Proceedings of the 13th International Conference of Environmental Science and Technology
Athens, Greece, 5-7 September 2013
MULTICRITERIA DECISION RULE FOR EVALUATING PHYSICAL
VULNERABILITY OF SELECTED AREAS TO FOREST DEGRADATION IN OGUN
STATE, NIGERIA.
SALAMI O. A.* and ANDE O. T **
*
Geographic Information Systems (GIS) Department
Regional Center for Training in Aerospace Surveys (RECTAS)
(Under the Auspices of United Nations Economic Commission for Africa)
OAU, Ile-Ife, Osun State, Nigeria.
**
Institute of Agricultural Research and Training,
Obafemi Awolowo University,
Moore Plantation,
Ibadan, Oyo State, Nigeria.
Email: [email protected]
EXTENDED ABSTRACT
With the increasing occurrence of natural hazards, deterioration of our physical environment
and also climate change, there has been an increased consciousness in the conservation of
our forests. Forest cover and density have proven to be important indicators of ecological
stability and they play a major role in the preservation of the natural world. In tropical regions,
deforestation and forest degradation are gradual progressive processes that are advancing
at worrisome rate, resulting in a consequent conversion of forest area into a mosaic of
mature forest fragments, pasture, degraded soil and loss of biodiversity. Hence, increase in
research that assesses the vulnerability of forests to changing environmental conditions. This
study was carried out using geo-information techniques in evaluating various physical factors
contributing to the degradation of forests in Ogun State. Datasets acquired include
landuse/landcover data, soil data, population data, meteorological data such as Mean annual
rainfall map, Rainfall intensity, Mean Onset of rainfall map, Mean Cessation dates of rainfall,
and Hydrologic ratio. Weighted linear combination was used to analyze factors and
implemented in a GIS system. A grid model was developed to partition the study area into
equal grid cells and this serves as a platform for combining criterion weights, scale values,
data evaluation and vulnerability values.
The total land area studied was 1798.88km2, using 2301 grid cells (900m by 900m). The very
highly vulnerable zone was 20.65% (386.27 km2), highly vulnerable zone, 41.63%
(760.86km2), moderate vulnerable zone, 17.27% (323.09km2) and the low vulnerable zone
was 20.45% (328.66km2) of the total land area. The evaluation of biophysical factors with
multi-criteria techniques reveals that the current land uses promote environmental
degradation. Therefore to avoid further degradation sustainable land uses such as agro
forestry, reforestation of degraded habitat, supervised logging, use of alternative sources for
energy other than firewood should be encouraged.
Keywords: Forest degradation, land use, vulnerability, multi-criteria evaluation, weighted
linear combination, grid model.
1.
INTRODUCTION
Deforestation and forest degradation are the major ecological problems in developing
countries. It is a dynamic process which can be attributed to various socioeconomic and
biophysical factors, resulting in the conversion of thick and healthy forest area into a mosaic
of degraded habitat [5]. In 1949, forest reserves occupied about 7% of Nigeria’s land area
and 15% of the area coverage of the then Western Region of Nigeria [6]. It has however
been reduced to about 10% and is on further decrease [7]. Forests in south-western Nigeria
are degrading at annual rate of 1.90% [8]. In Ogun State, forest degradation claims about
17,758 hectares of forest area per year [1]. Thus, a greater recognition of the seriousness of
global environmental change has led to an increase in research that assesses the
vulnerability of vegetation to changing environmental conditions. With the advancement in
space technology, the use remote sensing and geographic information systems for
environmental modeling presents a more effective and intelligent techniques in evaluating
complex, dynamic and continuous environmental variables affecting the sustainability of our
forest.
Fig 1: Location map of study area showing Abeokuta-North LGA, Abeokuta-South LGA and
Odeda LGA
2.
AIM AND OBJECTIVES
This study is aimed at using geo-information techniques in evaluating various physical
factors contributing to the degradation of forests in Ogun State. The objectives include
analyzing the physical factors causing forest degradation; use of a weighted linear method in
analyzing the combined effect of physical factors and adopt a grid model for quantitative
assessment of vulnerability of areas to forest degradation.
3.
METHODOLOGY
This study was performed in three local government areas in Ogun State which are
Abeokuta- South, Abeokuta North and Odeda. Datasets acquired include meteorological
data such as Mean annual rainfall map, Rainfall intensity, Mean Onset of rainfall map, Mean
Cessation dates of rainfall, and Hydrologic ratio. Other datasets include the
landuse/landcover data, soil data and population data. Spatial interpolation technique was
used to reveal the variability of all acquired meteorological datasets, soil data and population
data across the study area.
3.1.
Weighted Linear Combination
WLC lies on the concept of weighted average. The decision maker directly assigns the
weights of ‘relative importance’ to each attribute map layer. A total score is then obtained for
each alternative by multiplying the importance weight assigned for each attribute by the
scaled value given to the alternative on that attribute, and summing the products over all
attributes. Weighted linear combination was used to compute factors and implemented in a
GIS system. Weights were assigned to each factor in order of its importance and a total
score is obtained for each alternative by multiplying the importance weight assigned to each
attribute by the scaled value. The major physical factors which were used for the WLC multicriteria evaluation includes: Mean annual rainfall, Mean length of rainy season, Hydrologic
ratio, Soil data, Landcover data and NDVI. The weightage and principal component values of
the physical attributes were however incorporated into an integrated evaluation index model
[4] to deduce vulnerability of areas to forest degradation.
Evaluation of factors using an integrated evaluation index
E = α1Y1 + α2Y2 + ··· + αmYm ------------------------------ (1)
Where, Y1, Y2, --------------- Ym = principal component value
α1, α2 ------- αm, = corresponding contribution (weightage)
Table 1: Criteria for physical vulnerability evaluation to forest degradation
FACTOR
CRITERIA
INDEX
WEIGHTAGE
Mean Annual rainfall
<1048 mm
5
5%
(MAR)
1048mm to 1215 mm 3
>1215mm
1
Mean length of rainy season
(MLRS)
<220days
220 days to 240 days
>240 days
5
3
1
5%
Hydrologic ratio
(HR)
<0.58
0.58 – 0.69
>0.69
5
3
1
10%
Soil
(Soil)
Humus/Loam
Sand/gravel
Clay
1
3
5
10%
Land use
(LU)
Water body
Settlement
Arable lands
Light forest
Thick Forest
5
4
3
2
1
5%
4
10%
Normalized
differential No Vegetation
vegetation index (NDVI)
Unhealthy vegetation
Slightly healthy
Healthy
3
2
1
Integrated Evaluation Index for evaluating physical vulnerability of study area to forest
degradation = ((MAR*W1) + (MLRS*W2) + (HR*W3) + (Soil*W4) + (Landcover*W6) +
(NDVI*W7))
3.2.
Grid Model
The grid model was developed to partition the study area into equal grid cells and this serves
as a platform for combining criterion weights, scale values, data evaluation values and
vulnerability values. However, a grid system was designed to have the study area partitioned
into a grid spatial resolution of 0.9km (900m) by 0.9km (900m). This method was used in
selecting the spatial attributes of each of the entities overlaid on the grid model for easy
registration of selected attributes in the grid model database. Using clustering standards,
environmental vulnerability in the study was graded into five levels (low risk, moderate risk/
risky, high risk and very high risk [2], and each level was characterized.
This figure below gives a representation of how the soil map was overlaid on the grid model
and how soil values were registered in the grid database.
Fig 2: Interactive spatial selection of attributes in Abeokuta-North LGA, Abeokuta-South LGA
and Odeda LGA
4.
RESULT AND DISCUSSION
Biophysical factors have a significant impact on deforestation [3]. The integration of physical
factors such as mean annual rainfall, mean length of rainy season, hydrologic ratio, soil data,
landcover and analyzed using the weighted linear combination of the multi-criteria decision
rule helped to reveal the combined effect of biophysical factors on vulnerability of areas in
the study area to forest degradation. It was observed from the landcover categorization, that
Odeda LGA has the highest quantity of forest lands but the area has the least mean length of
raining season, mean annual rainfall, low hydrologic ratio and even the dominant soil type in
the area is lixisols which require fertilizer application for cropping and are susceptible to
erosion which implies that the thick and rich forest of Odeda LGA are most vulnerable to
forest degradation based on the integrated analysis of biophysical factors. The vulnerability
of Odeda LGA as confirmed from the rate of decline in thick forest and degraded/disturbed
forest of the landcover analysis. Other parts of the study area experienced less vulnerability
from biophysical factors because they have more favorable biophysical factors that enhance
vegetation growth or regrowth.
Fig 3: Physical vulnerability map of Abeokuta-North LGA, Abeokuta-South LGA and Odeda
LGA
Table 2: Comparing spatial extent of areas affected by various classes of physical
vulnerability
Evaluation
index range
Vulnerability
class
0.80 – 1.05
Low
risk 328.66
zone
Risky zone
323.09
20.45
Ikereku, olorunda, Asipa, Akaa,
17.27
High
risk 778.86
zone
Very
high 386.27
risk zone
41.63
Olukotun, Idi-emi, Ijaiye isale,
Asero,
Odeda, olugbo, alabata
20.65
Ilugun, olodo, killa, isolu, osiele
1.05 – 1.40
1.40 – 1.70
1.70 – 2.10
Size of
(km2)
area % Area Communities affected (LGA)
covered
5.
CONCLUSION
The interaction and interplay between these biophysical factors that serve as potential driving
forces that initiate forest degradation was analyzed to evaluate the vulnerability of the study
area to further deforestation activities through the use of a multi-criteria decision techniques.
This study reveals how the interplay between the various biophysical factors had contributed
to the depletion and conversion of most of the thick forest lands and degraded/disturbed
forest to farmlands/grasslands and settlements respectively.
However, it is not just enough to reveal the magnitude and vulnerability areas to forest
degradation in Ogun State but generating a platform for easy visualization and dissemination
of information, data collection and acquisition as well as an interactive decision support
system which has been achieved in this study. The adoption of a spatial decision support
system in this research has helped in providing a system for understanding the complexity of
spatial problems, critical evaluation, assessment in planning and decision making processes
in order to enhance environmental sustainability and assessment. These findings provide
quantitative basis and support for forest policy, management issues and institutional
analyses in planning and management of the forest in Ogun State.
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