Land Use Classification with Back Propagation Neural Network and The Maximum Likelihood Method: A Case Study in Ciliwung Watershed, West Java, Indonesia.

LAND USE CLASSIFICATION WITH BACK PROPAGATION

NEURAL NETWORK AND MAXIMUM LIKELIHOOD METHOD

(Case Study of Ciliwung Watershed, West Java Province)

YOSS ANDREAS ARMAN

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

2006

STATEMENT

I, Yoss Andreas Arman, here by declare that the thesis title:

Landuse Classification with Back Propagation Neural Network and Maximum Likelihood Method: A Case Study of Ciliwung Watershed, West

Java Province, Indonesia

contains correct results from my own work and that it has not been published ever before. All data sources and information used factual and clear methods in this project, and has been examined by the advising committee and the external examiner.

Bogor, August, 2006

ACKNOWLEDGEMENT

There are many people I should thank in regard to this work no doubt I will not be able to name them one by one. To these I can but beg forgiveness. I wish to thank the following:

1. My supervisor Dr. Ir. I.Wayan Astika, MSi and my co-supervisor Dr. Ir. Lilik Budi Prasetyo, MSc for their guidance, technical comment and constructive criticism through all months of my research.

2. Dr. Ir. Tania June MSc, Chairman of study program of MIT for her kindness and providing academic assistance. And also to MIT Staff Miss. Uma, Miss. Devy, Mr. Bambang, Pak Zen-Zen, Pak Zein, Mas Mulyadiana, Mas Hasan, and Mas Mulyadi.

3. Dr. Ir. Antonius Bambang Widjanarko, MSc as the external examiner of this thesis for the positive ideas and inputs

4. MIT lectures, Prof. Jacub Rais, Dr. Handoko, Dr. Lukman Azis, Dr. Bambang Sapto, Dr. Iwan Gunawan, Dr. Kaswadji, Dr. Azis, Dr. Kudang Seminar, Dr. Hartisari, Mr. Iwan Setyawan, Mr. Eddi Nugroho and all other IPB lectures who thought me very important knowledge for my future.

5. PPLH-IPB through Mr. Yudi Setiawan that has given me image data support. 6. My uncle Ir Fery Mayhendra for all support, especially for “The Spirit to

Fight”

7. I deeply appreciate the effort of MIT staff. I specially appreciate to MIT colleagues for giving me encouragement and supporting, Andes Jayarsa, MSc, Iksal Yanuasyah MSc and Mr Upank.

8. MIT students year 2002 and 2003 Mr. Deny, Mr. Asep, Mr. Bonie, Mr. Yahya, Mrs. Vevin, Mrs. Evie, Mr Epho, Mr. Albert, Mr. Putu, Mrs. Brilie and also Linda friend’s (Ona, Indah, Yuni, Adit, Teti and Fita) I really appreciate our togetherness and how we support each other to finish our study. And also to the All MIT student, who gave me support prepare this research. 9. My special gratitude is also extended to my lovely Mom “Femarlyn”, Dad

“Arman Yazid”, and Sisters “Reny Arlinda” and “Arsepta Andreani”, for their prayers, understanding, moral support, patience, encouragement, and everything.

10.My pink jasmine Linda Setyoningrum, thanks for your patience, support, love and understand me.

CURRICULUM VITAE

Yoss Andreas Arman was born in Pekanbaru, Riau Province, Indonesia at Maret 14rd, 1979. He finished elementary school from SD 1 PIUS Payakumbuh in 1991, afterwards he finished junior high school from SMP Fidelis Payakumbuh in 1994 and in 1997 he finished senior high school from SMU Negeri 2 Payakumbuh. He received his undergraduate diploma from Forest Management Department, Faculty of Forestry, and Bogor Agricultural University in 2002.

In the year of 2003, Yoss Andreas Arman is received as Graduate School Student in Information Technology for Natural Resources Management, Bogor Agricultural University. In 2006, he received his Master of Science degree in Information Technology for Natural Resources Management from Bogor Agricultural University. His thesis title was on “Landuse Classification with Back Propagation Neural Network and Maximum Likelihood Method”.

ABSTRACT

YOSS ANDREAS ARMAN (2006). Land Use Classification with Back Propagation Neural Network and The Maximum Likelihood Method: A Case Study in Ciliwung Watershed, West Java, Indonesia. Under the supervision of I WAYAN ASTIKA and LILIK BUDI PRASETYO.

Ciliwung Watershed is located at West Java Province; this area ranges from 106°47'29” to 107°0'25" E and from 6°24'16" to 6°46'23"N, having hard relief with slope 15% – 30%. Ciliwung watershed area has 117 km in length and 347 km2, it is one of water catchments that suffering damage relatively serious from the upstream to the downstream. Nevertheless, in less than 10 years since 1985, the conservation area has changed into settlement area or cultivation area.

The objectives of the research are to apply back propagation neural network and the maximum likelihood classification method for land use classification and compare the performance of the two methods. A one periods of Landsat-7 ETM+ (December 22, 2001) images with the path/row 122/065 was used for classification in the Ciliwung Watershed.

This research compared parametric method (maximum likelihood) and non-parametric method (back propagation neural network) that occupied the same Landsat-7 ETM+ and the same training area. Six bands (band 1, 2, 3, 4, 5, 7) from Landsat Image were used as input data for both classification methods. In this case, band 6 and 8 on the image not utilized for this research because both of band have different resolution with the others. Before Landsat-7 ETM+ used for classification process, it was corrected geometrically, atmospherically, and topographically. The purpose of these corrections was to decrease the error that can occur during the making of training area and classification process afterward. Landuse was classified into 8 classes: tea garden, settlement, paddy field, grass, forest, farm, bush, and water body. The target of the training area was based on Ikonos image interpretation, instead of using field data. There were 4000 pixels used for whole class categories.

There were several experiments was performed by using back propagation neural network method, which were by changing number of pixels (from 100 to 4000 pixels) and number of iteration (100-2000 iterations). Error matrix was used as accuracy measurement in order to compare both methods. Error matrix showed that both methods had difficulty in classifying water class and paddy field class due to the closeness of spectral value between those particular classes. This research is also showed that back propagation neural network giving a better accuracy level (81.5%) rather than maximum likelihood method (73.4%). Kappa statistic showed that classification result by using back propagation neural network method is nearly close to the real field condition due to the value is 0.759 (75%), which is close to 1 as an ideal case.

TABLE OF CONTENTS

STATEMENT... i

ACKNOWLEDGMENT ... ii

CURRICULUM VITAE... iii

ABSTRACT ... iv

TABLE OF CONTENTS... v

LIST OF FIGURE ... vii

LIST OF TABLE ... viii

LIST OF APPENDICES ... ix

1. INTRODUCTION... 1

1.1. Background ... 1

1.2. Objectives ... 3

1.3. Problem Statement ... 3

2. LITERATURE REVIEW... 5

2.1. Watershed Characteristic ... 5

2.1.1. Watershed Boundaries ... 5

2.1.2. Watershed Area... 5

2.2. The Impact of Land Use Change to Water Quality ... 6

2.3. Remote Sensing ... 6

2.3.1. Geometric Correction... 7

2.3.2. Topographic Correction ... 8

2.3.3. Remote Sensing Data ... 8

2.3.5. Landsat Thematic Mapper ... 9

2.3.6. Landsat-7 Enhance Thematic Mapper Plus (ETM+) ... 12

2.4. Classification... 13

2.4.1. Maximum Likelihood Classification Method ... 14

2.4.2. Back Propagation Neural Network Classification Method... 15

2.4.3. Classification Accuracy Assessment ... 21

3. MATERIAL AND METHODOLOGY ... 24

3.1 Time and Location ... 24

3.2 Data Source ... 24

3.3 Required Tools ... 26

3.4. Methodology ... 26

3.4.1. Insitu Measurement and Observation ... 28

3.4.2. Image Preprocessing ... 28

3.4.2.1. Geometric Correction... 28

3.4.2.2.1 Collecting GCP’S... 28

3.4.2.2.2 Transformation ... 29

3.4.2.2.3 Resampling... 29

3.4.2.2. Topographic Correction ... 29

3.4.4. The Method Comparison ... 39

4. RESULT AND DISCUSSION... 40

4.1. Digital Image Pre-Processing... 40

4.1.1. Atmospheric Correction ... 40

4.1.2. Geometric Correction ... 41

4.1.3. Topographic Correction ... 44

4.2 Digital Image Processing ... 49

4.2.1. Image Classification... 49

4.2.1.1. Classification Image with Maximum Likelihood ... 50

4.2.1.2. Classification Image with B.P.Neural Network... 52

4.3 Classification Accuracy Assessment ... 59

4.4. Comparison Neural Network and Maximum Likelihood Method... 73

5. CONCLUSION AND RECOMMENDATIONS ... 76

5.1 Conclusion ... 76

5.2 Recommendation ... 76

LIST OF FIGURES

No Caption Page

Figure 2.1 Electromagnetic Spectrum... 9

Figure 2.2 Back Propagation Neural Network... 16

Figure 3.1 Interest Area of Research ... 25

Figure 3.2 Flow Chart of General Methodology ... 27

Figure 3.3 Flow Chart of Image Preprocessing... 32

Figure 3.4 The Model of Back Propagation Neural Network Method... 38

Figure 4.1 Rectification of Landsat 7 ETM + Imagery Path/row 122/065... 42

Figure 4.2 Subset of Remotely Sensed Data to Focus Study Area ... 44

Figure 4.3 Digital Elevation Model... 45

Figure 4.4 Model of Topographic Correction ... 46

Figure 4.5 Histogram before Topographic Correction ... 47

Figure 4.6 Histogram after Topographic Correction ... 48

Figure 4.7 Landsat 7 ETM+ 2001 before and after Corrected ... 48

Figure 4.8 Training Area for Classification... 50

Figure 4.9 The Classification Result of Ciliwung Watershed Using Maximum Likelihood Classification Method ... 50

Figure 4.10 Creates Data for Neural Network ... 51

Figure 4.11 Training Data for Neural Network ... 54

Figure 4.12 Classify Process for Neural Network ... 54

Figure 4.13 The Classification Result of Ciliwung Watershed Using Back Propagation Neural Network Classification Method ... 58

Figure 4.14 Data Vector as Reference ... 62

Figure 4.15 Vector Cutting of Classification Result Using Back Propagation Neural Network... 63

Figure 4.16 Vector Cutting of Classification Result Using Maximum Likelihood... 64

Figure 4.17 Ommission Error for Back Propagation Neural Network Method.... 71

Figure 4.18 Ommission Error for Maximum Likelihood Method ... 72

LIST OF TABLE

No Caption Page

Table 2.1 The Characteristics of Landsat Thematic Mapper (TM)... 10

Table 2.2 The Characteristics of Landsat 7 ETM + ... 12

Table 2.3 The Characteristics Sensor of Landsat 7 ETM + ... 12

Table 2.4 Example of Confusion Matrix (Error Matrix)... 21

Table 3.1 The Example of Input (X) And Output (Y) ... 34

Table 3.2 The Example of Data Training... 36

Table 4.1 Comparing The DN Value Before And After Performed Histogram Adjustment Of Image 2003... 41

Table 4.2 The Geographical Coordinates of Ciliwung Watershed ... 43

Table 4.3 Minnaert Coefficient (k) Per Band... 47

Table 4.4 Overall Accuracy for Back Propagation Neural Network Method... 56

Table 4.5 The Producer’s Accuracy and User’s Accuracy for Back Propagation and Maximum Likelihood Classification Methods ... 65

Table 4.6 The Confusion Matrix for Back Propagation Neural Network with 4000 sample and 1000 Iterations... 66

Table 4.7 The Confusion Matrix For Maximum Likelihood Classification Method ... 67

LIST OF APPENDICES

Appendix Caption Page

Appendix 1.Metadata of Imageries (Landsat 7 ETM+) ... 80

Appendix 2.Neural Network Report with some samples and iterations ... 83

Appendix 3.Accuracy Assessment for Back Propagation Neural Network... 92

Research Title : Land use Classification with Back Propagation Neural Network and Maximum Likelihood Method (Case Study of Ciliwung Watershed, West Java Province)

Name : Yoss Andreas Arman

Student ID : G.051024011

Study Program : Master of Science in Information Technology for Natural Resource Management

Approved by, Advisory Board

Dr. Ir. I. Wayan Astika, M.Si Dr. Ir. Lilik Budi Prasetyo, M.Sc

Supervisor Co-Supervisor

Endorsed by,

Program Coordinator Dean of the Graduate School

Dr. Ir. Tania June, M.Sc Dr. Ir. Khairil A. Notodiputro, M.Sc

I. INTRODUCTION

1.1 Background

Water is a source of live for human being that has dynamic characteristic that flows from higher land to the lower land. The annihilation of higher land will cause the declining of water infiltration area and as a consequence, water will flow directly to the land surface and might cause land erosion and flooding. Flood occurrence that can be interpreted as rainwater excess, which flows from its storage, is a natural phenomenon where rainwater cannot be accommodated by land or surface storage in term of pool, lake or body river and drainage channel. Ciliwung watershed is one of areas that has been experiencing such damage.

Ciliwung watershed has a length of 117 km and an area of 347 km2, and is one of water catchments that suffers damage relatively serious from the upstream to the downstream. The most damaging area is in the upstream in Ciawi District and Cisarua District, where initially those areas categorized as conservation land. Nevertheless, in less than 10 years since 1985 (Harijono, 2002), the conservation area has changed into settlement area or tea plantation. This transformation occurred due to the weakness of local government control in releasing permit for land usage. It is recorded that over 40% villas located in upstream area do not have Building Constructing Permit (IMB), and generally the permit for some villas with IMB was obtained by unofficial manner (Syartinilia, 2004). This condition has become more complicated since the number of city society desiring to build a resort in upstream area, for them to have a nice and comfortable view in upstream area or puncak.

One of the difficulties of local government in decreasing land opening in upstream area is due to the reference differentiation regarding total of conservation area. Generally, there is a conflict of interest between Agricultural Department and Forestry Department. Each department has their own reference regarding the area that can be and cannot be utilized. In assisting the government in rearranging the upstream area, needs to have the same reference with high accuracy. GIS and remote sensing technology is one of the solutions to have an accurate and efficient land use map. Along with the improvement of GIS and remote sensing technology, the land changing that occurs in Ciliwung watershed can be identified and compared from time to time. Land use data and map of Ciliwung watershed can be derived from remote sensing data using classification techniques.

This research will investigate classification of image interpretation by using two methods, i.e. maximum likelihood method and back propagation neural network method. It is expected from one of those two methods can give fine or better accuracy in differentiating land cover located in Ciliwung watershed. Based on the previous researches, back propagation has high level of accuracy compared to maximum likelihood method. Koeshardono (1990), stated that back propagation neural network has 4% better in accuracy than maximum likelihood method, and so it is with the research that has been done by Widyastuti (2000) in mangrove area, where the result shown that back propagation neural network was more accurate than maximum likelihood method.

The researches above constitute the foundation of this study in order to prove how both methods can properly classify Ciliwung watershed land use, where it has been known for its heavy topographic characteristic.

1.2 Objectives

The main objectives of this research are:

1. To apply back propagation neural network and maximum likelihood classification method for land use classification and compare the performance of the two methods.

2. To produce a map of land use in the Ciliwung Watershed using the best result of the two methods.

1.3 Problem Statement

As mention earlier, population around the watershed will give big pressure to the land cover; this is not only for upstream area but for all area near the river. The land cover will be changed to land use, for example forest to settlement, this phenomenon can not be prevented because people need land for their living. For proper planning exercise information on the above aspect should be made available separately, however, the remote sensing digital data available from satellite image were found mixed up at many points. Although the land cover information from visual interpretation of the image can be extracted more accurately, difficulty had been is experienced in extracting information on land use like water conservation and others. The satellite based remote sensing has been very popular and different countries have launched their sensing satellite for this purpose. Although the visual interpretation of image had been used in many applications, it does not interpret the image pixel by pixel; instead it provides

aggregate information related to image feature of unknown objects. As a consequence, the information result for land use provided by human interpreter is less accurate and overlapping occurs in many places. For extraction from the remote sensing image the research present a relatively more comprehensive and scientific methodology using Back propagation neural network model. This method is expected to give best result and accurate information pixel by pixel.

II. LITERATURE REVIEW

2.1. Watershed Characteristics 2.1.1. Watershed Boundaries

The area upon which water falls, and the network through which it travels to an outlet is referred to as a drainage system. The flow of water through a drainage system is a only a subset of what is commonly referred to as the hydrologic cycle, which also includes precipitation, evapotranspiration, and groundwater (Seyhan, 1977).

A drainage basin is an area that drains water and other substances to a common outlet as concentrated drainage. Other common terms for a drainage basin are watershed, basin, catchments, or contributing area. This area is normally defined as the total area flowing to a given outlet, or pour point. An outlet or pour point is the point at which water flows out of an area. This is usually the lowest point along the boundary of the drainage basin. The boundary between two basins is referred to as a drainage divide

or watershed boundary (Ilyas, 1985).

2.1.2. Watershed area

Watershed is a land region which is limited by natural boundaries includes topography functioning to accommodate, keep and draining water accepted go to closest river system and then accumulating basin or lake or the se as the estuary (Seyhan, 1977).

According to Ilyas (1985), that watershed management is management of land and water rationally to get optimum benefit and

everlasting with minimum damage influent of this management will reflect at floods treats, river sediment. All of those will influence various activities and life sectors in the down stream.

2.2 The Impact of Landuse Change to Water Quality

On the watershed area, there are some land use, such as forest, plantation, and agriculture of dry farming, rice field, settlement, fisheries, industry and soon Manan (1992). The quality of water from the water territorial can be influenced by land use in there. The contamination of substance can degrade the water quality in part of river, especially from domestic waste, industrial disposal, activity of mining and waste from agriculture activity. A contamination that happen by land usage activity in upstream will give influenced in downstream.

2.3 Remote Sensing

Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation (Lillesand and Kiefer, 2000). The advance of other support technologies, especially in the related fields of electronic computing and the microchip, has begun to exercise what will doubtless grow to be a profound influence upon the design of what are often called ‘in situ’ sensor network, and through them upon the types of questions we may hope to solve though the analysis and interpretation of such conventional data. Remote Sensing can be defined as the science of observation from a distance. Thus it is contrasted with in situ sensing, which measuring devices are either

immersed in, or at least touch, the objects of the observation and measurement (Barett and Curtis, 1982).

Remote sensing is the technique of collecting information from a distance. By convention, “from distance” is generally considered to be large relative to what a person can reach out and touch, hundreds of feet, hundred of miles, or more. Remote sensing techniques are used intensively to gather measurements, satellite-based system can now measure phenomena that change continuously over time and cover large, often inaccessible areas (Aronoff, 1991).

Remote sensing systems, deployed on satellite, provide a repetitive and consistent view of the earth that is invaluable to monitoring the earth system and the effect of human activities on the earth (Schowengerdt, 1997).

The major spectral regions used for earth remote sensing are shown in this table. These particular spectral regions are of interest because they contain relatively transparent atmospheric “window”, through which (barring clouds in the non-microwave regions) the ground can be seen from above, and because there are effective radiation detector in these regions.

2.3.1. Geometric Correction

Geometric correction that most often used to make digital remote sensor data truly useful is geometric rectification. Geometric rectification is the processes of using Ground Control Points (GCP’s) are selected to transform the geometry of the images so that each pixel corresponds to a position in a real world coordinate system. Rectification is the process by

which the geometry of an image area is made planimetric (Haralicck, 1973 in Jensen, 1986).

2.3.2 Topographic Corrections

Besides geometric and radiometric error there are topographic mistakes caused by illumination differences and angle of earth surface (Smith and Brown, 1997). An area hit by a lot of sunshine will be seen brightness and on the contrary, although both of places have some cover type.

According to Jansa (1988), satellite image interpretation influenced by shadow effect caused by relief of earth surface. Some factors have an effect in topographic error, such as Sun Azimuth angle, elevation, slope and aspect of earth surface.

2.3.3 Remote Sensing Data

Each cover type on the earth has a specific characteristic to absorb, reflected and transmitted electromagnetic energy. Distinguished the fact between an object and others on the earth’s surface and then can be using in the remote sensing technologies.

Remote sensing satellite record reflected and emitted radiant flux from earth surface object or materials. Difference reflected and emitted each material are wavelength’s. The difference was used to know and interpreted what are materials or object on the earth’s surface.

2.3.4 Landsat Thematic Mapper (TM)

Landsat TM is a generation earth resources satellite, and combines reasonable spatial resolution (cell size of 30 meters by 30 meters) with the reasonable range of spectral band (7 bands in visible and near, short and mid-infrared wavelengths) Four detectors for the thermal-IR band provide four scan lines on each active scan. The TM sensor has a spatial resolution of 30 meters for bands 1 through 5, and band 7, and a spatial resolution of 120 meters for band 6 (Lillesand,1994). The thematic mapper spectral bands of Landsat - TM is shown in Table 2.1.

The Figure 2.1 below illustrates where in the EM spectrum the TM sensor can 'see'. The rectangles depict the bandwidth recorded within that region of the spectrum.

Figure 2.1. Electromagnetic Spectrum (Lillesand and Kiefer, 2000) Landsat TM image is useful for image interpretation for much wider range of application than Landsat MSS images. This is because the TM has both increases in number of spectral bands and an improvement in spatial resolution as compared to MSS (Lillesand, 1994)

Landsat data are being used to support a wide range of applications in such areas as global change research, agriculture, forestry, geology, resources management, geography, mapping, water quality, and oceanography. Landsat data have potential applications for monitoring the conditions of the earth’s land surface. The landsat TM archive has over 300,000 scenes with a data volume of over 50 terabytes (USGS, 1999). Table 2.1 The Characteristics of Landsat Thematic Mapper (TM) Spectral Bands

(Lillesand and Kiefer, 2000).

Band Spectral Resolution (_{Nominal Spectral Location}µm) & Principal Application

1 0.45 - 0.52 (Blue)

Provide increased penetration of water bodies, as well as supporting analyses of land use, soil, and vegetation characteristics. The shorter-wavelength cutoff is just below the peak transmittance of clear water, while the upper-wavelength cutoff is the limit of blue chlorophyll absorption for healthy green vegetation. Atmospheric scattering and absorption substantially influenced wavelengths below 0.45 µm

2 0.52 - 0.60 (Green)

This band spans the region between the blue and red chlorophyll absorption bands and therefore corresponds to the green reflectance of healthy vegetation.

3 0.63 - 0.69 (Red)

This is the red chlorophyll absorption band of healthy green vegetation discrimination. It is also useful for soil-boundary delineations. This band may exhibit more contrast than band 1 and 2 because of the reduced effect of atmospheric attenuation. The 0.69 µm cutoff is significant because it represents the beginning of a spectral region from 0.68 to 0.75 µm, where vegetation reflectance crossover take place that can reduce the accuracy of vegetation investigations.

4 0.76 - 1.90 (Reflective Infrared)

The lower cutoff for this band was placed above 0.75 µm. this band is especially responsive to the amount of vegetation biomass present in a scene. It is useful for crop identification and emphasizes soil/ crop and land/ water contrasts.

5 1.55 - 1.75 (Mid Infrared)

This band is sensitive to the turgidity or amount of water in plants. Such information is useful in crop drought studies and in plant vigor investigations. In addition, this is one of the few bands that can be used to discriminate between clouds, snow and ice, which are so important in hydrologic research.

Band

Spectral Resolution (µm) &

Nominal Spectral Location Principal Application

6 10.04 - 12.5 (Thermal infrared)

This band measures the amount of infrared radiant flux emitted from surfaces. The apparent temperature is a function of the emissivities and the true or kinetic temperature of the surface. It is useful for locating geothermal activity, thermal inertia mapping for geologic investigations, vegetation classification, vegetation stress analysis, and soil moisture studies. The sensor often captures unique information on differences in topographic aspect in mountainous areas.

7 2.08 - 2.35 (Mid-infrared)

This is an important band for the discrimination of geologic rock formations. It has been shown to be particularly effective in identifying zones of hydrothermal alteration in rock.

2.3.5 Landsat-7 Enhance Thematic Mapper Plus (ETM+)

Landsat 7 was officially integrated into NASA’s Earth Observing System (EOS) in 1994. It was launched on April 15, 1999 from Vandenburg Air Force base, CA, using a Delta-II Expendable launch vehicle into a sun-synchronous orbit.

Landsat 7 provides a unique suite of high-resolution observations of the terrestrial environment. It was designed to achieve three main objectives (NASA, 1999):

ƒ Maintain data continuity by providing data that are consistent in terms of geometry, spatial resolution, calibration, coverage characteristics, and spectral characteristics with previous landsat data;

ƒ Generate and periodically refresh a global archive of substantially cloud-free, sunlit landmass imagery; and

ƒ Continue to make landsat-type data available to U.S and international users at the cost of fulfilling user requests (COFUR)

and to expand the use of such data for global-change research and commercial purposes.

Landsat 7 is a three-axis stabilized platform carrying single nadir-pointing instrument, keeping the instrument pointed toward earth to within 0.05 degrees, and contains an improved Thematic Mapper sensor called the Enhanced Thematic Mapper (ETM+). The ETM+ instrument is a derivate of the Landsat 4 and 5 Thematic Mapper sensors (Jensen, 2000).

Table 2.2.The Characteristics of Landsat 7 ETM+ (Lillesand And Kiefer, 2000).

Band Spectral Resolution (µm) Spatial Resolution (m) at Nadir

1 0.450 - 0.515 30 x 30

2 0.525 - 0.605 30 x 30

3 0.630 - 0.690 30 x 30

4 0.750 - 0.900 30 x 30

5 1.55 - 1.75 30 x 30

6 10.40 - 12.50 60 x 60

7 2.08 - 2.35 30 x 30

8 (pan) 0.52 - 0.90 15 x 15

Table 2.3. The Characteristics Sensor of Landsat 7 ETM+ (Lillesand And Kiefer, 2000).

Sensor Technology Scanning Mirror Spectrometer

Swath Width 185 km (FOV=15 o

)

Off-track viewing No

Data Rate 250 images per day @ 31,450 km2

Revisit 16 days

Orbit and Inclination

705 km, sun-synchronous Inclination= 98.2o

Equatorial crossing 10:00a.m. + 15min Launch April 15, 1999: 6 year duration

Landsat 7 has a 378-gigabit solid-state recorder that can hold 42 minutes of sensor data and 29 hours of housekeeping telemetry data. This is necessary because the ETM+ obtains 150 megabits of data each second. Landsat data production facilities have had to be capable of handling very large quantities of data (Barett and Curtis, 1982).

2.4 Classification

Classification is a process in which all the pixels in an image that have similar spectral signatures are identified. It is typically used to process satellite imagery. There are two methods of classification: supervised and unsupervised classification. The difference between supervised and unsupervised classification is that, for supervised classification, we specify the classes we are interested in the supervised classification as is utility sorts out the image into these classes. On the other hand, for unsupervised classification, we can choose to give the utility class means to start with, or we can ask to make them up entirely its own (Anonymous, 1998).

Remote sensed image data sampled from satellite includes specific problems such as large image data size and difficulty in extracting characteristics of the image data. In the past, a statistical method is used without considering these problems. This method is based on an assumption that follows the Gaussian distribution. The other pattern recognition methods use intelligence based on neural network. By using neural network approach, one can get a good result for the remotely sensed data (Yoshida et. al. 1991 in Murai, 1995).

The back-propagation network (or back-propagation preceptor) is probably the most well known and widely used of neural network systems. The term “back-propagation” refers to the training method by which the connection weights of the network are adjusted. The back-propagation network is a type of multilayer feed-forward network (Anonymous, 1996).

2.4.1 Maximum Likelihood Classification Method

The maximum likelihood classification method is the most commonly used supervised classification method for remote sensing image data. This developed in the following statistically acceptable manner (Richard, 1986)

The maximum likelihood classifier quantitatively evaluates both the variance and co-variance of the category spectral response patterns when classifying an unknown pixel (Lillesand, 1994).

The maximum likelihood classification method, as shown in the following equation, includes a pixel “X” (value vector) classified into “k” class if the probability of “x” in “k” class is the highest compared to the other class (Koeshardono, 1999)

X Æ k if Lk(X) = max {L1(X), L2(X), L3(X),…….Lk(X)} Where:

Lk(X) = probability X to be “k” class

Lk

=

⎭ ⎬ ⎫ ⎩ ⎨ ⎧− 2 2 2 1 exp 2 1 ) 2 ( 1 k n d Ck

π ... (1)

2

k

d = Mahalanobis distance

2

k

X = pixel vector value (X = (x1,x2,x3,…..xn)t) k = class (k= 1,2,3…,k)

N = number of data band Mk = mean vector for class “k”

∑

= = m i ki k X m M 1 1

... (2)

ki

X = pixel vector value for training data “i” in “k” class M = the number of training data for “k” class

k

C = covariant matrix “k” class

(

)(

∑

− −

= 1 2

M X M X m

C_{k ki ki}

)

... (3)

k

C = determinant matrix C_k

1 −

k

C = inverse matrix C_k

t

Z = transposed matrix Z

2.4.2 Back propagation neural network Neural Network Classification Method

2.4.2.1 Back propagation neural network Model

Back propagation neural network is a learning algorithm using multilayer feed forward network with a different transfer function in artificial neuron. Back propagation neural network learning algorithm is usually implemented in multi (three or more) layer neural network. This learning algorithm accommodates both real and integer numbers for input and output. The general multilayer feed-forward network is fully interconnected hierarchy consisting of an input layer, one or more hidden layer and output layer. The hidden layer only receive internal inputs (inputs from other processing units) and hidden from outside world.

The back propagation neural network arises from the method in which correction are made to the weights (Patterson, 1996). During the learning phase, input patterns are presented to the network in some sequences. Each during pattern is propagated forward layer by layer until an output pattern is computed. The computed output is then compared to a desired value or target output and error value is determined. The errors are used as input to feedback connection from which adjustments are made to the synaptic weights layer by layer in back direction. The backward linkages are used for the learning phase, whereas the forward connection are used for both the learning and operational phase.

The processes inside the feed-forward back propagation neural network algorithm network are described in the Figure 2.2

X 0 X 1 X i h 0 h 1 h 2 h 3 h j Y1 vjk wij Y2

Xi : input variable of node i in input layer

hj : output of node j in hidden layer Yk : output of node k in output layer

(predicted value of node k) wij : weights connecting node i in

input layer and node j in hidden layer

vjk : weights connecting node j in hidden layer and node k in output layer. OUTPUT LAYER INPUT LAYER HIDDEN LAYER

2.4.2.2Back propagation neural network Learning Algorithm

In the back propagation neural network learning algorithm’ the training instance set for the network must be presented many times in order for the interconnection weight between the neurons to settle into a state for correct classification of input pattern. The basic learning algorithm of back propagation neural network modifies the interconnection weight on the network so that signal error is minimized (closer to zero).

Back propagation neural network learning algorithm can be done step by step as follows (Patterson, 1996):

1) Initialization:

a. Normalization of input data Xi and Target tk in form of (0, 1) range

b. Randomize of weight wij and vjk using (-1,1) value. c. Initialize of threshold unit activation, x0 = 1 and h0 = 1. 2) Active of input layer-hidden layer units with:

... (4)

∑

+

=

wijxi

e

hj

1

1

3) Active of hidden layer-output layer units with:

... (5)

∑

+

=

vjkhj

e

yk

1

1

4) To minimize error of weight, vjk must be adjusted. This process is called ‘backward’ step. Adjustment of vjk is done by

computing error of the nodes in output layer, denotes δk then adjusts weight vjk:

δk = yk (1-tk)(tk-yk) ... (6)

vjk = v jk + β.δk.hj... (7)

Where: β is constant of momentum tk is prediction value

5) This process to compute errors of the nodes in input layer, denoted τk, and adjust weight vjk

τk = hj (1-hj) ∑k δk. vjk... (8)

wij = wij + β . τk . xi ... (9)

6) Move to the next training set, and repeat step 2. Learning process is stopped if yk are close enough to tk. The termination can be based on the error E. for instance, learning process is stopped when E<0.0001

2

) (

5 .

0 _{p p}

kE = p tk −yk

∀

∑

... (10)

tkp = target value of p-th data from training set node k yp = prediction value of p-th data from training set node k The trained neural network can be used to predict target (t) by inputting values from input layer (x)

2.4.2.3 Back Propagation Neural Network Classification Method.

During the last decade, researchers have already done neural network for land cover classification using remote sensing

satellite. There are many researchers who use neural network in their research (Sadly, 1998):

a. Chang (1994) used a dynamic learning neural network for remote sensing applications and obtained a good result and concluded that neural network is a feasible classifier for a very large volume image.

b. Similar study was also carried out by Bischof (1992) and showed that the neural network outperforms the maximum likelihood method.

c. Yoshida (1994) proposed a neural network classification method for remotely sensed data analysis in order to improve neighborhood relations between pixels and decrease the error probability for pattern classification and obtained a more realistic and noiseless result compared to a conventional statistic method.

Back propagation neural network neural network usually includes an input layer, one or several hidden layers and an output layers as the biological neural network does. Three types of layers include (Fu, 1994):

¾ The input layer: The nodes, which encode the instance presented to the network for processing. For example, each input unit may be designated by an attribute value possessed by the instance.

¾ The hidden layer: The nodes, which are not directly observable and hence hidden. They provide nonlinearities for the network.

¾ The Output layer: The nodes which encode possible concept (or value) to be assigned to the instance under consideration. For example, each input unit represents a class of object.

The processing neurons in each layer are called processing units or simply known as units and neurons. The units of the input layer, hidden layers and output layer can also be called respectively input units, hidden units and output units, respectively. The direction of information transmission is from the input layer to the output layer. The neural network must be trained in advance in order to make discriminate analysis with it. The purpose of the training is to adjust the association strength (or coefficients of weights) between the neurons. The criteria of the training are to make the error between the computed output dependent vector and the known dependent vector of the trained patterns. The process of the training is just to transmit backward the error to the network, adjust the weight among the units between the output layer, the hidden layer and the input layer, that’s why this kind of network is called back propagation neural network (Zhou,1997).

The purpose of classification is to automatically categorize image pixels into classes based on land cover type. Back propagation neural network is a supervised classification method that was developed by Rumelhard et al. in 1986. Back propagation neural network is consists of many layers of neurons such as input layer, hidden layer (may be more than layer) and output layers. Each layer consists of many neurons.

2.4.3 Classification Accuracy Assessment

A classification is not complete until its accuracy is assessed (Lillesand, 1994). Classification accuracy assessment is necessary to compare the image classification with the reference data.

The reference data can be obtained from (Koeshardono, 1999):

o Ground truth obtained from the research area

o Test site obtained from visual interpretation satellite image o Aerial photography

Accuracy is determined empirically by selecting a sample pixel from the thematic map checking their labels against classes determined from reference data.

Usually this process is presented in a confusion matrix (error matrix). Lillesand (1994) gave an example of confusion matrix as shown Table 2.4.

Table 2.4. Example of Confusion Matrix (Lillesand, 1994).

Training Set Data (Know Cover Types)* Classification Data

W S F U C H

Row Total User’s Acc (%)

W S F U C H 480 0 0 0 0 0 0 52 0 16 0 0 5 0 313 0 0 38 0 20 40 126 38 24 0 0 0 0 342 60 0 0 0 0 79 359 485 72 353 142 459 481 99 72 87 89 74 75 Column Total 480 68 356 248 402 438 1992

Producer’s Accuracy (%) 100 76 88 51 85 82 Overall Accuracy = (480 + 52 + 313 +126 + 342)/1992 = 84 %

*……. W = Water S = Sand F = Forest U = Urban C = Corn H = Hay

The confusion matrix is prepared by classifying the training set pixels as shown in Table 2.4.The known category types of pixels used for training are listed versus the categories chosen by the classifier. From this information, classification errors of omission and commission can be studied. In an ideal case, all non-diagonal elements of the contingency table would be zero, indicating no misclassification. Commission errors are represented by non-diagonal elements of the tables where pixels are classified into a category to which they do not actually belong. Omission errors represent the reserve type of situation.

Producer’s accuracy result from dividing the number of correctly classified pixels in each category by the number of training set pixels used for that category (column total). User’s accuracy is computed by dividing the number of correctly pixels in each category by the total number of pixels that were classified in

that category (row total). Overall accuracy is computed by dividing the total number of correctly classified pixels

The Kappa statistic is a measure of the difference between the actual agreement between reference data and an automated classifier and the chance agreement between the reference data and a random classifier.

III. METHODOLOGY

3.1. Time and Location

The study was conducted from September 2005 to July 2006. Case study area was Ciliwung watershed of West Java Province, which covers an area of 130,000 ha, and is located between 106°47'29" East to 107°0'25" East in and between 6°24'16" North to 6°46'23" North (Figure 3.1).

3.2. Data source

There are three kinds of data that were used in this study:

1. Remote sensing data

The satellite imagery used in this study was Landsat satellite imagery

with series data (Landsat-7 ETM+ image, acquisition date of December

22nd, 2001). It is surrounding with the path/row 122/065, spatial

resolution is 30 x 30 meters (except band is 120 meters by 120 meters), and temporal resolution is 16 days with 7 bands in 2001 acquired from BTIC. (BIOTROP - Training and Information Center)

2. Topographic Map

The topographic map in vector data (1209-124,1209-141,1209-142,1209-144,1209-231) were used in this study, and they were published by Bakosurtanal that have a scale of 1:50.000

3. DEM (Digital Elevation Model) data

The data were provided by PPLH (Environmental Research Centre) IPB.

The data were obtained from check field survey using Global Positioning System (GPS). Field survey has purposed to take some of ground control point. The type of GPS is Garmin III plus and this can be used to fit the accuracy of the map.

3.3. Required Tools

The tools that used in this study consist of equipment, hardware and software as follow:

1. Equipment consists of Global Positioning System (GPS) Garmin III Plus

2. Hardware consists of personal computer (PC) Pentium 4 2.2 GHz 512 MB RAM, and color printer

3. Software was used are ER Mapper Image Processing Software, ESRI ArcView GIS Software

3.4. Methodology

There are four stages of methodology were done in this study namely 1) Collecting and pre-processing image, 2) Classification step by using maximum likelihood and neural network, 3) Analysis and continued by 4) Final result. The general outline of the research is shown in Figure 3.2

Final result

Max. Like Back Pro NN

No

Yes

Test Side: -Kappa Stat. -Visual Int.

- Cropping area of interest - Geometric correct - Topographic correct

References: -Ikonos

-Thematic Map

Preprocessing data

Classification

Processing data -Image enhance

-Band composite Comparison Corrected Imagery Geometric and Topographic Corrected Image Classification with NN Image Classification with M.Likelihood Image Classification with M. Likelihood

Image Classification with NN

Analysis Classification Step

Collecting Data and Pre-Processing Image Raw Image (Landsat TM) Good Accuracy Collecting Data: -Image -Digital Map -Thematic map -Contour Map

Mapping Watershed with the best result

3.4.1In situ Measurement and Observation

The field survey (in situ) data is very important when working with remote sensing images. The field data will be used to compare ground feature and corresponding image feature. Collect Ground Control Points (GCP’s), which are determined from the topographic maps.

3.4.2Image Preprocessing

Preprocessing the remotely sensed data prior to analyzing it is to remove some errors. Radiometric, geometric and topographic errors are the most common types of error encountered in remotely sensed imagery.

3.4.2.1 Geometric Correction

This research used polynomial rectification. It is usually used to transform an image from a RAW (or unknown) projection into a known projection. This is known as georeferencing or geocoding. Prominent features called control points are located on the RAW image and matched either with points on an image in the desired projection, or with coordinates typed in (possibly from a map).

The basic operation in geometric rectification: 1. Collecting GCP (Ground Control Point)

Ideally x’ would equal to xorig and y’ would equal to yorig but some

distortion usually happened in GCPs collection. Asimple way to measure such distortion is by computing the RMS error for each control point using the equation below:

where:

xorig and yorig = the original row and column coordinates of the GCP in

the image.

x’ and y’ = the computed or estimated coordinate in the original image.

RMS threshold is 0.5 pixel or RMSerror < 0.5 and less then 0.5 pixel is

expected. 2. Tranformation

It uses equation solution to transform the entire image. Polynomial (Control Point) is used as type of rectification.

3. Resampling

Resampling is used to determine the pixel values to fill into the output matrix from the original matrix (Lillesand and Kiefer, 2000). Resampling technique chosen is nearest neighbor. The value for a pixel in the output image could be assigned simply on the basis of the value of closest pixel in the transformed image.

3.4.2.2 Topographic Correction

Topographic factor is one of contributors in any image interpretation errors, and therefore, this effect must be removed. One of the

methods which have been used to correct to topographic effect is Minnaert

Function (Smith et.al., 1980), this representing correction between the

vector of vertical inclination and angle of the sun (the sun zenith angle). There are several steps used in this process, which are:

1. Collecting required data, i.e. Uncorrected Landsat-7 ETM+, Digital

image resolution), solar azimuth value of landsat metadata and solar elevation acquired from landsat metadata also.

2. After all the data were obtained, then a model was established in order

to be able in perform image topographic correction. The model will initially calculate cos i (cosine from effective incidence angle) by using all raster data and value acquired from metadata. Complete equation can be seen as follows:

cos i = cos e cos z +sin e sin z cos (фs – фn )... (12) where:

e = surface normal zenith angle or terrain slope

z = solar zenith angle /solar elevation angle (get from metadata)

фs = solar azimuth angle (get form metadata)

фn = aspect (get from DEM)

3. After effective incidence angle value was obtained, and the model

will perform calculation in order to acquire Minnaert Constant value. Minnaert constant value was obtained by using a linear equation, that

is: y = k x + b ...(13)

where: k = Minnaert constant

y = log (L cos e)...(14)

x = log (cos i cos e) ...(15)

b = logLn ...(16)

The model generated different value of Minnaert constant for each band.

4. After Minnaert constant and effective incident angle value were obtained, the model will perform topographic correction for each layer (per band) of Landsat-7 ETM+ 2001. The formula is:

L cos e = Ln cosk i cosk e...(17) where:

L = Digital number (DN) before corrected

Ln = Digital number (DN) after corrected

cos i = cossine from effective incidence angle (Equation 12)

cos e = surface normal zenith angle/slope of the terrain surface

k = Minnaert constant (Equation 13).

5. After the calculation using Minnaert function is finished, the model

Atmospheric Correction

Ground Control Point (GCPs) Polynomial

Rectification Geometric Correction

Topographic Correction Raw Imagery

Corrected Imagery Resampling

Figure 3.3. Flow Chart of Image Preprocessing (Lillesand and Kiefer, 2000) 3.4.3 Classification Method

Image cutting is done in order to extract the study area, because the original image covers a very large area, while the study areas are only part of the image. Cropping image is needed because the study area is limited only in Ciliwung watershed area. They was classified into 8 classes such as forest (C1), tea garden (C2), bush (C3), paddy field (C4), farm (C5), settlement (C6), grass (C7) and water body (C8).

1. Processing image data: image enhancement and band composite.

The goal of image enhancement is to improve the visual interpretability of an image by increasing the apparent distinction between features in the scene (Lillesand, 1994). This research used linear enhancement and 542 bands composite was used for training area only. This combination is called band combination natural color composite image. This was used

because band 5 is an indication of vegetation moisture content, band 4 is useful for determining vegetation types and band 2 is designed to measure green reflectance peak of vegetation for vegetation discrimination and vigor assessment. With this combination, more information for identifying the land cover was obtained.

2. Classification: this research is to compare back propagation neural

network classification method with a maximum likelihood classification method. The Artificial Neural Network (ANN) has the architecture parallel with some node and unit. The neural network used in this study has three layers representing the input, the hidden and the output. Every unit from one node to another can be linked by weight. The steps of back propagation neural network classification methods are :

a) Input layer: Landsat 7 ETM+ as the input units have eight bands or channels. They are divided to six thematic spectral (band 1 to 5, and 7), one thermal spectral (band 6), and one panchromatic spectral (band 8). In this study is only use thematic spectral or six band, that are band 1 (wavelength 0.45-0.52 µm), band 2 (0.45-0.52-0.60 µm), band 3 (0.63-0.69 µm), band 4 (0.76-0.90 µm), 5 (1.55-1.75 µm), and band 7 (2.08-2.35 µm). Besides spectral values, Landsat 7 ETM+ also consists of pixel which have pattern is rectangular and number of pixel is depending on area extent. Pixels that were used, it has resolution 30 x 30 m. So that, every pixel has spectral value that usually said a digital number (DN) and one pixel consist of

six digital numbers depend on image channel. The input are band 1 (X1), band 2 (X2), band 3 (X3), band 4 (X4), band 5 (X5), and band 7 (X6). Set of input (X) can show in tabular form as follows Table 3.1

Table 3.1. The Example of Input (X) and Output (Y) Pattern of C1, C2, C3, C4, C5, C6, C7 and C8.

No X1 X2 X3 X4 X5 X6 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 MARK

1 65 100 45 77 78 167 1 0 0 0 0 0 0 0 C1

2 78 24 24 58 79 210 0 1 0 0 0 0 0 0 C2

3 111 5 78 69 75 222 0 0 0 1 0 0 0 0 C4

4 175 10 44 36 76 212 0 0 1 0 0 0 0 0 C3

5 220 78 125 25 74 213 0 0 0 0 0 1 0 0 C6

10 80 69 210 14 45 214 0 0 0 0 1 0 0 0 C5

….

100 123 15 124 74 46 210 0 0 0 0 0 0 1 0 C7 125 145 14 156 85 42 200 0 0 0 0 0 0 0 1 C8 150 160 45 178 96 43 214 0 1 0 0 0 0 0 0 C2

….

800 170 56 192 65 123 100 1 0 0 0 0 0 0 0 C1 900 224 78 200 145 111 98 0 0 1 0 0 0 0 0 C3 …

1000 210 120 200 225 122 99 0 0 0 1 0 0 0 0 C4

Note: X1- X6 = Band 1- Band7 and C1 = Forest, C2 = Tea Plantation, C3 = Bush, C4 = Rice

Field, C5 = Farm, C6 = Settlement, and C7 = Waterbody

b) Hidden layer: The training phase is the most important aspect in neural network modeling because the weights and the network characteristics is defined to be used later on other

others datasets. In this case 6 nodes for processing element of

inputs were used to define eight possible output-training patterns (i.e. landuse classes). These eight output patterns correspond to the six inputs to generate the relationship in the form of weight in the ANN system. The back propagation

learning is inclusive of supervised, which determines the output from the input by using the training set (can see in Table 3.2 Training Set). The data for training is the training area that will get also from Landsat 7 ETM+ image after cutting area of interest, secondary information and fields check. The training area cutting from Landsat image depends upon the classes that will classify. Kind of data from training area is range of digital number per-pixel. For example, training area for forest has range of digital number 65-130 for band 1, 67-135 for band 2 and the others band without more distinguish of range. If from input pixel 1 and band 1 has digital number 100, hence pixel 1 band 1 is forest (pattern formed is 1). The data training can be shown in Table 3.1 So in this case number of data to be used as data training is 25% of total data. The best combination to obtain the best result is the learning rate 0, 9 and the momentum 0, 1 (Sadly, 1998). Before calculated in hidden layer was done, the input should be initialization by:

¾ Normalization of input data (Xi) and Target tk (will

explain next) in form of range (0,1).

¾ Randomize of weight wij and vjk using (0,1) value.

¾ Initialize of threshold unit activation, x0=1 and h0=1

The input that is to calculate can show in Equation (6). The

equation was actives of input layer to hidden layer units, then the hidden layer will produce the output. Every connection of

node the hidden layer to node the output layer has a weight; it will get by actives of hidden layer to output layer units

equations. The equations can show in Equation (7).To

minimize error of weight, vjk must be adjusted. This process is called ‘backward’ step. The equation to minimize error can

show in (8) and (10). If result from hidden layer given big

error, than iteration will be done from input. Iteration for this research had been done almost 100 until 1000. If not enough, hence iteration will continue until the error can not change

Table 3.2.The Example of Data Training.

Forest (Class 1) …. Bush (Class 7)

B1 B2 B3 B4 B5 B7 …. B1 B2 B3 B4 B5 B7

No

62-125 65-126 64-124 66-127 66-126 68-129 ….. 200-240 190-250 220-245 139-150 200-230 180-200

1 _{62 67 65 67 68 78 230 195 230 139 215 190}

2

70 126 111 112 69 90 215 200 231 145 225 200

3 _{100 100 115 124 70 88 235 204 232 150 230 185}

:

100 _{124 120 78 125 85 125 225 230 240 148 205 186}

:

250 _{90 126 124 127 123 129} …..

212 250 241 142 210 190

Note: B1 = Band 1, B2 = Band 2, B3 = Band3, B4 = Band 4, B5 = Band 5, B7 = Band 7

c) Output layer: The output (y) is eight classes (forest, tea garden, bush, paddy field, farm, settlement, water body and grass). Set of input (X) and output (Y) then composed in tabular form as follows Table 3.2

d) Target: Back propagation methods need the target (t) as comparator the output. Target was used for this research is the

training area for each class which has the accurate digital number. The target was get from training area. The trained neural network can be used to predict target (t) by inputting values from input layer (x)

e) Validation: validation is attempted to see the level of accuracy

and efficiency of a system. Validation step is conducted to test

the relationship between prediction and target output. It is done by checking the performance by calculating the accuracy between observed and predicted values of data sets:

% 100 (%) x

T Y

Accuracy = ... (18)

Where, Y is the number of valid prediction and T is the number of data to be predicted. Number of validation dataset will be used 10 % of total dataset. The intention of validation is to identify the value of classification result with the number of training set for each class

The model of artificial neural network by using back propagation is shown in Figure 3.4.

Input units Hidden layers Output Layer X1 X3 X2 X4 X5 X6 Landsat 7 etm+ Band 1 Band 2 Band 4 Band 7 Band 5 Band 3 X0 h0 h2 h1 h3 h4 y1 y2 y8 y5 y3 y7 y4 y6 F T B A R S W U h22 h21 h23 h24

Figure 3.4. The Model of Back Propagation Method

The steps of maximum likelihood classification method are (Lillesand, 1994):

a. Training Stage: The analyst identifies representative training areas and develops a numerical description of the spectral of each land cover type of interest in the scene

b. Classification Stage: Each pixel in the image data set categorized into the land cover class it most closely resembles. If the pixel is insufficiently similar to any training data set, it is usually labeled “unknown”

c. Output Stage: After the entire data set has been categorized, the result is presented in the output. Being digital in character, the results may be used in a number for difference ways.

3.3.4 The Method Comparison

In order to indicate the accuracy and reliability of the landuse classification with back propagation neural network and maximum likelihood, some types of analysis was used. There are:

Confusion matrix calculation, Lillesand (1994):

a. Overall accuracy: ∑ number of pixels in one class/grand

total of pixels

b. Producer’s accuracy: numbers of pixels in one class/

column total

c. User’s accuracy: numbers of pixels in one class/ row total

d. Kappa statistic: (observed accuracy-chance agreement)/

(1-chance agreement)

IV. RESULT AND DISCUSSION

4.1 Digital Image Pre-Processing 4.1.1Atmospheric Correction

In this study, atmospheric correction was performed on image 2001. Histogram adjustment was used to remove the atmospheric bias that may occur in the imagery in each band. The procedure made use of the Scattergram Cut-off Atmospheric Correction Technique. By principal, this technique makes use of the cut-off information that is determined from the bivariate scattergram.

A line of best fit drawn through the distribution between the two bands will intercept the shorter wavelength axis at a DN approximating the scattered component. Each cut-off value was used to readjust the minimum value in the histogram. This was accomplished by employing the simple formula “INPUT1 – cut-off value” on the formula editor of the software.

The result of histogram adjustment for radiometric correction is presented in Table 4.1 and the histogram performance before and after radiometric correction can be seen in Figure 4.1. DN value of original data has increased the minimum brightness all band. Generally, after performed histogram adjustment the minimum brightness value will be zero.

Table 4.1. Comparing the DN Value Before and After Performed Histogram Adjustment of Image 2001.

Band/Channel DN Value of Original Data Histogram Adjustment

1 56 – 255 0 – 199 2 40 – 255 0 – 215 3 28 – 255 0 – 227 4 29 – 211 0 – 182 5 23 – 255 0 – 232 7 13 – 255 0 – 242

The lowest digital number of 2001 imagery was zero, even when no objects in the scene truly have a reflectance of zero.

4.1.2 Geometric Correction

To determine control point in geometric correction and image rectification easier, it is needed to make a composite color image. The purpose of making composite color image is to find general illustration about data that will be processed further that is with manipulate visual appearance of the earth’s surface object. Composite color image that made is band combination-542. By using this composite color, the objects in the image to determining GCP’s is easier to be recognized and should be taken from exact and no change objects like small islands, intersection of road or river.

In this research, to conform the pixel grids and remove any geometric distortions in the Landsat imagery, the Landsat-7 ETM+ image, December 22, 2001 has been registered to the UTM Zone South 48, WGS 84 coordinate system. the Landsat-7 ETM + were registered to Then Topographic map utilizing similar sets of ground control points (GCP’s).

In selecting the GCP’s have to be careful, not only one should check that the object selected on the two images is similar, but also must be sure that the two have the same location on each image. The result of corrected image and collection of GCP’s can be seen on Figure 4.1.

a) Row Landsat imagery with Ground Control Point

b) Rectified imagery: Registered to the UTM zone 48, WGS 84 coordinate system

Figure. 4.1. Rectification Of Landsat 7 ETM + Imagery Path/Row 122/065, Image Date: December 22, 2001 With Composite Band 542

Even the smallest amount of RMS error has the potential to introduce some degradation to the change detection accuracy. This degradation has the potential to affect the boundaries of the landuse classes incorporated in the study as spurious differences can be detected because the land surface properties at wrong locations are evaluated instead of the real changes at the same location between one time and another. The spatial resolution of the imagery becomes an important factor in this assessment (Bottomley 1998).

Landsat-7 ETM+ image, December 22, 2001 were registered to an RMS error of less than 0.5 pixels or exactly the average of RMS error is 0.19 pixels (Appendix 4). This Root Mean’s Square error was deemed acceptable at the time of registration.

After geometric correction have already finished, it is better to subset of image that cover study area, because in some cases, Landsat 7 ETM + scenes are much larger than a research study area. In these instances it is beneficial to reduce the size of the image file to include only the area of interest (Figure 4.2). This not only eliminates the extraneous data in the file, but it speeds up processing due to the smaller amount of data to process. This is important when utilizing multi-band data such as Landsat 7 ETM + imagery. This reduction of data is known as subset (cropping). This process has been done for the both images. The image cut by using vector data from vector of west java which was produced by Bakorsurtanal. Table 4.2 shows the geographical coordinates of the cropping area.

Table 4.2. The Geographical Coordinates of Ciliwung Watershed.

Geo-position Top Left Bottom Right

Latitude 6o24’24.20”N 6o46’06.91”S

Longitude 104o47’41.37”E 107o00’01.59”E

Easting 421305.00E 460875.00E

a) Landsat imagery path/row 122/065 b) Ciliwung Watershed Figure 4.2. Subset of Remotely Sensed Data to Focus Study Area.

4.1.3 Topographic Correction

Topographic correction refers to the compensation of the different solar illuminations due to the irregular shape of the terrain. This effect causes a high variation in the reflectance response for similar land cover types: shaded areas show less than expected reflectance, whereas in sunny areas the effect is the opposite. Therefore, the process of topographic normalization may be critical in areas of rough terrain, as a preliminary step to the spectral and for multi-temporal digital classification of landuse types.

Ciliwung has diverse topography type starting from 0% slope to 55% slope. In Landsat-7 ETM+ image, the area where the slope is over 15% (such as Cisarua and Puncak) will have many shadows around it, especially forest area. In order to avoid misinterpretation during acquiring training area, then a process called topographic correction should be performed first. In classification, process in acquiring training area is very crucial part. Therefore, acquiring training area

In this study, topographic correction has been done by using Minnaert formula (Equation 19). Model can be shown below:

should represent the real spectral value for each desirable class. Topographic correction process in this research used Minnaert method where the calculation process done by using a model. Before the calculation process, all required data should be ready, which are DEM data, solar elevation angle value and azimuth angle value from Landsat-7 ETM+ metadata (acquisition date December 22, 2001). The picture of DEM used in this process can be seen in this Figure 4.3:

Low (81 m) High (3000 m)

Entering data

Image corrected

Minnaert constant

The value of solar azimuth angle for image 2001 is 122.4 while value of solar elevation angle is 56.6, Minnaert Constants can be seen in Table 4.3 as follow:

Table 4.3. Minnaert Constant (k) per Band

Band Minnaert Constant

1 -0.2644394098037027 2 -0.2371046780587166 3 -0.2144840868697183 4 -0.2724998585326885 5 -0.2780220865837141 7 -0.2099449197308662

The value of the Minnaert constant’s lies between 0 and 1. It is use to describe the roughness of the surface. Then Minnaert constant’s entered into the model and all of digital number (DN) per band which found on image will be corrected so that obtained an image which in the topography has been corrected. Figure 4.5 shows the histogram performance of images as follow:

Figure 4.6. Histogram after Topographic Correction

Histogram values indicate that there are a few value changes after topographic correction but this are not to obvious visually. The comparison was also done visually, that is no significant different, as shown in Figure 4.7:

After Before

Figure 4.7. Landsat 7 ETM+ 2001 before and after Corrected

From the figure above, there is no change in image before and after topographic correction process visually. But statistically, there is a change in spectral value

before and after topographic correction was performed. In general, topographic correction is very assistive to reduce interpretation error of image classification. 4.2. Digital Image Processing

4.2.1 Image Classification

In this research, the classification method used supervised classification, namely back propagation neural network and maximum likelihood. Both methods used Landsat 7 ETM + that had been geometrically corrected, radiometrically corrected and topographically corrected, and also used mode resample by nearest neighbourhood. The training areas were determined based on the characteristics of spectral, Ikonos image, and thematic map (Peta Penutupan Lahan, produced by PPLH (Enviromental Research Centre) IPB in collaboration with Faculty of Forestry-IPB. The pixels, which have the same spectral or visual characteristic, were categorized in the same group.

Based on the Government Regulation (PP No. 47/1997) concerning regional land use planning, Bogor-Puncak-Cianjur (BOPUNJUR) region is categorized as a specific area that needs special management and land use planning. According Government Regulation above, training area divided into 8 classes:

1. Tea Garden 2. Settlement 3. Paddy Field

4. Grass

5. Forest

6. Farm

7. Bush

Training area was taken by using Ikonos image 2001. Training area was not taken directly by ground check because the image that used too old (2001).

4.2.1.1 Classification Image with Maximum Likelihood Method

This method commonly used for image classification, this method is spectral value approach which found on training area or in the other word classification image done by supervised. Training area used for classification process was obtained from vector data. Vector data can be acquired by using Ikonos imagery as the source of training area. In order to reduce the mistake in training area setting, Ikonos imagery, which is used as the source should be corrected first by using Ground Control Point so that it will have the same position of Landsat imagery

Training area for every class is shown in Figure 4.8

The classification results are shown in Figure 4.9

Figure 4.9. The Classification Result of Ciliwung Watershed Using Maximum Likelihood Classification Method

4.2.1.1Classification Image with Back Propagation Neural Network Classification by using Back Propagation Neural Network model composed on several steps:

1. Creating Data

This Function creates and initiates a new back propagation neural network segment for back propagation neural network processing. This back propagation neural network segment can be trained to recognize classes with the back propagation neural network train program. In this process there are 2 types of data, which is spatial data in the form of Landsat 7 ETM + image and Training data area which is in form of vector. Data vector has made equal precisely with data training for method maximum likelihood of the size pixel 30 x 30 meters. The model has three parameters, which are input units, number of hidden units and number of samples. After repetition to several time, that will get the best combination are input (6), hidden layer (5) and number of sample (500), like a Figure 4.10. The input is coming from layer per-band on image and number of samples coming from number of pixels which is used to training.

2. Training

This command of back-propagation neural networks needs to be trained to learn the input patterns of interest. The momentum rate optionally specifies the momentum rate (between 0.0 and 1.0) for back propagation neural network training. The learning rate optionally specifies the learning rate (between 0.0 and 1) for back propagation neural network training. The learning and momentum rates affect how quickly the neural network stabilizes. The momentum rate can be used to speed up learning. A high momentum rate (0.9) trains with larger steps than a lower rate (0.1). The use of momentum term helps reduce oscillation between iterations, and allows a higher learning rate to be specified without the risk of non-convergence.

For this research, momentum of rate used was 0.9, while learning rate used was 0.1. Training Process require five parameters that are momentum rate, learning rate, maximum total error, maximum individual error and maximum number of iteration. After done to several times, it will obtained a combination for training that are momentum rate (0.9), learning rate (0.1), maximum total error (0.1), maximum individual error (0.1) and maximum number of iteration (1000). The setting is shown in Figure 4.11

Figure 4.11. Training Data for Neural Network 3. Classify

This function classifies multispectral imagery using a back propagation neural network. The classification can be restricted to pixels under specified rectangular window. If the window is not specified, then every pixel in the image is classified. In classification model have three parameters, which are null class, most likely classes’ images and resample mode. Best combinations for this research are null class (yes), most likely class images (1) and resample mode (nearest). For resample mode should be equal with maximum likelihood, which is nearest neighbourhood.

4. Report

This function generates a report for the specified neural network segment. The report will be showed a process of iteration until get the error that was expected. If the maximum total error which is expected can not reach, then the iteration will be done but the maximum total error still equal with the lowest error

In order to acquire a better result, then the process will be performed for several times of repetition by changing the number sample from 100 to 5000 samples. In addition, the number of iteration must also be changed for several times, starting from 100 iterations until 1100 iterations.

From several repetitions, it can be noticed that the more number of sample is used for learning process, the bigger total of error will be. The whole samples as described in the table above was running by using 1000 iterations. Number of iteration was increased to 1100 iterations, yet the maximum total error obtained was not significantly changed. As an example, for number of sample 500 and 1000 iterations, the maximum total error is 0.449, then the iteration was increased to 1100 iterations, the error is still 0.449. The conclusion is the maximum total error that can be achieved by model with 500 samples is 0.449, and this error will not change even though the number of iteration was increased to 1100. The maximum total error along with the increasing of number of sample will progressively far from the expected value, which is 0.1 (Figure 4.11).

From the explanation as stated above, it can be concluded that the model will stop to perform learning process in the training area if the value of

maximum total error or the expected number of iteration has been achieved. As known before, back propagation neural network method is a non-parametric classification, which means that classification process is using weighting for each pixel inside training area, instead of using statistical algorithm. Pixels were acquired randomly inside the training area, the more number of pixel used, then the more number of pixels that have high value. This will also cause individual maximum error getting bigger.

The accuracy of classification result by some changing in number of sample can be measured by using error matrix. Error matrix was obtained by drawing matrix diagram of the classification result and reference data. Reference data used is a vector data from training area. The result of error matrix for each changing in number of samples can be seen in Table 4.4. Table 4.4. Overall Accuracy for Back Propagation Neural Network Method

No. Number of Sample Overall Accuracy

1 100 68.62

2 500 69.89

3 1000 70.72

4 4000 73.34

5 5000 67.54

From the table above, it can be observed that, the more number of samples used in learning phase, the higher of accuracy of classification result by using neural network will be. The calculation for overall accuracy using error matrix can be seen in Appendix 3. Due to the level of accuracy with number of samples is 4000 was classified as high, and then later this classification result will be used as comparison to the maximum likelihood method. Nevertheless, after the number of samples was increased to 5000 samples of pixels, the overall accuracy is getting lower. This occurrence happened due to number of pixels used (5000) is over than

number of pixels used as the training area, which is 4000 samples of pixel, so that there are several number of pixels outside of the training area included in training process. Classification result for number of samples 4000 can be seen in this Figure 4.13 below.

Figure 4.13. The Classification Result of Ciliwung Watershed Using Back Propagation Neural Network Classification Method

4.3 Classification Accuracy Assessment

The measurement of accuracy level from landuse classification result is using a reference where the data truth is 100% assured. Other kind of reference that is commonly used, i.e. first, by aggregating reference data directly from the field; second, by using aerial photograph or other image that has same resolution with aerial photograph.

This research occupied vector data acquired from Ikonos Multispectral data (2001) with 4 bands (red, green, blue and near IR). Acquisition date of Ikonos image (as reference) is the same with acquisition date of Landsat7 ETM + (as classified data). “Accuracy assessment reference data should be collected as close as possible to the date of the collection of the remotely sensed data used to make the map “(Congalton, 1987). In order to minimize the error in creating vector on Ikonos data, then the geometry process of Ikonos will use the same Ground Control Point with Landsat Image.

There are five common sampling schemes that have been applied for collecting reference data (Congalton, 1987):

1) Simple random sampling 2) Systematic sampling 3) Stratified random sampling 4) Cluster sampling

In this study, stratified random sampling was used for collecting reference data. Stratified Random Sampling is similar to simple random sampling; however, some prior knowledge about the study area is used to divide the area into groups or strata, and then each of strata of is randomly sampled. In Stratified random sample each sample units in the study area has an equal chance of being selected. In the case of accuracy assessment, the map has been stratified into land cover or vegetation types (i. e., land cover types), no matter how small, will be included in the sample. In Stratified also pick random x,y coordinates to go and collect samples. The main advantage of Stratified Random Sampling is the good statistical properties that result from the random selection of samples. The aggregation of reference data (in form of vector) can be seen in Figure 4.14.

Tea Garden Settlement

Paddy Field Grass

(b)

Forest Farm

Bush Water Body

(d)

Figure 4.14. Vector Data as Reference

From the vector result as figured above, then the result of classification obtained from likelihood and back propagation neural network method will be cut by using that vector data. The result is shown in this Figure 4.15

Tea Garden Settlement Paddy Field

Grass Forest Farm

Bush Water Figure 4.15. Vector Cutting of Classification Result Using Back Propagation

Tea Garden Settlement Paddy Field

Grass Forest Farm

Bush Water

APPENDIX 3. Accuracy Assessment for Back Propagation Neural Network

¾ Learning Phase with 100 sample and 1000 iteration

REFERENCE

Class

Tea Garden Settlement Paddy Field Grass Forest Farm Bush Water Row Total

Tea Garden 835 5 113 8 425 3 27 8 1424

Settlement 1 1348 102 0 0 31 5 3 1490

Paddy Field 56 93 451 32 0 26 59 18 735

Grass 20 31 103 32 0 7 10 3 206

Forest 132 2 6 1 561 0 1 0 703

Farm 2 9 10 0 7 2 2 2 34

Bush 5 8 70 3 7 0 14 2 109

Water 0 2 11 10 0 0 2 0 25

Colum Total (pixel) 1051 1498 866 86 1000 69 120 36 4726

C

L

A

S

S

I

F

I

E

D

Appendix 3 (continued)

¾ Learning Phase with 500 Sample and 1000 Iteration

REFERENCE

Class

Tea Garden Settlement Paddy Field Grass Forest Farm Bush Water Row Total

Tea Garden 601 4 111 4 178 2 6 6 912

Settlement 32 1286 20 1 4 11 6 1 1361

Paddy Field 27 35 300 4 0 14 13 12 405

Grass 6 22 31 50 2 2 13 0 126

Forest 310 10 26 0 816 1 3 1 1167

Farm 2 44 21 4 0 31 0 2 104

Bush 72 69 346 16 0 6 76 13 598

Water 1 28 11 7 0 2 3 1 53

C

L

A

S

S

I

F

I

E

D

Appendix 3 (continued)

¾ Learning Phase with 1000 Sample and 1000 Iteration

REFERENCE

Class

_Garden Tea Settlement Paddy Field Grass Forest Farm Bush Water Row Total

Tea Garden 583 0 47 1 180 0 6 3 820

Settlement 47 1416 128 1 1 8 7 4 1612

Paddy Field 14 20 350 0 0 9 30 6 429

Grass 1 10 16 72 1 1 3 1 105

Forest 399 16 71 1 817 1 8 1 1314

Farm 1 21 115 3 0 46 6 10 202

Bush 3 5 111 3 0 1 57 10 190

Water 3 10 28 5 1 3 3 1 54

Colum Total (pixel) 1051 1498 866 86 1000 69 120 36 4726

C

L

A

S

S

I

F

I

E

D

Appendix 3 (continued)

¾ Learning Phase with 4000 Sample and 1000 Iteration

REFERENCE

Class

Tea Garden Settlement Paddy Field Grass Forest Farm Bush Water Row Total

Tea Garden 809 0 49 2 285 1 7 1 1154

Settlement 8 1415 60 0 0 2 2 1 1488

Paddy Field 18 2 315 0 1 3 22 9 370

Grass 6 5 37 81 0 4 9 3 145

Forest 196 1 14 1 714 0 3 1 930

Farm 4 68 130 1 0 56 1 8 268

Bush 8 0 225 1 0 2 72 9 317

Water 2 7 36 0 0 1 4 4 54

Colum Total (pixel) 1051 1498 866 86 1000 69 120 36 4726

C

L

A

S

S

I

F

I

E

D

REFERENCE

Class

Tea Garden Settlement Paddy Field Grass Forest Farm Bush Water Row Total

Tea Garden 762 2 64 3 428 0 9 4 1272

Settlement 34 1417 40 2 0 4 3 0 1500

Paddy Field 58 28 216 1 0 8 9 3 323

Grass 0 5 20 76 0 5 2 2 110

Forest 173 0 1 0 572 0 1 0 747

Farm 0 34 39 0 0 45 0 2 120

Bush 21 3 411 2 0 4 93 14 548

Water 3 9 75 2 0 3 3 11 106

Colum Total (pixel) 1051 1498 866 86 1000 69 120 36 4726

C

L

A

S

S

I

F

I

E

D

Overall Accuracy (%) 67.54

Appendix 3 (continued)