2.5 Prediction of LULC change
Based on the classified maps, this study also attempts to predict LULC
for 2023 and 2027. Three steps are involved in the prediction process.
They are estimation of transition probabilities, creation of transition
suitability maps and finally predicting LULC. A combination of Markov
chain with cellular automata (CA) method is employed as former technique
is unable to provide spatial dimension of a phenomenon. To simulate
future land covers, actual data of 2017– 2018 are used to
predict 2019 LULC which is then compared with observed data of 2019 to
check the effectiveness of model.
Transition probability matrix is derived through markov module as a
first step. LULC thematic maps of different periods are inputted to
estimate transition probabilities (Pijanowski et al., 2002). A
suitability map for each of LULC class defines transformation
suitability of a certain class from all other categories (Halmy et al.,
2015). Stressor and stimulus parameters are, therefore, required to
develop suitability map to account dynamic aspect of land cover change.
In this work, forest degradation is based on both stressor and stimulus
parameters. Stimulus variable includes number of Rohingya population,
stressor parameter comprises high elevation, and constraint is defined
by highly protected areas. The stressor, constraint and stimulus
variables are determined on the basis of previous studies (e.g., IOM and
FAO, 2017; IUCN Bangladesh, 2018), 2018 field works and local knowledge
of the sites (Table 3). Since not all LULC classes are subject to change
rapidly, six dynamic (Table 3) and one constraint variables are included
to isolate suitable locations or forest patches that could be degraded
under the influence of refugee occupancy.
As degradation of forest is accelerated by fuelwood collection and
illegal logging by the Rohingya communities, distribution of Rohingya
population is a key factor for a suitability map. Apart from population
variable, four distance variables (Table 3) are also considered. Due to
the fact that the Rohingya can travel up to 16 km (IOM and FAO, 2017),
and on average, 7 km to collect forest resources, a 7– km
buffer is constructed using center of each refugee camps. These buffers
are then intersected with population distribution to identify number of
people that can conceivably influence forest degradation. In other
words, if a forest area is within a distance of 7– km buffer of
three camps (C1, C2 and C3) and these camps contain 100, 200 and 150
people, then a particular forest cover has a total of 450 humans. These
populations are considered as potentially degraders. The results are
subsequently aggregated to a 100x100 m grid based on which a ranking is
performed. This helps determining forest covers subject to degradation
due to existence of the Rohingya communities. The higher the population
in each grid, the greater the likelihood of a forest to be degraded. In
the creation of forest degradation suitability maps, maximum weighting
(0.5) is assigned to population field whereas other parameters receive
rest of the weights (0.5), using a scale of 0-1. A weighted linear
combination method is then employed to develop transition suitability
maps.
The transition probability or transition suitability maps of
2017– 2019 are considered, wherein 2019 LULC is used as base.
Since CA Markov provides spatial distribution of LULC change, area of
each class to be changed to other classes are determined by transition
potential or transition suitable maps (Halmy et al., 2015). These
transition areas are divided by the number of time periods in the
simulation (1, 4 and 8 in this case). This operation provided areas to
be converted to another LULC class. The CA Markov with these principles
results predicted LULC data which are then assessed for accuracy by
considering kappa index of agreement and disagreement. LULC prediction
for the year of 2023 and 2027 are conducted, based on actual data of
2019 (Pontius and Millones 2011).