Performance of logistic regression model for predicting deforestation, case study cikepuh wildlife reserve and cibanteng natural reserve

PERFORMANCE OF LOGISTIC REGRESSION MODEL

AND SPATIAL METHOD

(Case: Predicting of Deforestation in

Cikepuh Wildlife Reserve

and Cibanteng Natural Reserve)

d

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

2006

PERFORMANCE OF LOGISTIC REGRESSION MODEL

AND SPATIAL METHOD

(Case: Predicting of Deforestation in

Cikepuh Wildlife Reserve

and Cibanteng Natural Reserve)

BONIE FAJAR DEWANTARA

A thesis submitted for the degree of Master of Science of Bogor Agricultural University

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

September 2006

STATEMENT

I am Bonie Fajar Dewantara stated that this thesis entitled :

Performance of Logistic Regression Model and Spatial Method (Case: Predicting of Deforestation in Cikepuh Wildlife Reserve and

Cibanteng Natural Reserve)

is result of my own works during the period January 2005 – September 2006 and it has not been published before. The contents of thesis have been examined by the advising committee and an external examiner.

Bogor, September 2006

ACKNOWLEDGEMENT

Alhamdulillah, Thanks to God, at the last this thesis has finished successfully, and I would like to thank to all people who have helped and assisted me during finishing the thesis. There are many people I should thank in regard to this work and no doubt I will not be able to mention them one by one, and I can buy beg forgiveness.

I deeply appreciate the efforts and thank to my supervisor Dr. Ir. Lilik B. Prasetyo, M.Sc and co-supervisor Idung Risdiyanto S.Si, M.Sc for their guidance, technical, comments and constructive criticism through all months of my research. My special gratitude also goes to Dr. Ir. I Nengah Surati Jaya, M.Sc as the external examiner and Dr. Ir. Hatrisari Hardjomidjojo, DEA, as the seminar and examination chairman (moderator) for their positive ideas, inputs, and criticism. And also my special gratitude goes to all my teachers, my lectures for sharing their knowledge and experiences.

I would like to thank to SEAMEO-BIOTROP management and staff, especially Dr. Ir. Tania June, M.Sc and MIT staff and management, technical and facility. Especially To Devi, Uma, and Bambang; Pak Jejen has been together gone to the field to collect ground truth data, and also Pak Asep in Ciracap for his home stay. Also, I thank to PPLH (Pusat Penelitian Lingkungan Hidup / Environmental Research Center) Bogor Agricultural University for the image data and Baplan (Badan Planologi / Forestry Planning Agency) Ministry of Forestry, for digital map data I would like to thank to Conservation International – Indonesia for the basic idea of image processing methodology and Wildlife Conservation Society – Indonesia Program for the ERDAS Imagine 8.7 and ArcView license usage. For my friends in MIT especially in the same batch 2002, I really appreciate our togetherness, our 24-hours-a-day works, and how to support each other to finish our assignment and study right on time.

Finally I feel deeply indebted to my lovely dear wife, Frida Yuliyanti S.Hut for her moral support and patience during the course, and especially also for both my sons Ariodanie Fudhail Hanif and Ariq Maulana Malik Ibrahim; my parents Adnan Hanif (alm) and Hj. Chadidjah, and all my family. I dedicated this thesis for the glory of knowledge and science of Indonesia.

Bonie Adnan September 2006

CURRICULUM VITAE

Bonie Fajar Dewantara was born in Belawan – Medan, North Sumatera, Indonesia at January 1st, 1971. He received his undergraduate diploma from Bogor Agricultural University in 1996, especially Forest Product Technology Department of Forestry Faculty.

Since 1996 to 1999 he worked at Risjad Salim International Bank, and from 1999 to 2002 worked at Carrefour Indonesia as Department Head, and from 2002 to 2004 worked at Ritel and Logistic Consultant PT. Wira Prima Abadi, and continued to consultant firm PT. Explorer Indonesia from 2004 – 2005 as Head of Forestry Division. Now, he has been working at Wildlife Conservation Society – Indonesia Program as GIS and Remote Sensing Analyst since 2005.

In 2002, he registered as a post-graduated student of Bogor Agricultural University, program study Master of Science in Information Technology for Natural Resources Management, and received his post-graduated diploma in 2006 with thesis title “Performance of Logistic Regression Model and Spatial Method (Case: Predicting of Deforestation in Cikepuh Wildlife Reserve and Cibanteng Natural Reserve”

ABSTRACT

BONIE FAJAR DEWANTARA (2006). Performance of Logistic Regression Model for Predicting Deforestation, Case Study: Cikepuh Wildlife Reserve and Cibanteng Natural Reserve. Under the supervision of LILIK BUDI PRASETYO and IDUNG RISDIYANTO.

Cikepuh Wildlife Reserve and Cibanteng Natural Reserve, since both conservation area was established in 1973 and 1925 have been facing complex problem caused by land use changed, deforestation, illegal hunting, forest fire, and so on. Deforestation itself is a complex socio-economic, cultural, and political event. This thesis focused on what factors affect the rate of deforestation by considering some common driving forces of deforestation and using logistic regression for predicting deforestation. It is clearly important to know where deforestation is likely to occur. The objectives of the thesis are to quantify the contribution of each deforestation driving factor such as distance from center of dweller, aspect, slope, distance from shore line, distance from existing road, and elevation, and to elaborate spatial projection of future trends of deforestation based on possibility of deforestation as the result of logistic regression equation.

The methodology is using Stacking Method from CI (Conservation International) CABS (Center for Biodiversity Applied Science) and developed together with WCS IP (Wildlife Conservation Society – Indonesia Program). Two image with different dates or one period was stacked and analyzed by visualization from both images. Signature area was extracted from the stacked-images by using shapefile polygon for forest to forest class, forest to non forest class, non to non forest class, water, cloud and shadow. Signature area should be represented certain spectral characteristic, so for obtaining number of class as many as possible, it could use 16 bit data type indeed 8 bits.

Classification method is supervised classification that was done by CART ERDAS Imagine plug in tool and See5, a stand alone decision-tree based classification program. The result of classification is thematic raster image with forest change attribute. Analysis was done in one attribute table of polygon vector cell (PVC), that is created by using Edit Tool Vector Grid, an extension from ArcView 3.3. All attribute of independent variables fill the squared-shaped polygon as called PVC, and the result probability of logistic regression as the result of the calculation as well.

Independent variable is divided to two binary category 0 and 1. 1 is a parameter that tends to occur deforestation such as less 1 km distance from road. 0 is stable condition that there is no change from forest to non forest. The result of possibility deforestation occurrence is if the road distance less than 1 km, tends to deforested occurrence 3 times compare the distance greater or equal 1 km. The smallest possibility of deforestation occurrence was contributed by predictor distance 1 km from river, and almost has no effect to deforested occurrence.

Regression logistic equation in this thesis can predict deforestation significantly, although some processes of polygon vector cell could not accommodated to assign data from attribute of independent variables to polygon vector cell exactly. Regression logistic model could predict deforestation better if distribution of independent variables that are assumed to tend to deforestation occurrence distribute evenly entire the study area.

Research Title : Performance of Logistic Regression Model and Spatial Method (Case: Predicting of Deforestation in Cikepuh Wildlife Reserve and Cibanteng Natural Reserve)

Name : Bonie Fajar Dewantara

Student ID : G.051020051

Study Program : Master of Science in Information Technology for

Natural Resources Management

Approved by, Advisory Board

Dr. Ir. Lilik Budi Prasetyo, M.Sc Idung Risdiyanto, S.Si, M.Sc Supervisor Co-supervisor

Endorsed by,

Program Coordinator Dean of Graduate School

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

TABLE OF CONTENTS

Page

Table of Content ……… ……… i

List of Figure ……….. iii

List of Table ………..………. vi

List of Appendix ………..……….. vii

I INTRODUCTION 1.1. Background ……… 1

1.2. Obejctives ……… 2

1.3. Hypothesis ………. 3

II LITERATURE REVIEW 2.1. Logistic Regression Model ……… 4

2.1.1. Logistic Regression Equation ……… 6

2.1.2. Significance Test for Parameter Predictors ……… 7

2.1.3. Model Interpretation ……… 9

2.1.4. Logistic Regression Coefficient and Correlation ………… 11

2.2. Remote Sensing, GIS and Change Detection ……… 12

2.2.1. Remote Sensing ……… 12

2.2.2. Change Detection……… 12

2.3.3. Geographical Information System ……… 13

2.3. Deforestation ……… 14

III MATERIALS AND METHODS 3.1. Time and Location ………... 16

3.2. Data Sources ……… 17

3.3. Supporting Tools / Program ………. 18

3.4. Methodology………. 19

3.4.1. Image Preprocessing………... 19

b. Geo-Referencing ……… 19

3.4.2. Image Processing ……… 21

a. Image Stacking……….. 21

b. Signature Area ……… 22

c. ERDAS Imagine – CART Classification ………….…… 24

3.4.3. Vector Processing ……….. 26

a. Creating Cell Vector……….. 26

b. Extracting Variables Data ………. 27

3.4.4. Logistic Regression Model ……… 29

3.5. Assumption of Research Study ……….. 30

IV RESULTS AND DISCUSSION 32 4.1. Image Processing ……… 32

4.1.1. Period 1990 - 1997………. 32

4.1.2. Period 1997 - 2001 ……… 37

4.2. Vector Processing ………. 40

4.2.1. Creating Vector Cell ……… 40

4.2.2. Data Extracting of Contour SRTM Data Image ... 41

4.2.3. Data Extracting of River Buffer Process ……… 43

4.2.4. Data Extracting of Road Buffer Process ………... 44

4.2.5. Data Extracting of Shoreline Buffer Area Process ………. 45

4.2.6. Data Extracting of Pupolation Center Buffer Area Process . 46 4.2.7. Data Extracting of Aspect Area Image ……… 48

4.2.8. Data Extracting of Slope Area Image ………. 50

4.3. Logistic Regression ……… 51

4.3.1. Logistic Regression Equation ……… 51

4.3.2. Significance Test of Model and Predictors ………. 53

4.3.3 Logistic Coefficient and Correlation ……… 59

4.4. Validation and Accuracy Assessment ………... 61

4.4.1. Validation ……… 61

V CONCLUSION AND RECOMMENDATION 66

5.1. Conclusion ……… 66

5.2. Recommendation ……… 67

REFERENCES 69

Appendix 1. Vector Map of Study Area ………... 74

Appendix 2. Illustration of attribute table in spatial processing ……... 75

Appendix 3. SPSS Output ………. 76

LIST OF FIGURE

No Caption Page Figure 3.1. Study area, is included Ciemas and Ciracap Subdistrict of

Sukabumi Province

17

Figure 3.2. Flow chart of research activities and procedures 20 Figure 4.1. Stacking process of Landsat image 1990 and 1997 32 Figure 4.2. Defining a training site with one polygon and inside that polygon

must be similar the spectral characteristic in both dates

33

Figure 4.3. Observing the one class of stable forest by signature mean plot, which is similarity of spectral characteristic

34

Figure 4.4. The result of classification process that is using ERDAS Imagine 8.7, CART and See5 for period 1990 - 1997

37

Figure 4.5. The result of classification process that is using ERDAS Imagine 8.7, CART and See5 for period 1997 - 2001

38

Figure 4.6. ERDAS Imagine 8.7 Modeler Maker, the model is to clip deforestation class in first period to the raster in second period.

39

Figure 4.7. The result of clipping process between deforestation class in Period 1990 -1997 will be as non-forest in Period 1997 – 2001.

39

Figure 4.8. The result of clipping process between boundary polygon of study area and square shaped of cells.

40 Figure 4.9. SRTM (Shuttle Radar Topography Mission) data of topography

was obtained from GLCF website, and displaying by ERDAS Imagine 8.7

42

Figure 4.10. The result of assigning data by location (spatial join) of contour or altitude data (LogR_Alt), Figure 4.14. where yellow cells is altitude < 250 m (1) and light blue is ≥250 m (0).

42

Figure 4.11. The result of assigning data by location (spatial join) of river buffer (LogR_Riv) 1,000 m, where yellow cells is river or group of river < 1,000 m (1) and light blue is ≥ 1,000 m (0).

Figure 4.12. The result of assigning data by location (spatial join) of road buffer (LogR_Road) 1,000 m, where yellow cells is road or group of road network < 1,000 m (1) and light blue is ≥ 1,000 m (0)

45

Figure 4.13. The result of assigning data by location (spatial join) of shore line (LogR_SL) 1,000 m, where yellow cells is shoreline buffered < 1,000 m (1) and light blue is ≥ 1,000 m (0)

46

Figure 4.14. The result of assigning data by location (spatial join) of shore line (LogR_CP) 1,000 m, where yellow cells is center population buffered < 10,000 m (1) and light blue is ≥ 10,000 m (0)

47

Figure 4.15. The result of assigning data by location (spatial join) of aspect (LogR_COM), where yellow cells is East, West,, and flat area and remaining compass is the light blue.

49

Figure 4.16. The result of assigning data by location (spatial join) of slope (LogR_Slp), where yellow cells less 25 degree and the blue light is 25 – 90 degree.

50

Figure 4.17. Classification plot (ClassPlot), another very useful piece of information for assessing goodness of fit for the model

60

Figure 4.18. Comparison between prediction deforestation and actual deforestation in 2001.

LIST OF TABLE

No. Caption Page

Table 2.1 References of Regression logistic method for prediction model. 5

Table 3.1 Binary data and categorization of variables as the factors of deforestation ……… 27

Table 3.2 Recapitulation table as the result of SPSS calculation of logistic regression ……… 30

Table 3.3. Correlation matrix, that defining the correlation among the variables ……….. 30

Table 4.1 Variables not in the Equation ……….. 52

Table 4.2 Variables in the equation ……….. 53

Table 4.3 Variables in the Equation of null model ……….. 54

Table 4.4 Omnibus Tests of Model Coefficients ……… 54

Table 4.5 Hosmer and Lemeshow Test. ……….. 55

Table 4.6 Contingency Table for Hosmer and Lemeshow Test …………... 55

Table 4.7 Classification Table ………. 56

Table 4.8 Variables in the Equation and Wald test ……….. 58

Table 4.9 Recapitulation of raster classification process ………. 62

Table 4.10 Recapitulation of polygon vector cell process ………. 63 Table 4.11 Error matrix resulting from classifying logistic regression model

………..

LIST OF APPENDIX

No Caption Page

Appendix 1. Vector Map of Study Area ……… 74

Appendix 2. Illustration of attribute table in spatial processing ……… 75

Appendix 3. SPSS Output ……… 76

I. INTRODUCTION

1.1. Background

Wildlife reserve is a kind of protected area that possesses the unique characteristic species and or biodiversity and to manage the habitat for their living sustainability (Act (UU) No 5, 1990). Cikepuh Wildlife Reserve is one of conservation area located in the southern of Sukabumi District, West Java Province. Cikepuh Wildlife Reserve is bordered in the northern by Cibanteng Natural Reserve. Cibanteng Natural Reserve is forest and natural grass land and suitable for wildlife habitat.

Cikepuh Wildlife Reserve and Cibanteng Natural Reserve, since both conservation area was established in 1973 and 1925 have been faced complex problem caused by land use changed, deforestation, illegal hunting, forest fire, and so on. Sahardjo (2000) indicated that Cikepuh Wildlife Reserve had suffered from degradation forest, reaching 80%. Most of this degradation has been caused by illegal logging for wood and paddy field. Deforestation itself is a complex socio-economic, cultural, and political event. Concern over the rate of deforestation has given rise to a literature that quantifies the impact of forces that drive deforestation. The literature has focused on two questions: (1) What factor affect the location of deforestation? And (2) What factors affect the rate of deforestation? It is clearly important to know where deforestation is likely to occur (Cropper, Puri, Griffiths 2001).

This thesis focused on the first question above by considering some common driving forces of deforestation and using a spatial model to look at broader condition and logistic regression model is proposed as an effective framework for the modeling prediction of forest cover and non-forest category associated with the spatial pattern and rates of deforestation.

Logistic regression model is a special type of regression models, which used to study the probability of membership in two contradictory classes or categories. It should be noted that logistic regression can be used

identically such as deforestation possibility or stable forest. In the application of logistic regression, each “observation” is a cell.

Recent development of GIS (Geographic Information System) technology enhances the analytical power needed for the study of land use and land cover change. Remote sensing is a science that records and analyses the radiation reflected or emitted by the objects on the earth surface. The nature of the object land cover type (forest) determines which proportion of the radiation in a specific part of the electromagnetic spectrum (different wavelength) will be reflected and recorded by sensor of the satellite. The changes of land use and land cover due to natural and human activities can be observed using current and historical remotely sensed data available form archives.

1.2. Objectives

The objective of this study is to translate the complexity of deforestation processes into simple model by using the statistical method of logistic regression model which analyze the probability of deforestation in each single cell. The purpose of this analysis is to measure the possibilities of changed forest (deforestation) or unchanged forest based on the predictor or variable factor of its driving force, such as distance from center of dweller, aspect, slope, distance from shore line, distance from existing road, and elevation.

As the main objective of this study is to observe the performance of logistic regression model and spatial method, and the specific objectives are:

1. to quantify the forest cover and deforestation.

3. to elaborate spatial projection of future deforestation trends based on possibility of deforestation resulted from predicting logistic regression.

1.3. Hypothesis

Logistic regression does not assume a linear relationship between the dependents and the independents variables. Logistic regression does not require linear relationships between the independent variables as well, so in this study the hypothesis for the logistic regression analysis will be:

At least one of independent variables such as distance from river road, shore line and center of population, altitude, aspect, or slope is not equal to zero, and can be used for predicting deforestation by the equation of logistic regression.

II. LITERATURE REVIEW

2.1. Logistic Regression Model

Model can be interpreted as simplification of a system. While system is the illustration of a process or some processes (some sub-system) regularly. Model only depicting some aspects from a system and not have to express entire process that happened in the system. More process that being explained, the model will be more complex beside more inputs required. By the reason, primary factors at one particular model are the target of when that model is created. Based on the goal/target, model can be divided to become three kinds of (1) to the understanding of process, (2) prediction, and (3) for management purpose (Handoko 1994).

There are two distinct approaches to the modeling of systems: (1) statistical models and (2) structural models. In statistical model, a relationship between observed output and known input of a system is established by postulating a general mating the parameter of the relationship by adjusting them to best fit the empirical data. Regression and correlation analysis are examples of this widely used method. While structural model attempt to describe the structure of the system, which is responsible for its behavior (Bossol 1986)

Logistic regression model is being used to analyze the relationship between the explanatory variables and the outcome/response. The outcome variable is categorized to be success or failure, i.e zero or one. It is assumed each observation is independent one another, so that the number of success events will be binomial distribution (Sutisna 2002). Also logistic regression is a technique which used to analyzed data that its response variable is binary or dichotomy scale (Hosmer and Lemeshow 1989 in Amanati 2001), and independent variable can be continue or categorical-scale of data (Amanati 2001)

According to Saadi and Abolfazi (2003) that a logistic regression model is a statistical model which a relation between a phenomenon (a dependent variable) and some of its factors (independent variables) will be

defined based on some observation. These observations are in fact a set of values measured or observed for the dependent and independent variables. Having the model specified and calibrated, the unknown value of the phenomenon can be calculated and predicted on the basis of known values of its factors.

Often, the spatial phenomenon under investigation can only be described by a categorical variable for example bird distribution indicating presence or absence of birds (Anonim 2004) or forest area being either stables or destroy (Saadi and Abolfazi 2003). Another word according to Saadi and Abolfazi (2003) mentioned that a regression model was a special type of regression models, which used to study the probability of membership in two contradictory classes. It should be noted that logistic regression can be used to determine the probability of any of the two possibilities (categories) identically. Previous regression technique is not suitable because the dependent variable is neither interval or ratio (Anonim 2004).

Table 2.1 shows the references of logistic regression model that being used to predict deforestation and other purposes.

Table 2.1. References of Regression logistic method for prediction model.

No Authors Research Title Methods Results

1

Chengling Xie Bo Huang

Christophe Claramunt Magesh Chnadramouli

Spatial logistic regression and GIS to model rural-urban land conversion

Log.Regression, GIS and Remote Sensing

Accuracy of correct prediction for developed area is 45.33 with overall accuracy 76.15% 2 Laura C. Schneider

and R. Gil Pontius Jr.

Modeling land-use change in Ipswich watershed, Massachusetts, USA

Log.Regression, GIS and Remote Sensing

Accuracy of Prediction 70%

3 Mesgari Saadi Ranjbar Abolfazi

Analysis and estimation of

deforestation using satellite imagery and GIS

Log.Regression, GIS and Remote Sensing

31.93 correctly predicted as unchanged pixels and 93.90% for changed

4 _{S. Lee}

Cross-verification of spatial logistic regression for landslide

susceptibility analysis: A case study Korea

Log.Regression, GIS and Remote Sensing

No Validation

5 _Sisilia

Simulasi perubahan penggunaan lahan pada daerah Merang dengan menggunakan metode regresi logistic (Simulation of landuse change in Merang area by using

Log.Regression, GIS and Remote Sensing

2.1.1. Logistic Regression Equation

According to Scheider and Pontius Jr (2001) in their research in Ipswich watershed, Massachusetts, USA, that in the application of logistic regression, each “observation” is a grid cell. The dependent variable is a binary presence or absence event, where 1 = changed forest (deforestation) and 0 = unchanged forest or non-forest, for a certain period of time. The logistic function gives the probability of deforestation as a function of explanatory variables. The function is a monotonic curvilinear response bounded between 0 and 1, given by a logistic function of the form:

)

(

p p

p p X X X X X X Y

E β_β β_β β_β β_β

π + + + + + + + + + = = ⎜⎝⎛ ⎟⎠⎞ ... exp 1 ... exp ) ( 2 2 1 1 0 2 2 1 1 0 … (1)

where π is the probability of deforestation in the cell, E(Y) the expected value (mean) of the binary dependent variable Y, β0 a

constant to be estimated, βi a coeficient to be estimated for each

independent variable Xi (where i = 1,2,3, …, p).

The logistic function can be transformed into a linear response with the transformation :

⎟ ⎠ ⎞ ⎜ ⎝ ⎛ − = π π π 1 log

' e … (2)

hence π =

(

β0+β1X1+β2X2+...+βpXp

)

… (3)

The transformation (Eq. (2)) from the curvilinear response (Eq. (1)) to linear function (Eq. (3)) is called a logit or logistic transformation. The transformed function allows linear regression to estimate each βi. Since each of the observations is a cell, the final

result is a probability score (p) for each cell (Scheider and Pontius Jr. 2001).

Model parameter can be estimated by an predictor maximum likelihood, iterative reweighted least squares, and discriminant analysis (Hosmer and Lemeshow 1989 in Amanati, 2001). Parameters testing of logistic regression is based on assumption that parameter βi

is normal distributed (Freeman 1987 in Amanati 2001). In this study maximum likelihood method is used to estimate the parameters βi.

2.1.2. Significance Tests for Parameter Predictor

There are numerous models in logistic regression: a constant (intercept) only that includes no predictors (null model), an incomplete model that includes the constant plus some predictors, a full model that includes the constant plus all predictors, and a perfect (hypothetical) model that would provide an exact fit of expected frequencies to observe frequencies if only the right set of predictors were measured (Tabachnick and Fidell 2001)

In SPSS program, there are two steps (model) as default. The first step, called Step 0, includes no predictors and just the intercept, and also called null model. And the second is the first step (or model) with predictors in it. In this case, it is the full model that we specified in the logistic regression command.

When it has no reasons for assigning some predictors higher priority than others, statistical criteria can be used to determine order in preliminary research. That is, if being wanted a reduce set of predictors but has no preferences among them, stepwise method can be used to reduced set (Tabachnick and Fedell 2001)

In this study, it uses Forward Stepwise of Maximum Likelihood method in SPSS process, to obtain the statistical reasons. SPSS allows to have different steps in a logistic regression model. The difference between the steps is the predictors that are included. This similar is to blocking variables into groups and then entering

begins with a full model and variables are eliminated from the model in an iterative process. The fit of the model is tested after the elimination of each variable to ensure that the model still adequately fits the data. When no more variables can be eliminated from the model, the analysis has been completed

Estimating parameter β0, β1, …, βp in the logistic regression model, it can be done using maximum likelihood method. The function of the model is given by : (Sutisna 2002)

For simply, the likelihood function can be written in the form of log-likelihood as follow:

Once an adequate model has been obtained, the next step is to test the significant of the parameter estimates. There are two types of test that can be used, G-test, a likelihood ratio-based tests statistic and Wald test.

G-test is used to test the significance all of the parameters in the model. The formula of G statistic:

where Lo = likelihood without independent variable

Lp = likelihood with independent variable

with hypothesis of test:

H0 = β0 = β1 = β2 = β3 = … = βp = 0

Under the null hypothesis H0 the G-statistic will follow a chi-square distribution with p degree of freedom (Hosmer and Lemeshow 1989 in Sutisna 2002), so a chi-square test is the test of the fit of the model

Wald test is used to test the significance of parameter βi,

where i = 1,2,3, .., p partially. This test is used, when the null hypothesis H0 in the G-statistic is rejected. The formula of Wald test:

With βˆi is the estimator for coefficient βi and SE

( )

βˆi is the

standard error of . Null hypothesis for coefficient regression is zero will be rejected if |W

i

βˆ

i| > Wα/2. Under the null hypothesis the Wald test

will follow normal distribution (Hosmer and Lemeshow, 1989 in Sutisna 2002).

2.1.3. Model Interpretation

SPSS will offer a variety of statistical tests. Usually, though, overall significance is tested using what SPSS calls the Model Chi

-square, which is derived from the likelihood of observing the actual data under the assumption that the model that has been fitted is accurate. It is convenient to use -2 times the log (base e) of this likelihood; it’s called -2LL. The difference between -2LL for the best-fitting model (full model) and -2LL (null model) for the null hypothesis model – initial chi-square (in which all the β values are set to zero) is distributed like chi-squared, with degrees of freedom equal to the number of predictors; this difference is the Model chi-square

that SPSS refers to. Very conveniently, the difference between -2LL

values for models with successive terms added also has a chi-squared distribution, so when using a stepwise procedure, it can use chi-squared tests to find out if adding one or more extra predictors

significantly improves the fit of our model (Departement of Psychology–University of Exeter 1997).

Model chi-square measures the improvement in fit that the explanatory variables make compared to the null model. Model chi-square is a likelihood ratio test which reflects the difference between error not knowing the independents (initial chi-square) and error when the independents are included in the model (deviance). When probability (model chi-square) ≤ .05, it reject the null hypothesis that knowing the independents makes no difference in predicting the dependent in logistic regression (CHASS-NCSU 2006).

Coefficient of logit model can be formulated as βI = g (x+1) –

g(x). Parameter of βi, depicts the change of g(x) logit function for the

changing of one unit of independent variable x, and as called log odds

(Hosmer and Lemeshow 1989 in Amanati 2001).

For significance of individual predictors, SPSS also offer what it calls Wald statistic, together with a corresponding significant level. The Wald statistic also has a chi-squared distribution (Departement of Psychology – University of Exeter 1997).

The ratio of the logistic coefficient β to is Standard Error (SE) squared, equals the Wald statistic. If the Wald statistic is significant (i.e less than 0.05) then the parameter is significant in the model.

Coefficient interpretation its self will be done for the significant predictors by seeing the value of each coefficient. If the coefficient is positive, it tends Y =1 to be greater than for occurring independent variable X = 1 than X = 0.

According to Hosmer and Lemeshow (1989) in Amanati (2001), coefficient of logit model is written βi = π (x + 1) - π (x). Parameter βi depicts the changes in logit π (x) for one unit changes of

independent variable X that call Odd Ratio. Log odds is difference between two values of logit, and being noted as:

ln [ψ (a,b)] = g(x=a) – g(x=b)

= βi (a-b)

One of the values of risk level is odd ratio (Freeman 1987 in Amanati 2001). For dichotomy variables, the estimator of odds ratio is:

ψ = [π(1) / (1- π(1))] / [π(0) / (1- π(0))] ln (ψ) = g(1) – g(0)

ln (ψ) = βi

ψ = exp [βi (1-0)]

With the result that, if a-b=1, so ψ = exp(βi). This odd ratio can be

interpreted as a tendency of Y=1 at x = 1 with the amount of ψtimes by comparing at x = 0 (Amanati 2001).

2.1.4. Logistic coefficients and correlation

Note that a logistic coefficient may be found to be significant when the corresponding correlation is found to be not significant, and vice versa. To make certain global statements about the significance of an independent variable, both the correlation and the logit should be significant. Among the reasons why correlations and logistic coefficients may differ in significance are these: (1) logistic coefficients are partial coefficients, controlling for other variables in the model, whereas correlation coefficients are uncontrolled; (2) logistic coefficients reflect linear and nonlinear relationships, whereas correlation reflects only linear relationships; and (3) a significant logit means there is a relation of the independent variable to the dependent variable for selected control groups, but not necessarily overall (College of Humanities & Social Sciences, North Carolina State University (CHASS-NCSU) 2006).

2.2. Remote Sensing, GIS and Change Detection 2.2.1. 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 have no any contact with the object, area, or phenomenon investigated (Lillesand and Kiefer 2000).

Remote sensing is the instrumentation, techniques and methods to observe the Earth’s surface at a distance and to interpret the images or numerical values obtained in order to acquired meaningful information of particular object on Earth (Buiten and Clevers 1993).

Before the image data can produce the required the information about the objects or phenomenon of interest, they need to be processed. The analysis and information extraction or information production is a part or overall remote sensing process, known as image processing. Digital image processing involves the manipulation and interpretation of digital image with the aid of a computer and a certain program or software.

The central idea image behind digital image processing is quite simple. The digital image is fed into computer one pixel at a time. The computer is programmed to insert these data into an equation, or series of questions, and then store the results of the computation for each pixel. These results form a new digital image that may be displayed or recorded in pictorial format may itself be further manipulated by additional programs (Lillesand and Kiefer 2000). 2.2.2. Change Detection

Change detection is the process of identifying differences in the state of an object or phenomenon by observing it in different times. The basic premise in using remote sensing data for change detection is that changes in the object of interest will result in changes

in radiance values or local texture that are separable from changes caused by other factors, such as differences in atmospheric conditions, illumination and viewing angle, soil moisture, etc. It may further be necessary to require that changes of interest be separable from expected or uninteresting events, such as seasonal, weather, tidal or diurnal effects. Some techniques also assume that the areas of change will be relatively small (Deer 2004).

2.2.3. Geographical Information System

A geographic information or Geographic Information System (GIS) is a system for creating, storing, analyzing and managing spatial data and associated attributes. In the strictest sense, it is a computer system capable of integrating, storing, editing, analyzing, sharing, and displaying geographically-referenced information.

(http://en.wikipedia.org/wiki/Geographic_information_system).

GIS depicts the real world through model involving geometry (spatial information), attributes, relation, and data quality. Spatial information is presented in two ways: as vector data in the form of points, lines, and areas (polygons) or as grid data in the form of uniform, systematically organized cell (raster) ( Bernhardsen 1992).

All geographic phenomenon have various relationships among each other and possess spatial (geometric), thematic and temporal attributes (de By 2000). Phenomenon are classified into thematic data layers depending on the purpose of database as for example land use data layers that can be analyzed (spatial analysis) involves question about data that relate topological and other relationship. Such question may involve neighborhood, distance, and few more characteristics that may exist among geographic phenomenon (de By 2000). So that in the attributes data that relates with spatial data can be manipulated, added, eliminated, and so on, for example in the attribute/tabular data is added by data resulted from calculating of

regression logistic. This condition, is especially for discrete grid/cell data that can be analyzed further from each cell for any purposes.

2.3. Deforestation

Forest decline is interpreted as deforestation, forest degradation or a combination of both. The Food and Agricultural Organization of the United Nations defines deforestation as the “sum of all transitions from natural forest classes (continuous and fragmented) to all other classes” (FAO 2006). The loss of forest cover attributed to these transitions must occur over less than 10% of the crown cover for the phenomenon to quality as deforestation (Contreras-Hermosilla 2000).

In addition to deforestation, forest degradation is an issues. According to FAO, changes within a forest class, for example for closed to open forest, which negatively affect the stands or, in particular, lower its production capacity, constitute forest degradation. Thus, forest degradation implies a major loss of forest productive capacity, even where there is little deforestation as such.

In this thesis deforestation is the forest decline, forest loss from one period of time without considering the reduction of tree crown cover less than 10% of total area for rather large areas and for long period of time and will not attempt a rigorous definition of “large area” and “long period of time”.

According to the World Resources Institute, the world has lost about half of its forest cover. Despite a number of initiatives to stop forest decline, the world continues to lose some 15 million hectares of forest every year. Deforestation over the period 1980-1999 reached 8.2% of total forest area in Asia. Most modern deforestation takes place in developing countries, particularly in tropical area (Contreras-Hermosilla 2000).

According to FWI/GFW (2001), actually Indonesia still has close forest in the year 1950. About 40 percentage of forest areas in the year 1950 have been cut away during 50 next year. If rounded up, forest cover in Indonesia have decreased from 162 million ha become 98 million ha. Rate of forest losing is increasing progressively. In the year 1980-an accelerating losing of forest in Indonesia mean about 1 million ha per year, later increase to become about 1,7 million ha per year at first years of 1990. Since year 1996, accelerating deforestation seems re-increasing to become mean 2 million ha per year.

Although Java Island contributes only about 7% of total land in Indonesia (MOF and FAO 1990), but it is unique because of its high population and intensive agriculture (Verburg, Veldkamp, Bouma 1999). And they contain over 60% of the population and produce some two-third of the country’s food supplies (Verburg, Veldkamp, and Bouma 1999).

Demography transition and growth of economics in Java Island where possesses population about 132 million people, have caused expanding of agriculture and wide of forest degradation from time to time. Obtained information has shown that from 16th century until mid of 18th century, natural forest in Java about 9 million ha. To the last year 1980th, natural forest cover in Java only reaching 0.96 million ha or 7% from Java land (Kartodihardjo et al. 2003).

Encroachment and deforestation in Cikepuh Wildlife Reserve, according to Eddy (2004) was aimed to change the land function into settlement and fixed cultivation, and almost half of total wide of Cikepuh Wildlife Reserve.

With regard to policy issues, the problem with deforestation policy in Indonesia in general is not so much the policy logging, but the conversion of land to other uses, where the other uses have no economic potential. This policy is implicated in leading to unsustainable activities and resulting in land degradation and environmental problems (Rustiadi and Kitamura 2004).

III. RESEARCH METHODOLOGY

3.1. Time and Location

The research would require six months, it had started from December 2004 to May 2005, from problem identification, finding related references to writing research proposal. It remains the processing, analyzing data and discussing the result.

The study area had been carried out place in Sukabumi province, and only two districts will be chosen as the unit of sub-district: Ciemas and Ciracap. Nature Preserve (Cagar Alam) Cibanteng and Wildlife Reserve (Suaka Margasatwa) Citepuh are inside both two subdistrict. These conservation areas will be study area. The ecosystem inside, there are low land forest and sub-mountain forest, and bounded by Indonesia Ocean, so that coastal ecosystem included.

The area is assumed that no organized illegal logging in large scale, only community in two districts and surrounding forest interact with the forest condition. The location is about 120 km far from Province Capital City of Sukabumi and about 240 km from Jakarta.

Geographically, the both districts area is about 0 – 1000 m from sea level (BPS 2000), bounded by longitudes 106o 20' - 106o 40' and latitudes 7o 05' - 7o 30'. The area of Ciemas Subdistrict is about 26,696 ha and Ciracap Subdistrict about 22,237.44 ha (BPS 1996)

The borders of that area are:

1. Northern part is adjacent to Pelabuhan Ratu and Lengkong Subdistrict. 2. West part is adjacent to Indonesia Ocean.

3. South part is adjacent to Indonesia Ocean.

In this research, the spatial analysis processing for both subdistricts are treated as the administration unit, but Cikepuh Wildlife Reserve and Cibanteng Natural Reserve had been treated as a study area and combined into one focus area. Map of the study area can be seen in Figure 3.1 and more detail in Appendix I

Figure 3.1. Study area, is included Ciemas and Ciracap Subdistrict of Sukabumi Province

3.2. Data Sources

Data required to support this research, included:

(1). Satellite imagery, from data Landsat TM with series data (multi-date), covering Districts of Ciemas and Ciracap. The path/row image is 122/65, acquisition date Nov. 9th, 1990, July 28th, 1997, received from PPLH (Pusat Penelitian Lingkungan Hidup / Environmental Research Center) IPB (Institut Pertanian Bogor / Bogor Agricultural University),

and May,12th 2001 downloaded from GLCF (Global Land Cover Facility) (http://glcf.umiacs.umd.edu/index.shtml)

(2). Topographic Map is obtained from SRTM (Shuttle Radar Topographic Mission) format and downloaded from GLCF (Global Land Cover Facility) (http://glcf.umiacs.umd.edu/index.shtml) with acquisition data 2000

(3). Digital Map of Village, Sub-district, was received from PPLH-IPB, in vector or shp format

(4). Digital Map of Nature Preserve (Cagar Alam) Cibanteng and Wildlife Reserve (Suaka Margasatwa) Citepuh boundary, was received from Baplan (Badan Planologi / Forestry Planning Agency) Ministry of Forestry.

(5). Demographic and socio-economic data, which is collected from BPS (Badan Pusat Statistik / Bureau of Statistical Center) Sukabumi Province and Head Office Jakarta.

3.3. Supporting Tools/Program

In this research, supporting tools used, as the terms of software and hardware are as the followings:

(1). Software:

• ERDAS Imagine 8.7 used for image processing

• ArcView 3.3, used for spatial data processing

• See5 (C5) Ver. 2, used for classification process and combined with ERDAS Imagine.

• SPSS version 11.5, used for statistical calculation and analysis. (2). Hardware:

• PC Dual Processor Xeon™ Intel® Pentium® IV CPU 2.99 GHZ, 1 Gb DDR-SDRAM, Video System NVIDIA GeForce 6600 256 Mb, working at Operating System Window XP Service Pack 2

• Global Positioning System (GPS) Garmin Type E-Trex Vista, property of PPLH IPB Bogor

3.4. Methodology

On the whole of research procedure will be illustrated in Figure 3.2. 3.4.1. Image Preprocessing

The objectives of image preprocessing are to remove some error because of the radiance measured by any given system over a given object on earth surface is influenced by such factors as changes in scene illumination, atmospheric conditions, viewing geometry, and instrument response characteristics.

(a) Radiometric/Atmospheric Correction. Histogram Adjustment is one of method that can be used. Histogram adjustment is used to minimize atmospheric bias. This process is common process to pre image processing, but in this thesis this process was not done since the ERDAS Imagine can accommodate to identify the similarity of DN (Digital Number) value for each area of interest for training sites or signatures area b using mean plot tool. Only image enhancement method that will be done for improving the visual interpretability in order to increase the apparent distinction between the features in that Landsat scenes, such as contrast stretching by standard deviation in ERDAS Imagine 8.7

(b) Geo-Referencing, raw digital image usually contain geometric distortions so significant that they can not be used directly as a map base without subsequent processing. The sources of these distortions range from variations in the altitude, attitude, latitude, and velocity of the sensor platform to factors such as panoramic distortion, earth curvature, atmospheric refraction, relief displacement, and non-linearities in the sweep of a sensor’s IFOV. The aim of geometric correction is to compensate for the distortions introduced by these factors so that the corrected image will have the highest practical geometric integrity.

Knowledge Base Classification Image Stacking

ERDAS (img) Importing and Reprojection Image Pre-processing Classification Processing Image Analysis Data Collecting Satellite Imagery Landsat TM: - 1990 - 1997 - 2001 Digital/Hardcopy Maps

- Topographic/Contour Map - Road, River, Coast Line - Adm Boundary

Secondary Data

Statistical Data

Deforestation 1997 - 2001

1990 1997

Overlaid Cropping using ERDAS Modeler

Maker

Visual Verification

Yes

Convert to GRID ArcView Signature Ares by using

Shapefile Polygon

ERDAS - CART Sampling Tool

CART See5 Classification

Converting to Shapefile

ArcView Polygon Vector Cell 1997-2001 Corrected Image Logistic Regression Analysis Vector Analysis No Deforestation 1990 -1997 ArcView Polygon Vector Cell 1990-1997 Model Implementation Validation Yes Spatial Modeling 2001 Image Stacking

Converting Continuous to Thematic (ERDAS)

SEE5 / C5 Classifier Construction

ERDAS Recoding

= Data = Process = Result = Period 1997-2001

= Decision = Process Direction = Main Process Boundary = Period 1990-1997

No Defining of

Independent Variable Value for each binary ofIndependent variable

Random distortions and residual unknown systematic distortions are corrected by analyzing well-distributed ground control points (GCPs) occurring in an image. From another corrected-image, GCPs are features of known ground location that can be accurately located on the uncorrected image to become a new corrected-image, also known as rectification process.

Actually this was not done in this research, the images from GLCF just need to be projected to UTM Zone 48 South and Datum WGS 84. In the process also was needed GCP (Ground Control Points), it has about 30 points with first polynomial order. A first order polynomial is normally suitable for a transformation between two near recti-linear map systems.

Landsat images are from year 1995 and 1997 acquisition are in bsq (binary sequential), so it needed to export bsq to img (ERDAS format) with certain and fix number of rows and columns. Image acquired in the year 2001, was treated as master, and while the others 1990 and 1997 were referred to as slave images.

3.4.2. Image Processing

The methodology of image processing in this research is using classification method that obtained from CABS (Center for Applied Biodiversity Science) CI (Conservation International) Washington DC and developed together with Wildlife Conservation Society (WCS) – Indonesia Program. In this method, the classification uses CART tool as one of ERDAS Imagine 8.7 plug in and stand alone program See5 (C5), as the additional program to support better classification.

(a) Image Stacking, Both Image data of 122065_19901109 and 122065_19970728 have stacked each other, became 12 or 14

122065_19901109_ 19970728 . This stacked-image is called Period 1990-1997. Period 1997 – 2001 that will be used for validating, was done with the same procedure to stack the images 122065_19970728 with 122065_ 20010512 became 122065_19970728_20010512.

This stacking process always has remaining edge as the result of process, since the wide of both images are not same. This edge must be cut by using ERDAS Imagine Modeler Maker

(b) Signature areas, to obtain the interest area was done by cropping Ciemas and Ciracap Subdistrict based on digital administrative boundary data, with the same projection, and the last subset/cropping again to obtained both conservation area Nature Reserve (Cagar Alam) Cibanteng and Wildlife Reserve (Suaka Margasatwa) Cikepuh as the study area. Signature areas or training sites are used to define a certain class based on visualization characteristic. Signature area was made in polygons shapefile format in ERDAS Imagine 8.7. Start to take a signature area or training site is to zoom the interest area and make one representative polygon that make sure the characteristic spectral inside that polygon is similar whether first date (1220065_19901109) has one class for instant forest and the second date (122065_19970728) also has one class for instant non-forest. So the entire signature areas is stand alone layer in vector format in ERDAS Imagine 8.7

In this study, 5 main land cover categories were distinguished:

- Forest, to identify forest that no change to another uses.

- Non-Forest, assumed as agriculture area, plantation, settlement, shrub or bush, bare land, and even seasonal flooding

- Cloud, but in the thesis, the land use or land cover will be estimated with the surround condition.

- Shadow, in the thesis, the land use or land cover will be estimated with the surround condition, since binomial logistic regression can not use cloud and shadow class. So water, cloud and shadow, at the last process will be included as non-forest category, if the cloud and shadow are among the non-forest area category

ID of each signature area polygon in shapefile format as attributes is one important thing to recode and classify the image. Two digits can be used for 8 bits of classification result and 4 digits for unsigned 16 bits. Four digits will be more to determine as many as classes in order to minimize DN (Digital Number ) overlapping for each class.

Note field is just for describing the ID, example for making one particular a forest class to differ with the others by visualization interpretation. The forest says swamp forest, according to the class that we want to extract only forest and non-forest, and because of this fact we make particular ID for example start from 1140, 1141, and so on. At the last process, after getting the best iteration, the image must be converted again to 8 bit data type and recode to common classes forest to forest (11), forest to non (12), non to non (22), water (44), cloud (55), and shadow (66). As mentioned before, that it is better to use 16 bits output data type, to anticipate overlapping amount training sites, one training site will represent one type of spectral characteristic. In this case, forest class was collected, and numbered with 4 digits. After obtaining all spectral characteristic of forest, it needs to verify the similarity of forest spectral characteristic. This task can be done by using Mean Plot of Signature in ERDAS Imagine 8.7. So the wrong signature area can be deleted and remaining only signature

areas which have had similarity of spectral characteristic. However this technique is only for unchanged class such as forest to forest, and can not be used for changed forest to non, because non forest class in this case may be depicted many land cover for example seasonal flooding, mixed agricultural or farming, plantation, and so on.

Mean plot of signature is a spectral profile of the mean data file value of each signature in all bands of the image to be classified was obtained by changing the polygon in shapefile format to AOI. It uses Copy Selection to AOI, from AOI menu in ERDAS Imagine 8.7 Viewer. After all classes assumed are right, the polygon shapefile will be converted to grid by using ArcView 3.3.

ArcView program is used for converting shapefile of signature areas polygon into grid format. This grid must be same the pixel size. Using ERDAS Imagine again is to import the Grid type into img format, with data type unsigned 16 bit in Import Options. Open one ERDAS Imagine viewer again to see the result of importing process. In this process, the projection metadata will loose, and it needs to be redefined. From ERDAS Image Information, change the Map Model, the unit into meters and projection into UTM (Universal Transfer Mercator) and change Spheroid and Datum WGS 84, UTM Zone 48 South. The result of above process is still continuous file type, and it must in thematic file type. This process uses Modeler Maker, and the model will change continuous to thematic file type, with the same data type 16 bit.

(c) ERDAS Imagine – CART Classification. Classification process uses CART, from ERDAS tool bar, and choose CART Sampling Tool , Independent Variable File is the original image file,

Dependent Variable File is thematic img file after converting process, uses See5, and the last define the third file output *.names, *.data, and *.test.

After that, process is continued by using stand alone program See5. From See5 main menu, choose File – Locate Data, and find the path of *.data as the result of CART Sampling Tool. Choose File – Construct Classifier, and CC Option window will appear, check list Boost with 10 default value and the remaining let them as default value, Pruning CF 10% and Minimum 2 Cases in Global Pruning option.

See5 will run the classification process according to the dependent and independent variable files. If the See5 process is success, the last classification process is to appear the thematic image. Back to ERDAS Imagine tool bar menu, choose CART again and click Run See5.

Run See5 in submenu CART tool in ERDAS is the last process of classification. The result can be interpreted to the original image, and if there are many mistaken, it can be iterated and so on. Iteration starts at defining the signature areas polygon again, and continue the process. This result also can be used to further process, to convert to vector format. Since until this process, data type 16 bit still inside, the raster must be converted again to 8 bit, in order to be easy to further process. The class ID in the raster attribute data must be 11, 12, 22, 44, 55, or 66. Recoding of ERDAS Imagine 8.7 is needed in this case.

Period 1997 – 2001 will be done by the same procedure. Image data 1997 is the same date with image data in period 1990 – 1997, it is 122065_19901109 and 122065_20010512 for image data 2001.

In this case, with the assumption that deforestation in Period 1990 – 1997 will be non-forest in Period 1997 – 2001, since the image data 1997 will be the first date and image 2001 will be the second date. Both images became one stacked image. By the reason, the deforestation class (class number 12) will be clipped by all classes of raster Period 1990 – 1997. Only class number 12 of Period 1997 – 2001 will be as input theme and Period 1990 – 1997 will be as clip theme. This process is done by using Modeler Maker ERDAS Imagine 8.7

3.4.3. Vector Processing

(a) Creating Cell Vector, In this study as the nest for attribute data of each variables is cells. The cell is polygon with square shape, with size 90 x 90 meters. These vector cells were created by using ET Vector Grid, a plug in extension of ArcView 3.3. In its process, this tool just need four input extents points, Xmin, Xmax, Ymin, and Ymax; and XY spacing or grid resolution 90 meters. The result of this process is a squared shape, which consist of cells or grids.

Each cell has to be filled by either value 0 or 1 for independent and dependent variables. Joining process to fill each cell, will be easy by using ArcView Extension, Geoprocessing Assign data by location (Spatial Join). The tool will join the ID of cell correlated by either value 0 or 1, in an attribute table, and was saved by a name Polygon VectorCell (PVC). Name of PVC is based on ArcView extension to create grid vector polygon, and to differ with raster grid in this thesis, grid is changed to cell. The results process are spatial and its attributes, that must be saved into a new shapefile.

(b) Extracting Variables Data

In this research, dependent variable and independent variable will be list in Table 3.1.

Table 3.1. Binary data and categorization of variables as the factors of deforestation

Variable Symbol Value Category Variable References

Unchanged Forest –

Changed Forest Y 0

Unchanged Forest – Unchanged Non Forest

Saadi, M. and R. Abolfazi. 2003

1 Changed Forest

(Deforestation)

Saadi, M. and R. Abolfazi. 2003

Elevation/Altitude X1 0 ≥ 250 m Assumed

1 < 250 m Assumed

Aspect X2 0 North and South Saadi, M. and R. _{Abolfazi. 2003}

1 East and West Saadi, M. and R.

Abolfazi. 2003 Distance from

population centers X3 0 ≥ 10 km

Sitorus, J., E. Rustiadi, M. Ardiansyah. 2001

1 < 10 km Sitorus, J., E. Rustiadi,

M. Ardiansyah. 2001 Distance from

Shoreline X4 0 ≥ 1 km Assumed

1 < 1 km Assumed

Slope X5 0 ≥ 25 - 90 degree Assumed

1 < 0 – 25 degree Assumed

Road X6 0 ≥ 1 km Assumed

1 < 1 km Assumed

River X7 0 ≥ 1 km Assumed

1 < 1 km Assumed

Extracting vector data starts from obtaining the polygon that contains the attribute data such as contour data ≥ 250 meters and < 250 meters. Sometimes this purpose needs editing manually of using ArcView extension in order to make easy the process. The binary data must be put into the Polygon Vector Cell as attribute data by using Assign data by location (Spatial Join). It is done the same thing to other variables: road, river, distance from population center, and distance from shoreline. Independent variable such as elevation was assumed that less than 250 meter

tends to occur deforestation. The references and research citation is presented in Table 3.1.

In ArcView 3.3, there is an extension Edit Tool 3.5, can accommodate the purpose. There are three method to assign the data by location, i.e Inside (attribute of polygon source must be inside of the polygon target), Center Inside (attribute of polygon source must be touched the center point of polygon target, and Intersect (attribute of polygon source must intersect to the polygon target). This tool is the same with Geoprocessing extension Assign Data by Location.

In order to anticipate over estimation of deforestation, the Inside method is applied to this research.

The process of extracting slope and aspect are different with others. Extracting both features are using ArcView 3D Analyst for creating TIN from Features. The next process that uses grid TIN is using ArcView ModelBuilder. ModelBuilder will identify input automatically of grid theme, in this case the result from gridding TIN file.

Extracting aspect from elevation grid file is provide default by ArcView ModelBuilder. It will produce classification of aspect in raster or grid format that also as thematic or discrete grid theme; with defined resolution 90 meters. The grid classes are:

• 1 = Flat

• 2 = North

• 3 = Northeast

• 4 = East

• 5 = Southeast

• 6 = South

• 7 = Southwest

• 8 = West

With the same procedure above, slope can be extracted. Slope is in degrees unit, meter for vertical unit and slope would be classified into ten classes.

• 1 = 0 – 5 degree

• 2 = 5 – 10 degree

• 3 = 10 – 15 degree

• 4 = 15 – 20 degree

• 5 = 20 – 25 degree

• 6 = 25 – 30 degree

• 7 = 30 – 35 degree

• 8 = 35 – 40 degree

• 9 = 40 – 45 degree

• 10 = 45 – 90 degree

3.4.4. Logistic Regression Model

Logistic Regression Model, is used to determine the probability of Unchanged Forest / Unchanged Non-forest and Changed Forest (Deforestation) that will occur by using transformed function of:

π’ = (β0 + β1 X1 + β2 X2 + … + βp Xp)

where: π’ = probability of dependent variable Changed Forest (Y)

β0 = constant of regression

β1 = coefficient of X1 independent variable

β2 = coefficient of X2 independent variable

βp = coefficient of pth of X independent variable

According to Saadi and Abolfazl (2003) that they used and selected a sampling set about 5% of total pixels, but in this research, sampling set will be obtained from all cells that are from the result of classification result. The entire sampling set that entered into Polygon Vector Cell attribute, will be processed calculation by SPSS Statistical computer program. Both possibilities value of variables must be input in SPSS editing table. Calculation will be done

been mentioned in Chapter II (Literature Review). The result of the calculation will be recapitulated such Table 3.2.

Table 3.2. Recapitulation table as the result of SPSS calculation of logistic regression

β Standard _Error Wald df (degree of freedom) Significant

level exp (β) Constant

X1 X2 X3 …

Beside calculating and printing the statistic of logistic regression model, SPSS also can calculate the correlation among or inter the variables, as the result correlation will be display in matrix form, such Table 3.3.

Table 3.3. Correlation matrix, that defining the correlation among the variables

Constant X1 X2 X3 …

Constant X1 X2 X3 …

Between two variable will have correlation if the correlation value more then or equal 0.5, and less then 0.5, there is no relationship between them.

3.5. Assumption of Research

1. Supervised Classification by visual interpretation is assumed to be closed to the real condition and assessed by accuracy assessment.

2. Cloud, shadow, and water, will be included as the closest class around, for example cloud and shadow that are inside the forest area category; they will be included as forest class. Oppositely, if cloud an shadow are among the non-forest category, they will be non-forest class. This process is done by recode tool of ERDAS Imagine 8.7

3. As the consequence of assumption no.2 above, could, shadow, and seasonal flooding are assumed as 0 value. Because of calculation process, logistic regression can not identify value unless 1 and 0.

4. Road is class of sub district road that be able to be passed only by car and truck, so only district and province street class are used in this study 5. Logistic regression does not assume a linear relationship between the

dependents and the independents.

6. The dependent variable doesn’t need not to normally distribution. 7. Vector data and projection result are assumed correctly.

8. Center of population is approached by subdistrict office as point feature with buffer 10,000 meters from the point.

IV. RESULTS AND DISCUSSION

4.1. Image Processing

4.1.1. Period 1990 - 1997

Both image data of 122065_19901109 and 122065_19970728 have been stacked each other, became 14 (with Thermal Band 6) bands/layers into one file of dataset 122065_19901109_19970728, and properly overlayed with the same projection UTM (Universal Transverse Mercator) zone 48 South, and Datum WGS 84.

This research image processing uses ERDAS Imagine 8.7, from doing subset, geometric correction, until classification process.

The result of stacking process and the origin sources Landsat images can be seen in Figure 4.1, with band combination 4-5-3 or 11-12-10.

Un-scale Image Un-scale Image

a. 1990 b. 1997 Figure 4.1. Stacking process of Landsat image 1990 and 1997.

Getting the signatures area or training sites is done by using polygon shape. Starting to take a signature area or training site, done by zooming the interest area and make one representative polygon that make sure the characteristic spectral inside that polygon is similar whether first date (1220065_19901109) has one class for instant forest and the second date (122065_19970728) also has one class for instant non-forest (Figure 4.2). This class will be forest to forest area or stable forest with class number 11.

Saving the training site as an attribute is one of the step, and this attribute will be useful as the attribute in further vector process. ID in attribute is one important thing to recode and classify the image as well. Two digits can be used for 8 bits of classification result and 4 digits for unsigned 16 bits. Four digits will be more to determine as many as classes in order to minimize DN (Digital Number) overlapping for each class.

Un-scale Image

a. 1990 b. 1997

Figure 4.2. Defining a training site with one polygon and inside that polygon must be similar the spectral characteristic in both dates.

Another process in naming the polygon for each class name is “note field”. Note field is just for describing the ID, for example is to make one particular a forest class with different with the others by visualization interpretation. The forest says dense forest, according to the class that we want to extract only forest and non-forest, and because of this fact we make particular ID for example start from 1140, 1141, and so on. Since in this classification using 16 bit data type, class 11 (forest –forest) might be 100 subclass from 1100 to 1199.

The important part of classification process is to define the signature area or training site. A training site must depict the entire similar pixels. Pixels of cloud for instant can not include in a training site of forest, although only one pixel (Figure 4.2).

It is better using 16 bits output data type rather than 8 bit, to anticipate overlapping amount training sites, one training site will represent one of spectral characteristic. In this case, forest class was collected, and numbered with 4 digits, after obtaining all spectral characteristic of forest, it needs to verify the similarity of forest spectral characteristic. This task can be done by using mean plot of signature in ERDAS Imagine (Figure 4.3).

(a) (b)

Figure 4.3. Observing the one class of stable forest by signature mean plot, which is similarity of spectral characteristic.

Observing the graphic in Figure 4.3, X axis is bands while Y axis is Mean of DN (Digital Number) value. It can be seen the categorizing or grouping of forest class according to the spectral characteristic. If one of those classes far from its group, it means that the training site polygon can not included in forest class (Figure 4.3a) The wrong classes must be corrected by deleting the class by class. ERDAS Imagine 8.7 program will automatically identify which class that is far from its group by distinguishing the color of training sites (Figure 4.3a).

The editing graphic of signature mean plot after deleting the misclassified is shown in Figure 4.3b.

This technique is only for unchanged forest, and can not be used for forest to non or even unchanged non-forest, because non in this case may be depicted many kinds of land cover for example seasonal flooding, mixed agricultural or farming, plantation, and so on.

Once the all signature areas are related to the all classes that being defined before, deciding to stop getting the signature, can be done and before that, make sure all the land cover or a particular spectral characteristic have been covered by the entire signatures area. Since the polygon shapefile is in vector format, it needs to convert to grid or raster format using ArcView 3.3. This grid must be same the pixel size.

Using ERDAS Imagine 8.7 again is to import the grid type into img format, with data type still in unsigned 16 bit. Because of this process, the projection metadata will be lost, and it needs to redefine again. Projecting into UTM (Universal Transfer Mercator) Zone South 48and also defines Spheroid and Datum Name as WGS 84.

Hosmer and Lemeshow Test

.000

0

.

.000

0

.

28.804

2

.000

26.016

3

.000

24.907

4

.000

44.944

6

.000

Step

1

2

3

4

5

6

Chi-square

df

Sig.

Contingency Table for Hosmer and Lemeshow Test

6252

6252.000

4762

4762.000

11014

2596

2570.541

1549

1574.459

4145

3656

3681.459

3213

3187.541

6869

2091

2014.536

1063

1139.464

3154

2655

2731.464

2149

2072.536

4804

505

556.129

486

434.871

991

1001

949.871

1064

1115.129

2065

2091

2016.910

1063

1137.090

3154

2354

2401.158

1787

1739.842

4141

505

549.390

486

441.610

991

301

327.932

362

335.068

663

1001

956.610

1064

1108.390

2065

1828

1779.887

941

989.113

2769

263

236.411

122

148.589

385

1991

2064.714

1542

1468.286

3533

789

775.948

601

614.052

1390

779

751.959

712

739.041

1491

602

643.082

844

802.918

1446

1240

1166.615

554

627.385

1794

588

615.602

387

359.398

975

1093

1048.434

656

700.566

1749

1161

1249.071

1008

919.929

2169

608

594.415

447

460.585

1055

642

668.763

639

612.237

1281

607

574.291

558

590.709

1165

313

334.809

513

491.191

826

1

Step 1

1

2

Step 2

1

2

3

4

Step 3

1

2

3

4

5

Step 4

1

2

3

4

5

6

Step 5

1

2

3

4

5

6

7

8

Step 6

Observed

Expected

LU_90_97 = 0

Observed

Expected

LU_90_97 = 1

Classification Table

a

6165

87

98.6

4552

210

4.4

57.9

6165

87

98.6

4552

210

4.4

57.9

5251

1001

84.0

3698

1064

22.3

57.3

4950

1302

79.2

3336

1426

29.9

57.9

5650

602

90.4

3918

844

17.7

59.0

5381

871

86.1

3884

878

18.4

56.8

Observed

0

1

LU_90_97

Overall Percentage

0

1

LU_90_97

Overall Percentage

0

1

LU_90_97

Overall Percentage

0

1

LU_90_97

Overall Percentage

0

1

LU_90_97

Overall Percentage

0

1

LU_90_97

Overall Percentage

Step 1

Step 2

Step 3

Step 4

Step 5

Step 6

0

1

LU_90_97

_Percentage

Correct

Predicted

The cut value is .500

a.

Variables in the Equation

1.185 .129 84.330 1 .000 3.269 2.539 4.209

-.303 .020 240.917 1 .000 .738

-.302 .040 57.033 1 .000 .739 .684 .800

1.212 .129 87.822 1 .000 3.362 2.609 4.332

-.188 .025 58.274 1 .000 .828

-.294 .040 53.714 1 .000 .745 .689 .806

1.296 .130 99.583 1 .000 3.655 2.834 4.715

.324 .045 52.875 1 .000 1.382 1.267 1.509

-.276 .028 100.436 1 .000 .759

-.251 .041 36.859 1 .000 .778 .718 .844

1.154 .135 73.546 1 .000 3.172 2.436 4.130

.355 .045 61.820 1 .000 1.426 1.305 1.558

.344 .080 18.497 1 .000 1.410 1.206 1.649

-.322 .030 118.291 1 .000 .725

-.247 .041 35.510 1 .000 .781 .721 .847

1.143 .135 71.960 1 .000 3.136 2.408 4.084

.341 .046 56.083 1 .000 1.406 1.286 1.537

.354 .080 19.550 1 .000 1.425 1.218 1.667

-.123 .053 5.392 1 .020 .884 .797 .981

-.218 .054 16.390 1 .000 .804

-.232 .042 30.593 1 .000 .793 .730 .861

1.130 .135 70.293 1 .000 3.097 2.378 4.033

.348 .046 57.956 1 .000 1.416 1.294 1.548

.354 .080 19.584 1 .000 1.425 1.218 1.667

.082 .041 3.968 1 .046 1.086 1.001 1.177

-.150 .055 7.506 1 .006 .861 .773 .958

-.238 .055 18.903 1 .000 .788

LOGR_ROA Constant Step 1a LOGR_RIV LOGR_ROA Constant Step 2b LOGR_RIV LOGR_ROA LOGR_SL Constant Step 3c LOGR_RIV LOGR_ROA LOGR_SL LOGR_CP Constant Step 4d LOGR_RIV LOGR_ROA LOGR_SL LOGR_CP LOGR_SLP Constant Step 5e LOGR_RIV LOGR_ROA LOGR_SL LOGR_CP LOGR_COM LOGR_SLP Constant Step 6f

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

Variable(s) entered on step 1: LOGR_ROA. a.

Variable(s) entered on step 2: LOGR_RIV. b.

Variable(s) entered on step 3: LOGR_SL. c.

Variable(s) entered on step 4: LOGR_CP. d.

Variable(s) entered on step 5: LOGR_SLP. e.

Correlation Matrix

1.000

-.152

-.152

1.000

1.000

-.099

-.607

-.607

-.035

1.000

-.099

1.000

-.035

1.000

-.128

-.556

-.441

-.556

-.033

1.000

.023

-.128

1.000

-.033

.090

-.441

.090

.023

1.000

1.000

-.025

-.590

-.464

-.364

-.590

-.096

1.000

.061

.242

-.025

1.000

-.096

.048

-.236

-.464

.048

.061

1.000

.160

-.364

-.236

.242

.160

1.000

1.000

-.043

-.288

-.361

-.154

-.835

-.288

-.098

1.000

.055

.244

-.044

-.043

1.000

-.098

.052

-.238

.035

-.361

.052

.055

1.000

.151

.130

-.154

-.238

.244

.151

1.000

-.056

-.835

.035

-.044

.130

-.056

1.000

1.000

-.034

-.311

-.368

-.152

-.749

-.187

-.311

-.104

1.000

.066

.241

-.083

.170

-.034

1.000

-.104

.048

-.239

.045

-.045

-.368

.048

.066

1.000

.151

.107

.075

-.152

-.239

.241

.151

1.000

-.055

.003

-.187

-.045

.170

.075

.003

-.246

1.000

-.749

.045

-.083

.107

-.055

1.000

-.246

Constant

LOGR_ROA

Step

1

Constant

LOGR_RIV

LOGR_ROA

Step

2

Constant

LOGR_RIV

LOGR_ROA

LOGR_SL

Step

3

Constant

LOGR_RIV

LOGR_ROA

LOGR_SL

LOGR_CP

Step

4

Constant

LOGR_RIV

LOGR_ROA

LOGR_SL

LOGR_CP

LOGR_SLP

Step

5

Constant

LOGR_RIV

LOGR_ROA

LOGR_SL

LOGR_CP

LOGR_COM

LOGR_SLP

Step

6

Constant

LOGR_ROA

LOGR_RIV

LOGR_SL

LOGR_CP

LOGR_SLP

LOGR_COM

Model if Term Removed

-7533.228 94.011 1 .000

-7486.223 57.422 1 .000

-7506.500 97.977 1 .000

-7458.161 54.055 1 .000

-7486.803 111.339 1 .000

-7457.511 52.756 1 .000

-7440.402 37.011 1 .000

-7461.894 79.995 1 .000

-7452.733 61.673 1 .000

-7431.133 18.473 1 .000

-7437.033 35.651 1 .000

-7458.305 78.195 1 .000

-7447.177 55.938 1 .000

-7428.969 19.524 1 .000

-7421.897 5.379 1 .020

-7432.570 30.693 1 .000

-7455.410 76.372 1 .000

-7446.131 57.815 1 .000

-7427.003 19.559 1 .000

-7419.207 3.967 1 .046

-7420.967 7.487 1 .006

Variable LOGR_ROA Step 1 LOGR_RIV LOGR_ROA Step 2 LOGR_RIV LOGR_ROA LOGR_SL Step 3 LOGR_RIV LOGR_ROA LOGR_SL LOGR_CP Step 4 LOGR_RIV LOGR_ROA LOGR_SL LOGR_CP LOGR_SLP Step 5 LOGR_RIV LOGR_ROA LOGR_SL LOGR_CP LOGR_COM LOGR_SLP Step 6 Model Log Likelihood Change in -2 Log Likelihood df

Sig. of the Change

Variables not in the Equation

57.169

1

.000

56.468

1

.000

23.262

1

.000

3.424

1

.064

11.410

1

.001

4.399

1

.036

138.281

6

.000

53.072

1

.000

9.591

1

.002

.399

1

.527

9.583

1

.002

4.313

1

.038

81.368

5

.000

18.609

1

.000

1.995

1

.158

4.340

1

.037

.067

1

.795

28.409

4

.000

1.859

1

.173

5.396

1

.020

.370

1

.543

9.812

3

.020

3.969

1

.046

.695

1

.404

4.421

2

.110

.453

1

.501

.453

1

.501

LOGR_RIV

LOGR_SL

LOGR_CP

LOGR_COM

LOGR_SLP

LOGR_ALT

Variables

Overall Statistics

Step 1

LOGR_SL

LOGR_CP

LOGR_COM

LOGR_SLP

LOGR_ALT

Variables

Overall Statistics

Step 2

LOGR_CP

LOGR_COM

LOGR_SLP

LOGR_ALT

Variables

Overall Statistics

Step 3

LOGR_COM

LOGR_SLP

LOGR_ALT

Variables

Overall Statistics

Step 4

LOGR_COM

LOGR_ALT

Variables

Overall Statistics

Step 5

LOGR_ALT

Variables

Overall Statistics

Step 6

Appendix IV.

Table of Frequency

Frequencies

Statistics

11014

11014

11014

11014

11014

11014

11014

0

0

0

0

0

0

0

Valid

Missing

N

LOGR_RIV LOGR_SL LOGR_CP LOGR_COM LOGR_SLP LOGR_ALT LOGR_ROAD

Frequency Table

LOGR_RIV

6733

61.1

61.1

61.1

4281

38.9

38.9

100.0

11014

100.0

100.0

0

1

Total

Valid

Frequency

Percent

Valid Percent

Cumulative

Percent

LOGR_SL

8255

75.0

75.0

75.0

2759

25.0

25.0

100.0

11014

100.0

100.0

0

1

Total

Valid

Frequency

Percent

Valid Percent

Cumulative

Percent

LOGR_CP

10194

92.6

92.6

92.6

820

7.4

7.4

100.0

11014

100.0

100.0

0

1

Total

Valid

Frequency

Percent

Valid Percent

Cumulative

Percent

LOGR_COM

6232

56.6

56.6

56.6

4782

43.4

43.4

100.0

11014

100.0

100.0

0

1

Total

Valid

Frequency

Percent

Valid Percent

Cumulative

Percent

LOGR_SLP

1788

16.2

16.2

16.2

9226

83.8

83.8

100.0

11014

100.0

100.0

0

1

Total

Valid

Frequency

Percent

Valid Percent

Cumulative

Percent

LOGR_ALT

2581

23.4

23.4

23.4

8433

76.6

76.6

100.0

11014

100.0

100.0

0

1

Total

Valid

Frequency

Percent

Valid Percent

Cumulative

Percent

LOGR_ROAD

10717

97.3

97.3

97.3

297

2.7

2.7

100.0

11014

100.0

100.0

0

1

Total

Valid

Frequency

Percent

Valid Percent

Cumulative

Percent