Chapter 1 - Remote Sensing

Question 1

Advantages of Remote Sensing

Answer 1

unobtrusive
systematic data collection - helps with sampling bias that is inherent in lots of GIS
provides fundamental information, unlike lots of GIScience

Question 2

Limitations of Remote Sensing

Answer 2

Oversold sometimes
Human error introduced in design specifications
Active remote sensing that emits EMR (LIDAR, RADAR, SONAR) can be obtrusive
Can be expensive (greatest expense is a well trained analyst)
Machines can become uncalibrated

Question 3

Remote sensing process

Answer 3

1. Hypothesis to be tested is defined using a specific type of logic and appropriate processing model
2. In situ data & collateral data necessary to calibrate remote sensor is collected
3. Remote sensor data are collected (ideally at the same time as calibration data)
4. In situ and remote sensor data are processed using analog, digital image processing, modelling, and n-dimensional visualization
5. Metadata, processing lineage, and accuracy are provided & results communicated

Question 4

Biophysical variables

Answer 4

Some can be measured with remote sensing such as:
water vapor in air
soil moisture
normalized difference vegetation index (nvdi)
etc.

Question 5

Hybrid variables

Answer 5

modeled by combining other variables together

Question 6

Active remote sensing (and what it’s input is a function of)

Answer 6

emit EMR, and reabsorb as L = f(lamba, sxyz, t, theta, P, omega)
where lamba = wavelength
sxyz = location of pixel and its size
t = temporal info
theta = angle between radiation source & terrain target of interest
P = polarization of back-scattered energy
omega = radiometric resolution

Question 7

Spectral resolution

Answer 7

number and dimension of specific wavelength intervals (bands)

Question 8

Nominal Spectral Resolution

Answer 8

What we call certain bands. Bands follow a gaussian distribution curve and are nominally defined/ separated at Full Width at Half Maximum of intensity

Question 9

Hyperspectral

Answer 9

records hundreds of bands

Question 10

Ultraspectral

Answer 10

records many hundreds of bands

Question 11

Spatial resolution

Answer 11

smallest angular or linear separation between two objects that can be resolved by a remote sensing system.
Can be calibrated by placing black and white tarps on the ground, obtaining areal photography, computing # of resolvable line pairs per millimeter

Question 12

Nominal Spatial Resolution

Answer 12

D = beta* H
diameter = instantaneous field of view * height above the ground
smaller = greater spatial resolving power
- historical landsat MSS data not useful for urban applications because of large spatial resolution
- rule of thumb: to detect a feature, resolution should be smaller than 1/2 of the smallest part of the feature

Question 13

Sampling density

Answer 13

Some systems such as LIDAR use pulses of EMR at various time intervals - the spatial resolution applies to the pulse and reception of the pulse, but the sampling density is more important for analysis & has to do with interval/ frequency of observations

Question 14

Temporal Resolution

Answer 14

How often samples of 1 location are taken

Question 15

Nadir

Answer 15

The point directly below a satellite. Some satellites can sample off-nadir, which introduces Bidirectional Reflectance Distribution Function issues

Question 16

Pulse length

Answer 16

length of time required to emit a pulse -> short = more precise distance measurement

Question 17

Radiometric resolution

Answer 17

Ability of a sensor to discern between greyscale levels, defined in bits

Question 18

Polarization

Answer 18

when waves aren’t distributed uniformly across all 360 degrees perpendicular to direction of propagation. Sunlight is weakly polarized, but becomes depolarized when it hits nonmetal objects.

Question 19

geniometer

Answer 19

documents changes in sensor radiance, L, caused by changing position of sensor & or source of illumination

Question 20

Pros/ Cons of suborbital viewing

Answer 20

Available on demand, higher resolution, available on demand

atmospheric turbulence can distort images & be difficult to correct for, expensive

Question 21

Analogue image processing pros/ cons

Answer 21

humans are great at compiling lots of info when looking at an image

precise measurement isn’t as easy, analysis isn’t repeatable, can’t store data,

Question 22

Radiometric correction of remote sensor data

Answer 22

noise or error introduced by the sensor may be able to be corrected for

Question 23

Geometric correction of remote sensor data

Answer 23

processes information so images are in proper place for GIS applications

Question 24

Hard vs. Fuzzy classification

Answer 24

hard = discrete mutually exclusive classes 
fuzzy = blend together

Question 25

Scene modeling components

Answer 25

1) scene model -> form & nature of energy & matter within scene & their spatial and temporal order 2) Atmospheric model -> relationship between atmosphere and energy entering & leaving 3) Sensor model -> behavior of sensor in response to energy influxes