UNIVERSITY OF HELSINKI
FACULTY OF SCIENCE
DIVISION of GEOGRAPHY
ADDIS ABABA UNIVERSITY
COLLEGE OF NATURAL SCIENCES
SCHOOL OF EARTH SCIENCES
The rapid population growth observed in the East African
highlands during the past century has had implications for
land use requirements with subsequent impacts on natural
vegetation cover, biodiversity, socio-economic stability and
food security (Brink and Eva, 2009).
In Ethiopia, studies estimate that 35–40% was covered by
natural forest and 66% of the country was originally covered
with forest or woodlands (Britenbach, 1961, Wood, 1990,
Kuru, 1990, Yirdaw, 1996). According to FAO (2010), 13 % of
the total area of the country (159104 km2 ) was covered with
forests in 1990 and this had decreased to ca. 11 % of the total
area (129104 km2 ) in 2010.
In this research, we aimed to simulate past century natural
vegetation cover of Ethiopia and to estimate the extent of
natural vegetation cover affected by agricultural expansion.
Ethiopia is situated at 34o30'–45 o30' E and 3o30'–15o N
covering an area of 1.1 million square kilometer in the
northeastern part of Africa (Figure 1).
Figure 1. The location map and topography of Ethiopia (DEM: GTOPO30).
The traditional Ethiopian classification of climate (Conway,
2000) is based on altitude and identifies three zones: i)
Kolla zone that is below 1800 m asl. with mean annual
temperatures of 20–28 °C; ii) Woina Dega zone, 1800–
2400 m asl with mean annual temperatures of 16–20 °C; iii)
Dega zone above 2400 m asl with mean annual
temperatures of 1–6 °C.
Material & Methods
In this research, the net primary productivity (NPP) was
modeled based on the climatic constraints of natural
vegetation growth derived from remote sensing and climate
data. This model was used to simulate productivity of
agricultural area in order to identify the original extent of
natural vegetation cover.
The method was divided in 4 main stages.
1. The natural vegetation was separated from agricultural areas and
its net primary productivity (NPP) was characterized based on
climatic productivity constraints (Nemani et al., 2003).
2. Random points of natural vegetation cover were created to
tabulate the three constraints and NPP.
3. Mltivariate regression was used to assess the relationship between
NPP and the climatic variables (water availability, solar radiation and
minimum temperature). The relative impact of these variables for
NPP of each vegetation cover was determined by standardized
coefficients (beta). The model was used for simulating NPP over
agricultural lands of Ethiopia
4. The simulated productivity map classified based on threshold in
order to show how the natural vegetation affected by agricultural
Figure 2. Land cover of Ethiopia in 2009 (FAO, 2009).
Climatic constraints of productivity
The productivity on the highest peaks of mountains, such
as the northern part of the Ethiopian highlands and the
southeastern part of the country is limited by the minimum
temperature. As these peaks are very small in area, the
productivity in the highlands is mainly constrained by the
vegetation cover of Ethiopia
Binyam T. Hailuab, Eduardo Eiji Maeda,a, Janne Heiskanena, Petri Pellikkaa,
aUniversity of Helsinki, Department of Geosciences and Geography, P.O. Box 68, Gustaf
Hällströmin katu 2 b, FI-00014, Helsinki, Finland
bAddis Ababa University, P.O.Box: 1176, Addis Ababa, Ethiopia
email: firstname.lastname@example.org, email@example.com
Figure 3. Environmental constraints for vegetation growth in Ethiopia.
Modelling productivity of natural vegetation
The multivariate regression shows that the productivity of
natural vegetation cover is significantly related (p<0.001,
R2=0.77) to the water availability. The productivity of
each vegetation class was significantly related to the water
availability (Figure 4)., (Table 1).
Table 1. The results of multivariate regression analysis for each natural vegetation class.
*COG = Closed to open grassland, BESDF = Broadleaved evergreen or semi-deciduous forest, COS = Closed to open shrubland, MFSG =
Mosaic Forest-Shrubland/Grassland, MGFS = Mosaic Grassland/Forest-Shrubland, OBDF = Open broadleaved deciduous forest
1WA = Water Availability, SR = Solar Radiation, Tmin = Minimum Temperature, C = Constant
Figure 4. The relationships of NPP and water availability for the
natural vegetation classes.
Modelling of natural vegetation productivity
From the multivariate regression model, the relative coefficients and intercept
were identified in order to formulate NPP validmodel. This model was used for
simulating the past natural vegetation productivity in the area covered by
agriculture (Figure 5b). Model ation showed that the simulated NPP and
original NPP were significantly related (p < 0.001 and R2 = 0.76).
Reconstructed natural vegetation of agricultural areas
The simulated NPP map of agricultural land in Ethiopia was classified based on
threshold values. in order to reconstruct the original extent of natural vegetation cover
classes (Figure 6a). The current agricultural landscapes were previously covered mainly
by broadleaved evergreen and deciduous forest, which encompassed 38.9% of the
current agricultural land. The least affected by agricultural expansion was sparse
vegetation and grassland with 5.7% area. The extent of broadleaved evergreen or semi-
deciduous forest, open broadleaved deciduous forest, closed to open shrubland, mosaic
forest-shrubland/grassland, sparse vegetation and grassland in Ethiopia was 18.8%,
12.4%, 20.6%, 31.5%, and 16.8%, respectively (Figure 6b).
Landscape reconstructions can be used to define a reference condition from which to assess the magnitude of land changes caused by human influence. Since the beginning of the last century, the population of Ethiopia has
increased drastically with large effects on the natural vegetation and biodiversity. However, the original land cover patterns in Ethiopia have not been precisely mapped, which hinder the identification of the biophysical
and socio-economic factors that contributed to the current landscape patterns. The objective of this study was to reconstruct the past century vegetation landscapes of Ethiopia (i.e. vegetation cover before agricultural
expansion) and identify which ecosystems have been most affected by land changes. First, the net primary productivity (NPP) was modelled based on the climatic constraints of natural vegetation growth (water
availability, solar radiation and minimum temperature) derived from remote sensing and climate data. This analysis showed that water availability is the most critical constraint for vegetation growth for all regions and
land cover types in Ethiopia. Then, the past vegetation was mapped based on predicted NPP. Our results show that i) the extent of broadleaved evergreen or semi-deciduous forest, open broadleaved deciduous forest, closed
to open shrubland, mosaic forest-shrubland/grassland, sparse vegetation and grassland was 18.8%, 12.4%, 20.6%, 31.5%, and 16.8%, respectively, and ii) current agricultural landscapes were previously covered mainly by
broadleaved evergreen and deciduous forest. The least affected by agricultural expansion were sparse vegetation and grassland. Our study provides novel insights on pre-agricultural expansion landscapes in Ethiopia with
critical information for scientists and other stakeholders working on the restoration and rehabilitation of degraded areas.
Key words: Pre-agricultural expansion, remote sensing, Net Primary Productivity, Ethiopia
In this study, all the information for mapping the past natural vegetation cover was
based on the remote sensing products (NPP, GlobCover land cover, solar radiation,
PET) and climate data (precipitation and temperature). Hence, our approach differs
from previous attempts to map potential vegetation in Ethiopia (e.g. Friis et al., 2010),
which are based on infor-mation from previous literature, field experience, as well as on
the analysis of information for about 1300 species of woody plants in the Flora of
Ethiopia and Eritrea.
We reconstructed the past century natural vegetation cover of Ethiopia including the area
affected by agriculture. We show that 36.1% and 38.9% of the current agricultural land were
previously covered by closed to open shrubland and broadleaved evergreen and deciduous
forest, respectively. This encompasses about 75% of the agricultural land. The land cover
least affected by agricultural expansion was sparse vegetation and grassland. The map of
the reconstructed natural vegetation of Ethiopia could be helpful for decision makers to
restore and rehabilitate the major affected areas. Furthermore, it can provide a better
understanding of the spatial patterns of the original vegetation and identification of socio-
economic factors that contributed for defining the current agricultural landscapes in
The authors would like to thank CHIESA project, which is funded by the Ministry for Foreign Affairs of
Finland. Dr Eduardo Maeda is currently funded by the Academy of Finland. Janne Heiskanen
acknowledge BIODEV project, which is funded by the Ministry for Foreign Affairs of Finland.
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Figure 6. (a) Reconstructed natural vegetation cover of agricultural land. (b) Reconstructed Land
cover map of Ethiopia (agricultural areas replaced by its simulated natural vegetation cover).
Figure 5. (a) MODIS NPP of the natural vegetation area of Ethiopia (agricultural area masked) and (b) SimulatedNPP for the agricultural area.
The 36th International Symposium on Remote Sensing of Environment,
11 – 15 May 2015, Berlin, Germany