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

25

16

5

4

7

49

input

output = sqr(input)

5

4

75

4

7

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11

input

output =

focalsum(input)

25

16

5

4

75

4

7

49

input

output = sqr(input)

5

4

75

4

7

input

output = zonalsum(zone, input)

zone

Zone 1

Zone 2

9

9

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9

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7 7 7

7 7 7

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7input

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

5

4

7input

output = trend(input)

9

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54

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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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1

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!