Home Articles Monitoring Mangrove forests using Remote Sensing and GIS

Monitoring Mangrove forests using Remote Sensing and GIS

ACRS 1999

Poster Session 5

Monitoring Mangrove Forests using Remote Sensing and GIS

Yousif Ali Hussin Mahfud M. Zuhair Michael Weir


Forest Science Division

International Institute for Aerospace Survey and Earth Science (ITC)

P.O. Box 6, 7500 AA, Enschede, The Netherlands
Fax: (31) 53-4874-399

Email: [email protected],
[email protected]


Keywords:
Mangrove, monitoring, coastal zone, deforestation, remote sensing, GIS,
radar, optical, satellite images


Abstract

Mangrove forests form one of the primary coastal ecosystems in the tropical and subtropical regions
of the world. Mangrove ecosystems are very sensitive and fragile. The pressures of increasing population, and the
resulting expansion of agriculture and industrial and urban development, have caused a significant proportion of the
world’s mangroves to be destroyed. Reliable and timely information is therefore required in order to monitor and
manage the remaining mangrove resources. This paper describes research to compare the ability of three different
radar satellite imaging systems and three different optical satellite systems to detect mangrove deforestation in the
delta of the Mahakam River, East Kalimantan, Indonesia.


1. Introduction

Mangrove forests form one of the primary coastal ecosystems in the tropical and subtropical region of the world.
They are biologically diverse and have therefore traditionally been utilized for food resources, firewood, charcoal,
timber and other minor products. However, mangrove ecosystems are very sensitive and fragile. In recent years, the
pressures of increasing population, and the resulting expansion of agricultural land and industrial and urban
development, have caused a significant proportion of the world’s mangrove resource to be destroyed. In addition,
significant areas of mangrove swamps in Indonesia and other regions of Southeast Asia have been developed to
create ponds for the commercial production of fish and shrimps.

Information is crucial in order to assess mangrove deforestation, to monitor the state of the remaining mangrove
forests and ensure their sustainable management. In mangrove areas, the task of collecting information by ground
inventory is extremely difficult, time consuming and, therefore, expensive. For this reason, remote sensing is an
attractive means of obtaining data for defining deforested areas and updating management plans. In Indonesia, a
serious drawback to using optical satellite images for mangrove monitoring has been the unavailability of cloud-free
images at the time of interest. The advent of radar systems is expected to resolve this problem. Unlike optical
systems, radar systems can be operated day or night and have all weather capability. They can therefore penetrate
clouds cover, fog, rain and atmospheric dust. Furthermore, radar energy has the ability to penetrate tree canopies and
some surface features. This paper describes research to compare three different radar satellite imaging systems
(ERS-1, JERS-1, and Radarsat) and three types of optical satellite data (Landsat TM, MSS and Spot XS) for
detecting and monitoring mangrove deforestation in the delta of the Mahakam River on the coast of East Kalimantan.


2. Materials and Methods

2.1 Study Area

Because it is an archipelago, Indonesia has a large area of mangrove (4,250,000 ha are found around the four main
islands). There are basically six categories of land use in the Indonesian mangrove areas: 1) forest reserves; 2) nature
conservation; 3) protection forest; 4) production forest; 5) fish/shrimp ponds; and 6) salt production areas. Mangrove
forests are traditionally a source of livelihood for the local population. The main problems of managing these
resources therefore centre on delineating the area and resolving local community ownership issues.

In the study area, the delta of Mahakam River in East Kalimantan, the commercial production of shrimps for export
was established in 1974. Every year, the company needs about 1,000,000 Kg of shrimps to reach their export target.
For example, in 1996 it purchased about 1,015,000kg from local fishermen. At that time, shrimp ponds supplied
about 4% of the demand, and fishermen operating at sea supplied most of the shrimps. The high demand for shrimps
for export has resulted in substantial price increases. In 1997, the price of good quality of shrimps was about
Rp50,000, or about US$15 per kilogram. This has encouraged local fishermen to build large areas of shrimp ponds.
During the 1990s, they encroached into the mangrove forest, clear-cut the mangrove vegetation by slash and burn
and then constructed dykes to surround the ponds. This activity tends to be concentrated in the areas covered by
Nypa palm forest The system of production in the Mahakam delta is extensive. For example, the fishermen do not
use water mills for oxygen circulation in the ponds but, instead, rely on the current flowing between high and low
tides, large areas are required for extensive production. As a result, the ponds produce only about 2-3 tons per
hectare. This is low compared with the 10 tons per hectare produced from shrimp ponds on the northern coast of
Java. Because intensive production systems are expensive, the local fishermen assume that sufficient mangrove
forest will remain to allow the establishment of large areas of ponds.


2.2 Remote sensing data

Data acquired by both passive and active remote sensing systems were used in the study. The specifications are
summarized in Table 1.


Table 1 Image specifications


Image Date
of
acquisition
Wavelength Spatial
reso-
lution
Polari-
zation
Inci-
dence
angle
Landsat MSS 15-04-1983 1) 0.5 – 0.6mm
2) 0.6 – 0.7mm
3) 0.7 – 0.1mm
4) 0.8 – 1.1mm
70 m
SPOT-XS 21-02-1987 1) 0.50-0.59mm
2) 0.61-0.68mm
3) 0.79-0.89mm
20 m
Landsat TM 1994 1) 0.52-0.60mm
2) 0.76-0.90mm
3) 1.55-1.75mm
30 m
JERS-1 19-09-1996 1) 23.5 cm 12.5 m HH 35°
ERS-1 28-05-1996 1) 5.6 cm 12.5 m VV 23°
Radarsat-1 1-10-1997 1) 5.6 cm 12.5 m HH 40°

ACRS 1999

Poster Session 5

Monitoring Mangrove Forests using Remote Sensing and GIS


2.3 Method

The research involved two main steps. In the first step, a broad classification of the general land cover, including
mangroves was made (Figure1). This indicated the best approach to dealing with the various types of imagery in
order to detect the mangrove deforestation. The second step concentrated on the specific problem of detecting
changes in the mangrove areas (Figure2). It examined different approaches for monitoring the nature of the changes
in order to produce maps showing the current and former conditions in the study area.


Figure 1. Method



3. Results and Discussions

The classes identified on each image using supervised classification are summarized in Table 2..


Table 2
Classes detected in each image through supervised classification and visual interpretation

Landsat MSS SPOT Landsat TM ERS-1 JERS-1 Radarsat
Broad-leaved
Nypa
Water
Ponds


agriculture



Broad-leaved
nypa
water
dry pond
orchard
mixed
agriculture



broad-leaved
nypa
water
fish pond
orchard
mixed
agriculture
clear-cut
half-cut
swampy

nypa
water
fish pond

mixed

clear cut


oil pipeline

nypa
water
fish pond

mixed

clear-cut
half-cut

oil pipeline

nypa
water
fish pond

mixed

clear-cut
half-cut

oil pipeline


Figure 2. Mangrove forest area change detection procedure

Three types of mangrove deforestation were found to occur in the study area:

  1. deforestation caused by the establishment of agriculture and/or orchards;
  2. deforestation caused by the establishment of oil pipeline networks;
  3. deforestation caused by the establishment of shrimp ponds.

Deforestation resulting from the establishment of agriculture crops or orchards can be detected on the Landsat TM
image of 1994 and on the SPOT_XS of 1987. Radar data (JERS-1, ERS-1 and Radarsat) cannot, however, detect
this type of deforestation. Mangrove deforestation caused by the establishment of oil pipeline networks cannot be
detected on any of the optical images. This is because the width of the oil pipelines is around 15m and because water
and swampy vegetation surround them. The spectral and spatial resolutions therefore prevent them from being
imaged on optical images. However, pipelines are visible on all the radar images because they behave as corner
reflectors as a vertically standing object on the ground. Mangrove deforestation caused by establishment of shrimp
ponds can be detected on all optical and radar images, although ponds under construction, in which only about half
of Nypa palm have been cut, can be detected only on the Landsat TM, JERS-1 and Radarsat images. ERS-1 image
was not able to detect the process of deforestation in Nypah palm because of its short wavelength, small incidence
angle and it is VV polarization. However, JERS-1 radar image is collected with longer wavelength, HH polarization
and medium incidence angle. While Radarsat image is collected using the same short wavelength of ERS-1, but with
HH polarization and a medium incidence angle. It is believed that incidence angle (e.g., in this case) played an
important role to detect the process of deforestation in the Nypah palm. As Table 2 shows that SPOT_XS image was
not able to detect this process of deforestation in the Nypah palm because this image does not cover any areas with
this deforestation process from Nypah palm to fish ponds.

The accuracy of each classification was assessed by comparison with data collected at sample points on the ground
and compiling error matrices. Based on data collected at sample sites in the field, the classification accuracy of each
image to detect deforested mangrove area was as follows:






Landsat MSS 1982 5 classes 76%
SPOT_XS 1987 7 classes 89%
Landsat TM 1994 9 classes 88%
ERS 1996 6 classes 83%
JERS 1996 7 classes 85%
Radarsat 1997 7 classes 84%v

ACRS 1999

Poster Session 5

Monitoring Mangrove Forests using Remote Sensing and GIS


3.1 Mangrove land use changes

For the purpose of detecting the mangrove land use a broad classification (mangrove forest, non-mangrove forest
and water bodies) was first made for each image. This was used to avoid possible problems that might occur if a
detailed classification with many classes was used. The result of the change detection analysis generated from
Landsat MSS 1982, Landsat TM 1994, and fused images (Landsat TM 1994 and ERS-1 1996 radar data) are
summarized in Table 3. The accuracy assessment of these various classifications is shown in Figure 3.

The mangrove forest area was seriously reduced from 1994 to 1996 in comparison with the period of 1982 to 1994.
From Table 3 it may be concluded that the evolution of mangrove areas was as follows: 96,228 ha, 91,827 ha, and
78,799 ha in 1982, 1994 and 1996 respectively. The trends of these changes in mangrove area are indicated in
Figure 4. From these results, it appears that about 13,028 ha (14.2% of the mangrove areas in 1994) disappeared in
the period of 1994 to 1996, whereas only about 4,401 ha (4.6% of the 1982 area) disappeared in period of 1982 to
1994. The total of mangrove areas that disappeared in between 1982 to 1996 is about 17,429 ha (18.1% since 1982).
Therefore, it may be concluded that the mangrove forest area has predominantly deteriorated between period of
1994 to 1996.




Figure 3. Accuracy of the broad classification used for the change detection analysis

Table 3. Delta Mahakam mangrove forest 
land cover in 1982 to 1996

Class Category MSS data
in 1982 (ha)
TM data
in 1994 (ha)
Fused image
of TM 1994
and ERS-1
1996 (ha)
*. Mangrove
*. Non mangrove
*. Water bodies
96,228
16,164
99,398
91,827
12,602
107,361
78,799
24,751
108,240
Total 211,790 211,790 211,790



Figure 4. Mangrove land cover changes 
between 1982 to 1996

From the field data collection at the study area it was found that most of the deforested areas were converted to fish
and shrimp pond establishment. This evidence was supported by the images classification result in which there are
increasing in water bodies during the period of 1982 to 1996.


4. Conclusions

4.1 Detecting mangrove deforestation

Results of this research show that it is possible to differentiate between mangrove forest and non-mangrove cover
types with all types of optical remotely sensed data. Specifically, it was found that:

  • all optical data were able to differentiate fishponds;
  • Landsat MSS and Spot XS data were able to differentiate only one agriculture class;
  • Landsat TM data was able to differentiate two agriculture classes;
  • none of the optical data were able to differentiate oil pipeline establishment.

Using radar data to detect deforestation, it was found that:

  • all radar data were able to differentiate fishponds and clear cut areas;
  • all radar data were able to identify the establishment of oil pipelines (using both spatial and backscatter information)
  • none of the radar data were able to differentiate any agriculture classes
  • JERS-1 and Radarsat images were able to differentiate partly cut Nypa palm (fishponds under construction).

Given the accuracy percentage and the number of classes resulted from these classifications, it seems that Landsat
TM, Spot XS and all radar images, but especially JERS-1 and Radarsat all give reasonable results in detecting
deforested areas. There were no significant differences between the accuracy of these five data sets although, as
might be expected, the accuracy of the results from Landsat MSS is soemwhat lower. It is possible that if optical
data are fused with radar data they may even give better results. Further research is therefore required to investigate
whether optical and radar data can complement each other for detecting deforested mangrove areas.


4.2 Monitoring mangrove deforestation

Results of this research show that it is possible to monitor mangrove deforestation with a reasonable accuracy using
optical and radar satellite images.

  • during period of 1982 to 1996 about 17,429 ha of mangrove forest disappeared and changed to another land
    uses;
  • mangroves deforestation in study area is mainly caused by shrimp ponds establishment;

In addition, there are two other human activities that cause deforestation of mangrove in the Makahan delta:
establishing agriculture fields and oil extraction activities. However, the area deforested due to these activities is
relatively small compared to that resulting from the establishment of shrimp ponds.