A MUNKAERŐPIAC SZEREPLŐI

REGIONAL BIOMASS RESEARCH
ACTIVITIES AT KÁROLY RÓBERT
COLLEGE (GYÖNGYÖS, HUNGARY)
Zoltán Baros – Péter Burai – Tamás Tomor
Institute of Agroinformatics and Rural Development
Banská Bystrica
13. 10. 2010
THE GREEN COLLEGE

Introduction
• A college specialised partly in agricultural higher
education
• Crisis and transition
• Re-defining itself as
a.) a Green College … or … the Green College
b.) a Knowledge Company
• Various biomass and RES-related research activities
• Also integrated into education

‘KRC’ = Károly Róbert College = Knowledge
Company
Gyöngyös

(R+D+I+B)
Research
Business
KRC=
knowledge
services
Development
Innovation

Content
1. BIOENKRC project – Organisation of
a bioenergy cluster
2. Agricultural suitability research
3. Biomass applications of
Hyperspectral- and LIDAR airborne
systems

1. The BIOENKRC project –
Organisation of a bioenergy cluster

• Financed from the Oszkár Asbóth Innovation Programme for
Pull Factors
• Duration: 2006-2009
• R&D activities covered:
3 ‘interrelated fields’
25 successive program tasks
110-144 researchers involved
• Aims:
The establishment and functioning of an bioenergy innovation
centre and cluster
Elaboration, application and adaptation of new technologies

Integrated (R+D+I+B) activities
Applications
Projects
Government
Sponsors
Management and
business
Sustainable energy management
Economy
Economical sciences
Sustainable development
Agricultural
sciences
Bioenergy
Multidisciplines of social
sciences
Society
Rural development
Tourism and
catering
Environmental sciences
Nature
Forest and game
management
Knowledge centre
services
Networks
Business

Integrated R + D programs in Bioenergy
Heat Biogas Bio fuel
Producing, processing of biomass, technologies
Economical evaluation
Analysis of environmental and rural developmental issues
Marketing and communication
Elaboration of complex system models

Bioenergy Innovation Cluster and
Knowledge Centre
Governmental
organizations
Regional
developing
organizations
R + D
institutions
Local
governments
Universities
Egererdő
Strategic partners
Cooperating partners
Bio-energy Innovation
Cluster
Középtiszai
LTD.-Kunhegyes
Corax-Bioner
LTD.
LTD.
Mátrai
Erőmű LTD.
Bátortrade
LTD.-
Nyírbátor
MGI
Károly Róbert
Gödöllő
College KR Oktató-
Kutató LTD.
Knowledge
Centre
Companies
Inter-Tram LTD.-
Mátészalka
Győri Szeszipari
LTD.
Multinational
companies
Innovation
organizations
Associations
Banks

Social Acceptance of RES
Knowledge on RES in the Micro-region of Gyöngyös
Solar energy
Wind energy
Hydro power
Biodiesel
Biomass combustion
Biogas
Short-rotation forestry
Biobriquette
Geothermal energy
Bioethanol
Photovoltaic
None
0 10 20 30 40 50 60 70 80 90

2. Agricultural suitability research

Topic definition
Present stage: due to the high number of those involved in agriculture, the
pressure to increase efficiency of production and the lack of habitat-related
information, the collection, supplement synthesized evaluation of the existing
information and their provision to those involved is necessary.
Strategic goal: provision of structured knowledge for farmers, consultants,
regional development and planning experts to the elaboration of agrienvironmental
management, rural development EU programmes to be
introduced, tendered and applied as well as to the implementation of their
goals.
Operative goal: elaboration of a model required to a complex centre of
consultancy organised on the basis of information technology in the of North
Hungarian Region that is capable of fulfilling demands for habitat-related
information by the agriculture.

The information system is capable of:
Providing to the optimal and efficient functioning of a givee nspatial unit
(plot, block, settlement, micro-region, region) :
• basic information (on the geographical, economic and social environment)
• land use and landscape management recommendations (sustainable land
and soil use, soil protection, ecological and economic information)
•Expertise supporting agricultural production (crop production, crop
nutrition or crop protection information)
Providing information background to the implementation of thematic tasks:
• regional planning and development, land consolidation
• spatial management of production (e.g. designation of plantations)
• obtaining licences from authorities
• access to funding

The information system contains:
• GIS database
• Expert data, descriptions and annotations
• System software, algorithms
• Easy-to-handle, ergonomic communication interface
The potential range of users include:
• Farmers, consultants
• Municipalities and their associations
• Regional and national planning institutes, ministries
• Institutes of policy administration
• Institutes with magisterial power
• Special services

The geographical framework
The representative sample
area:
• Area: 750 km 2
• Number of settlements: 25)
• Number of inhabitants: 77,773
• Number of agricultural enterprises and cooperatives:
4,367
• Total area of croplands: 30,006 hectares
(Source: KSH, 2005)

• Public administration layers (outer area, inner
area, real estate cadaster)
• Infrastructural layers (road network, railway
network, air wires)
• Relief layers (level curves, spot heights, digital
elevation models – TIN, GRID, exposure, slope
category)
• Land-use layers (CORINE CLC-50, LANDSAT TM ,
First and Second Military Surveys)
• Statistical layers (economic, social, demographical
data content)
• Hydrological layers (surface water bodies,
surface catchment areas, flood inundations,
emergency flood reservoirs, water management
construction works)
Some of the GIS layers produced

• Meteorological layers (rainfall, relative
humidity, number of sunshine hours,
temperature, heat sum, date of first
frost)
• Habitat layers (vineyard cadaster,
orchard cadaster)
• Nature conservation layers (protected
areas, NATURA 2000 areas)
• Mining layers (search areas, mining
grounds, grant areas)
• Other layers (layers produced based on
field surveying)

GIS analyses – Soil characteristics
Evaluation of the conditions for
farming based on the factors of
environment:
General agricultural suitability
classification
(agricultural potential).

GIS analyses – Soil characteristics
Evaluation of the conditions for farming based on the
factors of environment:
Habitat classification:
A species specific classification
of agricultural lands with soil
conditions and habitat
requirements of plant species
taken into considerations.

GIS analyses – Land-use stability
Data sources used:
• First Military Survey (1783)
• Second Military Survey (1856-1866)
• CORINE CLC-50 (1999)
• Landsat TM 5 images (2003)
Land-use category
/Values of the stable land-use
Area
(ha)
Share of the
present total
area (%)
Index of
highest land
covered (%) *
Built-up area (red) 831.3 14.27 14.27
Forest (yellow) 17899.6 41.48 41.48
Meadow (orange) 604.5 21.18 2.33
Cropland (green) 11403.7 36.04 36.04
Vineyard (pink) 798.4 9.47 9.47
Continuous water surface 0 0 0
Total (ha) 31,537 34.0
* Ratio covere in the study period as measured to the largest area ever

GIS analyses – Relief and land-use
The share of croplands in the micro-region per slope categories
Slope category Area (ha) Area (%)
0-5 25 168,6 83,88
6-12 3 973,7 13,24
13-17 643,8 2,15
18-25 139,4 0,46
26-35 14,6 0,05
35< 65,8 0,22
Total 30 005,9 100,00
30 000,0
25 000,0
20 000,0
15 000,0
10 000,0
5 000,0
0,0
0-5 6-12 13-17 18-25 26-35 35

3. Biomass applications of
hyperspectral- and LIDAR airborne
systems

Application of hyperspectral airborne images
What is spectral imaging?
(hyperspectral imaging, imaging spectroscopy)
Spectral axis
Spatial axis
Time
R
Spectrum for one
pixel
400 Wavelength 700
Each pixel is
associated with
simultaneous high
resolution spectral
information
produced by a
Spectral Camera
(Hyper)spectral imaging is a next generation imaging technology, which
produces the image of the target at tens or hundreds of wavelengths.

First compact visible and shortwave infrared
hyperspectal system in Europe
AISA DUAL Provide
• smallest airborne hyperspectral systems in
VNIR and SWIR
• high spectral and spatial mapping
• full hyperspectral data at high speed
• fast turnaround from data collection to
information
Acquires hyperspectral images in the full
400 – 2450 nm (VNIR + SWIR) spectral
range at 400 wavelengths.

Biomass mapping
Agricultural application
Agricultural parcel with heterogenic covered and brushed vegetation

Biomass mapping
Agricultural application
Canopy % was derived from the NDVI.
Canopy (%) = 157.92*NDVI – 2.37 (R 2 = 0.78, n=21)

Species mapping with hyperspectral image
Classification Aquatic Vegetation Mapping
True color RGB image
Aquatic vegetation map
Spectral Angle Mapping (SAM) was applied to classify images

Qualitative and quantitative studies
Thematic map of farmland
Chlorophyll analysis

LIDAR Laser and Scanning System
The LIDAR instrument consists of a
system controller and a transmitter and
receiver. As the aircraft moves forward
along the line-of-flight, a scanning mirror
directs pulses of laser light across-track
perpendicular to the line-of-flight.
Jensen, 2008

Digital Surface Model (DSM)
containing the elevation characteristics of all the trees,
shrubs, and man-made structures

Classified Digital Surface Model (DSM)
containing the elevation and also types of trees, shrubs.

Digital Elevation Model (DEM)
Accuracy: 5-15cm (z), 5-20cm (x,y) Point density: 5-40 point/m 2

Integrating land use and biomass map
Vegetation 3D Structure &
Biomass:
for Biodiversity and Habitat
Vegetation
Type
Upland conifer
Lowland conifer
Northern hardwoods
Aspen/lowland deciduous
Grassland
Agriculture
Wetlands
Open water
Urban/barren
High: 30 kg/m 2
Biomass
Low: 0 kg/m 2

THANK YOU FOR YOUR ATTENTION!