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September 2012 Roughness Mapping using Landsat Imagery

Intelligent concepts

for the usage of renewable energy

Av. Júlio de Castilhos, 440, sala 81 90030-130 Porto Alegre, RS, Brasil www.epienergia.com.br

Roughness mapping using Landsat Imagery

Ing. Fernando Altmann

September 2012 Roughness Mapping using Landsat Imagery 2

EAB New Energy Group

EAB New Energy (www.eab-newenergy.eu) operates in project, development and

operation of wind farms

Installation of the first wind turbine in 1994

Located in Freiberg, Saxony State, Germany

Operating the largest wind farm in the Czech Republic

The EAB group of companies erected and commissioned more than 240 wind turbines

in several countries

Group of international scope, is represented in several countries like Poland, Croatia,

Italy, Czech Republic, Argentina, Uruguay, Vietnam


EPI Energia P&I Ltda.

Located in Porto Alegre, Rio Grande do Sul State, Brazil

Brazilian company of EAB New Energy Group

Conception, Development, Execution and Administration of Wind Farm Projects

Technical and Economical Feasibility Study of Wind Energy Projects

Execution and Administration of Wind Measurement Campaign

Developing, Executing and Operating Wind Farm Projects in Brazil, Uruguay and

Argentina as well as in other South American Countries


EPI Energia also works in cooperation with distinct Brazilian and international companies as well as universities in the field of research and development

Collaboration for the Wind Atlas of the Rio Grande do Sul State; Conception, development and execution of Wind Farm Projects in the Northeastern and Southern Brazil; More than 600 MW wind farm projects developed; Currently with wind farm projects underway in Brazil, Uruguay and Argentina.

EPI Energia P&I Ltda.


Wind energy


Wind Energy

Main principle: transforming the kinetic energy of the moving air to eletric power.

Available energy: the quantity of available energy comes from the kinetic energy equation.

The available mass is function of the air density, cross-sectional area and the air flow speed.


Wind Energy

Where: v = speed [m/s]; v* = friction velocity [m/s]; K = von Karman constant(K=0,4); h = height above ground [m]; z0 = surface roughness lenght [m].

The air is a fluid

Fluid Mechanics

Atmospheric flow

Boundary layer development Logarithimic Wind Profile

Power Law Profile

Then, speed comes as:


Introduction

Wind farm simulation: CFD software and simplified flow calculation softwares (Windpro, Wasp e OpenWind)

Wind

Topography

Roughness

Wind measurement or Weibull

DEM SRTM or ASTER

Sources with poor resolution. MODIS, GLCF


Wind Energy - Roughness

Surface roughness and its obstacles (trees, buildings) influence the wind speed profile, resulting in a wind retardment near the surface.

Terrain mapping is essential to any wind energy project, as its topographic and roughness characteristics affect the wind profile behaviour and, consequently, the energy yield.


Roughness Classes

Roughness Class Rpughness lenght, zo [m] Landscape Type

0 0.0002 Water surfaces.

0.5 0.0024 Completely open terrain with a smooth surface, e.g.concrete runways in airports, mowed grass, etc.

1 0.03 Open agricultural area without fences and hedgerows and very scattered buildings. Only softly rounded hills

1.5 0.055 Agricultural land with some houses and 8 metre tall sheltering

hedgerows with a distance of approx. 1250 metres

2 0.1 Agricultural land with some houses and 8 metre tall sheltering

hedgerows with a distance of approx. 500 metres

2.5 0.2 Agricultural land with many houses, shrubs and plants, or 8

metre tall sheltering hedgerows with a distance of approx. 250

metres

3 0.4 Villages, small towns, agricultural land with many or tall

sheltering hedgerows, forests and very rough and uneven

terrain

3.5 0.8 Larger cities with tall buildings

4 1.6 Very large cities with tall buildings and skycrapers

Troen & Petersen, 2009


Overview project z0, m Land cover classes 0.4 Urban and Built-‐Up Land

0.1 Irrigated Cropland and Pasture

0.1 Mixed Dryland/Irrigated Cropland and Pasture

0.07 Cropland/Grassland Mosaic

0.15 Cropland/Woodland Mosaic

0.05 Grassland

0.07 Shrubland

0.06 Mixed Shrubland/Grassland

0.07 Savanna

0.4 Deciduous Broadleaf Forest

0.4 Deciduous Needleleaf Forest

0.5 Evergreen Broadleaf Forest

0.5 Evergreen Needleleaf Forest

0.4 Mixed Forest

0.03 HerbaceousWetland

0.02 Barren or Sparsely Vegetated

0.15 Wooded Tundra

0.1 Mixed Tundra

0.001 Ice or Snow

0.0002 Water bodies GLCF adapted from USGS


Study Overview

Wind Energy Air Flow Fluid mechanics

Roughness Roughness Land Cover

Low resolution land cover datasets sources.

Create a dataset with higher resolution


Objectives

Review, apply and develop methodologies concerning to land cover mapping from Landsat imagery

Produce a reliable roughness dataset to be used on wind farm projects


Wind energy market in Brasil is highly competitive. More than ever the energy yield must be accurately predicted.

Nonexistence of good resolution local roughness datasets.

Digital image processing with Landsat imagery can produce good resolution datasets

Reasons


Study area


Study area

Round hills, deciduous needleleaf forests, broadleaf forests, grasslands.


Study area


Remote Sensing

Classic definition: remote sensing is the acquisition of information about an object without being in touch with it.

Eletromagnetic radiation register, creating images.

Different targets have different responses to the different radiation bands.


Remote Sensing


Landsat System Since 1972 Serie of satellites NASA and USGS To monitor collect information about Earth from space Landsat 5 sensor TM

Lansdat


Technics used to identify, extract, condensate and enhance the information of interest to specific purpouses.

Pre-processing

Image Enhancement

Image transformations

Classification

Geometric and Radiometric corrections

Contrast enhancement Spatil filtering

Principal Components Analysis Vegetation index Tasseled Cap

Digital Image Processing


Classification

To assign a specific class ot theme to each pixel of the image, based on statistical characteristics of the pixel brightness values. Also called spectral pattern recognition.

Unsupervised Essentially statistical comparisson and clustering by simillarity. Supervised Samples identification of the interest classes an signatures comparisson.


Methodology

Structure and data sources Satellite imagery Topographic maps Google Earth GIS softwares Spring; MapWindow

Procedures Geometric correction Transformations (PCA and Tasseled Cap) Choice of channels to be used in classification Classification Maxlike Quality Assessment Dataset creation


Results

True color composition


Results


Conclusions

Satellite imagery classification well known technique, lots of information, great cost/benefit.

Roughness mapping from classification low cost, great value for companies

In general, remote sensing and GIS showed to be high efficiency and low cost tools to identify and to map land cover with a good resolution, in order to procude accurate roughness datasets to use in wind farm projects.


EAB New Energy Group 3Energy Service Group

September 2012 Roughness Mapping using Landsat Imagery

Intelligent Concepts

for the usage of renewable energy

epi Energia Projetos e Investimentos Ltda. Av. Júlio de Castilhos, 440, sala 81 90030-130, Porto Alegre, RS, Brasil Tel.: +55 51 32730191 E-Mail: [email protected] www.epienergia.com.br

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