GIS

Question 1

Accuracy Assessment: Sampling Scheme II?

Accepted Answer

1.random sampling •Random number generator is used to generate random x, y coordinates •Advantage: important assumption underlying many statistical techniques is satisfied •Disadvantage: random sampling may undersamplesmall but (possible) very important classes 2. systematic sampling •Sample along a line of coordinates (starting point chosen randomly) •Disadvantage: Not useful if periodicities exist in the data 3.stratified random sampling •Minimum number of samples are selected from each strata (i.e., land-cover category) •Assures even #s per class •Somewhat biased/circular

Question 2

Accuracy Assessment: Sampling Scheme III ?

Accepted Answer

4.stratified systematic unaligned sampling •Combines randomness and stratification with a systematic interval •Point(s) selected at random within a regularly spaced grid – commonly, one randomly placed point per regularly spaced grid cell

Question 3

Accuracy Assessment: Sampling Scheme IV?

Accepted Answer

5.cluster sampling •Sometimes it is difficult to go into the field to randomly selected sites; some of suggested that several samples can be collected at a single random location •BUT: each pixel in the cluster is not independent of the others •Clusters no larger than 10 pixels and certainly not larger than 25 pixels should be used (Congalton, 1988)

Question 4

Accuracy Assessment: Descriptive Statistics ?

Accepted Answer

Overall accuracy (Gesamtgenauigkeit) How many of the validation pixels were correctly classified (regardless of class)?User’s accuracy ~ Commission error (Nutzer-Genauigkeit) “How many of the pixels in the output image are correctly classified?” Producer’s accuracy ~ Omission error (Produzenten-Genauigkeit)“How many of my test pixels did I correctly classify for each class?” For each of the above, the accuracy is essentially the opposite of the error, such that: P Acc. + O Err. = 100% U Acc. + C Err. = 100%

Question 5

Accuracy Assessment: Kappa analysis?

Accepted Answer

Κappa Coefficient of Agreement: Kappa analysis yields a statistic, , which is an estimate of Kappa. •measure of agreement or accuracy between the remote sensing–derived classification map and the reference data as indicated by •(1) the major diagonal, and •(2) the chance agreement, which is indicated by the row and column totals (referred to as marginals)

Question 6

Unsupervised classification?

Accepted Answer

In an unsupervised classification, the identities of land-cover types to be specified as classes within a scene are not generally known a priori because ground reference information is lacking or surface features within the scene are not well defined. •The computer is instructed to group pixels with similar spectral characteristics into unique clusters according to some statistically determined criteria. •Then re-label and combine the spectral clusters into discrete classes.

Question 7

ISODATA Algorithm ?

Accepted Answer

The ISODATA algorithm is a modification of the k-means clustering algorithm, which includes: a)merging clusters if their separation distance in multi spectral feature space is below a user-specified threshold, and b)rules for splitting a single cluster into two clusters

Question 8

Unsupervised Classification Algorithms?

Accepted Answer

•k-Means•ISODATA (Iterative Self-Organizing Data Analysis Technique)

Question 9

Supervised Classification: Training Sites Selection ?

Accepted Answer

•Based on known a priori through a combination of fieldwork, map analysis, and personal experience •on-screen selection of polygonal training data (ROI), and/or •on-screen seeding of training data (not supported by all software). •The seed program begins at a single x, y location and evaluates neighboring pixel values in all bands of interest. Using criteria specified by the analyst, the seed algorithm expands outward like an amoeba as long as it finds pixels with spectral characteristics similar to the original seed pixel. This is a very effective way of collecting homogeneous training information. •From spectral library of field measurements

Question 10

Change detection – System considerations Summary I ?

Accepted Answer

Put simply: Don’t compare apples and oranges. Differences in the instrument or sensor – spectral bands from different sensors may have different band center wavelengths, or different spectral response functions Differences in the collection date and time – seasonal changes can impart big differences in scenes containing vegetation; differences in the season and time of day will also affect the solar azimuth and elevation. Differences in Atmospheric Conditions – weather conditions can affect atmospheric transmission and scattering (also seasonal changes; wind direction can be important – aerosols; water content in the atmosphere) Differences in Image Calibrations –work with images that are calibrated into the same units (such as radiance

Question 11

Change detection – System considerations Summary II ?

Accepted Answer

Differences in Image Resolution –differing pixel sizes can lead to false change detections Co-registration Accuracy – Accurately co-registered images are critical for change detection analyses

Question 12

Change detection – Algorithm ?

Accepted Answer

Change detection algorithms commonly include: •write function memory insertion •multi-date composite image •image algebra (e.g., band differencing, band ratioing) •spectral change vector analysis •post-classification comparison

Question 13

Vegetation Indices?

Accepted Answer

Vegetation Indices The Vegetation Index (VI) = DN NIR/DNRed(or DN4/DN3 for Landsat 5,7) is a ratio. Ratios suppress topographic shading because the cos(i) term appears in both numerator and denominator.

Question 14

Image Transformation - NDVI?

Accepted Answer

The most commonly used technique for identifying vegetation in satellite data is to compare the amount of visible light reflected by a particular place on Earth to the amount of infrared light..

Question 15

Image Transformation - LAI?

Accepted Answer

The Leaf Area Index or LAI is the ratio of total upper leaf surface of vegetation divided by the surface area of the land on which the vegetation grows. The LAI is a dimensionless value, typically ranging from 0 for bare ground to 6 for a dense broadleaf forest and up to 10+ for massive needleleaf forests.

Question 16

Verbesserung der Darstellung von digitalen Daten (Enhancements) ?

Accepted Answer

Enhancements are used to make it easier for visual interpretation and understanding ofimagery• Vergrößerung/Verkleinerung (Image Reduction/Magnification/Resampling) • Kontraststeigerung (Contrast Enhancements (Stretching, Filtering)) • Texturen herausbringen (Texture transformations) • Bildtransformationen und Bandverhältnisse (Image Transformations/band rationing)

Question 17

Vergrößerung und Verkleinerung?

Accepted Answer

Veränderungen der räumlichen Auflösung und/oder Größe der Pixel. Wird häufig auch als “resampling” bezeichnet.

Question 18

Digital Image Interpolation '?

Accepted Answer

1D example:Original Data -Interpolation provides a means of estimating the function at intermediate points, such as x=2.5. Nearest Neighbor •simplest interpolation method •locate the nearest data value, and assign the same value Linear Interpolation •one of the simplest methods •Linear interpolation is quick and easy, but it is not very precise Polynomial interpolation is a generalization of linear interpolation. • linear interpolant is a linear function • replace this interpolant by a polynomial of higher degree Artifacts are known to occur at the beginning and ending of a function (Runge’s phenomenon) Spline Interpolation • linear interpolation uses a linear function for each of intervals between points • Spline interpolation uses low-degree polynomials in each of the intervals • Chooses polynomial pieces such that they fit smoothly together The resulting function is called a spline.

Question 19

Nearest Neighbor ?

Accepted Answer

Nearest neighbor is the most basic and requires the least processing time of all the interpolation algorithms because it only considers one pixel – the closest one to the interpolated point.This has the effect of simply making each pixel bigger.

Question 20

Nearest Neighbor? +/-

Accepted Answer

Strengths: • Fastest of the resampling algorithms • Retains original data Weaknesses:•If used on normal raster images, straight lines (such as roads and geologic units) are stair-stepped •Generally the results look very “blocky” and not smooth

Question 21

Bilinear Interpolation ?

Accepted Answer

Bilinear interpolation considers the closest 2x2 neighborhood of known pixel values surrounding the unknown pixel. It then takes a weighted average of these 4 pixels to arrive at its final interpolated value.This results in much smoother looking images than nearest neighbor.

Question 22

Bilinear Interpolation +/-?

Accepted Answer

Strengths: •Most reliable of the interpolation routines Weaknesses•Intermediate in terms of speed •Can not be used for categorized data

Question 23

Bicubic Interpolation (Or: Cubic Convolution) ?

Accepted Answer

Bicubic Interpolation goes one step beyond bilinear by considering the closest 4x4 neighborhood of known pixels – for a total of 16 pixels.Since these are at various distances from the unknown pixel, closer pixels are given a higher weighting in the calculation.Bicubic produces noticeably sharper images than the previous two methods, and is perhaps the ideal combination of processing time and output quality.For this reason it is a standard in many image editing programs (including Adobe Photoshop), printer drivers and in-camera interpolation.

Question 24

Bicubic Interpolation +/-?

Accepted Answer

Strengths: •Most theoretically correct of the three standard algorithms Weaknesses•Most computer intensive of the three standard algorithms •Sometimes can cause “ringing” at sharp edges (more in artifact section) •“overshooting” and other artifacts

Question 25

Which resampling method to use?

Accepted Answer

If you want to retain your original DN•ONLY Nearest Neighbor resampling allows to retain the original data If you want to produce a smooth output image•Bilinear Interpolation is a good choice between speed and accuracy If you want to produce a very smooth image and are not concerned about artifacts •Cubic Convolution is producing very smooth, resampled images

Question 26

Kontraststeigerung (contrast enhancements) ?

Accepted Answer

In raw imagery, the useful data often populates only a small portion of the available range of digital values (commonly 8 bits or 256 levels). Contrast enhancement involves changing the original values so that more of the available range is used, thereby increasing the contrast between targets and their backgrounds.

Question 27

Kontraststeigerung– Lineare Verzerrung?

Accepted Answer

By manipulating the range of digital values in an image, graphically represented by its histogram, we can apply various enhancements to the data. linear contrast stretch: identifying lower and upper bounds from the histogram (usually the minimum and maximum brightness values in the image) and applying a transformation to stretch this range to fill the full range.

Question 28

Texture I ?

Accepted Answer

•Texture transformations capture patterns among pixels; often used in classifications

Question 29

Spatial Transforms – Introduction I How can we transform a noisy image to a clearer (or filtered) image?

Accepted Answer

Spatial Transforms provide tools to extract or modify the spatial information in remote-sensing images. •some use only local image information (Convolution, Filtering) •some use the entire image (Fourier Transformation) Useful conceptfor understanding spatial filtering is that any image is made from spatial components at different scales: •For example, any image can be decomposed into a Low-Pass (LP) version and a High-Pass version (HP): image = LP + HP (or: HP = image – LP) Convolution Filters • Underlying operation in a convolution filter is the use of a moving window on the image • An operation is performed within the window – a calculated value is put in the output image, usually the same location as the center of the window in the input image •(window moves to next pixel...) •Convolution is one of the most useful tools in image processing Convolving an image involves the following 2 procedures: 1.A moving window contains an array of coefficients or weighting factors •arrays (or boxes) are referred to as masks, operators, or kernels 2.Kernel is moved throughout the original image and the DN at the center of the kernel in the convoluted output image is obtained by multiplying each coefficient in the kernel by the corresponding DN in the original image.

Question 30

Convolution IV – Low Pass Filter?

Accepted Answer

•Array filters are complementary •Application of convolution: •Low-Pass filter (LPF): ‘smoothing’ an image •High pass filter (HPF): ‘enhancing edges and sharpening’ an image

Question 31

Spectral properties of water ?

Accepted Answer

• molecular water absorption dominates the UV and yellow through NIR •Scattering is important in the violet, dark blue, and light blue portions of the spectrum (400 - 500 nm). This is the reason water appears blue to our eyes.

Question 32

Remote sensing of water and atmosphere?

Accepted Answer

Important hydrologic variables that can be remotely sensed: • Water-surface area (streams, rivers, ponds, lakes, reservoirs, and seas) • Water constituents (organic and inorganic) - Water depth (bathymetry) • Water-surface temperature • Cloud cover • Precipitation (rain and snowfall)

Question 33

Spectral properties of water: Suspended minerals ?

Accepted Answer

• Silicon, aluminum, and iron oxides are found in suspension in most natural water bodies with particles sizes including fine clay ( 3 - 4 μm in diameter), silt (5 - 40 μm), fine-grain sand (41 - 130 μm), and coarse grain sand (131 - 1250 μm) • Sediment sources vary from agriculture erosion, weathering of mountainous terrain and wave erosion on shores as examples • most suspended mineral sediment is concentrated in the inland and near-shore water bodies • clear, deep ocean far from shore rarely contains suspended minerals > 1 μm in diameter.

Question 34

Spectral properties of water ?

Accepted Answer

Reflectance peak shifts toward longer wavelengths as more suspended sediment is added• Visible wavelength range of 580-690 nm provide information on mineralogy of suspended particles (compare clay to silt) • NIR wavelength range of 714-880 nm are useful for determining the concentration of suspended minerals in surface waters.

Question 35

Spectral properties of water 2?

Accepted Answer

• Plankton is the generic term used to describe all living organisms (plant and animal) present in a water-body that cannot resist the current (unlike fish). Plankton may be subdivided further into algal plant organisms (phytoplankton), animal organisms (zooplankton), bacteria (bacterio -plankton), and lower plant forms such as algal fungi. • Decomposition of plankton creates dissolved organic matter (CO2, nitrogen, sulfur, phosphorous) in both fresh and salt water • We can also find humic substances and tannins in rivers (derived from soils), producing yellow and reddish tinges to water color.

Question 36

Remote sensing of water and atmosphere?

Accepted Answer

Important hydrologic variables that can be remotely sensed: •Water-surface area (streams, rivers, ponds, lakes, reservoirs, and seas) •Water constituents (organic and inorganic) •Water depth (bathymetry) •Water-surface temperature •Cloud cover •Precipitation (rain and snowfall)

Question 37

Passive remote sensing of water?

Accepted Answer

Passive remote sensing of water • Depth mapped based on the ratio of light reflected off of the river bottom after transmitting through a column of water. • Ratio determined using green and red light. • Blue is deep, red is shallow

Question 38

Sea Surface Temperatures?

Accepted Answer

Heat transfer and Sea Surface Temperatures Energy transfer mechanisms: Radiation, Conduction, Convection Convection transfers heat vertically through mixing, potentially hundreds of meters. Also includes latent heat exchange with the atmosphere. Water bodies have high thermal inertias (only a few degree difference between day and nighttime). Two of the most important thermal infrared remote sensing instruments include: NOAA Advanced Very High Resolution Radiometer (AVHRR) NOAA Geostationary Operational Environmental Satellite (GOES). The Gulf Stream is one of the major oceanic circulations that strongly influences the climate western Europe. It is a powerful, warm, and swift Atlantic ocean current that originates in the Gulf of Mexico, exits through the Strait of Florida, and follows the eastern coastlines of the US before crossing the Atlantic Ocean.

Question 39

Strahlungsintensität der Sonne und Erde?

Accepted Answer

• The Sun approximates a 6,000 K black body with a dominant wavelength of 0.48 μm (blue-green) • Earth approximates a 300 K black body with a dominant wavelength of 9.66 μm • The 6,000 K Sun produces 41% of its energy in the visible region from 0.4 - 0.7 μm (blue, green, and red light) • The other 59% of the energy is in wavelengths shorter than blue light (<0.4 μm) and longer than red light (>0.7 μm) • Eyes are only sensitive to light from 0.4 to 0.7 μm • Remote sensor detectors can be made sensitive to energy in the non-visible regions of the spectrum.

Question 40

Radiation at the Top of Atmosphere?

Accepted Answer

Parameters influencing the amount of radiation at the top of earth-atmosphere systems: Solar radiationsurface type – e.g. vegetation, snow/ice, clouds, aerosols Infrared radiationsurface temperature, greenhouse gases (water vapor, carbon dioxide, methane, ozone) and clouds Microwave radiationSurface type (e.g. snow/ice, soil moisture), rain,water vapor, clouds

Question 41

Multiple Scattering, First Surface Reflection and Absorption ?

Accepted Answer

• When light encounters a single particle in can be reflected, refracted (transmitted) or absorbed • When light encounters multiple particles it can be scattered • Represents a balance between reflection and absorption • First surface reflection only retains the color of the source• Additional “color” is produced only by absorption •The balance between first surface reflection and absorption depends on particle size (Surface/Volume Ratio

Question 42

Particle Size Effects?

Accepted Answer

•Effects of a Decrease in Particle Size •increases reflectance •decreases the expression of absorbers •caused by an increase in the amount of first surface reflection vs volume scattering

Question 43

Particle Size and Snow Reflectance?

Accepted Answer

•Why is this important? •Snow particle size evolves as it metamorphoses –Fresh snow tends to be fine grained, high reflectance –Old snow tends to darken as grain size increases

Question 44

Various types of DEMs ?

Accepted Answer

DEMs can be generated from... 1.Interpolation of survey points (total station, GPS) 2.Stereo Airphotos, Stereo Satellite Imagery (Stereogrammetry, Photogrammetry) 3.Digitizing topographic maps (contour lines) 4.Radar interferometric images (SRTM – Shuttle Radar Topographic Mission) 5.LiDAR– LIght Detection And Ranging

Question 45

GPS – Global Positioning System ?

Accepted Answer

•GPS: active sensor in the L band [1.57542 GHz (L1 signal) and 1.2276 GHz (L2 signal)] •GPS has one purpose: to determine the geographic x,y,z and time of a GPS detector. Every point on the planet has a unique geographic location, and can be measured at a specific time.

Question 46

Remote Sensing Systems: Human Eye ?

Accepted Answer

•Spatial Resolution: ~1-3 cm @ 20 m •Temporal Resolution: 22 images per second •Spectral Range and Resolution: 0.4-0.7μm (400-700nm) and millions of colors •Radiometric Resolution: ~16-32 shades B/W or ~100 colors

Question 47

The remote sensing “problem” ?

Accepted Answer

Energythat comes from the sun: • is radiated by atomic particles at the source (the Sun), • propagates through the vacuum of space at the speed of light, • interacts with the Earth's atmosphere, • interacts with the Earth's surface, • interacts with the Earth's atmosphere once again, and • finally reaches the remote sensor where it interacts with various optical systems, filters, emulsions, or detectors.

Question 48

Reflectance II ?

Accepted Answer

Specular reflection where surface is reflected is essentially smooth – the “mirror” effect Diffuse reflection on rough surface.[e.g. white paper, white powders and other materials reflect visible light in this diffuse manner.] Lambertian surface (LambertscheOberfläche) -one for which the radiant flux leaving the surface is constant for any angle of reflectance to the surface normal.

Question 49

Scattering ?

Accepted Answer

Rayleigh Scattering •particles with sizes < 1/10 λlight•Nitrogen and oxygen molecules upper troposphere The intensity of Rayleigh scattering varies inversely with the fourth power of the wavelength (λ-4). In other words, shorter wavelengths scattered more, especially blue. Mie scattering plays a role too. Mie Scattering •particles with sizes ~ λlight•Dust, pollen, smoke and water vapor throughout atmosphere Non-selective scattering •particles with sizes >> λlight•Water droplets and large dust particles

Question 50

LiDAR– Why use a laser?

Accepted Answer

ALS (Airborne Lidar System) take advantage of two of the unique properties of laser light: 1.The laser is monochromatic. It is one specific wavelength of light. The wavelength of light is determined by the lasing material used, usually 532 or 1064nm. Advantage: We know how specific wavelengths interact with the atmosphere and with materials. 2.The light is accurate and directional. A laser has a very narrow beam which remains concentrated over long distances. A flashlight (or Radar) on the other hand, releases energy in many directions, and the energy is weakened by diffusion. Advantage: The beam maintains its strength over long distances. 3mrad divergence = 30 cm at 1 km and 1.5m at 5 km

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