Short description of the ACEU model as used for the case
study in central Mozambique
Estimating risk – the ACEU model
In this study we hypothesise that land areas are at greater risk of deforestation and
degradation if they are easily accessible, are suitable for cultivation, have an extractable
value, and are unprotected. An overall level of risk is thereby determined as the product of
the risks associated with each of the four ACEU parameters:
Ri = AiCi EiU i
(Eq. 1)
where the overall risk level (R) for a defined land area or zone (i) is a function of the risks
associated with ease of access (A), suitability for cultivation (C), extractable value (E) and
protection status (U) of that zone. Each of the four ACEU parameters are defined and
assigned a level of risk as follows:
Access risk (A)
Access was assumed to be related to the proximity to towns and roads. The risk level
(a value between 0-1) at a given distance from the road or town centre was determined by
assuming an exponentially decreasing risk with increasing distance from roads and towns
(Fig. 1). To encompass the area in which most forest disturbance was predicted to occur
along roads, the risk was assumed to be negligible at > 5 km distance from roads. The risk
from proximity to towns was assessed for Gorongosa town, and was predicted to be
negligible at a distance > 15 km from the town centre. These limits on risk were determined
from other studies conducted in neighbouring provinces (Jansen et al. 2008), and based on the
population size and density of Gorongosa town. The distance at which access risk from
Gorongosa town was estimated to be minimal was 15 km (a total area of 707 km2), as we
estimate that the population size of Gorongosa would occupy an area of ~700 km if the
population density (170 people km-2) was similar to that reported for a town of similar
population size in Uganda (Tabuti et al. 2003). The final access risk (A) was determined as
the combined risks of proximity to towns and roads, which was capped at a maximum value
of 1.
1
Fig. 1: Conceptual model of exponentially decreasing risk with increasing distance from the centre of towns and
roads. The conceptual models assume that risk is very low or equal to 0.01 at 5 km distance from roads (solid
line), and 15 km distance from Gorongosa town (dashed line).
Cultivation risk (C)
A study conducted in the neighbouring province (Jansen et al. 2008) found that land
cover change was closely related to the land surface slope, where cultivation occurred
predominantly on gentle slopes. Slope is also related to other relevant factors such as soil
erosion, decreased accessibility, and higher input costs for cultivation. Therefore, the slope of
the land surface was used as an indicator of the suitability of the land for cultivation. Using
topographic data (SRTM) we calculated the mean slope within several delineated agricultural
fields using GIS software. This analysis yielded a mean slope of 3o with a range of 0-10o for
all agricultural fields. We create a frequency distribution of the number of fields at a given
slope, standardising the frequency on the y-axis to reflect a risk level of between 0-1 (Fig. 2).
A generalized logistic model was fitted to the frequency distribution data, so that the
cultivation risk (C) for a given area is a function of the mean slope in degrees for that area.
Cultivation risk (C) is thereby assumed to decrease in a sigmoidal fashion from high risk at
low slope angles and low risk at high slope angles (Fig. 2).
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Fig. 2: Standardised frequency (white bars) distribution of the number of cultivated fields with increasing slope
of the land surface for the Sofala province, central Mozambique. A generalised logistic model (solid line) fitted
to the frequency distribution shows a decreasing risk level with increasing slope angle of the land surface in a
sigmoidal fashion.
Extractable value (E)
In order to estimate the extractable value of an area we applied a simple assumption;
areas with a greater fraction of land covered by high biomass density will have a higher
extractable value. The assumption is that high biomass areas have experienced less extraction
and degradation in the past, and therefore still retain high value for extraction. High biomass
density was defined as areas with greater than average (> 20 Mg C ha-1) biomass densities.
To determine the fraction of land covered by high biomass density, we use a carbon density
map (units in tC ha-1) created from radar imagery from the year 2007. Probability density
functions of AGB C density values for a delineated area were created (Fig. 3), where the area
under the curve was made equal to 1 (i.e. the total land area was equal to 1). We apply a
threshold at 20 tC ha-1, and calculate the area under the curve which is > 20 tC ha-1. This
fractional area gives an estimate of the extractable value (E) from 0-1. Negative pixel values
occur due to errors with converting radar backscatter to biomass density, and any value < 10
tC ha-1 is considered bare ground.
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Fig. 3: Probability density function (solid line) of AGB density (Mg C ha-1) of all the pixels in a delineated area
in the year 2007. Frequencies are standardised so that the area under the curve is equal to one. Extractable value
(E) risk was estimated as the area under the curve (grey area) that is above a biomass density threshold of 20 Mg
C ha-1 (dashed line), i.e. what fraction of the land area has high density biomass.
Protection status (U)
The Gorongosa national park and several avoided deforestation areas were defined as
managed and/or protected areas. We assigned the risk associated with the protection status
(U) within protected areas as being very low, and were therefore given a risk value of 0 if
protected, and 1 if un-protected. This assumes that any land outside of clearly defined
protected areas is at risk, and that any land which is protected has no risk at all. This
assumption simplifies the risk calculation for areas which are protected, as any other risks are
thereby negated, whereas areas that are un-protected are subject to the combined ACE risks.
Creating discrete areas in the study site for estimation of risk
In order to map areas at risk, the study area (defined by the radar image extent) was
divided into several discrete zones (Fig. 4). By mapping discrete areas we are able to assign a
risk level within each zone, rather than estimate risk as a continuous variable over thousands
of grid cells. Using GIS software (ArcGIS) several buffers around Gorongosa town and the
main road were created based on distance, and known protected areas were overlain onto the
distance layers. All areas where slope was > 10o (SRTM data) were grouped into one zone.
Thereby, a map of delineated zones was created based on unique combinations of overlaid
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spatial vector data, and each zone was given a unique identifying number (1 to 36). This
stratified approach simplifies the risk calculation process
process and negates errors associated with
estimating biomass change from radar data on a pixel level, by using mean changes over
thousands of pixels rather than single pixel values.
values
Fig. 4: Map of overlaid spatial vectors of town and road buffers, protected areas, and areas where slope > 10o.
Each unique combination of overlaid vectors constitutes an individual zone, labelled 1-36,
1 36, where zone 36 are all
areas where slope was > 10o. Vector data courtesy of ARA CENTRO and Envirotrade Ltd.
Estimating risk as a qualitative value
In order to visualise the risks associated with each discrete area in the study site,
site and
how they compare to all other discrete areas within the study area, we ranked the calculated
risks from the ACEU model (values between 0-1)
0 1) to values between 1 to 36 (i.e. the total
number of discrete areas in the study site).
site). Thereby, areas with the same calculated risk from
the ACEU model will have the same rank number (e.g. all protected areas had an ACEU risk
of zero, and a rank of 1), or have a ranked risk determined by the order of increasing ACEU
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risk (e.g. the highest calculated ACEU risk will have a rank of 36). This ranking enhances
differences between zones and indicates discrete areas that may be of higher risk than others
within a defined study area. We assign risk as a qualitative measure on a scale from very high
risk (risk ranking of 31-36, or those areas with the highest risks values 0-1) decreasing in
steps of risk ranks of 5 to very low risk (risk ranking of 1-5, or the areas with the lowest risk
values 0-1), giving 7 discrete risk categorisations with an equal number of ranks in each (i.e.
5 ranks in each category). The final risk map for the study area is then created using GIS
software (Fig. 5).
Fig. 5: Map for Gorongosa district, central Mozambique, of estimated qualitative risks of deforestation and
degradation under the assumptions of the ACEU model.
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References
Jansen LJM, Bagnoli M, Focacci M (2008) Analysis of land-cover/use change dynamics in
Manica Province in Mozambique in a period of transition (1990-2004). Forest
Ecology and Management 254:308-326
Tabuti JRS, Dhillion SS, Luye KA (2003) Firewood use in Bulamogi County, Uganda:
species selection, harvesting and consumption patterns. Biomass & Bioenergy
25:581-596
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