004
of
es.
nd
Îts
ro,
ing
of
nt,
ues
| to
nal
stal
and
leta
age
Key
377]
n in
ons.
001.
ANS,
ient,
and
003.
)arse
etric
orest
of
ERS
ional
e
mote
mote
SAR
'SSIng
July
SUB-PIXEL AND MAXIMUM LIKELIHOOD CLASSIFICATION OF LANDSAT ETM-
IMAGES FOR DETECTING ILLEGAL LOGGING AND MAPPING TROPICAL RAIN
FOREST COVER TYPES IN BERAU, EAST KALIMANTAN, INDONESIA
Virginia P. Atmopawiro Yousif Ali Hussin
Department of Natural Resources, The International Institute for Geoinformation Science
and Earth Observation (ITC), Hengelosstraat 99, 7500 AA,
Enschede, Netherlands, Fax: (31)53-4874-388, Hussin@itc.nl, Atmopawiro@itc.nl
TS Ths8 WGI, WGVII
KEY WORDS: Su-pixel classifier, Maximum Likelihood classifier, Tropical forest, Detection, Illegal logging
ABSTRACT:
ce
The tropical forest is depleting at a fast rate due to deforestation and degradation. Illegal logging was reported to be the cause of
50% of the deforestation. Illegal logging is a very pressing issue in Indonesia that is threatening the sustainability of forest
management. The detection of the single felling tree which can be characterized as a specific type of illegal logging can provide
information for the assessment of related Criteria and Indicator (C&I) of Sustainable Forest Management (SFM) and therefore
support the certification of Sustainable Forest Management. This study aims to detect single tree felling in the tropical forest using
Landsat-7 ETM+ satellite data and two types of classifiers i.e. maximum likelihood classifier and the sub-pixel classifier.
Furthermore, it aims to assess the output of the first objective to support SFM through evaluation of specific C&I. Field data of new
logged points representing single tree felling was collected during fieldwork in East Kalimantan, Indonesia in September 2003. The
Landsat image was classified using maximum likelihood and sub-pixel classification. The results showed that the accuracy of the
sub-pixel classification was higher than the maximum likelihood classification of the 30 m resolution image with an overall accuracy
and kappa of 89% and 0.75 versus 79 % and 0.57 respectively. Consequently, more accurate detection of single tree felling can be
achieved using the sub-pixel classifier and Landsat-7 ETM+ image. The extracted information can be characterized as planned or
illegal with the use of GIS and expert knowledge which helped to identify specific indicators of SFM related with illegal single tree
felling. The measurement of these indicators will ultimately support the SFM assessment.
1. INTRODUCTION
Forests are one of the world’s most important renewable natural
resources that serve various economical, social and
environmental functions. Tropical forest, which comprises 47%
of the worlds total forest area, has the highest economic and
environmental value. Although, tropical forests have high
importance due to its values, they are decreasing quantitatively
as well as qualitatively because of various problems.
Deforestation and forest degradation have been emerging as
more and more important issues of the world's forestry sector.
An area of 16.1 million ha of forests was lost every year during
the 1990s, of which 15.2 million ha were in the tropics. The
continuous depletion of forest resources is not only creating a
serious threat to the regular supply of forest products but also
resulting in a lot of negative environmental impact e.g. global
warming, biodiversity loss etc. However, the world community
has already realized the consequences and started to emphasize
the sustainability of forest resources. United Nations
Conference on Environment and Development (UNCED) held
in June 1992 in Rio de Janeiro was the significant milestone in
this regard.
Indonesia is rich in its forest resources. About 60% of country’s
total land area is covered by forest representing approximately
10% of the world’s total tropical forest area. Timber has been
an important source of national income since commercial
logging started in the early 1960s. Concession holders carry out
most of the management and harvesting activities. Selective
Cutting and Planting (TPTI) is the commonly used silvicultural
System in natural production forests of Indonesia. A series of
activities has been established by the national guidelines for the
933
implementation of the system to achieve the goal of sustainable
forest management.
But, there are a lot of problems toward achieving the goal of
sustainable forest management in Indonesia. Massive
deforestation due to transmigration and illegal felling is one of
the big problems. It has been estimated that about 50% of
[Indonesian total timber production comes from illegal means.
The situation is worsening these days due to the change arising
from the economic crisis, a decline in law and order, legal
change arising from a movement calling for democracy, reform
and change and new decentralization law. The new laws have
empowered the district government to issue the small forest
concession and even to collect some revenue on their own
decision
The importance of remote sensing (RS) to generate information
for forest management has been widely recognized. It is the
only way to acquire repetitive biophysical data for large
geographic area at reasonable cost, accuracy and effort.
Many studies have been carried out on the use of RS products
to detect tropical deforestation. These studies mainly
concentrated with land cover change from forest to non-forest
etc and have been proved very useful for that purpose. But the
possibility of using RS data to detect selective logging is poorly
studied. As the selective felling is the adopted silvicultural
practice of the Indonesian Forest Management System, only
land cover change does not fully support the detection of spatial
extent and intensity of such logging. In addition, Illegal loggers,
who are only interested with timber quality and easy
accessibility, generally carry out the selective logging. Though,
it is clear that the selectively logged points become similar to
