deforestation study of phnom aural cambodia using change detection techniques

GEOG 4440/ENVS 4521

Term Paper

Deforestation Study of Phnom Aural, Cambodia,

Using Change Detection Techniques

Submitted by: Katrina Gordon

Student # 210380053

Submitted to: Gang Hong

Lab session: 02

Due Date: April 8, 2013

Introduction

The rate of deforestation in Cambodia is one

of the highest in the world. In 1969, Cambodia's

primary rainforest was estimated to encompass 70%

of its land mass. As of 2007, it dropped to 31%

(Cambodia 2013). Phnom Aural is the tallest peak of

the Cardamom Mountains located in central

Cambodia west of the Mekong River and is a region

that has experienced a rapid rate of deforestation

despite being a protected area (Mansfield and Parnell

2009). The specific study region consists of Phnom

Aural the mountain region north east of the study

region, the major city of Phnom Penh and extending

to Prey Veaeng the city east of the Mekong River as

seen in Figure 1. Cambodia consists of three main

types of forest: relatively open deciduous forest;

closed semi-deciduous forest; and closed coniferous

forest (Piazza 2007). The Phnom Aural Region of

Cambodia consists of a mix of deciduous and

coniferous forests. The dominant trees, which can

reach a height of 20 m, include oaks such as Litho-

carpus spp, Quercus cambodiensis and Castanopsis

cambodiana (Cambodia 2013).

Deforestation in Cambodia can be attributed

to a variety of sources and time periods. Historically,

the Vietnam War (1965-1975) was a major

contributor to deforestation as the U.S army sprayed

“Agent Orange” to remove foliage and reveal the

opposition (Phat et al. 1998). This was particularly

devastating to areas around the Mekong river where

approximately 2 million hectares of forest were

destroyed (Phat et al. 1998). Additionally, the rapid

increase of population has continuously led to the

increase of demands for biofuels such as firewood

and agricultural lands. Logging is also a major

industry in Cambodia. In 1999, the Cambodian

government issued a declaration on Measures to

Management of Forests and the Elimination of Illegal

Forest Activities (Energy, Environment and

Resources Chatham House 2013). This declaration

only allows tree harvesting by licensed companies

and individuals and prohibits harvesting on national

parks and protected land such as Phnom Aural.

Abstract

This study takes place in the Phnom Aural Region of Cambodia in South Asia at the coordinates of 12.0333°

N, 104.1667°E for the years 1990, 2003 and 2010. The Phnom Aural Region has been characterized by high rates of

deforestation and Cambodia in general has been recognized as having one of the highest deforestation rates globally.

The causal factors of deforestation in this region have been attributed to the Vietnam War with the use of Agent

Orange, agricultural purposes, population growth and illegal logging (Phat, Ouk & Ueki, 1998). The objective of this

study is to monitor deforestation concentrated in and around the Phnom Aural Region of Cambodia north-west of

Phnom Penh in addition to draw conclusions on the effectivity of forestry laws in Cambodia through the applications

of unsupervised classification remote sensing techniques in addition to, the execution of a Matrix analysis (MAT) and

a statistical Local Area Reports to detect change in the region. The study ultimately found unsupervised classification

methods were effective in studying vegetation changes however, the use of canopy structure could improve the level

of detail of the study and would be more indicative of the type of forestry that exists.

Figure 1. Study Area: Phnom Aural, Cambodia

However, the implementation of policies and

regulatory frameworks is hampered by a lack of

institutional capacity and illegal logging continues to

be an issue which questions the effectiveness of the

legislation (Energy, Environment and Resources

Chatham House 2013).

There are a variety of remote sensing

applications used to study forestry. Both

unsupervised and supervised classifications are

evident in a variety of forestry studies and are often

studied over a period of time. The alterations over a

specified time period are then understood better

through change detection techniques such as local

area reports which quantify land classification

statistics. Furthermore, a more sophisticated analysis

can be determined using a Matrix Analysis which

determines where exactly changes in the landscape

are occurring and by a statistical percentage of land.

There are numerous examples of studies using

classification techniques to map land classes

specifically in the use of forestry. For example,

Matuyama and Yamaguchi analyzed deforestation in

Mato Grosso, Brazil in 2010 caused by soya bean

plantations and relied on an unsupervised

classification with an NDVI classification to view

changes to the region over the span of 1991 to 2009.

Kumar et al in 2010 applied a supervised

classification technique to monitor deforestation in

Ranchi, Jharkland, India in addition to relying on a

statistical area report to describe how much of the

land was changing over that period. Classification

using ISODATA was used in a study conducted by

Nijomo (2008) who used ISODATA which calculates

class means evenly distributed in the data space then

iteratively clusters the remaining pixels using

minimum distance techniques (Geo Hul 2008). The

study performed an unsupervised classification of the

Congo Basin using ISODATA and then mapped

areas of change. These methods proved effective and

less time consuming in forestry studies that were not

able to get in situ measurments. Going beyond

specific image processing techniques, Defourny et al

(1993), identifies other key factors to ensure accuracy

of conclusions especially in tropically sensitive

regions subjected to wet and dry seasons. Defourny et

al studied land cover change in Cambodia and

discusses seasonal changes in Cambodia that affect

data collection. For example heavy cloud cover is

often seen through May to October as a result of their

Monsoon season making images from November to

the following March optimal images in addition to

avoiding seasonal additions of vegetation in images

skewing results.

The purpose of this study is to assess the

effectivity of Cambodia’s deforestation laws through

spatial analysis of deforestation in Phnom Aural

region of Cambodia. The use of unsupervised

ISODATA classifications will be relied on to define

areas of vegetation and where they exists throughout

the years of 1990, 2003 and 2010 to track patterns of

deforestation. Local Area reports in addition to a

Matrix Analysis (MAT) will be relied on as a form of

change detection to better understand these patterns.

This study is important as forestry studies of

Cambodia are rather limited despite the high rate of

deforestation.

Methods

Data was acquired using Global Land

Survey images from United States Geological Survey

(USGS). After researching the worst areas of

deforestation in Cambodia Phnom Aural was selected

and entered in the USGS search engine. Phnom Aural

is located at 12.0333° N, 104.1667°E.While the years

1975, 1990, 2000, 2003 and 2010 were retrieved,

only 1990, 2003 and 2010 could be accepted as the fit

within the same Path and Row (WRS Path 126, Row

52) and were images taken in Cambodia’s dry season

(November to March) which was important to

maintain an accurate and fair representation of

vegetation conditions. Meta data on Phnom Aural

images used can be found in the appendix i. Climatic

conditions are important to consider due to the wet

monsoons in this tropical region and would affect

results based on seasonal vegetational changes.

Figure 2, depicts the image processing techniques

which began with image retrieval of the Phnom Aural

region from 1990 (Figure 3), 2003 (Figure 4) and

2010 (Figure 5) were overlaid and clipped so that the

extent of the region that is being studied is consistent

throughout the years. Atmospheric correction was

applied using ATCOR in PCI Geomatica to help

reduce atmospheric noise and enhance the images.

Band combinations were set to 4,5,2 to contrast

differences in vegetation so that the degree of

photsynthetically active areas of vegetation display in

shades of deep red (Quinn 2001). The varying

shades of vegetation allowed for a better

classification of the vegetation. Simply knowing

where vegetation exists is trivial in a tropical region.

Being able to differentiate between healthy

vegetation and sparse regrowth allowed for a better

analysis. The band combination of 4,5,2 was also

used as a reference in carrying out the unsupervised

classification. The unsupervised classification was

applied using ISODATA to classify with a maximum

of 20 classes and 20 iterations. Classification

involved five different land classes: Dense

Vegetation, Sparse Vegetation, Bare Soil/Urban,

Water, and Clouds. Originally, Soil and Urban

Figure 2. Image processing model.

Figure 3. 1990 True colour image of Phnom Aural,

Cambodia. This image depicts Phnom Aural as

region characterized by a green and highly vegetated

environment.

True Colour Images of Phnom Aural,

Cambodia


Cambodia. This image depicts Phnom Aural with a

much more bare environment.


Cambodia. This image depicts Phnom Aural as being

similar to the 2003 conditions just slightly

accelerated.

had been classified separately, however, due to the

amount of dirt roads, many urban areas were

classified as bare soil that actually made up urban

areas Phnom Penh, Cambodia’s Capital city. For the

purpose of this study, in that deforestation is the

target variable, both bare soil and urban were

classified under one class.

To assess the accuracy, of classification, two

methods were relied on. First, a pan-sharpened image

was used as a reference image for the 2003 image of

Phnom Aural, Cambodia which had increased clarity

through a higher pixel resolution of 15 meters in

comparison to the 30 meter pixel resolution in the

classified images. Pan-sharpening of remote sensing

multispectral imagery has been found to directly

influences the accuracy of interpretation of

classification (Makaru et al. 2012). Pan-sharpened

ETM+ images such as the 2003 image of Phnom

Aural, have been found to enable better

discrimination of finer change detail than the original

ETM+ multispectral images (Makaru et al. 2012).

This is because a pan-sharpen image incorporates the

spatial detail features present in the panchromatic

image and missing in the initial multispectral one

(Makaru et al. 2012). The second method used true

colour images as the reference for the 1990 and 2010

images as a visual comparison this is because the

1990 and 2010 images were not supplied with a

panchromatic band. Pan-sharpening is a more widely

accepted version of an accuracy assessment however,

in the absence of the panchromatic band, a secondary

accuracy assessment method was needed. Neither

methods define a true accuracy assessment as they

are not referencing data that is truly determined such

as in situ data but in the absence of in situ data, they

provide a secondary method to assist in better

classifying the images. In both methods, a set of 100

random points were reassessed to determine of

classification was accurate represented.

In terms of change detection, a Local Area

report was relied on which quantifiably produced

area statistics in terms of percentage of land

classified in addition to square kilometers. To further

understand deforestation in the Phnom Aural region,

a Matrix Analysis was used to determine where

exactly change was occurring in addition to how

much vegetative land was being logged or turn into

urban areas.

Results

Figures 6, 7, 8 depict the unsupervised

classifications of Phnom Aural, Cambodia. From

1990 to 2003 it is clear that there is a progressive

amount of deforestation experienced. From 2003 to

22010 the deforestation shows much less progression

but an increase none the less. Table 1, quantifies

Figure 8. 2010 Unsupervised Classification of

Phnom Aural, Cambodia. This image depicts Phnom

Aural as having a high reduction in vegetation

however, it is evident that the reduction has slowed.



Aural with a dramatically reduced appearance of

vegetation.



Aural as region with copious amounts of vegetation.

Unsupervised Classifications of Phnom

Aural, Cambodia

Area % Total Area (km2) Area % Total Area (km2) Area % Total Area (km2)

Combined Vegetation 78.8 17615.63 Combined Vegetation 62.73 14023.28 Combined Vegetation 50.43 11274.5

Soil/Urban 17.2 3845.61 Soil/Urban 33.9 7578.01 Soil/Urban 45.71 10218.42

Water 3.5 782.25 Water 3.38 754.68 Water 3.85 860.92

Clouds 0.5 112.48 Clouds 0 0 Clouds 0.01 2.13

Total 100 22355.97 Total 100 22355.97 Total 100 22355.97

1990 20102003

these changes into the percentage taken up by the

land class and the square kilometers. The report was

generated by the Local Area Report in PCI.

MAT was also used to identify where the

changes of vegetation are occurring in the Phnom,

Aural Region of Cambodia. Figures 9, 10 and 11 map

out where strictly vegetation that has changed into a

clear cut area or an urban feature shown in grey.

Discussion

The results would indicate that deforestation

in the Phnom Aural region is progressing with the

most rapid rate occurring in the earlier period of

1990-2003. From 2003 to 2010 there is a slower

progression of deforestation. This also corresponds

with the enactment of the forestry laws from 1999

and onwards making it plausible that although the

legislation did not comprehensively halt deforestation

it did prove to be effective enough to slow the

progress. This can be confirmed in the Local Area

Report statistics. In 1990, Phnom Aural and the

surrounding region consisted of 78.8% vegetation

and dropped almost 16.07% to 62.73%. From 2003 to

2010 vegetation dropped another 12.3%. Overall

from the period of 1990 to 2010 vegetation was

reduced by 28.37%.

The MAT analysis provided some very

interesting information of where change was

occurring. It seems that the protected parks while not

free from deforestation and therefore illegal logging,

have stayed the most intact such a Phnom Aural, the

upper left area of the study region. It seems like in

addition to logging, it is urban sprawl from the main

city Phnom Penh and even east into Prey Veaeng that

experience the most amount of vegetation change.

From 1990 to 2003 the application of MAT

discovered that 65% of the combined vegetation

classes was changed into the Soil/Urban class. From

2003 to 2010 43% of the vegetation from 2003 was

Table 1. Local Area Report Statistics for the Phnom Aural, Cambodia images from 1990, 2003, 2010 respectively.

Dense and Sparse vegetation were combined to make an overall statistic due to the degree of subjectivity involved

in the visual analysis in determining dense vegetation from sparse vegetation which could have led to areas of

overlap and therefore misinterpretation.

transformed into the Soil/Urban class. This is

generally seen in the outskirts of the major cities

Phnom Penh and Prey Veaeng in the study site.

When looked at from an overall vegetation change

from 1990 to 2010, 88% of the original vegetation

from 1990 was changed into the Soil/Urban class.

This is highly indicative of deforestation and

confirms that Cambodia has been experience a very

high rate of deforestation. The legislation has

certainly reduced the rate at which Cambodia’s

forests are being lost, however, it is limited to the

protected areas.

In terms of the Accuracy of each classified

image, each image demonstrated a high degree of

accuracy. The 1990 image was found to have an

overall accuracy of 87% and a Kappa Statistic of

0.79 which would signify a substantial agreement

between the producers and users accuracy. Despite

the pan-sharpened image having a higher pixel

resolution, it was found to be the least accurate with

an overall accuracy of 86% however, the Kappa

Statistic was also 0.79. This could be due to the

overall colour change that resulted after the image

was pan-sharpened making it more difficult to

visually assess what land class was truly present. The

2010 Image was found to have the highest degree of

the overall accuracy being 91% and a Kappa Statistic

of 0.86 signifying an almost perfect agreement. The

higher accuracy is most probably due to the severe

degree of deforestation drawn by strict boundaries of

forestry parks such as Phnom Aural. Again, it is

important to remember that the accuracy assessment

involved in all three images cannot be defined as a

true accuracy assessment so these statistics would

likely be subject to change if in situ data were

presented. In all cases, the dense and sparse

vegetation were more easily confused as seen in the

error matrix appendix iii, confirming the importance

of combining the vegetation groups to discuss an

overall deforestation. Complete results of accuracy

statistics can be seen in appendix iv.

Conclusion

It is evident that the Phnom Aural and

surrounding region of Cambodia has experienced

very rapid rates of deforestation especially around the

major cities. Since the implementation of forestry

laws in Cambodia in the late 90’s, deforestation has

slowed making a case that such implementation of

legislation has assisted to reduce the deforestation

experienced however, there is still a considerable

amount of deforestation occurring and further action

to enforce forestry legislation should be pursued.

This study provided information on overall levels of

vegetation changes in the Phnom Aural, Region of

Cambodia but could be improved by implementing a

canopy analysis of Cambodia’s forestry. Cambodia

naturally is a tropical environment so even in the

presence of logging, vegetation to a lesser degree

could still exist. Therefore, studying tree canopies of

vegetated areas would allow for a better indication of

the forestry structure and whether old growth or

primary forest are being clear cut or altered into

urban areas. Overall, using an unsupervised

classification determined by ISODATA, proved to

be an effective and less labour intensive strategy to

study deforestation where in situ data is absent

however, final results should be taken in a more

general context to understand the overall changes that

are occurring rather than as an exact measurement.

Data Set Attribute Attribute Value

Entity ID P126R052_4X19891115

Acquisition Date 15-Nov-89

Satellite Number Landsat4

Sensor ID TM

Datum WGS84

Zone Number 48

Orientation NUP

Resampling Technique CC

Product Size 329.1

File Size 143156301

Sun Azimuth 137.63

Sun Elevation 50.05

WRS Path 126

WRS Row 52

Center Latitude 11°33'10.31"N

Center Longitude 104°45'16.80"E

NW Corner Lat 12°25'39.12"N

NW Corner Long 104°05'16.27"E

NE Corner Lat 12°11'01.97"N

NE Corner Long 105°45'02.62"E

SW Corner Lat 10°55'05.15"N

SW Corner Long 103°45'46.19"E

SE Corner Lat 10°40'36.99"N

SE Corner Long 105°25'03.37"E

Center Latitude dec 11.5528643

Center Longitude dec 104.7546671

NW Corner Lat dec 12.4275333

NW Corner Long dec 104.0878538

NE Corner Lat dec 12.1838819

NE Corner Long dec 105.7507289

SW Corner Lat dec 10.9180979

SW Corner Long dec 103.7628318

SE Corner Lat dec 10.6769423

SE Corner Long dec 105.417604

1990


Landsat Scene Identifier LE71260522003038SGS00

Sensor Mode N/A

Station Identifier SGS

Day/Night DAY

WRS Path 126

WRS Row 52

Date Acquired 07/02/2003

Start Time 2003:038:03:08:26.0288125

Stop Time 2003:038:03:08:53.1304374

Image Quality VCID 1 9

Image Quality VCID 2 9

Cloud Cover 0

Cloud Cover Quad Upper Left 0

Cloud Cover Quad Upper Right 0

Cloud Cover Quad Lower Left 0

Cloud Cover Quad Lower Right 0

Sun Elevation 48.6229095

Sun Azimuth 130.0988312





NE Corner Lat 105°45'21.24"E















2003Full Aperture Calibration N

Gain Band 1 H

Gain Band 2 H

Gain Band 3 H

Gain Band 4 L

Gain Band 5 H

Gain Band 6 VCID1 L

Gain Band 6 VCID 2 H

Gain Band 7 H

Gain Band 8 L

Browse Exists Y

Data Category NOMINAL

Data Type Level 1 ETM+ L1T

Elevation Source GLS2000

Output Format GEOTIFF

Ephemeris Type DEFINITIVE

Corner UL Latitude Product 12.52881 (12°31'43.72"N)

Corner UL Longitude Product 103.65901 (103°39'32.44"E)

Corner UR Latitude Product 12.53092 (12°31'51.31"N)

Corner UR Longitude Product 105.81550 (105°48'55.80"E)

Corner LL Latitude Product 10.62746 (10°37'38.86"N)

Corner LL Longitude Product 103.66804 (103°40'04.94"E)

Corner LR Latitude Product 10.62924 (10°37'45.26"N)

Corner LR Longitude Product 105.81001 (105°48'36.04"E)

Panchromatic Lines 14021

Panchromatic Samples 15621

Reflective Lines 7011

Refelective Samples 7811

Thermal Lines 7011

Thermal Samples 7811

Ground Control Points Model 59

Geometric RMSE Model 5.692

Geometric RMSE Model X 2.573

Geometric RMSE Model Y 5.077

Gain Change Band 1 HH



Gain Change Band 4 LL


Gain Change Band 6 VCID 1 LL

Gain Change Band 6 VCID 2 HH


Gain Change Band 8 LL

Map Projection L1 UTM

Datum WGS84

Ellipsoid WGS84

UTM Zone 48

Grid Cell Size Panchromatic 15

Grid Cell Size Reflective 30

Grid Cell Size Thermal 30

Orientation NORTH_UP

Resampling Option CUBIC_CONVOLUTION

Appendix i


Entity ID LT51260522009014BKT00

Acquisition Date 14/01/2009

WRS Path 126

WRS Row 52

Satellite Landsat5

Zone Number 48

Datum WGS84

Resampling Technique CC

Orientation NUP

Scene Size 179827506

Product Type L1T

Sun Azimuth 136.8860938

Sun Elevation 45.0801299

Gap Fill Percent

Gap Fill Acquisition Date

Registration Acquisition Date





NE Corner Lat 12°13'51.13"N

NE Corner Long 105°48'41.94"E















2010

Class Name Dense Vegetation Sparse Vegetation Soil/Urban Water Clouds Total

Dense Vegetation 31 2 0 0 0 33

Sparse Vegetation 2 8 0 0 0 10

Soil/Urban 2 3 45 0 0 50

Water 0 0 0 7 0 7

Clouds 0 0 0 0 0 0

Total 35 13 45 7 0 100

2010 Error Matrix


Dense Vegetation 17 2 o 0 0 19


Soil/Urban 0 2 44 0 0 46

Water 3 0 0 2 0 5

Clouds 0 0 0 0 0 0

Total 25 27 46 2 0 100

2003 Error Matrix


Dense Vegetation 37 6 0 0 0 43


Soil/Urban 0 0 14 0 14

Water 0 0 0 2 0 2

Clouds 0 0 0 0 0 0

Total 44 40 14 2 0 100

1990 Error Matrix

Appendix ii Appendix iii

Class Name Producers Accuracy User's Accuracy

Dense Vegetation 84.09 86.05

Sparse Vegetation 85 82.93

Soil/Urban 100 100

Water 100 100

Clouds 0 0

Overall Accuracy 87%

Overall Kappa Statistic 0.79%

1990 Accuracy Statistics


Dense Vegetation 68 89.47

Sparse Vegetation 85.19 76.67

Soil/Urban 95.65 95.65

Water 100 40

Clouds 0 0





Dense Vegetation 88.57 93.94

Sparse Vegetation 61.54 80

Soil/Urban 100 90

Water 100 100

Clouds 0 0




Appendix iv

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deforestation study of phnom aural cambodia using change detection techniques

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