Geography C188 Final

Front 1

Remote Sensing

Back 1

Process of collecting data about objects or landscape features w/o coming to direct physical contact with them.

Most remote sensing performed from orbital or sub-orbital platforms using instruments which measure reflected electromagnetic radiation.

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Image Interpretation: 1st order elements

Back 2

tone - variation from white to black
color - multitude of combinations of hue, value and chroma.
resolution - ability of the entire photographic system, including lens, exposure, processing, and other factors, to render a sharply defined image.

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Image Interpretation: 2nd order elements

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-geographic arrangement of objects
size: discriminate of objects and features (cars vs. buses, brush vs. trees)
shape: can provide clues that aid identification
-spatial arrangement of tone/color
texture: frequency of change and arrangement of tones.
pattern: spatial arrangement of objects

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Image Interpretation: 3rd order elements

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-locational or positional elements
site: how objects are arranged with respect to one another; or with respect to various terrain features (aspect, topography, geology).
association: some objects commonly associated with one another so you can indicate or confirm existence of another. (ex. railroad tracks + coal piles + smoke stacks + cooling ponds = coal fired power plant)
-interpreted from lower order elements
height
shadow: help identify objects. (ex. low sun angle for geologists)

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Electromagnetic Spectrum

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The full range of wavelengths that is made up of the several different wavelengths of light emitted by the sun.
ROYGBIV

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Spectral Resolution

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The number and size of the bands of the electromagnetic spectrum which can be recorded by an sensor.

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Radar Resolution

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Range resolution
Azimuth resolution
These are determined in part by the width of the synthesized antenna and the wavelength.

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Hyperspectral Imaging

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A powerful and versatile means for continuous sampling of broad intervals of the spectrum

Front 9

Earth Observing System (EOS) satellite Terra

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Five state of the art sensors that will study interactions among the Earth's atmosphere, lands, oceans, life and radiant energy (heat & light).
TERRA Instruments: ASTER, CERES. MISR, MODIS, MOPIT

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Shuttle Radar Topography Mission (SRTM)

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Collected radar data over 80% of all land.
Two types of antenna panels: C-band (Digital Elevation Models (DEM). ex. NED) , X-band

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Lidar

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Light Detection and Ranging (laser radar).
Transmits and receives electromagnetic radiation, but at a high frequency. Operates in ultraviolet, visible and infrared region of the em spectrum.

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Airborne Laser Scanning

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active remote sensing sensor that measures distance with reflected laser light.
two types of lidar systems: waveform and discrete-return.
create high resolution DEMs

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Lidar Example

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Contra Costa County - Lidar (4inch)

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What is a GPS?

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Global Positioning System is a global satellite based radio navigation system consisting of 27 earth-orbiting satellites in 6 different orbital paths, and their ground stations.
GPS satellites = NAVSTAR satellites

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How does GPS work?

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1. Trilateration from satellites
2. Distance measured using the travel time of radio message
3. Timing - atomic clocks in satellites
4. Satellite's position in space
5. The signal's travel through the atmosphere

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Trilateration from satellites

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A third measurement from another satellite puts the location of the point at one of the two points where the third sphere intersects the circle.

Front 17

Distance measured using the travel time of radio message

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The satellite and the receiver are synced to generate the same code at the same time.
speed of light * time = distance

Front 18

Correcting Errors

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Rough trip through atmosphere, to the ground
Orbital errors
Satellite geometry
Intentional Errors - DOD
Therefore you need to Error Budget

Front 19

Code-Phase GPS vs. Carrier-Phase GPS

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code phase: pseudo random code generated in both GPS receiver and satellite. Both matched up.
carrier phase: count exact number of carrier cycles between the satellite and the receiver. high precision, GPS surveying.

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Differential GPS

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Registering and recording deviation from base station. Deviation is error added to signal for each time moment.

Front 21

Justification for Land Suitability Modeling

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Living in Nature: used to live off what land could provide, adapt to prevalent natural conditions
Environmental Ignorance: tech innovation has brought freedom of choice of habitation and loss of importance of adjusting settlements to natural and local conditions.
Some people have lost sensitivity towards environment -> natural disasters.

Front 22

McHarg's Suitability Analysis

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Undertaken using Spatial Feature Overlay Logic.
Technique: layer cake; polygon overlay operations central to assessing capability of a given parcel of land being suitable for a given land use.

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The Overlay Method

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aka Polygon Overlay
1. Measurement and representation of phenomena on landscape (vegetation, elevation, etc. )
2. Registration (common geometric ref. for maps)
3. Source maps > layers representing natural and social phenomena
4. Classification into factors: Constraints and Opportunities
5. Third class: Knock-Out-Constraints; land use is strictly prohibited
6. Factor maps overlayed to produce Composite maps

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Hand-crafted Overlay Method

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Paper or mylar map overlay

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Spatial Feature Overlay Logic

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Spatial disaggregation/aggregation
disaggregation: creating more fragments; geometry
aggregation: combining more fields in one record; data tables

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Set Theory: Intersection and Union

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Intersection - Area of overlap
Union - Combined area

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Operations requiring polygon overlay

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Planar Enforcement: process of building points, lines and areas from digitized spaghetti wherever intersections occur between lines.

Front 28

Line Processing Problems

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-Conflation: procedure of reconciling positions of corresponding features in different layers
-Edge Matching: a procedure to adjust position of features that extend across map sheet boundaries. builds a single continuous map
-Editing Functions: functions to add, delete, and change geoposition of features. two lines crossing
-Line Coordinate Thinning: thinning of redundant or unnecessary points to reduce data volume.
-Classification and Generalization: thresholding data fields into clusters

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Sliver Polygons

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Pairs of lines which should coincide but don't b/c of differences in digitizing.
spurious polygons / "coastline weave"
Solved using Fuzzy tolerance, as if line fuzzy
Part of Editing Functions and Line Processing Problems

Front 30

Discrete vs. Continuous Data

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-Discrete: measurement scale consisting of a # of separate values (ie. points, lines, polygons)
-Continuous: measurement scale where it is permissible to calculate intermediate values (interpolated surfaces derived from discrete samples)

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Notions of Proximity

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-the notion of proximity or neighborliness with other observations
-proximity of all neighbors affects potential for interaction
-notions of proximity effective in modeling clustering of populations
-1st law of geography: "Everything is related to everything else, but close things are more closely related" - W. Tobler

Front 32

Theiseen or Voronoi Polygons

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Buffered circles impacting each other to form a set of cells, a cellular network consisting of polygons.

Each cell has the same Z value

Front 33

Delaunay Triangulation

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-the dual of the Thiessen or Voronoi Polygons
-a proximal method that satisfies the requirement that a circle drawn through the three nodes of a triangle will contain no other node.

Front 34

TIN

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Triangulated Irregular Network
-represents space using a set of non-overlapping triangles that border one another and vary in size and proportion
-once TIN is built, the elevation can be interpolated, slope and aspect for each triangle can be calculated.

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Slope in TIN

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Calculates the maximum rate of change between each point and its neighbors

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Aspect in TIN

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Identifies the steepest down-slope direction from each point to its neighbors.
Clockwise in degrees from 0 (due north) to 360 (due north coming full circle)

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Contour to TIN

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vertices of the contour lines are used as mass points for triangulation.
sometimes flat triangles appear b/c of streams or rivers.

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LIDAR for TIN

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3D Point Clouds

Front 39

Viewshed Analysis

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identifies the facets in an input data set that can be seen from one or more observation points or lines

Front 40

Universal Soil Loss Equation

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Used to predict longtime average soil losses and runoff
A = R K L S C P
A = computed annual soil loss
R = rainfall-runoff erosivity factor
K = soil erodability factor
LS = topographic factor combining slope length and slope angle
C = land cover and management
P = erosion-control practice for crops

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TIN Case Study

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Erosion modeling for the island of St. John, U.S. Virgin Islands.
On island topographic relief is extreme. Lots of hurricanes.
Solution: Erosion Model to find areas at risk of potential erosion

Front 42

What is a network?

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-A system of connected linear features through which resources or events flow (ex. roads)
-Precise flow route and speed may be affected by multiple factors (ex. speed limits)

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What is a network composed of?

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See picture

Front 44

Location-Allocation

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-Models attempt to improve or optimize demand conditions by allocation of supply

Front 45

Types of Location-Allocation Models

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Simple: supply and demand points in space, demand allocation to closest supply for optimal solution

Spatially Unconstrained: demand and supply interact in straight lines and frictions (ease of travel) constant

Discrete or Constrained: embedded within a line network (transportation network), each segment and intersection has indiv. constraint parameters.

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Classes of Location-Allocation Problems

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Point-Point class
Point-Polygon class

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Types of Networks

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Straight-line
Branching
Circuit

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Network-based Analysis

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Capacity Analysis: load the network and look for bottlenecks and capacity problems

Gravity Model: determine (many-to-many) flows along multiple-link routes

Front 49

Point-polygon location problems

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Unconstrained allocation: Voronoi or Theissen polygons

Contrained allocation: weighted Voronoi

Front 50

Networks Modeling Case Study

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Sherman Island, Sacramento-San Joaquin Delta

modeled levees failing and its affect on accessibility of emergency response.

Solution: fire station (rescue worker) in each high risk neighborhood.

Front 51

Spatial Interpolation

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Estimating the value of a variable of interest an an un-sampled location based on the values measured at sampled locations.

Front 52

Why Spatial Interpolation?

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We cannot sample everywhere.
-too expensive, tedious, physically impossible, inaccessible, cloud cover, forest canopy

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Typical Inputs/Outputs of Interpolation

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-Points to Points
-Points to Lines: contours
-Points to vector polygons
-Points to raster grids

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Sampling Strategies

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-Random samples
-Uniform samples
-Cluster sampling: cluster center identified randomly or systematically, with cluster of samples around center
-Adaptive sampling: fewer samples taken in homogeneous areas; higher sampling densities where feature of interest is heterogeneous

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Main Characteristics of Interpolation Methods

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-Global vs. Local

-Exact vs. Inexact

-Deterministic vs. Stochastic

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Global vs. Local Estimators

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Characteristic of Interpolation Methods

Global: use all sample points to estimate values at unsampled locations

Local: estimates are based on neighboring points

Front 57

Exact vs. Inexact Estimators

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Characteristic of Interpolation Methods

Exact: the values at input sample locations have same values in the output surface

Inexact: output surface where values at the original sample locations may be estimates

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Deterministic vs. Stochastic Methods

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Characteristic of Interpolation Methods

Deterministic: based on a mathematical model
-Natural neighbors: Thiessen polygons
-IDW: inverse distance weighted
-spline functions

Stochastic: based on a geostatistical model that incorporates random variation and accounts for spatial autocorrelation.
-kriging

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Thiessen Polygon Interpolation

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-local estimator
-exact estimator
-deterministic

Front 60

IDW Interpolation

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Inverse Distance Weight
-IDW derives an estimate based on user defined parameter for the # of sample points, I
-the user can also input a power parameter, n

-local, exact, deterministic

Front 61

Splines

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Creates the smoothest possible line along the set of points, (flexible ruler)

For surface creation, spline functions are like bending a rubber sheet to pass through all sample points

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Kriging

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Statistically based estimator of spatial variables.

Incorporates:
1. spatial trend: increase or decrease towards a direction
2. spatial autocorrelation: tendency for points near each other to have similar values
3. random variation

3 components combined in mathematic model to create estimation function

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Descriptive Spatial Statistics

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Mean Center
Mean Distance Circle
Standard Error Distance Circle
Max Distance Circle

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Central Moments

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1st central moment: Spatial Mean
2nd central moment: 1st, 2nd standard deviation

Front 65

Spatial Metrics: Direction

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1st and 2nd standard deviational ellipse: measure of whether a distribution of features exhibits a directional trend

Front 66

Average Nearest Neighbor

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Calculates a nearest neighbor based on the average distance from each feature to its neighboring feature

Front 67

Convex Hull

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A set of points enclosed by connecting the outermost points in the set using exterior angles >= 180 degrees.

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Concave Hull

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A set of points enclosed by connecting the outermost points in the set using exterior angles < 180 degrees

Front 69

Spatial Metric

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Delauney Triangulation, Spatial Descriptor

A maximum planar skeleton

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Spatial Metric

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Beta Skeleton, Gabriel Graph (after K.R. Gabriel)

Endoskeleton graph

the smaller the beta, the more lines

Front 71

Notions of Proximity to create boundaries

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based on notions of proximity or neighborly

delineates the transition zone about the boundary

draws boundaries between homogeneous areas

Example of methods: Voronoi tessellation method, Gravity model method

Front 72

Boundary and Transition Zones

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Voronoi tesselation: predict the potential of any potential of any given polygon being associated with a particular population

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Boundary and Transition Zones

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Gravity model: weighted and unweighted models

Front 74

Beta-skeletons

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Generalize process of using disc or empty circles in a plane.
Have properties similar to construction of DT
Can construct spectrum of endoskeletons, family of beta-skeletons

Front 75

Street Center Application

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Use both lune and disc based neighborhood methods to generate street centerlines (lune, DT) and intersections (disc, GG)

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Minimum Spanning Tree

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1. closest neighbors connected

2. closest neighborhoods connected

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Endoskeleton graphs

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-Nearest neighbor graph
-Minimum spanning tree
-Relative neighborhood graph
-Gabriel graph
-Delaunay Triangulation

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Exoskeleton graphs

Back 78

-Min. bounding box, circle, ellipse
-Convex hull, alpha-hulls