Spatial Inference of Vegetation Vulnerabilityfor the
Ecological Economical Zoning of Minas Gerais
Luis M. T. Carvalho1
Moisés S. Ribeiro2, Luciano T. de Oliveira1
Thomaz C. A. Oliveira1, Julio N. Louzada3
José R. S. Scolforo1, Antonio D. Oliveira1
1Departamento de Ciências Florestais2Departamento de Engenharia
3Departamento de Biologia
Ecological Economical Zoning of Minas Gerais
Introduction
ZEE → Zones subject to a certain model of use according to
degrees of natural vulnerability and social potentiality.
ZEE-MG → implemented by the Government of Minas Gerais to
support policy making by means of a statewide diagnosis of
economical, social, ecological and biophysical sustainability.
NATURAL VULNERABILITY → the capacity of resisting or
recovering from impacts caused by human activities.
Ecological Economical Zoning of Minas Gerais
Introduction
ZEE/MG
Vulnerability Potentiality
Institutional
Productive
Biotic
Physical
Natural
Human
Flora
Fauna
Soils
Erosion
Water
Climate
Ecological Economical Zoning of Minas Gerais
Objectives
to investigate alternative methods of spatial inference, viz. fuzzy
logic and neural networks for generating maps of vegetation
vulnerability for the State of Minas Gerais, and
to evaluate their suitability to be used instead of weighted
overlay.
Ecological Economical Zoning of Minas Gerais
Study site and data sets
The study area comprises the whole State of Minas Gerais.
Data compiled and included in the ZEE-MG were structured in a
GIS using the raster data model with a spatial resolution of
270x270m.
Indicators of vegetation vulnerability were derived from a
30x30m resolution land cover map (Scolforo & Carvalho, 2006)
and from priority conservation areas (Drummond et al., 2005)
Ecological Economical Zoning of Minas Gerais
Flora
Conservation Heterogeneity RelevanceConservation
Priority
Indicators of Vegetation Vulnerability
Ecological Economical Zoning of Minas Gerais
30m
6 : Regional total
270m
Rocky Field Cerrado stricto sensu Semideciduous Forests
Indicators 1 to 9: Regional Relevance
Ecological Economical Zoning of Minas Gerais
Grass land Rocky grass land Open savanna
Savanna stricto sensu Savanna woodland Savanna palm land
Deciduous forest Semi deciduous forest Evergreen forest
Ecological Economical Zoning of Minas Gerais
30m
11
270m
Native Vegetation Others
Indicator 10: Conservation Degree
Ecological Economical Zoning of Minas Gerais
Ecological Economical Zoning of Minas Gerais
30m
3
270m
Campo rupestre Cerrado stricto sensu Floresta Estacional Semidecidual
Indicator 11: Spatial Heterogeneity
Ecological Economical Zoning of Minas Gerais
Ecological Economical Zoning of Minas Gerais
Conservation Priority classes Vulnerability classes
None Very low
Corridor Low
Potential Medium
High High
Very high, Extreme and Special Very high
Indicator 12: Conservation Priority
Ecological Economical Zoning of Minas Gerais
Ecological Economical Zoning of Minas Gerais
Methods
Albers Conic Equal Area Projection (datum SAD-69).
Spatial inference using weighted overlay, fuzzy logic, and neural
networks.
Vulnerability represented by the models outputs were classified
as (1) Very low, (2) Low, (3) Medium, (4) High, and (5) Very high.
Ecological Economical Zoning of Minas Gerais
Weighted Overlay
Simple and straightforward technique.
Weights represent the importance of each variable, as well as the
importance of each classe according to a given objective.
Allows the inclusion of expert knowledge.
1
2 3
1 1 1 1
1 1
1
2
2
2 2
3 3
3
3
32
2
2 2
2 2
2 2
Peso = 75% Peso = 25%
+ =
Ecological Economical Zoning of Minas GeraisIndicator Indicator weight Class Class weight
Regional relevance 8 Very low 1
Low 6
Medium 10
High 12
Very high 12
Degree of conservation 12 Very low 1
Low 6
Medium 10
High 12
Very high 12
Spatial heterogeneity 4 Very low 1
Low 6
Medium 10
High 12
Very high 12
Conservation priority 12 Very low 1
Low 2
Medium 6
High 12
Very high 12
Ecological Economical Zoning of Minas Gerais
Fuzzy Logic
Input data values are rescaled using the assumption of
continuous membership values (i.e., fuzzyfication).
Environmental data are normally modeled using the symmetric
fuzzy models as generated by Kandel (1986):
21/1)( bxdxAFPx
Ecological Economical Zoning of Minas Gerais
Fuzzy Logic
Fuzzy operators allow the combination of layers containing fuzzy
values through a process of fuzzy overlay.
Operator Fuzzy Gamma:
yycombinação PAFSAF 1*
icombinaçãoSAF 11
icombinaçãoPAF
Ecological Economical Zoning of Minas Gerais
Fuzzy Logic
Operator Fuzzy Convex Sum.
If A1,.....,Ak are subsets of X, and w1,......,wk are non negative
weights then the convex combination of A1,....,Ak is:
AjjA w
Ecological Economical Zoning of Minas Gerais
Neural Networks
Clustering algorithms of the machine learning field.
Models of biological neurons and networks.
Unsupervised clustering
Self Organizing Maps (with and without k-means)
Fuzzy ArtMap
Ecological Economical Zoning of Minas Gerais
Neural Networks
Parameter SOM (without k-means) SOM (with k-means)
Input layer neurons 12 12
Output layer neurons 9 36
Initial neighborhood radius 5.24 9.49
Minimum learning rate 0.5 0.5
Maximum learning rate 1 1
Iterations 874,080 628,992
Quantization Error 0.0241 0.0187
SOM neural network parameters:
Ecological Economical Zoning of Minas Gerais
Neural Networks
Parameter Fuzzy ArtMap
F1 layer neurons 24
F2 layer neurons 6
Choice parameter 0.01
Learning rate 1
Vigilance parameter 0.95
Iterations 48,923
Fuzzy ArtMap neural network parameters:
Ecological Economical Zoning of Minas Gerais
Results and Discussion
Weighted overlay x Fuzzy logic:
Ecological Economical Zoning of Minas Gerais
Results and Discussion
Weighted overlay x Neural networks:
Ecological Economical Zoning of Minas Gerais
Ecological Economical Zoning of Minas Gerais
Conclusions and Future Studies
The evaluated methods are less intuitive, dependent on a number of
arbitrary parameters, demand more computational power, and do not
provide significant improvements when compared to the map produced
using weighted overlay,
Fuzzy logic seems to be a promising approach and further research will
be carried out in order to test different fuzzification methods, as well as
different fuzzy operators,
Neural networks will be disregarded due to the difficulties in setting the
necessary parameters, and
A framework to collect field data will be developed to provide a robust
base to carry out vulnerability map comparisons
Thank You !
Contact: [email protected]