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The 5th ICEED, KIGALI-RWANDA-UNILAK AUGUST, 2018 EAJST Special Vol.8 Issue2, 2018 by Enan N. P81-102

81

Prediction of Land Use/Cover Change in the Congo Nile Ridge Region of Rwanda

Author: Enan Nyesheja Muhire 1,2,3,4, *

Affiliation:

1State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and

Geography, Chinese Academy of Sciences

2 University of Chinese Academy of Sciences, Beijing 100049, China

3 Faculty of Environmental Studies, University of Lay Adventists of Kigali (UNILAK),

P.O. Box 6392 Kigali, Rwanda

Corresponding author: [email protected] (E.N.M.), Mobile :+250788306534

Abstract: Simulation and mapping the changes of LULC became the major apprehension

to the environmental planners. In this research, we explored LULC dynamics in the Congo

Nile Ridge Region of Rwanda, from 1990 to 2010 and simulate future LULC in 2020 and

2030 with Land Change Modeler (LCM), MLP Neural network, Markov Chain (MC), and

GIS approaches. To map LULC changes and assess spatial trend, Landsat (TM and ETM+)

imageries of 1990, 2000, and 2010 were classified using supervised with likelihood

classification techniques in six classes. Kappa Statistics and overall accuracy for all LULC

(1990, 2000 and 2010) classification maps were over 94% and 93% respectively. Prediction

revealed that in 2020 and 2030, future LULC would expand for built-up 80 ha and 8,950 ha

for cropland, whereas grassland and forestland will decrease approximately to 2,550 ha and

6,480 ha, respectively; water bodies and wetland will remain constant. The findings from

this research can inform decision-making process concerning environmental protection.

Keywords: land use/cover; prediction; remote sensing; Rwanda

mailto:[email protected]


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1. Introduction

Land use refers to the purpose for which and

with the way in which, human beings

utilized land and its resources(REMA

(Rwandan Environmental Management

Authority), 2015). Land cover refers to the

habitat or vegetation types, such as forest,

water surfaces. Monitoring of Land User

and Land Cover (LULC) Changes can,

therefore, be valuable in ecologically

sensitive zones for protection of

biodiversity, improvement of land use

management and planning policies (Di

Gregorio, 2005; Lambin et al., 2003).

Moreover, the misuse of land cover

accelerates degradation of land,

eutrophication, desertification, variation

and change of climate, siltation, and

biodiversity loss (Akinyemi, 2017; Berakhi,

2013; Gillet et al., 2016; Shrestha and

Dwivedi, 2017). According to Wu et al.

(2012), from 2000 to 2050, the agriculture

land is estimated to be decreased in the

future in Central Africa, South Asia and in

the Eastern United States. It is therefore

believed that anthropogenic activities,

climatological change effects, inappropriate

land use management and natural processes

are the foremost causes of the LULC

dynamics (Arsanjani et al., 2013; Koranteng

and Zawila-Niedzwiecki, 2015; Lambin et

al., 2003).

As previously confirmed, globally, Land

Use and Land Cover Changes (LULCC) is

one of the main risks to aquatic living as

well as terrestrial habitats, in a special way

for forestland in Central Africa (Basnet and

Vodacek, 2015; Douglas, 2017; UNEP,

2016; Zubair et al., 2017). The research

conducted in Central Africa for tracking

LULC change in cloud prone area, the forest

cover loss in the epochs 1988 to 2001 and

2001 to 2011 were estimated of about 216.4

and 130.5 thousand hectares, respectively.

The triggering factors for the loss were

associated with deforestation during the

civil war in the Republic of Congo (Basnet

and Vodacek, 2015). Also, land

degradation has been the main problem in

Rwanda and particularly in the Congo Nile

Ridge Region of Rwanda which recorded

the peak land degradation level with

plentiful tropical precipitation (Goudie,

2013; Roose and Ndayizigiye, 1997). The

fourth population and housing census in

2012, indicated that Rwanda population was

10,515,973, with 52% female and 48%

male. Since 2002 till the 2012 census, the

population of Rwanda increased 2.4 million,

representing an average rate of growth

equaling to 2.6% annually, thus, a

population density of 415 persons/km2 in

2012 (NISR-Ministry of Finance and

Economic Planning, 2012). The high

population pressure to land also results in

land changes by increasingly disturbing the

The 5th ICEED, KIGALI-RWANDA-UNILAK AUGUST, 2018 EAJST Special Vol.8 Issue2, 2018 by Enan N. et al P81-102

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biophysical and biogeochemical processes

of the earth’s surface (Basnet and Vodacek,

2015).

According to Nyandwi and Mukashema

(2011), in Congo Nile Ridge Region of

Rwanda, the two national park forests of

Nyungwe and Gishwati were decreased

approximately 74 % from 1988 to 2007, due

to settlement and agriculture provided by

the government of Rwanda to refugees

returning from exile after the 1994 genocide

perpetrated against Tutsi in Rwanda

(Lemarchand and Tash, 2015).

Furthermore, land conversion to cropland in

Africa is very predominant (Brink and Eva,

2011), and the Congo Nile Ridge Region

of Rwanda, as well as some other East

African countries, are dominated by land

dynamics in forest of tropical areas (Rattan

and Rattan, 2006), owing to the

transformation of land to cropland and built-

up (Berakhi, 2013). Additionally, LULC

dynamics monitoring and future prediction

necessitate a very particular attention by

land managers at all levels (local and

regional) as well as policymakers. This

helps to make knowledgeable decisions that

successfully stabilize the positive features

of development and its negative effects for

preserving environmental resources and

increasing socio-economic welfare (Di

Gregorio, 2005; Kamusoko et al., 2009).

LULC dynamics detection is becoming a

very important concern to conservationists,

environmentalists, policymakers, and land

use planners due to its significant impact on

natural ecosystems (Verburg et al., 2000).

For mapping and monitoring LULC

changes, Remote Sensing (RS) technology

is cost-effective, and it has been used

globally, locally and regionally (Giri, 2012).

The user-friendly applications with

effective LULCC modeling approaches are

significantly required for monitoring,

analyzing, validation, simulation and

predicting LULC (Aguejdad et al., 2017;

Kamusoko et al., 2009). More researchers

investigated the changes of LULC to

improve natural resources conservation in

Rwanda (Kamusoko et al., 2009; Nyandwi

and Mukashema, 2011). Spatial studies

related to LULC using technologies as

Remote Sensing (RS), Geographic

Information systems (GIS), LULCC

approaches with spatial statistics, transition

matrices, and spatial metrics have the

capability of measuring, analyzing,

mapping the spatial pattern of LULCC,

predicting the future land use and land

cover, modelling the relationship of LULC

and its driving factors (Arsanjani, 2011;

Bhatta, 2010; Rwanga and Ndambuki,

2017).

Different kinds of modelling systems of

LULCC were implemented to simulate

LULC for future prediction, such as Markov


84

Chain (Kumar et al., 2014; Yirsaw et al.,

2017), CLUES (Han et al., 2015; Verburg et

al., 2002), Land Change Modeler (Mishra et

al., 2014), Cellular Automata (Al-shalabi et

al., 2013), agent-based (Iizuka et al., 2017).

Among all the models, LCM has been

applied for Modelling LULCC for urban

and non-urban areas at vast spatial scales,

and also its feasibility as well as its

applicability, has been verified and tested in

significant numbers of research papers

(Iizuka et al., 2017). However, the spatial

model used in this research Multi-Layer

Perception (MLP) Neural Network, which is

able to simulate multiple-land use classes

(Houet and Hubert-Moy, 2006), and

Markov Chain (MC) the subroutine model

of Land Change Modeler (LCM) IDRISI

platform which are embedded in TerrSet

Software (Eastman, 2012). In this

framework, it is imperative to forecast the

LULC changes over the time and predict the

LULCC future status. Therefore, the

objectives of this research are:(i)

highlighting the most LULC dynamics in

quantity and clarifying its changes; (ii)

recognizing spatial patterns of land use and

land cover changes and its expansion

proportion; (iii) simulating the future LULC

change map for 2020 and 2030. The

outcomes of this investigation help to reveal

categories of LULC significant conversions

and disclose the prognosis of future LULC

change for 2020 and 2030. For sustainable

environmental development and wellbeing

of households, the current investigation can

be momentous. In addition, the results from

this study can inform decision making

process concerning the protection of

environment and it can serve as a baseline

for further researchers aiming at monitoring

LULCC and prediction of changes in areas

with similar LULC dynamics.

2. Materials and Methodology

2.1. Site Description

Congo Nile Ridge Region of Rwanda is

located between latitude 28°55ʹ30ʹʹS and

29°26ʹ00ʹʹS, longitude 10°33ʹE and 20°33ʹE

as shown in Figure 1, occupying an entire

area of 7991.32 square kilometers. The

region stretches eastwards to the

surroundings of Lake Kivu. The area under

study is landlocked and comprises nine

administrative districts namely, Nyaruguru,

Nyamagabe, Karongi, Rutsiro, Rubavu,

Nyabihu, Ngororero, Rusizi, and

Nyamasheke. The topography is hilly and

mountainous and has an altitude ranging

from 1700 meters to 4507 meters. The

highest peak is 4507 meters on mountain

Karisimbi (Byers, 1992; Mikova et al.,

2015). Rwanda has mountains that are

volcanic in nature in the northern frontier

and the Congo Nile Ridge Region of

Rwanda which extend over an unstable

mountainous area, while the central plateau

is dominated by scattered undulating hills


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(Carr and Halsey, 2000; Van Damme et al.,

2013).

Figure 1. Locating the area of the study of Congo Nile Ridge Region of Rwanda

(CNRRR).

In regions of a high altitude, the annual

average temperatures range between 15°C

and18°C in the western and northwestern

mountains. The volcanic region has much

lower temperatures that can go below 0°C in

some places. The study area experiences a

rainfall climate of the tropics, with the

lowest rainfall of 1000 to 1200 mm/year

averagely. The highest average rainfall

rising up to 1800 mm/year, occurs in the

western mountain ranges of Rwanda, where

most of the rainfall runoff is generated. The

year is divided into four seasons; two are

wet (Mar-May and Sept-Dec) and the other

2 seasons are dry (Jan-Feb and Jun-Aug)

(Basnet and Vodacek, 2015; Karamage et

al., 2017; Nsabimana and Habimana, 2017;

Ntwali et al., 2016). Winds are generally

around 1‐3 m/s (Mikova et al., 2015).

2.2. Data Acquisition

2.1.1. Land Use/Cover

The use of satellite imagery has shown

immense potential in the study and analysis

of LULC in regional as well as global scales

(Basnet and Vodacek, 2015; Chen et al.,

2013). This investigation used the free

accessible data of Landsat TM/ETM+ of the

Congo Nile Ridge Region of Rwanda from

the Landsat chronicle. The developing

season is, by and large, more helpful for


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mapping vegetation than lethargic periods.

The essential planting season in the area of

investigation is amid the March– May and

Sept-December wet seasons. Along these

lines, the essential Landsat data were those

acquired in June– August as the yields

developed. At the point when scenes were

not available amid the essential growing

time frame, data with insignificant overcast

cover were acquired from the second dry

season. Therefore, six satellite

images/scenes were downloaded from

Landsat data through United States

Geological Survey (USGS) Global

Visualization Viewer web site

(http://glovis.usgs.gov/) for conducting this

research. The area of the study stretches in

one path and two different rows of Landsat

image tile (path 173 and rows 61 and 62).

All data flow and information processes for

the methodology used in LULC dynamics

analysis and future prediction in the study

area are summarized in the Figure 2.

Figure 2. Methodology used of the study for LULC dynamics prediction.

To cover the whole study area, two satellite

scenes set for each year were downloaded,

and thus six cloud-free or minimum cloud

images (Landsat 5: TM 1990 and Landsat 7:

ETM + 2000 and 2010) in the dry season

(December – January) in order to minimize

seasonal influences (Table 1). The

coordinate system of the images is the

Universal Transverse Mercator (UTM),

Zone 35S, referenced to the World Geodetic

System (WGS) 1984 ellipsoid. Moreover, it

has a spatial resolution of 30m. The

http://glovis.usgs.gov/


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characteristics of the selected images are

tabulated in Table 1. At times, it was

essential to wipe out clouds and shadows by

making masks and utilizing complementary

images.

Table 1. Specifications of images of Landsat data information used in Congo Nile Ridge

Region of Rwanda with a spatial resolution of 30 m.

Sn Satellite/ Sensor Acquisition Date Cloud

Cover

(%)

Path Row

1

2

Landsat 4/TM 1989/08/04

1989/08/04

4

2

173

173

61

62

1

2

Landsat 7/ETM+ 1999/12/06

2000/06/15

7

9

173

173

61

62

1

2

Landsat 5 / TM

Landsat 5/ TM

2010/08/22

2010/08/22

12

14

173

173

61

62

2.2.2. Driving Factors

Four driving factors, namely, distance from

main roads, distance from rivers, elevation,

aspect and slope were utilized for LULC

future change prognosis through Land

Change Modeler. Elevation, aspect and

slope were derived from Digital Elevation

Model (DEM) of 30m resolution acquired

from the Shuttle Radar Topography

Mission(SRTM) of the National

Aeronautics and Space Administration

(NASA) the radar mapping of Earth’s

topography (National Aeronautics and

Space Administration United States). The

distance from roads and distance from rivers

were acquired as shapefiles from DIVA-

GIS website (DIVA-GIS). The map of

distance from mains roads and rivers was

created using Euclidean distance embedded

in ArcMap-Spatial analysist tools through

the shapefile of the road network datasets

and shapefile of river respectively of the

area under investigation. The slope and

aspect were derived from STRM DEM

(Basnet and Vodacek, 2015;

Nikolakopoulos et al., 2006). All variables

were prepared in raster format with the same

projection and resolution (30*30 m of cell

size) as done for LULC classification.

2.3. Image Processing

Image preprocessing was achieved by

stacking imagery, band selection and

consortium. To fill the gaps and harmonize

the data, interpolation and extrapolation

techniques were applied (Vázquez-Quintero

et al., 2016). Landsat imageries of

1990,2000 and 2010 were radiometrically

adjusted using the top of atmosphere

process (Akinyemi, 2017). This process is


88

very important for imageries applied to

compare different periods of times in a year,

as in the case of LULC change with Land

Change Modeler (LCM) where earlier

LULC and later LULC are needed to

analyze the changes between the two

periods (1990-2000, 2000-2010 and 1990-

2010) (Iizuka et al., 2017; Vázquez-

Quintero et al., 2016).

Equation (1) and (2) were used for the

radiometric conversion of sensors TM of the

satellite Landsat (Iizuka et al., 2017).

Lλ = (( Lmaxλ − Lminλ )/(QCALmax (1)

−QCALmin)) ∗ (QCALmin − QCALmi ) + Lminλ

ρλ =π ∗ Lλ ∗ d2

cos θs ∗ USUNλ

(2)

in which, Lλ is the spectral radiance, ρλ

is top of the atmosphere, QCAL is the

calibrated and digital conversion number,

𝐿𝑚𝑖𝑛λ = to the spectral radiance scales to

QCALmin, 𝐿𝑚𝑎𝑥λ represents the

spectral scales radiance to QCALmax,

QCALmin is equivalent to the maximum

quantized calibrated pixel value, QCALmax

is the maximum quantized calibrated pixel

value, d stands for the distance from the sun

to earth, 𝑈𝑆𝑈𝑁λ indicates the minimum

solar exoatmospheric irradiance, also θs

represents the solar zenith angle. Lλ is in

units of watts per square meter per steradian

per micrometer (w/m2/sr/µm). (Xiong et al.,

2017). The false color composite was

presented in the red, green and blue,

respectively, was used to show differences

between diverse LULC categories (Chander

et al., 2007). Besides, after atmospherical

rectification, each image set containing 2

scenes of each was mosaicked using seam

line mosaicking to create a single scene of

each year. Finally, the single scene

generated for each year was clipped

according to the shapefile of the study area

as a raster in ArcMap.

2.4. Classification of Land Use/Cover

The modified Anderson Level I

classification Scheme(Churches et al., 2014;

Lui and Coomes, 2015) which is the

worldwide accepted and widely used LULC

classification scheme were used in this

research. This was done to achieve desired

LULC categories from satellite imageries of

1900, 2000 and 2010. In the context of

estimating LULCC over time and future

prediction of the area under investigation,

the first phase in Land Change Modeler of

IDRISI method is change analysis,

transition potential and change prediction.

In Land Change Modeler software, in order

to compare two maps of different periods of

time, it is mandatory for both imageries to

have the equal number of LULC categories,

same background, and similar projection

and coordinate system to meet the common

land type structure (Bhatta, 2010).


89

Referring to Table 2, the LULC maps of

Congo Nile Ridge Region of Rwanda for

1990, 2000, and 2010 were generated using

Anderson’s classification system level one

through supervised training classification

method (Anderson, 1976). Six LULC

categories were therefore identified,

including forestland, grassland, cropland,

settlement, wetland and water bodies. For

the current study, a supervised classification

approach and the maximum likelihood

classification algorithm (Mwangi et al.,

2017) were used as indicated in the Equation

numbered four and five. To generate the

desired categories from the Landsat images

we applied the maximum likelihood images

classification as it is indicated by equations

numbered three and four. Hence, the

classification based on maximum likelihood

is considering together with the covariances

and variances of the category signatures

when assigning each cell to one of the

categories representing in the signature file

(Friehat et al., 2015). As a rule, the

maximum likelihood assigns pixel to the

categories based on probability (Srivastava

et al., 2012). The probability density

function for a category (Wi) is derived by:

P(X/Wi)=1

((2𝜋)12)σi

exp (−1

2∗

(𝑋−µi)2

σi2 ) (3)

in which, P is density function probability,

Wi is the LULC category, X represents the

values of brightness, µi is the average of the

estimation of all values in LULC category

training category.

For n-dimensional multivariate ordinary

thickness equation is written as:

P(X/Wi)=𝟏

((𝟐𝝅)𝟏𝟐)|𝑽i|

𝟏𝟐

exp (−𝟏

𝟐∗ (𝑿 − 𝐌)𝑻 𝑽i

−𝟏 (𝑿 − 𝐌i)) (𝟒)

in which, Vi is covariance matrix

determinant, Vi-1 is the matrix of covariance

inverse, (X-Mi) T is the transpose of the

vector X-Mi, Mi is the mean vector, V is the

covariance of the matrix. Figure 3 is the

outcome of classification procedure,

depicting maps of LULC of the area of

investigation for the different periods under

consideration in this research

.


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Table 2. Classification schemes of LULC in Congo Nile Ridge Region of Rwanda.

LULC

Category

Descriptions

Forestland This is a developed plantation approximately with the elevation

greater than five meters, and it is greater or equal to 60 % ground

surface covered by trees and canopy is even with green foliage.

Forestland category contains natural montane forests and plantation

forests such as Pine as well as stands of trees remaining with

croplands.

Grassland This category of land contains primarily of shrubs, grasses, bushes and

scrub. It is mainly used as pasture grassland suitable for pasturage.

Cropland Cropland fields land area, cultivated lands used for mixed crops.

This land grows a variety of food crops and fiber with greater or equal

to 80 % of land cover in crop-producing fields and wetlands.

Built-up This type of land is made up of about 80 to 100 percent of

buildings of residential, non-residential and other infrastructures such

as roads, play-grounds without or with vegetations, and car parking.

These are raised by cooked bricks, uncooked bricks, concrete material

or mud. The buildings are surrounded by a small open space with

flowers, grasses and trees.

Wetland Mix of water with herbaceous and other vegetation; swamps

Water bodies Flowing water, Perennial water body, rivers, lakes, ponds, dam

and other water reservoirs with greater or equal 95 percent of water

cover.

In this classification scheme, six classes are

identified, namely, Forestland, Grassland,

Cropland, settlement, Wetland and Water

bodies.

2.5. Classification Accuracy Assessment

For accuracy assessment evaluation, the

overall accuracy and Kappa statistics were

calculated (Basnet and Vodacek, 2015; Yeh

and Qi, 2015). In total, 360 points samples

were utilized as a part of the precision

evaluation of the classified images of 1990,

2000 and 2010. Each of the six LULC

classes were utilized as stratum to create the

arbitrary sampling points. In all, sixty

arbitrary points were utilized for each land

category (forestland, grassland, cropland,

settlement, wetland and water bodies). A

Kappa statistic for the stratified random

sampling was also used using the equation

(5)(Foody, 2004; Mubea and Menz, 2014).

K =N ∑ (Xii) r

i−1 −∑ (Xi+)( X+i) r

i=1

N2−∑ (Xi+)( X+i) r

i=1

(5)

in which, r stands for the quantity of rows in

the matrix, Xii represents the quantity of

observations in row-i and column-i (the

diagonal elements), X+i and Xi+ represent

the marginal totals of row-r and column-i,

respectively, and N stands for observations

quantity. The accuracy assessment

summary of produced accuracy, user

accuracy, overall accuracy and the kappa


91

statistics of both imageries classified are

depicted in Table 3.

2.6. Land Use/Cover Prediction

The simulation can be done for the future

date; the MLP neural network was applied

to learn the status of transition with driving

potential variables of the LULCC. The

potential transition map resulting from MLP

neural network was used in combination

with Markov Chain (MC) for generating the

future date projection land use/cover. The

MC is a stochastic procedure model that

defines in what way likely one state is to

transform to another state, which stands to

transition probability matrix (Arsanjani et

al., 2013). In Markov Chain, two different

LULC maps at earlier and later land

user/cover should exist, and it is possibly to

compute the transition probability between

earlier and later lands (Zhang et al., 2011).

The prediction of the future LULC in LCM

is done in two very important phases, the

first phase is the transition potential sub-

model and the second one is the prediction

of change for a specific future date

(Aguejdad et al., 2017). The potential

change modeling is developed by applying

transition of sub-model grouped into

important variables affecting land use and

land cover change categories, in our study,

the MLP neural network sub-routine is used

(Aguejdad et al., 2017).

Future change prediction is therefore

performed by Markov Chain, after

determination of the prediction date

(Arsanjani et al., 2013; Zubair et al., 2017).

The validation is used for assessing the

quality of predicted output map. Markov

Chain transition probability matrix is

computed by the Equation (7) and

(8)(Stewart, 2009).

∑ Aij

𝑛

𝑖=1

= 1𝑖, 2,3, … … … 𝑛 (7)

A = (Aij) |

𝐴11 𝐴12 … 𝐴1𝑛

𝐴21 𝐴22 𝐴2𝑛

𝐴𝑛1 𝐴𝑛2 𝐴𝑛𝑛

| (8)

(8)

in which, Aij is the transition probability

from one land use and land cover to another

land use and land cover, n is the category of

land use and land cover of the area under

investigation, Aij is the value between 0 and

1. The accuracy assessment is accepted

when it is achieved within the vicinity

equaling to 80% (Eastman, 2009; Eastman,

2012).

LCM in TerrSet was used for predicting

LULCC of the area under investigation for

2020 and 2030. Applying the historical

maps of change at the earlier time

(LULC1990) and another land use/cover at

later time (LULC2000), with the

combinations of layers of distance from

roads, distance from rivers, slope, aspect,

and elevation, were used to project LULC

map of the year 2010 (LULC2010).


92

Accordingly, the map predicted of land use

and land cover of 2010 was validated by

relating it to the actual map of 2010

classified. Thereafter, with the positive

validation, the land use and land cover map

of 2000 and 2010, were then applied to

predict LULC in 2020, while the LULC of

1990 and LULC of 2010 were utilized to

model the future land use and land cover in

2030.

3. Results and Discussion

3.1. LULC Classification 1990,2000 and

2010

The consecutive equal time periods of 10

years between imageries of Landsat during

1990–2010 (1990, 2000 and 2010) were

adopted to synchronize with LULC

dynamics analysis. Over the entire study

area of 7991.35 square kilometers, as it is

shown in Figure 3 and Tables 2 and 3, six

lands use and land cover classes (forestland,

grassland, cropland, built-up, wetland and

water bodies) were classified using

supervised maximum likelihood for both

three years (1990, 2000 and 2010). The

overall accuracy for 1990,2000 and 2010

maps was 95.28 %, 96.67% and 94.72%,

respectively, whilst the Kappa statistics was

94.33%, 96.00% and 93.67%, respectively

(Table 3). The Kappa statistics (>80 %) for

the three maps of three different time frames

indicate a high degree of accuracy with

minimal error propagation in classification

method. Producer’s and user’s exactitudes

of specific categories were consistently

high, extending from 83.67% to 100% and

86 to 100%, respectively (Table 3).

Therefore, forestland (59.31 %), cropland

(23.55 %), grassland (4.35 %) and water

bodies (12.69 %) were the foremost

represented LULC classes in Congo Nile

Ridge Region of Rwanda in 1990, whereas

built-up (0.07%) and wetland (0.03%) were

the slightest represented in the area under

investigation of 799,135 hectares.


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Figure 3. LULC Mapping of 1990, 2000 and 2010.


94

Table 3. Summary of Landsat classification accuracy evaluation (Overall accuracy,

user accuracy and Kappa statistics) for 1990, 2000 and 2010.

Year LULC

Category

Producer

Accuracy

(%)

User

Accuracy

(%)

Overall

Accuracy

(%)

Kappa

(%)

1990

Forestland

Grassland

Cropland

Settlement

Wetland

Water bodies

100.00

91.67

96.67

93.33

90.00

100.00

98.36

94.83

86.57

98.25

98.18

96.77

95.28 94.33

2000

Forestland

Grassland

Cropland

Settlement

Wetland

Water bodies

100.00

95.00

100.00

93.33

91.67

100.00

100.00

98.28

92.31

96.55

98.21

95.24

96.67 96.00

2010

Forestland

Grassland

Cropland

Settlement

Wetland

Water bodies

100.00

90.00

100.00

96.67

83.67

100.00

98.36

93.10

89.55

96.67

96.08

95.24

94.72 93.67

3.2. Land Use/Cover Forecasting for 2010,2020 and 2030

The predicted land use /land cover of 2010

(Figure 4) was generated using land use and

land cover of 1990 and 2000, and after that

it was validated based on the actual LULC

of 2010 (Figure 4), in order to verify the

accuracy before continuing with the

simulation future land use and land cover for

2020 and 2030. Conferring to Pontius and

Schneider (2001), validation is playing a

significant role in the processing of a

modelling system of prediction. Validation

stipulate how the simulated land use and

land cover map is correlated to actual map

or the map of reference (Koranteng and

Zawila-Niedzwiecki, 2015).


95

Figure 4. The actual land use and a land cover map of 2010 and the predicted land use

and land cover of 2010 map applying the land use and land cover maps of 1990 and

2000, both used for validation of simulated LULC map of 2010.

Therefore, the model was achieved with a

Kappa overall of 83.57% and a percentage

of correctness of 89.35 %. The accuracy

assessment results achieved in this

prognosis was satisfactory and showed a

great model with magnificent expectation

(Koranteng and Zawila-Niedzwiecki,

2015). Furthermore, there are few

inconstancies in simulated LULC 2010 for

built-up category. The concentration of

built-up category to the southeast of the

forecasted map of land use and land cover

2010 was compared to the actual LULC map

of 2010. However, applying the LULC of

2000 as an earlier period and LULC of 2010

as a later period, the land use/cover of 2020

was then simulated (Figure 6). The

prediction of land use and land cover in

2030 (Figure 6) was executed using the

earlier land use/cover of 2000 and the later

land use/cover of 2010.

Figure 5 displays the prognosis of 2020 and

2030 maps of land use and land cover. In

2020, forestland and cropland continue to be

the peak in land use and land cover category

with approximate rates amount of 333,559

hectares and 317,976 hectares, respectively.


96

Cropland is the uppermost gaining, while

the built-up is slightly increasing. Then,

water bodies and wetland remain

unchangeable. As shown in Figures 5 and 6

as well as in Table 4, the forecasted land use

and land cover of 2030, depicted that the

forestland and grassland lands reduced the

extent of about 6480 hectares and 2550

hectares, respectively, whereas built-up and

cropland increased approximatively 1.12 %

and 0.01 %, respectively, while water bodies

and wetland remained constant.

Table 4. Projected Land Use and Land Cover for 2020 and 2030.

2020 2030 Change

LULC

Category

Extent

(%) Extent(ha) Extent (%) Extent(ha) Extent(ha)

Forestland 41.74 333559 40.93 327079 -6480

Grassland 5.23 41795 4.91 39245 -2550

Cropland 39.79 317976 40.91 326926 8950

Built-up 0.3 2397 0.31 2477 80

Wetland 0.01 80 0.01 80 0

Water

bodies 12.93 103328

12.93 103328

0

TOTAL 100 799135 100.00 799135

The trend of LULC from 1990 to 2030 as

depicted from Figure 7, extends across a

period of 40 years. Since 1990 to 2030, the

figure shows a significant decreasing and

increasing changes for forestland and

cropland respectively. The forestland is

highly losing; whereby it changed from

about 473,969 hectares in 1990 to

approximately 327,079 hectares in 2030 as

per LULC simulation.


97

Figure 5. Simulated land use/cover maps:2020 (using LULC of 2000 and LULC of

2010) and

2030 (using LULC of 1990 and LULC of 2010).

Figure 6. Predicted LULC for 2020,30 and LULC since 1990- 2010.

0.00

20.00

40.00

60.00

80.00

100.00

120.00

1990 2000 2010 2020 2030

Ch

ange

in %

Forestland Grassland Cropland


98

Figure 7. The trend of LULC of Congo Nile Ridge Region of Rwanda from 1990 -2030.

The simulation of the changes elaborated

that the cropland continues to increase so

that it will probably reach the forestland

(which used to be dominant from 1990-

2010) in 2030. Given that the forestland

loses and cropland increases, it is obvious

that forestland remains dominant but with a

high likelihood for cropland to become

dominant after 2030, if no adequate

measures will be put in place. In that case,

the cropland will be estimated to 326,926

hectares whereas forestland will be 326,926

hectares. For the year 2030, forestland is

higher 153 hectares than cropland

comparing to 285,786 (35.76 %) in 1990.

Additionally, the results showed that from

2020 to 2030, grassland will decrease from

41,795 hectares to 39,245 hectares

equivalent to a loss of 2,550 hectares.

Inversely, the built-up will increase about 80

hectares, while wetland and water bodies

will remain unchanged (Figures 6,7).

4. Conclusions

The main goals of this research emphasized

on prediction of LULC in Congo Nile Ridge

Region of Rwanda for 2020 and 2030 years.

Additionally, this study was implemented

using LCM where the future LULC

prediction, Markov chain embedded in Land

Change Modeler was applied, and Multi-

Layer Perceptron (MLP) Neural Network

sub-routine of LCM was used for transition

potential map based on driving factors

(distance from roads, distance from rivers,

slope, aspect and elevation) and previous

change trend. The outcomes of this study

confirmed ample proofs of transition of land

cover changes.

y = -5.433x + 65.329R² = 0.8272

y = 0.2002x + 4.4156R² = 0.1641

y = 5.096x + 17.602R² = 0.8641

y = -0.006x + 0.036R² = 0.75

y = 0.072x + 12.62R² = 0.7289

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

1990 2000 2010 2020 2030

Ch

ange

in %

Forestland Grassland CroplandBuilt-up Wetland Water bodiesLinear (Forestland ) Linear (Forestland ) Linear (Grassland )Linear (Cropland) Linear (Wetland) Linear (Wetland)


99

LULC changes were assessed by

increases/decreases in different land

categories in the future land use and land

cover prediction for 2020 and 2030 except

for two lands (water bodies and wetlands)

remaining constant. Furthermore, the

prediction for 2020 and 2030 of land use and

land cover revealed that forestland will

continue to lose as well as grassland to the

extent of 6,480 ha and 2,550 ha

correspondingly, whilst cropland and built-

up will increase around 8,950 ha and 80 ha

respectively, and both wetland and water

bodies will be remained constant.

From the findings of the current study, it

would be recommended that forestland in

Congo Nile Ridge Region of Rwanda

should be dedicated to the mechanisms of

protection and conservation as well legal

means. In addition, other managed and

effective regulations should be established

to curb the deforestation and destruction of

forests as to safeguard the forests in the

study area. Concurrently, some mechanisms

need to be put in place like planned

harvesting of trees, wood cutting for

firewood, built-up, and adoption of new

alternative sources of energy rather than

firewood. This would therefore reduce the

continuing stress and excessive

consumption of forests. Notwithstanding,

thorough enforcement of existing policies,

rules and regulations related to construction

measures needs to be enhanced. To achieve

this, the government of Rwanda should

embark on new approaches aiming to reduce

the mismanagement of the limited land

resources. These include: grouped and

planned settlements, construction in vertical

ways instead of current horizontal which is

consuming a lot of fertile lands which

should be used for agriculture. Accordingly,

the population growth should be controlled,

because the population increase exacerbates

pressure on land use and land cover.

Introduction of well-planned farmland

activities accommodate more than 80% of

the overall population in the country.

5. Acknowledgments

This study has been bolstered by the China–

East Central Africa Joint Research Center

Project of Chinese Academy of Sciences.

The authors express plentiful gratitude to

USGS and NASA team for providing

information and data used in this research.

The authors would like also to thank

Professor Xi Chen from Xinjiang Institute

of Ecology and Geography of Chinese

Academy of Sciences, for supervising,

motivating, improving the quality of the

study and his unreserved scientific support

in order to accomplish this paper. Finally,

the special thanks are additionally addressed

to the joint cooperation between the

University of Lay Adventists of Kigali

(UNILAK and Xinjiang Institute of Ecology

and Geography of Chinese Academy of


100

Sciences (XIEG-UCAS), which offered a

financial support for achieving the

Doctorate Studies (PhD) of which this study

is a fruit.

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