research methodology gis basics 1/2 - uef
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Research Methodology – GIS Basics 1/2
GIS data representation –vector/raster/dynamic segmentation
Topology ?????
Coordinate Systems Geographic vs. Projected
•Geographic Coordinate Systems (GCS)
• Location measured from curved surface of the earth
• Measurement units latitude and longitude
– Degrees-minutes-seconds (DMS)
– Decimal degrees (DD) or radians (rad)
•Projected Coordinate Systems (PCS)
• Flat surface
• Units can be in meters, feet, inches
• Distortions will occur, except for very fine scale maps
Geographic Coordinate System
•Parallels - east to west – 0° at the Equator (0 °-90 °)
•Meridians – north to south – 0° at the Prime Meridian (0 °-180 °)
•Latitude and longitude are angular measurements made from the center of the earth to a point on the surface of the earth
Map Projection
•A map projection is a
mathematical formula for
representing the curved surface
of the earth on a flat map.
– wide variety of projections possible
– each projection will create a different type of distortion
Universal Transverse Mercator (UTM)
•60 zones
– 6o wide
– Defined by central meridian (example: 120° W)
•Preserves direction and small shapes (conformal projection).
•Extent is from 84°N to 80 °S.
•UTM coordinates are easily recognized by 6 digit for the x, and 7 digit for the y ( most of the time at latitudes of 15° and greater in the Northern Hemisphere)
80 S°
84 N°Secant!
Coordinate systems in ArcGIS
DateIdentification 7
Steps
1) Define projection
2) Project if needed
GPS-methods• Absolute positioning (one receiver)
• Relative positioning (at least 2 receivers)
– Differential Global Positioning System (DGPS) is an enhancement to Global Positioning System that uses a network of fixed, ground-based reference stations to broadcast the difference between the positions indicated by the satellite systems and the known fixed positions. ( http://en.wikipedia.org/wiki/Differential_GPS)
• These stations broadcast the difference between the measured satellite pseudoranges and actual (internally computed) pseudoranges, and receiver stations may correct their pseudoranges by the same amount.
• The correction signal is typically broadcast over UHF radio modem.
– Real Time Kinematic (RTK) satellite navigation is a technique used in land survey and in hydrographic survey based on the use of carrier phase measurements of the GPS, GLONASS and/or Galileo signals where a single reference station provides the real-time corrections, providing up to centimetre-level accuracy. (http://en.wikipedia.org/wiki/Real_Time_Kinematic)
• When referring to GPS in particular, the system is also commonly referred to as Carrier-Phase Enhancement, CPGPS. RTK systems use a single base station and a number of mobile units.
• The base station re-broadcasts the phase of the carrier that it measured, and the mobile units compare their own phase measurements with the ones received from the base station.
GIS Analysis
• New information will be produced from spatial databases
• Spatial analysis is a process, where several thematic map layers are combined and overlaid. Spatial Queries are dobe according to location and attributes.
• GIS system is also modelling tools
• Simulation models are normally utilising spatial and non-spatial data as input
Vector GIS tools
• Connection to database
• Generalisations or filtering
• Classification
• Area, length of line, distance between objects
• Overlay analysis
• Buffer analysis
• Network analysis
• Interpolation
• DEM and 3D analysis
• Change
Database queries
• Attributes are normally stored to separate relational database
• Ominaisuustieto tallennettu useimmiten erilliseen relaatiotietokantaan
• Communication between application and using query language
• Relational databases utisize (Structured Query Language)
• First commercial product Oracle (1979)
• Usage of SQL:
– Interactive use with commands or user-interface window
– Embedded SQL-commands in software modules
Database queries in GIS interface
• Search compartments, which have
– Diameter of trees > 23 cm
– Volume of stand> 200 m3/ha
SQL-lause:
select Compartment, Sub.Number from Comp_table
where (D13 > 23 and V_total >
2000)
Tulos:
199263 15
199263 34
SQL
Structured query language
Structure of relational database
• Principles are based of logic and basic mathematics
• Thematic objects organized into tables, where we have records in columns and fields/attributes are in columns.
• Tables utlise primary keys in identifation of objects and in connecting/joining tables together. Foreign keys are used in connecting tables.
• Table structure and logic between tables using constrains
Kuviot-taulu
Id Kuvio metsatyyppi Pohjapinta_ala
1 23 1 23
2 35 1 35
3 67 2 3
Puut-taulu
Id Kuvio d1.3 tilavuus
1 23 30 0.2
2 23 21 0.17
3 35 60 1.3
4 35 50 1.0
5 67 10 0.06
6 67 3 0.00
Perusavain
Viiteavain
Sarake
Sarakenimet
Queries:SELECT
Select which columns are needed
[distinct] removal of doubble lines
from from which table
where which lines from tables
group by categorize output
having specify certain categories
order by sorting instructions
Sample
SELECT h.tunnus, h.snimi, h.svuosi, SUM(tunnit) AS summa
FROM Henki h, Prhe p
WHERE h.tunnus = p.tunnus
AND (kunta=‘Lehmo’ OR palkka > 6500) AND h.svuosi =
(SELECT MIN (svuosi) FROM henki)
GROUP BY h.tunnus, snimi, svuosi
HAVING SUM(tunnit) > 500
ORDER BY snimi
Search all people in Lehmo municipality, who has salary> 6500
and worked over 500 hours . Id, name and hours are needed.
Vector GIS analysis
Overlay analysis
A AND B A OR B
A NOT B A XOR B
(A AND B) OR C A AND (B OR C)
• We can produce from two or more original map layer a new product where needed multiple criterias are met.
• Overlay is utising logical operators (=, <, >, and, or, nor, not, xor) and arithmetic operators.
Overlay analysis
Area-area Line - area Point - area Point - line
Buffering
• We can create layers, where specific distance from objects is defined (buffer-function) or search object which are closer tha specified distance (near-function)
• Vector buffer is always a polygon
• Raster buffer contain always a distance from object as cell value -> suitable for modelling
Buffer analysis
piste viiva aluepoint line area
Sample: Distance is spatial growth model, 2DTopographic map Ditches 5m and 20 buffers around roads
Distance from roads and effect to growth (Lukkala 1929, Seppälä 1969,
Miina 1994
Lukkala (1929): when distance< 5 m from ditch -> growth=9.6 mm and
when distance 6-20 m from ditch -> growth=6.2mm
Sample: Growth model
• Distance form ditch can be calculated in GIS system
• Steps:
1. Extrach ditches from topographic database to single layer
2. Generate buffer
3. Overlay buffer and stand register
4. Create different values of growth attributes from buffer zones and other areas
5. Summarize zonal sum back to stand border level
Generalisation
• For visualisation
• Operations in the picture
Change analysis
• Time series
– Snapshots from each timestamp
– Large changes are visible
– Classification problems
• Movement map
– Location is certaing times, eg. Forest fire movement
• Change map
– Change maps are done
– Small changes are visible
– Difficult to understand overal situation
Landsat TM based forest inventory in China
•Feasibility study conserning to forest inventory alternatives in pilot area.
•Landsat TM-imagery were used to demonstrate alternative methods.
Vectorized results of the unsupervised change detection with AutoChange
Raster GIS Analysis
Map algebra and locality of analysis
• Map analysis is utilising map-algebra, which is defined using arithmetic operators and spatial functions.
• Four categories of analysis:
– Local functions
– Focal functions,
– Zonal functions
– Global functions
Local functions
•Output value of each cell is a function of the corresponding input value at each location
– value NOT location determines result
– e.g. arithmetic operations and reclassification
– full list of local functions in GRID is enormous
• Trigonometric, exponential and logarithmic
• Reclassification and selection
• Logical expressions in GRID
• Operands and logical operators
• Connectors
• Statistical
• Other local functions
Local functions
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input
output = sqr(input)
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output =
focalsum(input)
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output = sqr(input)
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input
output = zonalsum(zone, input)
zone
Zone 1
Zone 2
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output = trend(input)
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Local
Global
Focal
Zonal
Some examples
input
output = tan(input) output = reclass(input) output = log2(input)
Focal functions
•Output value of each cell location is a function of the value of the input cells in the specified neighbourhood of each location
•Type of neighbourhood function
– various types of neighbourhood:
• 3 x 3 cell or other
– calculate mean, SD, sum, range, max, min, etc.
Zonal functions
•Output value at each location depends on the values of all the input cells in an input value grid that shares the same input value zone
•Type of complex neighbourhood function
– use complex neighbourhoods or zones
– calculate mean, SD, sum, range, max, min, etc.
zonal sumzonal sum
(53 + 57 + 33 + 78 + 31 + 12 + 32 + 9 + 9 + 33 + 76) = 423
focal meanfocal mean
(27 + 8 + 22 + 16 + 21 + 16 + 6 + 44 + 8) / 9 = 18.7
Global functions
•Output value of each location is potentially a function of all the cells in the input grid
– e.g. distance functions, surfaces, interpolation, etc.
– Again, full list of global functions in GRID is enormous
• euclidean distance functions
• weighted distance functions
• surface functions
• hydrologic and groundwater functions
• multivariate.
Global functions
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output = trend(input)
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Grid analysis: Map algebra (or “How it works”)
• Map Algebra arithmetic:
Map Calculation 1 = (Dem * 3)
Multiplies each input cell by 3
Map design - Reporting
• Thematic maps and statistics
• Map components need to be planned
– Objects, symbols
– Purpose of map
• Objects
– Lines, areas, legends
• Visualisation
– Scale, projection, north arrow, general description
polygons
arcs
points
labels
Geographic features Cartographic elements
title
neatlines
legend
north arrow
scale bar
23
342
109
12
35
56
500 10000
forest stand
road
well site
No rth
Area m a p
Fo res t s ta n d m a p
Key leg end
W ell A
W ell B
Maantieteelliset kohteet Kartografiset elementit
alueet
viivat
pisteet
tekstit
otsikko
kehys
selite
pohjoisnuoli
mittakaavajana
Map design - Use of colors in maps (Imhof 1962)
1.Clear and strong colors are not good, when they cover large areas.
2.Bright and clear colors near the white areas and also not recommended in large extent
3.Large area and basic colors are best when they quite neutral and grey-smoothed versions. They allow visibility of small bright highlighted objects.
4.Do not use two large areas only. You can easily mix one color to all objects.
5.Color design normally utilise basic colors, smooth colors and harmonise differences.
Research Methodology – GIS Basics 2/2
Research Methodology – GIS Basics and Use of GIS models
Topographic Functions
•Used to describe changes and properties of surfaces such as elevation
•Most common topographic functions
– Slope: Rate of Change of elevation (first derivative)
– Aspect: Direction that a surface faces
– Gradient: Direction of maximum slope
– Example: Desirable building sites have low slope and southern aspect
Topographic slope
Topographic Position Index(TPI)
TPI
More Topographic Functions
• Intervisibility (viewshed)
– Determine the areas than can be seen from one or more viewer locations
– Used for scenery management, choosing parcels with view
– Can use screen areas as well as topography
• Screen areas could include buildings, forested areas, etc.
• Illumination
– Mimics the effects of shining a light on a 3-D surface
– Creates a shaded relief (or color shaded relief) model
– Illumination source has a defined position relative to the surface
• Default usually from the NW at a 45 degree angle
•Perspective View
– Creates a 3-D block diagram showing how surface appears from a defined position
• Typically displayed with parallel lines or a mesh
Visibility
Visibility
Visual Models Näkemä = 0,207Vmä -0,445Vku +4,179Hx -0,007RLtaim +23,477
missä Vmä = männyn tilavuus (m3/ha)
Vku = kuusen tilavuus (m3/ha)
Hx = puuston keskipituus (m) RLtaim = taimikon runkoluku (kpl/ha)
• 20 m 40 m
Visibility
• 60 m 80 m
Sample lines
Transportation costs
Energy wood potential – Comparison of empirical and theoretical diameter distribution
•Potential higher that using traditional methods
•The amount of residual wood was 24% higher in permanent sample plots than estimated using method used in Pasanen et al. (1997).
• Vainio, P., Tokola, T., Palander, T. and Kangas, 2009. A GIS-based stand management system for estimating local energy wood supplies. Biomass and Bioenergy 33:1278 – 1288
13.11.2015Esityksen nimi / Tekijä 58
Permanent
field sample
plots
Forest
Stand
Database
and Maps
Digital road
maps & mill
sites
Tree height
estimation
Harvesting RulesSimulator for
alternative
harvesting
schemes
Volume and
Biomass Models
Stand Model for
Fuel Wood
Stand Fuel
Wood
Estimates
Transportation
from stand to road
side
Transportation
from road side to
mill
Total
transportation
cost for each
stand
Modeling of stand
residualsCostpath analysis of
transportation costs
Cost/benefit
of stand
fuel wood
Digital mill
sites
Fuel wood per
mill/year
Allocation of stand
resources to mill
sites
Harvestable
area and Stand
ID per mill/year
Mill site analysis
Travel time
• Drive time analysis showing areas that can be reached within 5, 10 and 15-minute drive times.
• Drive time analysis showing the location of demographic samples.
• The blue colored dots represent the demographic sample sites that fall within a 10-minute drive time.
• Green colored dots represent demographic sample sites that fall within the 5-mile radius, but fall outside of the 10-minute drive time polygon.
• Red colored dots that fall within the 15-minute drive time polygon represent demographics that would not be included using a traditional 5-mile radius approach.
Definition of the cost surface
• In this case we have used travel time measured in minutes as unit and road category as main classifier without any territorial barrier.
• The first step is to establish the average speed according to the road category and then to calculate the cell crossing time by using the following equation:
• where:
• CCT - Cell Crossing Time (minutes)
• P - Pixel Size
• TS - Travelling Speed (Km/h)
• For example if one is travelling in a 2-lane IP, the result is the following:
Road Category
Average
Speed
Cell Crossing
Time (minutes)
IP highway 110 0.0545
IP 2 lane 80 0.0750
IC highway 110 0.0545
IC 2 lane 70 0.0857
National Road 60 0.1000
Regional Road 55 0.1091
Municipal Road 50 0.1200
Establishing Optimal Paths
•Stream -- like a raindrop, the “steepest downhill” path identifies the optimal path. If the surface is a proximity map, the optimal path identifies the shortest (not necessarily straight) route between any location on the surface and the closest starting cell (point, line or area).
•Drain – accumulates the optimal paths from many locations. Higher “Drain” values indicate locations that have numerous optimal paths passing through it.
Transportation costs
Bioenergy mills and allocation zones
Use of GIS in Spatial planning
Biomass components from Lidar inventory data
•The approach used for assigning the plots to operation classes resulted in moderate accuracies (75%).
•Predicting the biomass components removed in forest treatments, with RMSEs of 33.0-69.4% in the case of final cutting and 71.0-228.0% in the case of thinning.
• Kotamaa, E., Tokola, T., Maltamo, M., Packalén, P., Kurttila, M. and Mäkinen, A. 2010. Integration of remote sensing-based bioenergy inventory data and optimal bucking for standlevel decision making. European Journal of Forest Research 129 (5):875-886.
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Plot level data / ALS-derived data
Distributions of tree height, diameter,
volume, species, top
Calculating the auxiliary attributes
Number of stems ha-1 > 3800
Mean d >22 ELIF
ELSE
IF Energy wood thinning
Final cutting
Thinning
IF
Cut all
Thin until number of
stems ha-1 is 1800
Defining the min. value for thinning
ELSE
Basal area ha-1 < the min. value
No operation
Thinning
Thin until
basal area ha-1 < basal area objective
Calculating
the biomass of felled trees
Lehtonen et al. 2004
Repola et al. 2007
Total aboveground
Total sum of components
Living branches
Top
Dominant tree species
Age
Mean diameter, d
Dominant tree height
Basal area ha-1
Number of stems ha-1
Forestry operation class
Stem
Pukkala et al. 2007
Dead branches
Bark
Foliage
Stumps
Transportation costs according to
possible loads
Spatial optimization
•Grouping of the stands in blocks for the first period (top) and for the second period (below)
Use of GIS model - Terrain mobility
Terrain mobility model
Optimal route during winter 2002-2003
Use of alternative DEM - Average total resistance
coefficients for a loaded tractor over 50 m sections
0,000
0,100
0,200
0,300
0,400
0,500
0,600
0,700
50 200 350 500 650 800 950 1100
Driven distance, m
Avera
ge t
ota
l re
sis
tan
ce c
oeff
icie
nt
for
50
mete
rs s
ecti
on
Measured Prediction_l Prediction_n
The solid grey line
shows the measured
values, the dotted line
(Prediction_n) values
predicted from the
25x25 m DEM and the
dashed line
(Prediction_l) values
predicted from the
more accurate terrain
profile.
Predicted resistance by soil quality
Loaded tractor
-0.2
0.0
0.2
0.4
0.6
0.8
Soil type
Ro
llin
g r
es
ista
nc
e c
oe
ffic
ien
t
Average Prediction
Rock Sand Fine sand Fine siltTill
Average
measured rolling
resistance values
(squares) on each
soil type for a
loaded forwarder,
together with
predicted values
(circles) based on
a digital soil type
map and the
rolling resistance
coefficients
Use Of GIS - Tropical Plantation
forestry
The forestry data collection and database practices for the inventory data
•Document and information management system may be modern and contain payment and operation specific information
•Operation monitoring system is not tightly connected to main database system
•Connection to maps coming
Coming tasks, Work lists
Modelling tools
•Using ModelBuilder •Using Python
Thanks!