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         Geoplanning 
Vol 5, No. 1, 2018, 17-34                                                                                                                                                           Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning 

doi: 10.14710/geoplanning.5.1.17-34 

MONITORING AND PREDICTING LAND USE-LAND COVER (LULC) CHANGES WITHIN 
AND AROUND KRAU WILDLIFE RESERVE (KWR) PROTECTED AREA IN MALAYSIA 
USING MULTI-TEMPORAL LANDSAT DATA 

J. Gambo a,b, H. Z. M. Shafri a        , N. S. N. Shaharum a, F. A. Z. Abidin c, M. T. A. Rahman c 

a Department of Civil Engineering and Geospatial Information Science Research Centre (GISRC),  
  Universiti Putra Malaysia (UPM), Malaysia 
b School of General Studies, Binyaminu Usman Polytechnic, Nigeria 
c Department of Wildlife and National Parks (DWNP), Malaysia 

 

Abstract: Natural and anthropogenic activities surrounding a Protected Area (PA) 

may cause its natural area to change in terms of Land Use-Land Cover (LULC). Thus, 
there is need of environmental change monitoring within and around PA because of its 
significant values to ecosystem at conservation scales. Effects and influences of local 
community within and around PA turn into the major problems for natural resource 
and conservations management as well as environmental impact assessment. 
Ascertaining the complex interface in relations to changes and its driving factors over 
period of time within and around PA is significant in order to predict future LULC 
changes, build alternative scenarios and serve as tools for decision making.  The main 
objective of this work was to evaluate temporal change detection and prediction of 
LULC as well as the trends of changes from 1989 to 2016 within and around Krau 
Wildlife Reserve (KWR).  The cloud issues were mitigated by producing cloud free 
image and object-based image analysis (OBIA) was adopted after a comparison with 
pixel-based analysis for overall accuracy and kappa statistics. The comparison of 
classified maps had produced a satisfactory results of overall accuracies of 91%, 86% 
and 90% for 1989, 2004 and 2016 respectively. The natural/dense forest between 
periods of 1989-2016 was decreased whereas built-up and agricultural/sparse forest 
were increased. The simulation model of Land Change Modeler (LCM) was utilized with 
digital elevation model (DEM) and past LULC maps to project future LULC pattern using 
Markov chain. The predicted map trend showed an increase of dense forest converted 
to agricultural/sparse forest in the north-western, and urban/built-up in east-southern 
part of KWR. The study is important for the conservation of habitat species and 
monitoring the current status of the KWR. 

 Copyright © 2018 GJGP-UNDIP  

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 

Gambo, J., et al. (2018). Monitoring and Predicting Land Use-Land Cover (LULC) Changes within and around Krau Wildlife Reserve (KWR) Protected 

Area in Malaysia using Multi-Temporal Landsat Data. Geoplanning: Journal of Geomatics and Planning, 5(1), 17-34. 

doi: 10.14710/geoplanning.5.1.17-34 

1. INTRODUCTION 

Protected areas (PAs) represent a massive investment around the world both at national and international 
levels in looking after our environment. Awareness and dialogue about protection and conservation of 
these environmentally sensitive areas be well informed and shared understanding among all the 
beneficiaries involved within and around. Natural areas have been effected greatly in many world locations 
by natural and human activities. Thus, monitoring, detecting and forecasting of land features modifications 
are significant for sustainable management, biodiversity, conservation, and development of PA (Bozkaya et 
al., 2015). Geographic spaces which, because of their particular environmental values for conservation 
purposes, deserved a special forms of safety ranging from total closure, except for protection purposes, to 
various forms of intervention required to maintain or restore habitats, to direct human use, remove 

OPEN ACCESS 

Article Info: 
Received: 20 August 2017 
in revised form: 06 March 2018 
Accepted: 30 April 2018 
Available Online:  30 April 2018 
 

Keywords:  
LULC, OBIA, Protected Area, Krau 
Wildlife Reserve, Land Change 
Modeler 
 

Corresponding Author: 
Helmi Zulhaidi Mohd Shafri 
Coordinator of Remote Sensing 
and GIS programme 
Department of Civil Engineering 
Faculty of Engineering 
Universiti Putra Malaysia (UPM) 
43400 Serdang, Selangor, 
Malaysia 
Email: hzms04@gmail.com  

  

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invasive species, re-introduce extirpated spaces, or facilitate visitation by scientists or the public for 
purposes of research, monitoring and mapping, recreation and education. Tourism was also considered to 
consist of the facilitation of recreational visitation: availability of food services, guide to access roads, 
accommodation, services, and water supply and regular sanitation is referred to PA (Dudley & Stolton, 
2008). PA is also clearly defined by International Union for Conservation of Nature (IUCN) 2008, as a 
geographical space, dedicated, acknowledged and managed, through legal or other effective means, to 
achieve the long-term future conservation of nature with associated ecosystem services and cultural values 
or an area of land and/or sea especially dedicated to the protection and maintenance of biological diversity, 
and of natural and associated cultural resources, and managed through legal or other effective means 
(Dudley & Stolton, 2008). UNEP (2004) described PA as an area of land or sea especially dedicated to the 
protection and maintenance of biological diversity and conservation of natural and related cultural 
resources, managed through legal or other positive means. 

Natural forests are the unique land cover types of ecosystems especially in most of the wildlife reserve that 
provide important ecological services for habitat species within the reserve area. Rural community along 
the PA relies on forest resources for their livelihood (Despot Belmonte & Bieberstein, 2016). Frequent 
changes in PA has led to degradation and fragmentation of wildlife habitat. In Malaysia reserved areas have 
been traditionally established with creation of Chior Wildlife Reserve in 1903. However, based on the 
Master List there are 490 protected areas listed for Malaysia: 271 PAs for Peninsular Malaysia, 173 for 
Sabah (including 3 in FT Labuan) and 46 for Sarawak. These encompass land area (3,510,239 ha) with a total 
size of 4,586,273 ha (Table 1) (Interim Master List of Protected Area in Malaysia). Some PAs are 
administered by the department within the Ministry of Natural Resources and Environment (NRE) and 
others are administered and managed at the states and NGOs level. According to the NRE, terrestrial PAs 
currently cover over 1.8 million ha in Peninsular Malaysia. They can be divided into four legal categories: 
Areas reserved for a public purpose under the land laws; Permanent reserved forests (PRFs) under the 
forestry laws; National parks and state parks under the parks’ laws; and Sanctuaries or reserves under the 
wildlife laws (UNDP, 2012) KWR is one of the threatened PA in Malaysia due to frequent illegal logging (Lin, 
2016) of important forest tree which alter the condition of natural forest over a decade (Ahmad, Abdullah, 
& Jaafar, 2012). 

Geospatial technologies have potentials for mapping changes in Pas (Willis, 2015), environmentally 
sensitive areas, and biosphere reserve area. Mapping and monitoring PAs and their surrounding areas at 
both local and regional scales are crucial given that the vulnerability to anthropogenic activities, including 
climatic change, and important for conservation and biodiversity management. Monitoring using geospatial 
technologies and field information, can play a significant role in developing baselines for understanding 
condition of habitats and related species diversity (Bush et al., 2017) as well as measures the gain and 
losses, associated with specific activities around the protected area (Nagendra et al., 2013).  The challenges 
for detecting and monitoring of LULC using optical remote sensing data especially in tropical region were 
the cloud cover and haze (Nagendra et al., 2013), but this study utilized image patching using multi-date 
Landsat data. KWR, for example has been particularly input for reason such as recent report about unstop 
illegal logging and mining around KWR, the use of geospatial data has been used before (Ahmad et al., 
2012), but limited and outdated information about the loss future prediction and less number of LULC 
classes by (Ahmad et al., 2012), also understanding the current status of natural forest and conditions of 
habitat species.  This could then be of use as updated study of and also utilized the products for proper land 
use planning, biodiversity and conservation management of KWR based on recent report of illegal logging in 
KWR (Norawi, 2017) and also illegal gold mining Lakum Forest Reserved within the river streams Sg. Teris 
around KWR. Thus, this study aims to provide the most up-to-date study on the status of KWR and evaluate 
changes that have occurred over the period and prediction (Mishra, Rai, & Mohan, 2014; Kumar et al., 
2015; Reveshty, 2011). Technique of OBIA will be investigated and compared with traditional pixel-based 
method to ascertain on the proper method to be used in generating the required information. 

 

 

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Table 1. Terrestrial and Marine PA Coverage in Malaysia 

State Terrestrial (ha) Marine (ha) 

Johor 235,407.80 61,869.80 

Kedah 2.00 10,720.10 

Kelantan 127,946.70 0.00 

Melaka 106.80 2,401.80 

Negeri Sembilan 57,323.90 1,893.80 

Pahang 855,160.90 55,800.80 

Perak 286,673.20 0.00 

Perlis 4,441.20 0.00 

Penang 1,414.10 1,339.30 

Selangor 106,673.10 0.00 

Terengganu 139,844.10 110,931.40 

Federal Territory 0.00 0.00 

Kuala Lumpur 156.30 0.00 

Putrajaya 0.00 0.00 

Labuan 0.00 9,288.30 

Sabah 1,555,022.70 118,768.50 

Sarawak 616,288.20 226,914.00 

Malaysia 3,986,345.60 599,927.80 

 

2. DATA AND METHODS 

Study area base map was prepared by using subset of landsat satellite image of 2016 (Islam et al., 2018). 
The systematic workflow indicate entirely steps conducted and adopted throughout the research period, 
starting with selection of study area, data collection (landsat 1989, 2004 and 2016).  Data analysis were also 
applied to both imageries, initially from image correction (atmospheric correction and geometric 
correction). Due to presence of cloud cover in 2016 image, image patching was also applied using 
SmartGEO Fill Tool. The detailed description of the entire methodology workflow was done in sub headings 
of data and methods as included; data and preprocessing, dealing with cloud and image patching, image 
classification and change detection approaches. The general systematic flowchart of the steps conducted in 
this study is shown in Figure 1. 

2.1. Study Area 
The research area of interest is known as Krau Wildlife Reserve (KWR), located nearby Mountain Benom 
with a streams/tributaries drained to Lompat, Teris and Krau River in the district of Temerloh and Jerantut 
of Pahang, Malaysia. It is geographically bounded to the south-east of Taman Negara forest which is the 
largest natural forest in. KWR covers approximately 62,395 ha as the largest wildlife reserve which makes it 
the third largest PA in Peninsular Malaysia (Figure 2) with elevation of 2,107 metres ranging from the top of 
Benom Mountain in Kuala Lompat, to 43 metres to the reserve area. The Krau Wildlife Reserve Office, 
Institute of Biodiversity and National Elephant Conservation Centre located in the southern part with an 
entrance through Lanchang while Jenderak Wildlife Conservation Centre is located in the east of the reserve 
bordering Felda Jenderak Selatan (DANCED & Jabatan Perlindungan Hidupan Liar dan Taman Negara, 2001). 
KWR was managed and controlled by the Department of Wildlife and National Parks. Because of its diversity 
of the landscape and biodiversity fullness within such a compact area makes KWR a unique centre of 
habitat species, natural forest with different flora and fauna (DANCED & Jabatan Perlindungan Hidupan Liar 
dan Taman Negara, 2001). In International Union for Conservation of Nature (IUCN) Management 

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Categories for protected area, KWR in list Category 1a: Strict Nature Reserve/Wilderness Area its meant 
protected area mainly for science or wilderness protection. Nevertheless, activities surrounding the area 
lead KWR to faces many problems related to conservation purposes including encroachment and 
conversion of natural forest, illegal harvesting of non-timber products, degazettment, and over-hunting 
(Ahmad et al., 2012). 

1989
Imageries

2004
Imageries

2016
Imageries

Mosaic

Image Patching

Geo Smart Fill

Cloud Free Images

Subset 1989

Subset 2016Subset 2004

Image Segmentations

Segmented Image
for 1989

Segmented Image
for 2004

Segmented Image
for 2016

Training Object Selection for Segmented Images
(1989,2004 and 2016)

Nearest Neighbor Image Classification for Segmented Images
(1989,2004 and 2016)

Time Series Classified Maps
(1989,2004 and 2016)

Reference points for
Subset (1989.2004,2016)

Accuracy Assessment
(1989,2004 and 2016)

RESULTS (MAPS, TABLES & FIGURES
Change Analysis

(1989-2004 and 2004-2016)
predictions

(2028 and 2040)

Landsat TM
Imagery
(1989)

Landsat TM
Imagery
(2004)

Landsat OLI
Imagery
(2016)

Atmospheric and Geometric Corrections

STUDY AREA

(Krau Willdlife Reserve)

  

Figure 1. Methodology Workflow Adopted in this Study 

 

 

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Figure 2. The KWR Location in Peninsular Malaysia 

2.2. Datasets 
The downloaded Landsat 8 OLI and Landsat TM Level-I imagery of the area from U.S Geological Survey 
(USGS) website (http://earthexplorer.usgs.gov/) are shown in Table 2. To analyze LULC changes in on a 
yearly basis, the Landsat imageries of the year 1989, 2004 and 2016 were obtained with an interval of 15 
and 12 years. Due to the heavy rainfall and cloud cover in Malaysia as tropical region, obtaining optical 
sensor imageries of 10% cloud free is quite difficult, for this reason in this study four different scene of 2016 
Landsat 8 OLI and two different scene of 2004 Landsat TM were utilized and produced 0-1% cloud free 
image of study area within the same dry seasons between June, July and August, the preprocessing 
operation started from atmospheric correction and image patching of Landsat TM 2004 and Landsat OLI 
2016 due to presence of cloud in area of interest of this study using PCI Geomatics Smart Geofill tools. 
Using region of interest (ROI) created in Google Earth Pro, the area of interest was subset following the 
image patching. The completion of pre-processing stages, the study progressed to the image analysis 
process, first stage image segmentation was conducted by object-based image classification. The 
segmentation algorithm of multiresolution in eCognition Developer 9.0 applied to each individual image for 
generating image objects. 

Table 2. Satellites Data Used in this Study 

SENSOR ID DATE ACQUIRED Time Path/Row Resolution 

TM 4 LANDSAT  6/15/1989 3:01:32 127/057 30 

TM 5 LANDSAT  6/16/1989 2:49:51 126/057 30 

TM 5 LANDSAT 8/2/2004 3:07:27 126/057 30 

TM 5 LANDSAT 7/18/2004 3:10:35 127/057 30 

OLI 8 LANDSAT  7/3/2016 3:28:03 127/057 30 

OLI 8 LANDSAT 6/26/2016 3:21:49 126/057 30 

OLI 8 LANDSAT 6/26/2016 3:22:12 126/058 30 

OLI 8 LANDSAT 6/1/2016 3:27:51 127/057 30 

 

The fusing of neighboring segments together to ensure a heterogeneity threshold is stretched to one-pixel 
image segment within segmentation algorithm. Nearest Neighbor image classification was applied by 
following three stages that included creating class hierarchy, training data sets, and training samples were 

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taken for each LULC type to be classified in the image. Testing samples were randomly selected from three 
different Landsat image of the study (Waiyasusri, Yumuang, & Chotpantarat, 2016). With reference to our 
previous training samples (Hackman, Gong, & Wang, 2017).Second stage was the classified images of 
different dates were used for change detection and landcover projection in Land Change Modeler (LCM) in 
Idrisi Selva software. Five different LULC types comprising of water, dense forests, urban/built-up, 
agriculture/sparse forest and bare soil were recognized as the final classes in Table 3. 

Table 3. Level LULC within and around KWR, as Derived from Landsat Satellitesin 1989, 2004 and 2016 

Land cover classes Descriptions 

Water River, Dam, Pond, Stream, Reservoir, Pool etc. 

Dense Forest Natural and dense Forest 

Built-up Building, Road etc. 

Bare soil Open land, Harvested land etc. 

Agriculture/Sparse 
Forest 

Rubber plantation, oil palm plantation, Banana plantation, shrubs/Grass, Cropland, Orchard and low 
density forest etc. 

2.3. Dealing with Cloud and Image Patching 

It was possible to utilize uncorrupted Landsat scenes within the same season with corrupted one. In each 

scene, some pixels in the areas with high altitude regions of KWR had been covered by clouds, haze, 

predominantly in the northern part of the study area. It was easy to identify scenes with cloudy areas 

because of the available cloud percentage information. However, we had to visually look for areas where 

pixels had been corrupted by haze or live fires and the resulting thick smoke using false composites 

(Hackman et al., 2017).  We used Smart Geofill tool which allowed user to copy a specified area of an image 

layer with non-cloud effects, make changes to it and then paste the selection to another layer of the image 

with cloud effects using color balancing method either overlap or histogram trim (Smart GeoFill Geomatica 

2015 Tutorial), and can also adjust settings for color balance, blend width, contrast, and brightness of the 

selected area to enhance or adjust its appearance in the destination layer (Hruby et al., 2016). Meanwhile 

(Hackman et al., 2017) has masked out areas with corrupted pixels before the classification which had an 

extremely thick layer of cloud. In this study image patching was applied for all available 2004 and 2016 

multi-date Landsat data images with heavily cloudy (Figure 3) showed the 2004 multi-date landsat before 

image patching and after applied using Smart GeoFill). Nonetheless, except for the landsat scenes taken on 

June 15/16, 1989. 

 

      

 

 

 

 

 

 

 

 

Figure 3. (A) Before Image Patching, (B) After Image Patching 

A  B  

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2.4. Image Classification and Change Detection Approaches 
The main aim of image classification was to automatically categorize all pixels in an image into land cover 

classes (Figures 4). The classification legend was made based on spectral characteristics. Image 

segmentation was conducted by object-based image classification. Traditionally, pixel-based image analysis 

was utilized for image classification on both low and moderate resolution satellites data, OBIA incorporates 

not only the spectral information, but also included the shape, size, spatial, texture and contextual of the 

data. OBIA merged both spatial and spectral information about the features to extract land use for specific 

objects (Kindu et al., 2013), which grouping many pixels in to one image objects during segmentation stage 

to avoid the salt-pepper effects (Desclée, Bogaert, & Defourny, 2006). While pixel-based directly focus to 

one single image objects. In recent research, many studies utilized these technologies such as OBIA in 

change detection at different scales, with both low and high resolution satellites sensor such as landsat data 

(Dutta, Reddy, Sharma, & Jha, 2016; Waiyasusri et al., 2016; Balaji, Geetha, & Soman, 2016; Ranjan et al., 

2016) and data like ALOS (AVNIR2) was used for land use changes (Munthali & Murayama, 2011) and the 

OBIA approach the same of work by Zhang et al (2017) maintained OBIA approach for change analysis in 

florida everlades water conservation area using landsat data. The object-based image analysis showed the 

expansion/reduction of land use types when applied the classified image in change detection algorithms as 

done by Son et al. (2015). 

The multiresolution segmentation algorithm in eCognition Developer 9.0 was applied to generate image 

objects for each individual image. The segmentation algorithm starts with making homogeneous object 

clusters with one-pixel image segment, and considerably merges neighboring segments together until a 

heterogeneity threshold is reached. The heterogeneity threshold identification depended on user-defined 

scale parameter, as well as the shape and compactness weights. The scale of the segmentation determined 

the quality of segmentation, and classification. The image segmentation is scale-dependent. For this study 

different scales were applied before the selection of appropriate classification. Figure 4 shows two 

segmented results with different scales assigned. Image (A) has the scale parameter of 40, and maintained 

the default values of shape 0.1 and compactness equal to 0.5. For image (B) we tried to change different 

segmentation scale from default of shape, compactness and scale parameter to 70, but the segment 

between the features have an overlap. In this study we adopted the image (A) segmentation scale because 

each land use and land cover was segmented properly. 

 

 

 

 

 

 

 

 

 

 

 

 

        

Figure 4. Segmentation Testing Results using Different Scale Parameters 

A  B 

 

0.1, 05 and 70 0.1, 05 and 70 

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Nearest Neighbor Image classification was done by following three stages that included creating class 

hierarchy, training data sets, and accuracy assessment. Training samples were taken for each LULC type to 

be classified in the image. Following the adaptation of Nearest Neighbor object based image analysis in this 

study, a pixel-based image analysis was applied to 2016 OLI landsat image using support vector machine 

(SVM) as to compare with OBIA approach. The comparison in terms of the overall and kappa statistics 

showed good results for both OBIA and pixel-based techniques (for OBIA 90%, 0.87 and SVM 98%, 0.87). 

However, the visualization of two classified image indicated that there were misclassifications between 

dense forest and agriculture/sparse forest as well as bare soil and built-up area.  

     

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5. (A) OBIA Classified Map, (B) Pixel-based SVM Classified Map 

The pixel-based SVM result produced the highest value of overall accuracy and OBIA classified image 

obtained lower accuracy. However, the OBIA classification results showed better accuracy and realistic 

representations when compared to the topographic map of KWR collected from Department of Survey and 

Mapping Malaysia (JUPEM) and also Google Earth Map. Because of these comparison and validation (figure 

5). This study adopted the OBIA image classification for all three temporal Landsat satellite data 

classifications. 

2.5. Markov model 
The stochastic model that the model output is depending on the probabilities of a transition of current 

change scenarios of Pi – j, between states i and j. The multiple land covers land uses categories in a 

landscape with transition probability Pi j , would be the land-cover type (pixels) i probability in time t0 

A  B 

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changes to land-cover type j probability in time t1 is called Markov chain model (Bozkaya et al., 2015). The 

Markov transitions probabilities, expressed as; 

                                                              (1) 

The derived transition probabilities is from a sample of transitions that occurred between two certain time 

intervals data and these probabilities was shown through the matrix P of following transition. The equation 

below proportion probability of land cover of the second date, and calculated using the equation. 

  

                                                                               (2) 

 

Where vi j x Pi j is the proportion of land cover of the later date, Pi j is the matrix of the probability of 

landcover transition, Vi is the proportion of land cover of the current date (vector), i is the type of land 

cover of the first date, j is the type of land cover of the second date, P11 is the probability that land cover 1 

at the first date will change into land cover 1 by the second date, P12 is the probability that land cover 1 at 

the first date will change into land cover 2 by the second date and so on, and m is the number of land-cover 

types in the study area (Bozkaya et al., 2015). 

3. RESULTS AND DISCUSSION 

3.1. Accuracy Assessment of Classified Images 

The Nearest Neighbor OBIA image classification of the temporal images, generated land cover maps and 

accuracy assessment report of confusion matrix were performed on  (1989, 2004 and 2016) classified 

images indicated a satisfactory overall accuracy and a kappa statistics as work of (Kindu et al., 2013; Son et 

al., 2015; Yu, et al, 2016) in (Tables 4, 5 and 6). An overall Kappa Statistics of 0.88, 0.82 and 0.87 was 

achieved for 1989, 2004 and 2016 LULC with Classification accuracy of 91%, 86% and 90% respectively. In 

Table 4 built-up has a lower producer accuracy, followed by dense forest with 71.43% user accuracy. Bare 

soil has producer’s accuracy of 73.33% while dense forest has the highest producer’s accuracy of 100% 

showed in Table 5. All the remaining LULC classes were having their accuracies above 60%. The user’s 

accuracies of all the LULC types were above 60% with water and bare soil having the highest accuracy of 

100%. 

        

 

 

 

 

 

 

 

 

 

 

Figure 6. Classified LULC Maps (1989, 2004 and 2016) 

1989 2004 2016 

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Table 4. Accuracy Results for the Landsat-5 1989 Image Derived from OBIA Classification Methods 

Note: Descriptions of LULC classes; U= Unclassified, W= Water, BS= Bare soil, DS=Dense forest, BU=Built-
up, AFS= Agriculture/Sparse forest. 

Table 5. Accuracy Results for the Landsat-8 Image 2004 Derived from OBIA Classification Methods 

classified Reference 
Producer's accuracy % User's accuracy % 

 
U W F BU BU ASF Total 

Unclassified 0 0 0 0 0 0 0 0.00 0.00 

Water 0 23 0 0 0 0 23 76.67 100.00 

Dense forest 0 2 40 1 0 2 45 100.00 88.89 

Built-up 0 1 0 22 1 1 25 73.33 88.00 

Bare soil 0 2 0 7 27 3 39 90.00 69.23 

Agriculture/ 

Sparse forest 
0 2 0 0 2 34 38 85.00 89.47 

Total 0 30 40 30 30 40 170 
  

Overall  Accuracy        
 

86% 

Kappa coefficient       
 

0.82 

Note: Descriptions of LULC classes; U= Unclassified, W= Water, BS= Bare soil, DS=Dense forest, BU=Built-
up, AFS= Agriculture/Sparse forest. 

Table 6. Accuracy Results for the Landsat-8 Image 2016 Derived from OBIA Classification Methods 

Classified Reference 
Producer's Accuracy % User's Accuracy % 

 
U W BS DF BU ASF Total  

Unclassified 0 0 1 0 1 0 2 0.00 0.00 

Water 0 27 0 0 0 0 27 90.00 100.00 

Bare land 0 0 23 0 1 0 24 76.66 95.83 

Dense forest 0 1 1 39 0 3 44 97.50 88.64 

Built-up 0 1 4 0 27 0 32 90.00 81.82 

Agriculture/ 

Sparse forest 

0 1 1 1 1 37 41 92.50 90.24 

Total  0 30 30 40 30 40 170   

Overall Accuracy         90% 

Kappa coefficient         0.87 

Note: Descriptions of LULC classes; U= Unclassified, W= Water, BS= Bare soil, DS=Dense forest, BU=Built-
up, AFS= Agriculture/Sparse forest. 

 

Classified Reference 
Producer's Accuracy % User's Accuracy % 

 
U W BS DF BU ASF Total 

Unclassified 0 0 0 0 0 1 1 0.00 0.00 

Water 0 27 0 0 0 0 27 90.00 100.00 

Bare soil 0 0 40 0 0 0 40 97.56 100.00 

Dense forest 0 3 0 25 7 0 35 86.21 71.43 

Built-up 0 0 0 2 23 0 25 76.67 92.00 

Agriculture/ 

Sparse forest 
0 0 1 2 0 39 42 97.50 92.85 

Total  0 30 41 29 30 40 170 
  

Overall Accuracy 
        

91% 

Kappa coefficient 
        

0.88 

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3.2. Change Detection 

Trend analysis of the KWR (within and around) reveals changes in area of the five LULC of 27 year period of 

the study in Table 7 and Figure 7 and 8. The land cover changes being taken by both natural activities and 

community activities surrounding KWR between the study periods were measured by using modifications 

from the late classified map to early classified map as normally applied by all researches including (Islam et 

al., 2018). Table 7 below showed the results changes in hectares and percentages that has been revealed in 

the past three distinct years of the study generated and computed in terms of maps and tables through 

LCM. Areas covered with dense forest showed an immense changes of -7.05% between 2004 -2016 sooner 

than 1989-2004 with interval of 15 years period with an only 5.63%. The significant proportions of changes 

in agriculture/sparse forest from 4137.93 ha in between 1988-2004 to 18772.92 ha between 2004-2016 

with relation to earlier trend changes of dense forest to bare soil between the period of 1989-2004. 

Furthermore, there was an increase of built up areas and water. The land cover change detection maps in 

figure 3 and 4, indicated the changes amongst five classes recognized in this study. 1989 t0 2004 changes 

result showed that the highest changes was between dense forest to bare soil/open land around the KWR 

area despite all environmental and biodiversity management measures. However, between the periods of 

2004 to 2016 the results indicated that the bare soil area and dense forest were converted more to 

agriculture/sparse forest with little encroachment around the PA boundary (Ahmad et al., 2012; de Oliveira 

et al., 2017; Zhang et al., 2017). 

Table 7. LULC Changes Between the Study Periods in Hectares and Percentages 

Land cover classes 
1989-2004 2004-2016 1989-2004 2004-2016 

Area (ha) Area (ha) Area (%) Area (%) 

Water -983.43 2140.92 -0.31 0.67 

Forest -17996.4 -22629.78 -5.63 -7.05 

Built-up -2334.78 14436.99 -0.73 4.50 

Bare soil 17289.18 -12718.80 5.39 -3.96 

Agriculture 4137.93 18772.92 1.28 5.85 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Figure 7. LULC Changes Between 2004-2016 of KWR (within and around) 

 

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Figure 8. LULC Changes Between 2004-2016 of KWR (within and around) 

3.3. Gain and Losses between 1989 to 2004 and 2004 to 2016 

It is clearly indicated from Figure 9 that there were significant negative changes and transitions within and 

around the boundary of KWR for various LULC classes during the period from 1989 to 2004 and 2004 to 

2016. The main gain and losses occurred basically between the dense forest to bare soil from 1989 to 2004 

while between 2004 to 2016 showed that the losses in bare soil are positively changed to gain in 

agriculture/sparse forest. This make the analysis valid since changes started from removal of forest at the 

beginning then planting agriculture products as started by previous study that agricultural activities and 

illegal logging is the major land use activities taking place around the Krau Wildlife Reserve. Figure 8 

illustrated the increase and decline that occurred between LULC adopted in this study in hectares from 

1989 to 2004 and 2004 to 2016. The green bars represent the gain per class measured in hectares, and the 

left side brown bars describe the loss (decline) of each class in the same unit. In terms of net changes 

between the periods of study Figure 9 also indicated. Between 1989 and 2004 there is increase in the 

amount of bare soil (20,803 ha), more of the dense forest were proportionally lost to about 39,706 ha.      

The agriculture/sparse forest had the maximum extent of gains (49,000 ha) between 2004 and 2016 while 

about 38,324 ha was lost for dense forest within the same period (Figure 8). Figure 10 and figure 11 indicate 

the net changes contributions of each land use categories between 1989 to 2004 and 2004 to 2016. 

0 20000 40000-20000-40000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
las

se
s

Gains and losses between 1989 and 2004Gains and losses between 1989 and 2004

-1355358

-39706 21678

-83796036

-3524 20803

-33841 37930

 

0 20000 40000-20000-40000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
las

se
s

Gains and losses between 2004 and 2016Gains and losses between 2004 and 2016

-4042538

-38324 15752

-4593 19007

-20560 7856

-30281 49008

 

Figure 9. Land Use/Land Cover Gain and Losses in (Ha) from 1989 to 2004 and 2004 to 2016 

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From figure 8 it is clear that there are significant changes and transitions among various LULC categories 

during the period from 1989 to 2016. The main changes and transitions are mostly occurred among dense 

forest, bare soil and agriculture/sparse forest (Reddy et al., 2017). 

0 6000 12000 18000-6000-12000-18000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
la

ss
es

Net Change between 1989 and 2004Net Change between 1989 and 2004

-997

-18029

-2343

17280

4090

 

0 10000 20000-10000-20000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
la

ss
es

Net Change between 2004 and 2016Net Change between 2004 and 2016

2135

-22572

14414

-12704

18727

 

Figure 10. Land Use/Land Cover Net Changes in (Ha) at KWR from 1989 to 2004 and 2004 to 2016 

The contributions of other categories to their net change is presented in figure 11 and figure 12 below. It 

has been clearly shown that dense forest contribute about 6,925 ha to bare soil between 1989 to 2004 and 

dense forest for both period of study explained the majority of the total increase in agricultural/sparse 

forest areas (11,868 ha and 16,269 ha). Nevertheless, other LULC contribute to the changes throughout the 

study period as shown in figure 11 and figure 12 respectively. 

       0-40-80-120-160-200-240-280-320-360-400

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
la

ss
es

Contributions to Net Change in WaterContributions to Net Change in Water

0

-381

-80

-155

-381

     

       0-2000-4000-6000-8000-10000-12000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
la

ss
es

Contributions to Net Change in Dense forestContributions to Net Change in Dense forest

381

0

384

-6925

-11868

     

0-200-400-600-800-1000-1200

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
la

ss
es

Contributions to Net Change in Built-upContributions to Net Change in Built-up

80

-384

0

-1286

-753

 

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0 1000 2000 3000 4000 5000 6000 7000 8000 9000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
la

ss
es

Contributions to Net Change in Bare soilContributions to Net Change in Bare soil

155

6925

1286

0

8913

 

0 3000 6000 9000 12000-3000-6000-9000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
la

ss
es

Contributions to Net Change in Agriculture/Sparse forestContributions to Net Change in Agriculture/Sparse forest

381

11868

753

-8913

0

 

Figure 11. Contribution to Net Changes for All LULC Classes from 1989 to 2004 

0 200 400 600 800 1000 1200 1400

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
las

se
s

Contributions to Net Change in WaterContributions to Net Change in Water

0

1446

-36

94

631

 

0-3000-6000-9000-12000-15000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
la

ss
es

Contributions to Net Change in Dense forestContributions to Net Change in Dense forest

-1446

0

-4380

-477

-16269

 

0 1000 2000 3000 4000 5000 6000 7000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
la

ss
es

Contributions to Net Change in Built-upContributions to Net Change in Built-up

36

4380

0

3139

6859

 

0-2000-4000-6000-8000-10000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
la

ss
es

Contributions to Net Change in Bare soilContributions to Net Change in Bare soil

-94

477

-3139

0

-9948

 

0 4000 8000 12000 16000-4000-8000

Water

Dense forest

Built-up

Bare soil

Agriculture/Sparse forest

LU
LC

 C
las

se
s

Contributions to Net Change in Agriculture/Sparse forestContributions to Net Change in Agriculture/Sparse forest

-631

16269

-6859

9948

0

 

Figure 12. Contribution to Net Changes in Agriculture/Sparse Forest from 2004 to 2016 

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3.4. Prediction 

The simulation model of LCM was used to simulate LULC modifications pattern (Mishra et al., 2014) within 

and around KWR for 2028 and 2040 based on Markov transition probabilities showed in appendix A and B in 

appendix respectively. The probability of diagonal cells (Table 8 and Table 9) represent an area which 

remain under the same class (Areendran et al., 2017). However, the predicted LULC (Reddy et al., 2017) 

showed the forest area keep reduced in the next projected time period (2028 and 2040) and has the highest 

probability of changes from dense forest converted to agriculture/sparse forest and built-up in Figure 13. 

Table 8. Markov Probability of changes for 2028 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 13. Predicted Land Cover Maps of KWR Area over 22 Years with Interval of 12 Years (2028 and 2040) 

Markov Probability of changes for 2028 

LULC Class Water Forest Built-up Bare soil Agriculture 

Water 0.52 0.14 0.08 0.00 0.25 

Forest 0.01 0.78 0.03 0.01 0.17 

Built-up 0.00 0.07 0.42 0.05 0.45 

Bare soil 0.00 0.07 0.17 0.04 0.72 

Agriculture 0.01 0.12 0.09 0.05 0.73 

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The simulated projected LULC maps in Figure 13 can be utilized as tools of decision making toward the 

protection, conservation and implementation of law enforcement in KWR, because the results indicate that 

if proper actions not taking the rate of degradation of dense forest will keep increasing and can lead to 

decline of both plants and animals habitat species (Conservation and Environmental Management Division, 

2006) Malaysia. 

Table 9. Markov Probability of Changes for 2040 

Markov Probability of changes for 2040 

LULC Class Water Forest Built-up Bare soil Agriculture 

Water 0.2742 0.2195 0.1056 0.0202 0.3805 

Forest 0.0129 0.6395 0.0516 0.0189 0.2771 

Built-up 0.0081 0.1419 0.2319 0.0464 0.5717 

Bare soil 0.0092 0.1575 0.1458 0.0455 0.642 

Agriculture 0.011 0.1931 0.1191 0.0434 0.6334 

 

4. CONCLUSION 

Mapping and predicting the LULC changes in a PA is very important for monitoring the activities within and 

around it. This can minimize the negative impact and help to plan for future managements to safeguard the 

KWR. The mapping analysis of KWR using multi-temporal satellites data showed and predicted the gradual 

loss in natural forest area within and around from 1989-2016 and verified by field visits and interviews with 

KWR officials. Oil palm and rubber plantations are one of the main factors leading to the encroachment 

around the KWR boundary. Moreover, the size of low dense forest/agriculture land, of the analyzed years 

(1989, 2004 and 2016) was increased and was found in predicted results of 2028 and 2040. The changes 

within and around KWR showed a massive degradation and if left unattended through current situation 

based on projected land use and land cover, it will be detrimental for biological conservation of wildlife in 

the PA. The changes and encroachment around KWR boundary have a link with the dynamics of political 

and social issues of local communities surrounding the wildlife reserve.  

This study also suggested the implementation of buffer zones which may be one of the key solutions for a 

better conservation of PAs to protect from the negative effects of illegal activities within and around the 

KWR. It is also recommended that it increase the number of forest rangers in the KWR to monitor the 

encroachments by local and indigenous communities. Future work can improve the work further by utilizing 

more data from various systems such as radar, lidar and very high resolution data. Furthermore the results 

for the PA analysis in this study can be imported to a GIS for further analysis and model development. 

5. ACKNOWLEDGMENTS 

The authors would like to thank UPM for the facilities and funding for research and travel in completing this 

task. In addition, we thank the Department of Wildlife and National Parks, Peninsular Malaysia (Perhilitan) 

and the Institute of Biodiversity (IBD) for providing useful data and information. 

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