1

CHANGE DETECTION OF MANGROVE FOREST COVER IN THE CITY OF PUERTO

PRINCESA, PALAWAN BETWEEN 2003 AND 2013 USING LANDSAT IMAGERY

Homer M. Pagkalinawan a, Roseanne V. Ramos b

a MS Geomatics Engineering, Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City

1101 - homer.pagkalinawan@gmail.com b MS Geomatics Engineering, Department of Geodetic Engineering, University of the Philippines Diliman, Quezon

City 1101 - rowshan09@gmail.com

KEY WORDS: Mangroves, Change Detection, Landsat imagery, unsupervised classification, Puerto Princesa

ABSTRACT:

Mangrove forests provide a significant contribution in the protection of the coastal environment. These are unique portions of the

ecosystem that serve as sanctuary both for terrestrial and marine species. From 1918 to 1995, there was a significant decline in the

mangrove forest cover area and is expected to further decrease by 2030 if the trend continues. In recent years, people have been

aware of the continuing decline of mangrove forest cover area and efforts were made to alleviate it. In this paper, the extent and

distribution of the mangrove forests in the City of Puerto Princesa, Palawan was determined using unsupervised classification of

Landsat imageries taken in 2003 and 2013. Extraction of mangrove forest was limited to a particular region, given that this type of

forest is expected to exist on intertidal zones. Using the unique property of mangrove forest on false color composite image of near-

infrared and shortwave infrared bands, corresponding classes were identified. The change in the spatial distribution of mangrove

forests was also determined and served as preliminary results of human-initiated mangrove protection projects. Extracted mangrove

forests from the 2003 and 2013 images were 3,201.8 and 4,577.2 hectares, respectively, with accuracy ranging from 60 to 85%. The

difference of 1, 375.5 hectares was attributed from 1,688.8 hectares of other land cover types converted to mangroves and 313.4

hectares of deforested mangrove forests. The observed increase in the mangrove forest area is in line with the goals and different

projects initiated by the local government.

1. INTRODUCTION

1.1 Background

Mangroves are salt tolerant trees and shrubs that grow within

the sheltered marine intertidal zones of the tropics and

subtropics (Long and Giri, 2011). This type of forest cover

offers numerous ecosystem goods and services to coastal

populations such as firewood, charcoal, alcohol, medicines, and

thatching used for construction (Brown and Fischer, 1918).

They also provide vital ecological services such as bioprotection

from coastal erosion (Naylor, et.al 2002), nursery and feeding

sites for marine species (Ronback, 1999) and possible reduction

of the devastating impacts of tropical storms and tsunamis

(Alongi, 2008).

According to the Philippine Clearing House Mechanism for

Biodiversity, mangrove areas are found in Palawan, Aurora,

Isabela and Quezon in Luzon, Aklan, Samar provinces in the

Visayas, and Zamboanga, Surigao and Sulu provinces in

Mindanao. The largest remaining mangroves areas are located

in Mindanao (with 112,745 hectares), Palawan (with 37,432

hectares), Samar (with 65,119 hectares) and Luzon (16,769

hectares).

Their records also show that there is a significant decline in

mangrove forest cover. In the year 1918, there are

approximately 450,000 hectares of mangrove areas. Then in

1995, there are about 120,000 hectares only. It was concluded

that if the trend continues, there will only be less than 100,000

hectares by the year 2030.

With the people’s awareness of the decreasing volume of

mangrove forest cover, there have been several projects aiming

to reforest, maintain and expand mangrove forests through

various community projects in areas like Puerto Princesa City,

Palawan. It is expected that through these projects, mangrove

forest cover will increase and the depletion of this important

specie will be avoided.

Figure 1. Philippine Map highlighting mangrove concentration.

2

Figure 2. Total Area of Mangroves from 1918(estimated)

to 2030 (projected).

1.2 Land Cover Classification

In order to quantify the changes in this type of land cover,

various studies were conducted to provide current, reliable and

accurate information on its areal extent through geographic

information system (GIS) and remote sensing (RS) applications.

From the 2011 study by Long and Giri entitled “Mapping the

Philippines’ Mangrove Forests Using Landsat Imagery”, an

unsupervised classification technique was implemented to

extract mangrove forest cover from Landsat images. The

classification technique used is the Iterative Self-Organizing

Data Analysis Techniques (ISODATA), which requires user to

choose the initial estimates of class means. Each pixel is

assigned to classes with a similar mean, and the process of

assigning pixels to a class is repeated until reaching the

maximum number of iterations set by the user. The result of the

study is a three-class land cover classification (mangrove,

terrestrial non-mangrove, and water) from merged clusters with

spectral similarities based on ISODATA.

1.3 Objectives

In this research project, we aim to quantify the areal extent and

to be able to map the distribution of mangrove forests. We also

intend to detect the change in the spatial distribution of the

mangroves through temporal analysis of satellite images

acquired on the years 2003 and 2013. Knowing the extent of the

temporal change in mangrove forest cover will help identify

critical areas for its proliferation.

2. METHODOLOGY

2.1 Study Area

The scope of the project is the City of Puerto Princesa in

Palawan (Figure 3). Mangrove forests can be found in almost all

shorelines in the province. In the latest publication of the

Palawan Council for Sustainable Development (PCSD) State of

the Environment Report, it indicated that from out of the

province’s total land area of 1.485 million hectares, about

58,400 hectares of that is covered by mangroves and is

considered the highest in the country.

Figure 3. Landsat 8 Satellite Image of City of Puerto Princesa.

The local government is one of the frontier in promoting the

protection and increase of mangrove forests with community-

based plantation and livelihood projects since 2002.

2.2 Data

Publicly available satellite images taken by the Landsat Data

Continuity Mission were downloaded through the USGS Global

Visualization Viewer website and served as our primary source

of data for information extraction and analysis. One image

scene covers the entire study area. For the 2003 image, a scene

captured on March 12, 2003, with 4.85% cloud cover, by the

Landsat 7 sensor was downloaded. For the 2013 image, a scene

captured on June 3, 2013, with 11.34% cloud cover, by the

Landsat 8 sensor was also downloaded.

Vector data of mangrove areas were obtained for the

comparison and accuracy assessment of the classification

results. Shapefiles of the mangrove forests in Palawan acquired

on 2000 and 2010 were provided by the Fauna and Flora

International Philippines and the Forest Management Bureau,

respectively.

2.3 Methodology

The acquired satellite images were processed in order to extract

mangrove forest areas and other land cover types. The process

involved conversion to radiance and reflectance (pre-

processing), unsupervised classification and assigning of land

cover type to each classes. Results of classification were then

assessed for their accuracy. Based on the results, maps were

created showing the location of the mangrove forests for each

year as well as the change in the extent of mangrove forest

cover area between the two periods.

0

100,000

200,000

300,000

400,000

500,000

1918 1995 2030

Mangrove Areas in the Philippines (in ha)

3

Figure 4. Project Flowchart.

2.3.1 Characterization of Mangrove Forest Cover:

Mangrove forest cover was identified using a false color

composite of one near infrared band and two shortwave infrared

bands. For the Landsat 8 image, the band combination is 5-6-7

while its Landsat 7 counterpart is the combination 4-5-7. In the

composite images below, mangroves appear to have a brighter

color orange when compared to other vegetation cover.

Figure 5. Characterization of Mangrove Forest Through

Composite Images

2.3.2 Pre-Processing and Calibration: Pre- processing and

calibration of the Landsat images were done using RS software

applications. Downloaded images were calibrated by converting

their DN values to radiance. For the 2003 image, the built-in

calibration tool for Landsat 7 images in ENVI was used while a

manual calibration was made for 2013 images. The manual

calibration was applied using the Band Math tool and was based

on the following equation:

where:

Lλ = spectral radiance

ML = band-specific multiplicative scaling factor from metadata

Qcal = DN value

AL = band – specific additive scaling factor from metadata

For the correction of atmospheric effects in the images, ENVI’s

built-in Fast Line-of-sight Atmospheric Analysis of Spectral

Hypercubes (FLAASH) was used. The calibrated bands were

combined through the Layerstacking tool. The resulting

multiband image was converted to a band-interleave-by-line

(BIL) format as input for the FLAASH processing tool.

2.3.3 Masking: Since mangroves are expected to be located

on intertidal zones only, a buffer zone was created to serve as a

mask for image classification. Based on observations, the mask

was created from the union of a buffer of 2,000 meters from the

shore inland and 2,000 meters radius buffer of inland bodies of

water within the 2,000 meters shore buffer.

Figure 6. Mask Buffer Creation.

This buffer zone together with mask for clouds, clouds’

shadows and water bodies composed the mask used for this

project.

2.3.4 Classification: The unsupervised classification

method ISODATA was implemented on the output reflectance

file produced from the FLAASH technique. The number of

classes was set at a minimum of one hundred (100) and a

maximum of one hundred fifty (150). The threshold value was

set at 0.95 with fifteen (15) iterations.

2.3.5 Post-Classification: The results of the classification

were subjected to sieving (removal of isolated pixels) and

clumping (grouping of related classes). Classes were visually

inspected through their spectral signatures and were analyzed if

they represent the pixels identified as mangroves. Other classes

were also determined such as built-up areas, bare soil,

vegetation, water, clouds and shadow. The classes were

combined according to their respective group and were trimmed

down to the seven (7) categories mentioned.

2.3.6 Accuracy Assessment and Analysis: The extracted

mangroves classes from each image were compared with the

reference datasets to determine the accuracy of the

classification. The mangroves classes from the 2003 and 2013

image were compared with the polygon shapefiles of mangrove

areas dated 2000 and 2010, respectively. The results of the

comparisons were used to quantify the changes in the spatial

distribution of the mangrove forests.

2.3.7 Creation of Maps: The extracted mangrove classes

from both images were converted to vector data. ArcGIS tools

were used for the preparation of the mangrove forest cover

maps and change detection maps.

3. RESULTS AND DISCUSSION

3.1 Land Cover Classification

The classes from the classification results were categorized and

combined into 7 groups: mangroves (sea green), built-up areas

(red), bare soil (brown), vegetation (green), water (blue),

clouds(pink) and shadow (gray).

4

Figure 7. Subset of the Classification Image (limited to City of

Puerto Princesa).

Figure 8. A zoom window of the classification image showing

the different land cover classification.

Figure 9. Zoom windows showing the different land cover

classification - mangroves (sea green), built-up areas (red), bare

soil (brown), vegetation (green), water (blue), clouds(pink) and

shadow (gray).

3.2 Accuracy Assessment

A confusion matrix for each classification was generated to

quantify the accuracy of the results. The reference datasets were

converted to regions of interests (ROIs) in ENVI to compute the

following matrices for the 2003 image (Table 1) and 2013

image (Table 2).

Table 1. Classification Confusion Matrix for the 2003 Image.

Table 2. Classification Confusion Matrix for the 2013 Image.

Overall accuracy yielded for the 2003 image was 75. 29 %

while the 2013 image has 65.72% overall accuracy. Kappa

coefficients for the two images were 0.3625 and 39.76,

respectively. For the mangrove land cover, the 2003 image

obtained 62.5% in producer’s accuracy and 78.95% in user’s

accuracy. For the 2013 image, user’s and producer’s accuracy

for mangroves are 65.87% and 81.3%.

Table 3. Overall Accuracy and Kappa Coefficient

Statistic 2003 Image 2013 Image

Overall

Accuracy (%) 75.29 65.72

Kappa

Coefficient 0.3625 0.3976

Table 4. Classification Accuracy for the 2003 Image

Class

Producer's

Accuracy (%)

User's

Accuracy (%)

mangroves 62.5 78.95

built-up 42.91 3.5

soil 22.96 46.1

vegetation 85.19 89.8

water 22.53 3.4

Table 5. Classification Accuracy for the 2013 Image

Class

Producer's

Accuracy (%)

User's

Accuracy (%)

mangroves 65.87 81.3

built-up 49.83 25.02

soil 27.04 55.82

vegetation 82.58 77.23

water 27.91 14.93

3.3 Creation of Maps

Using the formatting tools and by adding annotations, the

following mangrove forest cover map were created. The

mangroves built-up bare soil vegetation water Total

unclassified 448 2 86 857 0 1,393

mangroves 30,267 4 2,147 5,693 224 38,335

built-up 497 2,776 24,764 50,988 385 79,410

bare soil 31 2,101 20,689 21,838 218 44,877

vegetation 12,165 1,417 41,023 485,841 590 541,036

water 5,021 170 1,399 5,114 412 12,116

Total 48,429 6,470 90,108 570,331 1,829 717,167

Cla

ssif

ied

Map

(2003)

Confusion MatrixReference Data (2000)

mangroves built-up bare soil vegetation water Total

unclassified 2677 829 4153 11383 260 19,302

mangroves 38251 129 1980 6039 653 47,052

built-up 2422 16101 25103 20225 489 64,340

bare soil 2379 5308 36774 19928 1493 65,882

vegetation 9049 9684 66384 293338 1384 379,839

water 3293 263 1588 4301 1657 11,102

Total 58,071 32,314 135,982 355,214 5,936 587,517C

lass

ifie

d M

ap

(20

13

)

Confusion MatrixReference Data (2010)

5

following land cover were combined into one class representing

terrestrial non-mangroves area – built-up areas, bare soil,

vegetation, water.

Figure 10. 2003 Mangrove Forest Cover Map

Figure 11. 2013 Mangrove Forest Cover Map

3.4 Change Detection

From 2003 to 2013, 1,688.8 hectares was converted to

mangrove forest while 313.4 hectares was deforested, according

to the classification results. Thus, it resulted to a net increase of

1,374.5 hectares in mangroves forest area. However, it worth

noting that 612.9 hectares of 2013 mangroves forests were

covered with clouds and its shadows on the 2003 image.

Likewise, 45 hectares of 2003 mangrove forest were covered by

clouds and shadows on the 2013 image. Refer to Table 6 for the

breakdown of change detection.

Table 6. Other Land Cover (2003) Converted to Mangroves

Land Cover Area (ha)

Built-Up 55.2

Bare Soil 7.6

Non-Mangrove

Vegetation 740.4

Water 263.8

Clouds &

Shadows 621.9

TOTAL 1,688.8

Table 7. Mangrove Conversion to Other Land Cover

Land Cover Area (ha)

Built-Up 12.8

Bare Soil 14.8

Non-Mangrove

Vegetation 119.0

Water 58.7

Clouds &

Shadows 108.1

TOTAL 313.4

Below are the two maps showing areas of conversion to

mangroves and deforestation of mangrove areas.

Figure 12. Reforested Forest Cover Map

6

Figure 12. Deforested Forest Cover Map

4. CONCLUSION AND RECOMMENDATIONS

In this project, the authors were able to extract mangrove forest

areas from two satellite image of different period using the

proposed method, yielding an accuracy ranging from 62 – 83%

for this particular and cover type. There was a detected ne

increase of mangrove forests in the area which is in line with

mangrove proliferation projects such as those started in 2002.

On both periods, there are significant portions of mangrove

forests that are covered by clouds and its shadow. Thus, it is

recommended to use multiple image data in order to minimize

the clouded or area or even eliminate them. It is also

recommended to perform classification of mangrove forest on a

period between 2003 and 2013 to see if the pattern of their

proliferation.

5. REFERENCES

References from Books:

Smith, J., 1989. Space Data from Earth Sciences. Elsevier,

Amsterdam, pp. 321-332.

References from Other Literature:

Smith, J., 1987b. Economic printing of color orthophotos.

Report KRL-01234, Kennedy Research Laboratories, Arlington,

VA, USA.

Smith, J., 2000. Remote sensing to predict volcano outbursts.

In: The International Archives of the Photogrammetry, Remote

Sensing and Spatial Information Sciences, Kyoto, Japan, Vol.

XXVII, Part B1, pp. 456-469.

References from websites:

Moons, T., 1997. Report on the Joint ISPRS Commission

III/IV Workshop “3D Reconstruction and Modeling of

Topographic Objects”, Stuttgart, Germany.

http://www.radig.informatik.tu-muenchen.de/ISPRS/WG-III4-

IV2-Report.html (28 Sep. 1999).

Long, J. B. & Giri C. (2011), Mapping the Philippines’

Mangrove Forests Using Landsat Imagery. Sensors, 2011(11),

pp 2972-2981, doi: 10.3390/s110302972

Brown, W.H.; Fischer, A.F. Philippine Mangrove Swamps;

Bureau of Printing: Manila, Philippines, 1918; p. 132.

Naylor, L.A.; Viles, H.A.; Carter, N.E.A. Biogeomorphology

Revisited: Looking towards the Future. Geomorphology 2002,

47, 3-14.

Rönnback, P. The Ecological Basis for Economic Value of

Seafood Production Supported by Mangrove Ecosystems. Ecol.

Econ. 1999, 29, 235-252.

Alongi, D.M. Mangrove Forests: Resilience, Protection from

Tsunamis, and Responses to Global Climate Change. Estuar.

Coast. Shelf Sci. 2008, 76, 1-13.

Philippine Clearing House Mechanism for Biodiversity (2009).

Coastal, Marine and Island Biodiversity: Mangroves. Retrieved

from http://www.chm.ph, accessed Aug 6, 2013.

ENVI, ENVI Classic Tutorials: Classification Methods.

Retrieved from http://www.exelisvis.com, accessed July 1,

2013.

USGS Global Visualization Viewer (2013).

http://glovis.usgs.gov

Environmental Systems Application of Geomatics Engineering

(EnviSAGE) Research Laboratory

Acknowledgement

The authors would like to acknowledge the following

individuals for their contributions and support in this research

work:

Our professors and instructors Dr. Ariel C. Blanco, Asst. Prof.

Jeark Principe and Engr. Sheryl Rose C. Reyes, M. Sc. for their

technical expertise and constructive criticisms,

Engr. Angelica Monzon of the Fauna and Flora International

Philippines for providing the reference datasets acquired in

2000, and

Mr. Brent Fallarcuna for providing the reference datasets from

the Forest Management Bureau acquired in 2010.