Leopard
APN Address: internet.fido.ca
APN Username: fido
APN Password: fido
BlackBerry Desktop Manager: Uninstalled completely
Tether Method: Bluetooth
Computer WiFi: Disabled
Cell Phone Provider: Fido
Country: Canada

Troubleshooting:

If your BlackBerry “failed to find available port”, then your APN settings are definitely wrong. Go to Main Menu -> Settings -> Advanced Settings -> TCP/IP, and fill in the relevant details from the previous section of this article.
If in doubt, double-check that your phone’s Bluetooth connection is turned on, your computer’s Bluetooth connection is on, and the two are paired correctly.
If still in doubt, grant Tether all permissions on your BlackBerry. Go to Main Menu -> Settings -> Applications, find Tether in the list, edit its permissions, and allow everything in the “Connections” category.

Images:

Soaring a Grob

Tue, 12 Oct 2010 20:11:33 +0000

Decided to go soaring last Saturday - haven’t been in a glider for about a year and a half, and the Rideau Valley Soaring Club offers intro flights in their German-made Grob 103, which is way more advanced than the Schweizer SGS-233A training glider I learned on.

I arrived mid-morning and helped the members there get a student up in a glider for a couple flights, then had to go pull out some cash from a bank because the club doesn’t accept credit.

The flight itself was terrific. We elected to release from the tow plane at a pretty high altitude, about 3,000’ agl, to give us the best chance at a long flight. Unfortunately, within about 20 minutes we had not found any decent lift and had dropped to 1,100’ agl. While preparing to land, though, we hit some nice lift and over the next 45 minutes had a fun time working the thermals. Ultimately we climbed up to about 2,500’ before heading in for a landing.

The flight was 1.2 hours in total, which was well worth the intro flight fee. It reminded me of how exciting soaring can be - the Air Cadets do not promote soaring itself, just the basics of gliding. I think I just might get a membership at the RVS club next year and see how far I can get :)

Landscaping: Completing a Project

Sat, 26 Jun 2010 07:41:08 +0000

All of us know what a subdivision looks like - we’ve all driven through them and visited friends who live in one. All the houses (ignore their similarity for a second) look perfectly finished, with siding or faux stonework and sod covering the yard and lining the driveway. Well I am in the dark about most of the building process, but I can speak about the efforts of the landscaping crews who prepare the ground and lay sod and are often the last people to work on a property.

This summer I departed from the office-type job I held last year and took on a landscaping job for a company based out of Stittsville that makes a habit of choosing commercial and city projects rather than single residential locations. My employer, Meyknecht-Lischer, is also a subcontractor to a custom home builder, Cardel Homes. In Ottawa, Cardel sells subdivision homes in places such as Kanata, Richmond, and Orleans.

I was placed on a three-man crew, which eventually grew to have six guys including myself. I remember my first task was to dig out a window well. Mostly, though, our crew routinely finishes houses: prepare the subgrade earth, add four inches of topsoil, and lay down rolls of sod. I wish I had pictures of this since it is amazing how much the look of a property can change with only the yard actually being improved. I will have to have a point of taking some before I exit the company the first week of August.

And now a word about the “how”.

Scene One: We arrive on site, walk around, and comment on the job. Okay, truthfully it’s the foreman who does most of the walking and commenting :)
Scene Two: Everyone grabs a shovel and flattens the subgrade - usually rock in Kanata, and clay in Orleans.
Scene Three: Dump trucks drop off loads of topsoil on front lawns and a bulldozer does most of the spreading. The minions (me + others) then push topsoil up to the walls of the house and use rakes to make everything slope the right way.
Scene Four: If we are sodding large areas, we use a sod layer (I don’t know the people in the picture), otherwise we lay smaller rolls by hand. The company water truck is called. Wow, look at that lawn!

Occasionally, our crew is assigned some city work, say replacing stone paving blocks around the War Memorial at Confederation Square in downtown Ottawa, or repairing sod at old City Hall. Incidentally, I took pictures of each stage of work at the latter site.

Something else I would like to offer is a description of the speed we work at. Although the progress made in a (typically 10-hour) day obviously varies greatly, the ratio of preparing for sodding to actually sodding is usually about 50:50. I should add that given that ratio, it is odd how tiring I find raking and grading compared to laying sod. We can sod either the front or the back lawns of 5-7 houses in a day.

Finally, a shout out to Eva for finding the job for me, Chris for his priceless humour, Tyler for his very good voice imitation skills as well as his memory for lines from movies, Lanre for both his experience and not minding our sometimes shocking jokes about francophones, Dan for his skills with a rake, and Kevin for the phrases “have a boo” and “have an eye”.

UPDATE: Ten months later, I came back and snapped a picture with my phone. Here’s the “after” picture of our work:

Trimtab.ca

2 Pro Tips for Your Next Airline Flight

Pro Tip 1: Book each leg as its own itinerary

Pro Tip 2: When late, never give up on a flight

Use OS X for Conveniently Remembering Developer Info

Carleton University GEOM4003 (Remote Sensing) Study Guide

Getting an American Pilot Certificate Using a Canadian PPL

Flight Time and Ratings Summary

Licenses and Ratings:

Aircraft Flown:

Time Breakdown:

First Thoughts on the Diamond Katana DA20

Weekend Hike in the Frontenac Provincial Park

Table of Contents

Intro: The Park, Weather, and GPS

Planned Route

The Hike: Scenery, Terrain, and Wildlife

GPS Track Log: Successes and Mistakes

How to Get Tether Working with a Mac and a Fido BlackBerry Bold 9000

Soaring a Grob

Landscaping: Completing a Project