PERFORMANCE OF LOGISTIC REGRESSION MODEL AND SPATIAL METHOD

Transcription

1 PERFORMANCE OF LOGISTIC REGRESSION MODEL AND SPATIAL METHOD (Case: Predicting of Deforestation in Cikepuh Wildlife Reserve and Cibanteng Natural Reserve) BONIE FAJAR DEWANTARA GRADUATE SCHOOL BOGOR AGRICULTURAL UNIVERSITY 2006

2 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

3 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 Bonie Fajar Dewantara

4 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.

5 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

6 CURRICULUM VITAE Bonie Fajar Dewantara was born in Belawan Medan, North Sumatera, Indonesia at January 1 st, 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 as Head of Forestry Division. Now, he has been working at Wildlife Conservation Society Indonesia Program as GIS and Remote Sensing Analyst since 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

7 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.

8 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 Study Program : Master of Science in Information Technology for Natural Resources Management Approved by, Advisory Board Dr. Ir. Lilik Budi Prasetyo, M.Sc Supervisor Idung Risdiyanto, S.Si, M.Sc Co-supervisor Endorsed by, Program Coordinator Dean of Graduate School Dr. Ir. Tania June, M.Sc Prof. Dr. Ir. Khairil A. Notodiputro, MS Date: September 29 th, 2006.

9 TABLE OF CONTENTS Page Table of Content i List of Figure.. iii List of Table... List of Appendix.... vi vii I INTRODUCTION 1.1. Background Obejctives Hypothesis. 3 II LITERATURE REVIEW 2.1. Logistic Regression Model Logistic Regression Equation Significance Test for Parameter Predictors Model Interpretation Logistic Regression Coefficient and Correlation Remote Sensing, GIS and Change Detection Remote Sensing Change Detection Geographical Information System Deforestation 14 III MATERIALS AND METHODS 3.1. Time and Location Data Sources Supporting Tools / Program Methodology Image Preprocessing a. Radiometric Correction.. 19 i

10 b. Geo-Referencing Image Processing 21 a. Image Stacking.. 21 b. Signature Area 22 c. ERDAS Imagine CART Classification Vector Processing.. 26 a. Creating Cell Vector.. 26 b. Extracting Variables Data Logistic Regression Model Assumption of Research Study.. 30 IV RESULTS AND DISCUSSION Image Processing Period Period Vector Processing Creating Vector Cell Data Extracting of Contour SRTM Data Image Data Extracting of River Buffer Process Data Extracting of Road Buffer Process Data Extracting of Shoreline Buffer Area Process Data Extracting of Pupolation Center Buffer Area Process Data Extracting of Aspect Area Image Data Extracting of Slope Area Image Logistic Regression Logistic Regression Equation Significance Test of Model and Predictors Logistic Coefficient and Correlation Validation and Accuracy Assessment Validation Accuracy Assessment 64 ii