Geog 370 Final Exam

GPS.

fieldwork. primary data collection. want to be able to judge data quality

GPS

3 dimensional measurement system. uses radio signals from satellites. receiver generates signals at the same time. calculates time difference.

US GPS

created by dept of defense

trilateration

based on distance using minimum 3 satellites

NAVSTAR

31 active satellites. flown by air force. much higher than other satellites

ground control stations

satellites are monitored and controlled by ground sttaions. broadcast orbital data and clock corrections

satellites constellation

orbit earth every 12 hours. 6 orbital planes. keep 4-6 above horizon at any time

receivers

can read signals from several navstar satellites at once. use timing of radio signals to calculate position.

varying degrees of accuracy depending on

quality of receiver and its GPS chip

user operation

atmospheric conditions

current status of system

radio signals travel at speed of light

satellites and receivers generate exactly the same signal at exactly the same time

signal travel time=

offset time of satellite signal relative to receiver signal. multiply time by speed of light

trilateration

1 distance= sphere

2 distances= circle

3 = 2 pts

4= 1 pt

dont need mroe than 3 measurements, but want 4+ for timing problem and error minimization

position dilution of precision

if satellites are all close, worse measurement. idea pdop when theyre far apart

errors

satellites clock errors

ephemeris error (inaccuracy in location of objects)

receiver processing errors

tropospheric effects

multipath effects (reflecting structures)

(multiple all my pdop)

15-25m total

accuracy depends on what kind of gps equipment you use

common: recreational gps devices or mapping grade gps receievers

difference:

quality of gps chip (big)

antenna

data logging (entire NMEA system?)

sophistocation of attribute data entry software

error reduction strategies

point averaging

collect points over time and average them. can be done w most receivers

Wide area augmentation system (WAAS)

Provide GPS signal corrections, but only for US hemisphere & latitudes. Up to five times better! fine for medium scale mapping, but not high precision mapping. expensive too. only for use in planes. does not work on ground because you could be blocked

horizon elevation masking

ensure satellites are at least a user-set angle about the horizon

reduces ionospheric/tropospheric error

PDOP masking

ensure current PDOP error estimate is below a user-set threshold

reduces pdop error

differential correction

removes most error sources

accurate measurement of relative positions of 2 receivers (base and rover) tracking the same set of satellites, post-processing or real time. errors <2m.

pdop masking, diff corection, and Horizon elevation masking

require more advanced gps equipment

receiver types

survey grade

expensive, submeter accuracy, differential correction, good attribute data entry

mapping grade

moderately expensive, sub-10m accuracy, differential correction, good attribute data entry

recreational grade/smartphones

least expensive, ten meter accuracy, poor attribute data entry

remote sensing

using a sensor far from the area to get data

ex

digital satellite data

aerial plane data

aerial drone data

advantages

synoptic perspective- view of large areas

repeat coverage

disadvantage

poorer spatial resolution

EM radiation interacts with physical matter

some wavelengths are absorbed or reflected. can estimate matter type by analyzing spectral signatures in satellite data

panchromatic

satellite platform contains a single band of data. higher resolution

multispectral

multiple bands of data. lower spatial resolution. can compare pixel values

data value for each pixel=

brightness value. lower BV=lower level of EM radiation

high BV

white

low BV

black

at most 3 bands of data can be displayed at once

red, green, blue

combination gives colored image

more bands

less resolution

false color

all red and bluish

spatial resolution

ground represented by pixels

temporal resolution

how frequently a sensor platform obtains imagery (based on orbit). how often is returns

plane sensors have more variation in temporal resolution

high = 24 hrs-3 days

medium= 4-16 days

low= > 16 days

spectral resolution

EM spectrum wavelength intervals recorded by a sensor (blue visible, near-infrared, radar, etc)

radiometric resolution

how precisely remotely sensed data is recorded. possible range of brightness values. number of possible data values recorded for each sensor (affects precision of EM spectral values)

0-1023 higher than 0-127

spatial resolution means tradeoff with

spectral resolution

perspective is also important

nadir or vertical perspective vs off-vertical or oblique

nadir

map-like

orthogonal

oblique

how we see it standing. must link to nadir perspective to interpret remote sensing data

remote sensing process

energy source

interaction with atmosphere

interaction with target

remote sensor records energy

data transmission and image production

application of information

image analysis and interpretation

visual interpretation

with higher spatial resolution data

human interpretation, errors (looking at data and guessing what it is)

apply quantitative techniques

with data in multiple spectral bands (higher spectral resolution). not perfect due to environmental conditions

quantitative vs visual

tradeoff. neither is perfect. both are necessary

spectral signatures

vegetation and water (like fingerprints) different types of soils, grasses, etc

vegetation indices

normalized difference vegetation index (NDVI)

used for vegetation monitoring

categorized NDVI

GIS dataset, thematic map layer or dataset, not just spectral information, shows in a gradient where vegetation is (such as in Africa)

soil adjusted vegetation index (SAVI)

enhancement of NDVI

adds an adjustment factor for soil

land-cover classifications

take spectral data and analyze to classify landcover classes (forest, barren, etc)

requires multispectral data

done using

statistical clustering algorithms

each landcover class is made of one or more clusters

supervised classification

identify training sites of known landcover types

determines characteristics, clusters based on similarity

unsupervised classification

software separates data based on clusters, you assign a class label to each cluster

minimum-distance-to-means classifier

separates by euclidean distance into classes that it is most like based on training sites

original remote sensing platforms

balloon and plane based

used for military

aerial photography

plane based remote sensing

high spatial resolution imagery

required special skills to interpret

less common now, still used for LIDAR

early satellite remote sensing was from NASA

first environmental mapping satellite platforms

then commercial programs, focusing on high spatial resolution

then increase in gov env sensing efforts focusing on special spectral resolutions

now drones

Landsat

land surface mapping. grandparent of earth environmental observation satellites.

SPOT

land surface mapping

IKONOS

early high spatial resolution imagery

WorldView

current cutting-edge spatial resolution

NASA Earth observation system

collection of satellites and sensors

Landsat MSS 1,2,3

spectral bands: green, red, 2 near-infrared

SPOT XS

multispectral

french commercial satellite data company

SPOT PAN

panchromatic

GEO-EYE IKONOS

first nonclassified really high spatial resolution imagery. commercial development. panchromatic and multispectral bands

Digital globe

quickbird, worldview

increasing spatial res

New EOS remote sensing platforms

from NASA (Terra and Aqua) monitor environmental change. set of sensors grouped on satellite platforms

Terra

N to S across equator in morning

Aqua

s to n across equator in afternoon

Terra sensor intruments

CERES

clouds and earths radiant energy system

MISR

multi angle spectroradiometer (sunlight, pollution)

MOPITT

measurements of pollution in the troposphere

MODIS

moderate resolution imaging spectroradiometer. cloud cover, global dynamics (hurricanes)

ASTER

advanced spaceborne thermal emission and reflection radiometer. surface temp, reflectivity, elevation

Aqua sensors

AIRS

3d maps of air, temp, clouds. awesome spectral res

AMSU-A

microwave temp/humidity sensor

HSB

humidity sounder for brazil

AIRS

advanced infrared sounder

CERES AND MODIS

AMSU-A

advanced microwave sounding unit

AMSR-E

water cycle

Aura sensor instruments

mostly atmospheric chem

special terrain data

not high resolution

only elevation

not available for other countries

imaging RADAR

active imaging

sensor emits microwave radiation toward earths surface, radar backscatter returns to sensor and is measured

landsat

passive sensor

long radar wavelength can penetrate clouds/atmosphere and ground surface

useful for mapping topography

can also map subsurface conditions

space-derived RADAR topographic data

digital elevation models (DEMS)

built from satellite data

global coverage

shuttle radar topography mission data (SRTM)

sub-surface RADAR data

PALSAR

japanese active RADAR sensor

airborne-derived topographic data: LiDAR

light detection and ranging

laser rangefinder

captures height measurements

filtering can remove tree canopy

great accuracy

costly

(ecuador beach pics)