Net Primary Production Spatial Distribution in Kalimantan

SERGE CLAUDIO RAFANOHARANA

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY BOGOR

STATEMENT

I, Serge Claudio Rafanoharana, hereby declare that this thesis entitled

Net Primary Production Spatial Distribution in Kalimantan

Is a result of my work under the supervision advisory board and that it has not been published before. The content of the thesis has been examined by the advisory board and external examiner.

Bogor, December 2011

Serge Claudio Rafanoharana

ABSTRACT

SERGE CLAUDIO RAFANOHARANA. Net Primary Production Spatial Distribution in Kalimantan. Under the supervision of HARTRISARI HARDJOMIDJOJO and IBNU SOFIAN.

Net Primary Production (NPP) flux was estimated based on the 16-days data of Moderate Resolution Imaging Spectroradiometer (MODIS) for 10 years from 2001 to 2010 by using the National Aeronautics and Space Administration - Carnegie Ames Stanford Approach (NASA-CASA) model. The values of the yearly average of NPP in Kalimantan from 2001 to 2010 were 762.77 gC m-2 yr-1, 703.74 gC m-2 yr-1, 778.68 gC m-2 yr-1, 745.6 gC m-2 yr-1, 797.54 gC m-2 yr-1, 713.27 gC m-2 yr-1, 790.01 gC m-2 yr-1, 768.53 gC m-2 yr-1, 752.94 gC m-2 yr-1, and 867.65 gC m-2 yr-1 respectively. The results have shown that high NPP was found in the forest area with a value from 1,200 gC m-2 yr-1 to 1,600 gC m-2 yr-1. It was followed by palm oil plantation with NPP of 1,000 gC m-2 yr-1 to 1,300 gC m-2 yr

-1

. For peatland, the value of NPP varied between 200 gC m-2 yr-1 and 600 gC m-2 yr-1. For water body, the results have shown that NPP varied from 0 gC m-2 yr -1 to 100 gC m-2 yr -1. For residential area, NPP was between 0 gC m-2 yr-1 and 700 gC m-2 yr-1. It increased as long as we got far from the center of the city. In term of mining area, the value of NPP was very low varying between 400 gC m-2 yr-1 to 600 gC m-2 yr-1. This high value might be the result from reforestation and from the vegetation surrounding the mining area since the resolution of the MODIS data used is 250-meter. The effect of inter-annual variation of El Nino is not clearly seen. However the NPP is decreasing during El Nino period. On the contrary, NPP is increasing during the La Nina periods. The NPP reached its highest value in March and April during the monsoonal transitional period, and decreased to the lowest in September and October.

ABSTRAK

SERGE CLAUDIO RAFANOHARANA. Distribusi Spasial Net Primary Production di Kalimantan. Dibimbing oleh HARTRISARI HARDJOMIDJOJO dan IBNU SOFIAN.

Nilai estimasi Net Primary Production (NPP) dihitung berdasarkan data per 16 hari dari MODIS selama 10-tahun menggunakan National Aeronautics and Space Administration - Carnegie Ames Stanford Approach (NASA-CASA) model. Nilai rata-rata tahunan NPP di Kalimantan pada tahun 2001-2010 adalah berturut-turut 762,77 gC m-2 thn-1, 703,74 gC m-2 thn-1, 778,68 gC m-2 thn-1, 745,6 gC m-2 thn-1, 797,54 gC m-2 thn-1, 713,27 gC m-2 thn-1, 790,01 gC m-2 thn-1, 768,53 gC m-2 thn-1, 752,94 gC m-2 thn-1, dan 867,65 gC m-2 thn-1. Hasil menunjukkan bahwa nilai NPP relatif tinggi ditemukan pada kawasan hutan dengan nilai 1,200 gC m-2 thn-1 sampai 1,600 gC m-2 thn-1, kemudian diikuti nilai NPP dari perkebunan kelapa sawit (1,000 gC m-2 thn-1 sampai 1,300 gC m-2 thn-1) Untuk lahan gambut, nilai NPP bervariasi antara 200 gC m-2 thn-1 sampai 600 gC m-2 thn-1. Untuk badan air, nilai NPP berada di antara 0 gC m-2 thn -1 sampai 100 gC m-2 thn -1. Untuk pemukiman, nilai NPP berada di antara 0 gC m-2 thn-1 sampai 700 gC m-2 thn-1. Nilai estimasi NPP lebih rendah di pusat kota dibandingkan dengan nilai NPP untuk daerah di sekitar kota. Untuk wilayah pertambangan, nilai NPP berada di antara 400 gC m-2 thn-1 sampai 600 gC m-2 thn-1. Nilai NPP yang tinggi di daerah tambang ini kemungkinan dihasilkan dari vegetasi di sekitar daerah pertambangan. mengingat resolusi data MODIS mencakup 250 meter. Efek antar-tahunan variasi El Nino tidak jelas terlihat, namun nilai NPP menurun selama periode El Nino. Sebaliknya, nilai NPP meningkat selama periode La Nina. Nilai NPP tertinggi dicapai pada bulan Maret dan April selama periode transisi musiman, dan mencapai level terendah pada bulan September dan Oktober.

SUMMARY

SERGE CLAUDIO RAFANOHARANA. Net Primary Production Spatial Distribution in Kalimantan. Under the supervision of HARTRISARI HARDJOMIDJOJO and IBNU SOFIAN.

Temperature of the Earth is controlled by the balance between the input from energy of the sun that hits the earth and the loss of this energy reflected back into space. On average, about one-third of the solar radiation is reflected back to space. Of the remainder, some is absorbed by the atmosphere, but most is absorbed by the land and oceans. Study about NPP for tropical forest is very important, because Indonesia is one of the countries located in the tropical area which has large area of tropical forest. Information on net primary production in tropical forests is needed for the development of realistic global carbon budgets, for projecting how these ecosystems was affected by climatic and atmospheric changes.

The objective of this research is to estimate the Net Primary Production (NPP) using Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) over Kalimantan between 2001 and 2010 and to validate the estimation of NPP with the ground check.

The research was conducted from March until October 2011. The study area was Kalimantan which is bounded between longitude 108o 40’ 58’’ E and 118o 59’ 45’’ E and latitude 4o 24’ 8’’ N and 4o 22’ 30’’ S with an area of approximately 537,442.55km2. MODIS satellite data was used for this research. The data was collected starting from the year 2001 to 2010. MOD13Q1 type in a Terra platform was used with a vegetation indices product. The data was in a tile fashion with a resolution of 250m and a temporal granularity of 16 days. In addition, FPAR data and different meteorological data such as temperature and rainfall data were downloaded from 2001 to 2010.

NPP in this research was estimated based on the utilization of remote sensing data which is MODIS data to provide information of the monthly NPP flux, defined as net fixation of CO2 by vegetation, which is computed in National

Aeronautics and Space Administration - Carnegie Ames Stanford Approach (NASA-CASA) model on the basis of light-use efficiency. Monthly production of plant biomass was estimated as a product of time-varying surface solar irradiance Sr, and EVI from the MODIS satellite, and a constant light utilization efficiency term emax that was modified by time-varying stress scalar terms for temperature T and moisture W effects.

In this study, the Enhanced Vegetation Index data were obtained from MODIS. Low EVI was assumed as sparsely vegetated land, high EVI was assumed as densely vegetated land, and EVI where values were equal to or below zero were assumed to be typically caused by water bodies. In October 2005, the amount of monthly mean of EVI reached its highest amount with about 0.55, while the lowest amount was in October 2006, around 0.46. However, during this 10-year period, there were a lot of changes to the proportion of EVI. The value of EVI reached its peak in March 2006, September 2007, March 2008, and

September 2010 with a value of 0.53, 0.52, 0.53, and 0.52 respectively. However, it decreased rapidly to 0.46 by the end of 2010.

The rainfall data used for this research was derived from the Tropical Rainfall Measuring Mission (TRMM) data which is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its associated latent heating. The rainfall measuring instruments on the TRMM satellite include the Precipitation Radar (PR), an electronically scanning radar operating at 13.8 GHz; TRMM Microwave Image (TMI), a nine-channel passive microwave radiometer; and Visible and Infrared Scanner (VIRS), a five-channel visible/infrared radiometer. Monthly Average precipitation in Kalimantan varied from 44 mm to 376 mm per month. The highest precipitation was found during the months of December and January. At the beginning of each year, the monthly value of precipitation was above 250 mm; while it was less than 200 mm in the middle of each year especially in July. The monthly average precipitation reached its peak in January 2009 where the value was about 376 mm, but a dramatic fall at 107 mm followed it in September 2009. In August 2004, the monthly average precipitation had its lowest amount with about 44 mm.

The temperature data used for this study was from NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) at monthly value. The monthly average temperature in Kalimantan ranged between 25.04°C to 26.38°C except in May 2010 where it reached 26.72°C. The pattern of monthly average temperature showed that monthly average temperature in Kalimantan reached its highest temperature value during May and June. The value of monthly average temperature in Kalimantan decreased from November to February for every year from about 25.7°C to 25.1°C. Similarly in July of every year, it fell slightly from about 25.9°C to 25.4°C; and for the other period of the year especially from February to May, an upward trend occurred with a value of about 25.05°C to 26.3°C and reached its peak in May or June. The monthly average temperature had its highest value in May 2010 and its lowest value in January 2009 with a value of 26.72°C and 25.04°C respectively. In general, each year variation’s trend was almost the same over the 10-year period from 2001 to 2010.

The monthly average Fraction of Absorbed Photosynthetically Active radiation FPAR could be used to explain about growing season length during normal climate condition or during abnormal climate condition. It measures the proportion of available radiation in the photosynthetically active wavelengths that are absorbed by a canopy. The monthly mean of FPAR peaked around June and July of every year and after that a rapid fall was noticed. However, the each-year FPAR revealed that the value of FPAR is not really stable. There was a lot of variations: many rises and decreases but over the 10-year period, the value of monthly average of FPAR was not generally less than 0.23 except for some cases where the amount was under 0.23 for instance in December 2003, October 2006, February 2008, and January 2009 with a value of 0.18, 0.16, 0.19, and 0.19 respectively.

Estimation of NPP using NASA CASA model has shown that high value of NPP has occurred in April and May of every year and low NPP during September and October. In addition, NPP over Kalimantan ranged from 0 to 1,800 gC m-2 yr-1. The results have shown that high NPP was found in the forest area

with a value from 1,200 gC m-2 yr-1 to 1,600 gC m-2 yr-1. It was followed by palm oil plantation with NPP of 1,000 gC m-2 yr-1 to 1,300 gC m-2 yr-1. For peatland, the value of NPP varied between 200 gC m-2 yr-1 and 600 gC m-2 yr-1. For water body, the results have shown that NPP varied from 0 gC m-2 yr -1 to 100 gC m-2 yr -1. For residential area, NPP was between 0 gC m-2 yr-1 and 700 gC m-2 yr-1. It increased as long as we got far from the center of the city. In term of mining area, the value of NPP was very low varying between 400 gC m-2 yr-1 to 600 gC m-2 yr-1. This high value might be the result from reforestation and from the vegetation surrounding the mining area since the resolution of the MODIS data used is 250-meter. Geographic and physic condition of the area affected the distribution of NPP. If the region is very mountainous, the value of NPP was very low at about 200 gC m-2 yr-1 and 400 gC m-2 yr-1. However, in the border of Kalimantan, the value of NPP was high reaching 1,400 gC m-2 yr-1 and even 1,800 gC m-2 yr-1. We found out that plenty of forests were still remaining in those areas on the border but we couldn’t verify for the whole area. There was a dramatically drop of NPP in the month of October 2006. It was reported that the 2006 Southeast Asian haze event was caused by continued uncontrolled burning from slash and burn cultivation in Indonesia, and affected several countries in the Southeast Asian region.

The results were validated by doing ground check in three main locations which were South Kalimantan, East Kalimantan, and Central Kalimantan. A sample for water body has been taken on the Mahakam River which is the largest river in East Kalimantan, Indonesia. The sample for forest has been taken in different areas. However, some parts of the very deep forest one was in Tandilang Forest, located in the village called Sulang’ai, Sub-district of Batang Alai Timur, Hulu Sungai Tengah Regency, in South Kalimantan Province. The ground check for peatland was done in a vast peatland area called “Jl Asang Permai Gambut”. The ground check for palm oil was done at the “Kebun Kelapa Sawit Pelaihari”, a palm oil plantation owned by PT. Perkebunan Nusantara XIII (PERSERO). Since it was impossible to go through the mining area, we took the point as close as the mining area as possible and we also took some points from some remaining areas and conditions of previous mining activities. For the residential area, it was done in Pangkalan Bun, capital of the Kotawaringin Barat regency, in the western part of Central Kalimantan.

To improve the prediction of NPP, it is recommended to apply more parameter data such as land cover data from high resolution satellite data. It is important to conduct field experiments and observations for advancing our understanding of the interactions between the carbon and nitrogen cycles in the tropics.

Copyright © 2011, Bogor Agricultural University Copyright are protected by law,

1. It is prohibited to cite all of part of this thesis without referring to and mentioning the source;

a. Citation only permitted for the sake of education, research, scientific writing, report writing, critical writing or reviewing scientific problem. b. Citation does not inflict the name and honor of Bogor Agricultural

University.

2. It is prohibited to republish and reproduce all part of this thesis without written permission from Bogor Agricultural University.

NET PRIMARY PRODUCTION SPATIAL DISTRIBUTION

IN KALIMANTAN

SERGE CLAUDIO RAFANOHARANA

A thesis submitted for the degree Master of Science in Information Technology for Natural Resources Management Study Program

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY BOGOR

Research Title : Net Primary Production Spatial Distribution in Kalimantan

Student Name : Serge Claudio Rafanoharana

Student ID : G051098121

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

Approved by, Advisory Board

Dr. Ir. Hartrisari Hardjomidjojo, DEA Dr. Ibnu Sofian, M.Eng

Supervisor Co-Supervisor

Endorsed by,

Program Coordinator Dean of the Graduate School

Dr. Ir. Hartrisari Hardjomidjojo, DEA Dr. Ir. Dahrul Syah, M.Sc.Agr

Date of Examination: Date of Graduation:

ACKNOWLEDGMENTS

First and foremost I owe great thanks to God Almighty for bestowing on me the knowledge and determination to do all the tasks related to this endeavor. He gave me patience and has always been a generous Guide.

I would like to thank Dr. Ir. Hartrisari Hardjomodjojo, DEA as Program Coordinator and also my supervisor, Dr. Ibnu Sofian, M.Eng as my co-supervisor for their guidance, comments, corrections, and constructive inputs through all months of my research. My appreciation also goes to Prof. Dr. I Nengah Surati Jaya, M.Agr as the external examiner for his valuable critics, inputs, and corrections.

I would like to thank the Developing Countries Partnership Scholarship (DCPS) as my sponsorship during my study here in Indonesia, the administrative staff at Bogor Agricultural University, and the MIT secretariat for their supports in term of administration, technical and facility.

I express my thanks to all MIT lecturers who gave me not only knowledge but also new perspective and vision. Thanks also go to all MIT students for the togetherness and cultural exchange during our study.

As always, my deepest appreciation goes to my beloved wife Miora Ramilijaona Rafanoharana for her prayer, love, patience and support during my study. Special thanks also go to my parents and siblings for their encouragement and support.

Hopefully, the results of this research would provide a positive and valuable contribution for anyone who reads it.

Bogor, December 2011

CURRICULUM VITAE

The Author was born in Antananarivo, Madagascar on December 12th 1981. He finished his bachelor degree in Computer Sciences and Applied Statistics from the University of Antananarivo, Madagascar. In 2008, he obtained his Diplôme d'Etudes Superieures Spécialisées (DESS) in Information System and New Technology after working for the University of Antananarivo for two years. He obtained also his Professional Certificate as a webmaster and web designer from the Conservatoire National des Arts et Métiers (CNAM) de Paris, France. He obtained a scholarship from the Developing Countries Partnership Scholarship (DCPS) Program to pursue his MSc Degree in Information Technology for Natural Resources Management, an international program under the collaboration of Bogor Agricultural University and Southeast Asian Regional Centre for Tropical Biology (SEAMEO BIOTROP). He completed his master program study in 2011. His final thesis is entitled “Net Primary Production Spatial Distribution in Kalimantan”.

TABLE OF CONTENTS

Page

TABLE OF CONTENTS... ii

LIST OF TABLES ... iv

LIST OF FIGURES ... v

LIST OF APPENDICES ... vii

I. INTRODUCTION ... 1

1.1 Background ... 1

1.2 Objectives... 2

1.3 Expected Output... 2

II. LITERATURE REVIEW... 3

2.1 Net Primary Production ... 3

2.2 Climate change... 3

2.3 MODIS... 4

III. METHODOLOGY... 5

3.1 Time and Location ... 5

3.2 Required Tools ... 5

3.3 Data Source ... 5

3.4 Method ... 6

3.4.1 Data collection ... 6

3.4.2 Data Processing... 7

3.4.3 Measuring vegetation: Enhanced Vegetation Index (EVI) ... 8

3.4.4 Estimation of Net Primary Production (NPP)... 9

3.4.5 Validation with the ground check ... 12

IV. RESULTS ... 16

4.1 Enhanced Vegetation Index (EVI) ... 16

4.1.1 Climatology process and Anomalies of EVI... 17 ii

4.1.2 Spatial distribution of Enhanced Vegetation Index ... 18

4.2 Temperature ... 20

4.3 Precipitation ... 22

4.4 Fraction of Absorbed Photosynthetically Active Radiation (FPAR).... 24

4.5 Net Primary Production (NPP) ... 27

4.5.1 Monthly mean of NPP... 27

4.5.2 Yearly mean of NPP... 28

4.5.3 Climatology Process and Anomalies of NPP... 29

4.5.4 Correlation of NPP with Climate Variability... 32

4.5.5 Spatial distribution of NPP in Kalimantan... 35

4.6 Validation... 39

4.6.1 Ground check for water body... 40

4.6.2 Ground check for forest ... 41

4.6.3 Ground check for Peatland... 43

4.6.4 Ground check for Palm Oil ... 43

4.6.5 Ground check for Mining area ... 45

4.6.6 Ground check for Residential area... 45

V. CONCLUSION AND RECOMMENDATION ... 47

5.1 Conclusion ... 47

5.2 Recommendation ... 48

REFERENCES... 49

APPENDICES ... 53

LIST OF TABLES

Page

Table 1 The general pattern of monthly EVI in Kalimantan ... 19

Table 2 The general pattern of monthly Temperature in Kalimantan... 22

Table 3 The general pattern of monthly Precipitation in Kalimantan (in mm)... 24

Table 4 The general pattern of monthly FPAR in Kalimantan ... 26

Table 5 The general pattern of monthly NPP in Kalimantan... 31

Table 6 Burnt area in Kalimantan in September and October 2006 (Langner and Siegert 2006) ... 38

LIST OF FIGURES

Page

Figure 1 Location of the study area... 5

Figure 2 General Method of the Research ... 7

Figure 3 Time series of monthly mean of EVI in Kalimantan... 16

Figure 4 Time series of monthly mean of EVI and monthly average of 10-year period ... 17

Figure 5 Time series of anomalies for EVI from 2001 to 2010 ... 18

Figure 6 Yearly mean of EVI in the year 2001, 2005, 2006, and 2010 ... 20

Figure 7 Monthly average temperature over Kalimantan from 2001 to 2010 ... 21

Figure 8 Monthly average precipitation over Kalimantan from 2001 to 2010 ... 23

Figure 9 Time series of FPAR over Kalimantan from 2001 to 2010... 25

Figure 10 Trends of monthly distribution of NPP in Kalimantan... 27

Figure 11 Trends of annual distribution of NPP in Kalimantan ... 28

Figure 12 Time series of monthly mean of NPP and monthly average of 10-year period ... 29

Figure 13 Time series of anomalies of NPP from 2001 to 2010... 30

Figure 14 Time series data of NPP and Temperature in Kalimantan ... 32

Figure 15 Correlation between NPP and Temperature data in Kalimantan... 33

Figure 16 Time series data of NPP and Precipitation in Kalimantan ... 34

Figure 17 Correlation between NPP and Precipitation data in Kalimantan... 34

Figure 18 Spatial distribution of monthly average of NPP for 10-year period... 36

Figure 19 Digital Elevation Model and 3D view of the study area ... 37

Figure 20 Spatial distribution of monthly average of NPP for 2006 ... 37

Figure 21 Location for ground check and validation of NPP ... 40

Figure 22 Ground check location for water body ... 41

Figure 23 Ground check location for forest (a)... 41

Figure 24 Ground check location for forest (b) ... 42

Figure 25 Forest Map of South Kalimantan... 42

Figure 26 Ground check location for peatland... 43

Figure 27 Ground check location for palm oil plantation (a)... 44 v

Figure 28 Ground check location for palm oil plantation (b) ... 44 Figure 29 Ground check location for mining area ... 45 Figure 30 Ground check location for residential area... 46

vii

LIST OF APPENDICES

Page 1 Batch file program for data processing ... 53 2 GPS points for ground truth and validation in Kalimantan ... 56

I. INTRODUCTION

1.1 Background

Temperature of the Earth is controlled by the balance between the input from energy of the sun that hits the earth and the loss of this energy reflected back into space. On average, about one-third of the solar radiation is reflected back to space. Of the remainder, some is absorbed by the atmosphere, but most is absorbed by the land and oceans (Maslin 2004). Certain atmospheric gases are critical to the temperature balance and are known as greenhouse gases. The existence of greenhouse gases includes water vapor, carbon dioxide, ozone, methane, and nitrous oxide could a blanket effect and would increase the temperature of the earth. Global temperatures have risen by 0.76oC since 1850, with doubled warming rate compare to the past century (Partington 2007).

The increasing concentration of atmospheric greenhouse gases could also threatened the existence of human beings. Understanding the response of ecosystem to climate change would be necessary for human beings to protect the environment and for the production of food and energy. Vegetation could act as carbon source or sink to greenhouse gases (Schimel et al. 2000, Braswell et al.

1997). According to Partington (2007) and also Dutschke and Wolf (2007), it was mentioned that deforestation is considered as the second most important human-induced source of greenhouse gases. This was being responsible for approximately 20% of total emissions. Much knowledge has been gathered on drivers and causes of deforestation and forest degradation at recent years, also methodological tools have been developed to monitor large areas and predict the quantification of carbon benefits from reduced deforestation and forest degradation (REDD).

Net Primary Production (NPP) is defined as the net flux of carbon from the atmosphere into green plants per unit time (Zhiqiang et al. 2004). Annual NPP is the net amount of carbon captured by land plants through photosynthesis each year. Studying the response of NPP to the environment could help understand the functions of ecosystem and its feedback to the changes of climate and social environment. Study on ecosystem productivity is also the key to sustainable

utilization and development of agriculture and forestry (Lobell et al. 2002, Mingkui and Woodward 1998). Net Primary Production of terrestrial ecosystems is important in estimating land carrying capacity, which is critically relevant to the social and economic development of a country. An objective and critical review of past efforts to estimate forest NPP calls into question the precision and accuracy of such estimates (Clark et al. 2001), new techniques such as eddy flux correlation (Goulden et al. 1996) and more explicit identification of NPP components (Clarck

et al. 2001) have led to improved estimates. Temporal trends lead to differences in estimated NPP and its allocation, depending on stand age or development stage (Kira 1975); hence, comparisons among species of forest types must be done with some caution.

MODIS data could be used to estimate the spatial distribution of NPP. MODIS offers a unique combination of features such as detection of a wide spectral range of electromagnetic energy; measurement at three spatial resolutions and time of measurements take in place. These advantages allowed MODIS to complete an electromagnetic picture of the globe every two days. MODIS’ frequent coverage could complement other imaging systems such as Landsat’s Enhanced Thematic Mapper Plus, which reveals the Earth in finer spatial detail, but can only image a given area once every 16 days. The research is aimed to estimate the NPP using MODIS for Kalimantan area.

1.2 Objectives

The objectives of this research are:

- to estimate the Net Primary Production (NPP) using Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) - to validate the estimation of NPP with the ground check

1.3 Expected Output

The expected output is the estimation of the total Net Primary Production, based on MODIS data in Kalimantan during 2001 to 2010. We also expect to obtain the spatial distribution of NPP, and validate the estimation with the ground check for several areas.

II. LITERATURE REVIEW

2.1 Net Primary Production

NPP is a measurement of plant growth obtained by calculating the quantity of carbon absorbed and stored by vegetation. NPP is equal to photosynthesis minus respiration. It is sometimes expressed in grams of carbon per square meter per year. It is a major component of the carbon cycle. Net primary productivity (NPP) is also defined as the net flux of carbon from the atmosphere into green plants per unit time. NPP refers to a rate process, i.e., the amount of vegetable matter produced (net primary production) per day, week, or year. It is a tool for measuring forest productivity and establishing carbon budgets. The data obtained by calculating NPP can be used as the basis for estimating the impact of both natural disturbances and management activities on forest productivity, assessing the effects of climate change on forests, and assessing the role that these forests can play in achieving our greenhouse-gas reduction objectives (Lobell et al. 2002, Mingkui and Woodward 1998). MODIS NPP is an annual value and provides a means of evaluating spatial patterns in productivity as well as interannual variation and long term trends in biosphere behavior (Turner et al. 2006).

2.2 Climate change

Plants capture and store solar energy through photosynthesis. During photosynthesis, living plants convert carbon dioxide in the air into sugar molecules they use for food. In the process of making their own food, plants also provide the oxygen that is very important for human beings. Plant productivity also plays a major role in the global carbon cycle by absorbing some of the carbon dioxide released from the people activities sech as burning coal, oil, and other fossil fuels. The carbon plants absorbed would becomes part of leaves, roots, stalks or tree trunks, and ultimately, the soil.

Climate change is a long-term change in the statistical distribution of weather patterns over periods of time that range from decades to millions of years. It may cause a change in the average weather conditions for example greater frequency of rains or longer time of drought season (Maslin 2004). Factors that

affected the climate are climate forcing such variations in solar radiation, deviations in the earth's orbit, and changes in greenhouse gas concentrations. There are a variety of climate change feedbacks that can either amplify or diminish the initial forcing. Some parts of the climate system, such as the oceans and ice caps, respond slowly in reaction to climate forcing because of their large mass. Therefore, the climate system can take centuries or longer to fully respond to new external forcing.

2.3 MODIS

MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths. These data improved our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.

The pair of Vegetation Indices from MODIS data highlights some of the refinements of the MODIS Enhanced Vegetation Index (EVI, right) over the traditional Normalized Difference Vegetation Index (NDVI, left) that has been used with previous satellite instruments. NDVI tends to “saturate” over dense vegetation such as the rainforests of South America, failing to distinguish variability. The MODIS EVI provides a more detailed look at variability within such highly vegetated regions. Production of MODIS NDVI provides continuity with data sets from heritage instruments, while EVI provides detail of global vegetation variability. Neither the NDVI nor the EVI product eliminated all obstacles. Clouds and aerosols can often block the satellites’ view of the surface entirely, glare from the sun can saturate certain pixels, and temporary malfunctions in the satellite instruments themselves can distort an image.

III. METHODOLOGY

3.1 Time and Location

The research was conducted from March until October 2011. The study area was Kalimantan which is bounded between longitude 108o 40’ 58’’ E and 118o 59’ 45’’ E and latitude 4o 24’ 8’’ N and 4o 22’ 30’’ S with an area of approximately 537.442,55 km2. The Figure 1 shows the area of study (inside the red boundary).

Figure 1 Location of the study area

3.2 Required Tools

One of the important steps for the study is the selection of the appropriate software. For that, we used GRADS 2.0 and MODIS Tool. In addition, appropriate hardware selection is also important so that its compatibility with the selected software might not cause any problem during all processes. Computers supported with high-speed Internet connection were required for this research. The research was done at the laboratory of Master of Science in Information Technology for Natural Resources Management (M.Sc. in IT for NRM) at SEAMEO BIOTROP.

3.3 Data Source

MODIS satellite data was used for this research. The data was collected starting from the year 2001 to 2010. MOD13Q1 type in a Terra platform was used with a vegetation indices product. The data are in a tile fashion with a resolution

of 250m and a temporal granularity of 16 days. In addition, FPAR data was downloaded from 2001 to 2010.

MODIS might be so far, the most complex instrument built and flown on a spacecraft for civilian research purposes. The MODIS sensor provides higher quality data for monitoring terrestrial vegetation and other land processes than the previous AVHRR, not only because its narrower spectral bands that enhance the information derived from vegetation, but also because leading scientists are working as a team to improve the accuracy of the data from low level reflectance data, to high level data, such as land cover, fire, land surface temperature, vegetation indices (NDVI and EVI; EVI is the enhanced vegetation index), FAPR / LAI and GPP / NPP (Justice et al. 2002)

3.4 Method

3.4.1 Data collection

MODIS data was used for this study. We login the MODIS/Terra Multiple Data Ordering Page to download MODIS data for our study sites. Since Kalimantan has a wide area, it could not be covered by one single image of MODIS data. In order to cover the whole area of Kalimantan, the MODIS data could be downloaded in a tile fashion, from which each tile covers approximately 10 latitude by 10 longitude.

For Kalimantan area, four images MODIS data was needed for EVI. MODIS Tool and GRADS 2.0 were the software used to process MODIS data. The Figure 2 shows the general methodology adopted for this research.

MODIS EVI

Data Processing

Estimation of EVI

Spatial Distribution of NPP

Validation

Recommendation Mosaicking

End Start

Ground check

Figure 2 General Method of the Research

3.4.2 Data Processing

There was several remote sensing techniques applied in this research. The first step of the work was mosaicking the images. This was done by using MODIS Tool and GRADS. Those software provided interactive capabilities for placing

non geo referenced images within a mosaic, and automated placement of geo referenced images within a geo referenced output mosaic and conduct the other data processing. The same process was done for MODIS EVI and other data.

3.4.3 Measuring vegetation: Enhanced Vegetation Index (EVI)

In December 1999, NASA launched the Terra spacecraft, the flagship in the agency’s Earth Observing System (EOS) program. Aboard Terra flies a sensor called the Moderate-resolution Imaging Spectroradiometer, or MODIS, that greatly improves scientists’ ability to measure plant growth on a global scale. MODIS provides much higher spatial resolution (up to 250-meter resolution), while also matching Advanced Very High Resolution Radiometer (AVHRR)’s almost-daily global cover and exceeding its spectral resolution. In other words, MODIS provided images over a given pixel of land just as often as AVHRR, but in much finer detail and with measurements in a greater number of wavelengths using detectors that were specifically designed for measurements of land surface dynamics.

Consequently, the MODIS Science Team prepared a new data product– called the Enhanced Vegetation Index (EVI) that improved upon the quality of the NDVI product. The EVI took full advantage of MODIS’ new, state-of-the-art measurement capabilities. The EVI is calculated similar to NDVI, but with corrections for some distortions in the reflected light caused by the particles in the air as well as the ground cover below the vegetation. The EVI data product also does not become saturated as easily as the NDVI when viewing rainforests and other areas of the Earth with large amounts of chlorophyll.

Vegetation indices derived from remote sensing data provide information about consistent, spatial and temporal comparisons of global vegetation conditions which was used to monitor the Earth's terrestrial photosynthetic vegetation activity. For example, the enhanced vegetation index (EVI) provides a measure of greenness of the vegetation that can be used to predict net primary production.

The Enhanced Vegetation Index (EVI) improves on the venerable NDVI. Derived from state-of-the-art satellite data provided by the MODIS instrument, EVI improves on NDVI's spatial resolution, is more sensitive to differences in

heavily vegetated areas. The EVI is related to the optical measures of vegetation, a direct measure of photosynthetic potential resulting from composite chlorophyll, leaf area, canopy cover, and structure. It is developed to optimize the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences. The equation takes the form,

EVI = G * (ρNIR – ρRed) / (ρNIR + C1 * ρRed – C2 * ρBlue + L)

Where,

EVI = Enhanced Vegetation Index G = Gain factor (=2.5)

ρNIR = Near Infrared Reflectance

ρRed = Red Reflectance

ρBlue = Blue Reflectance

C1 = Atmosphere Resistance Red Correction Coefficients (=1)

C2 = Atmosphere Resistance Blue Correction Coefficients (=6.0)

L = Canopy Background Brightness Correction Factor (=1)

The input reflectance to the EVI equation may be atmospherically-corrected or partially atmosphere atmospherically-corrected for Rayleigh scattering and ozone absorption. C1 and C2 are the coefficients of the aerosol resistance term, which uses the blue band to correct for aerosol influences in the red band. The canopy background adjustment factor, L, addresses non-linear, differential NIR and red radiant transfer through a canopy and renders the EVI insensitive to most canopy backgrounds, with snow backgrounds as the exception.

3.4.4 Estimation of Net Primary Production (NPP)

This estimation was based on the value of EVI. The methods for measurement of primary production vary depend on the focus that we would like to calculate, the gross or net production, terrestrial or aquatic system. The approach for estimation of NPP was conducted using relationship of monthly

production of plant biomass (Potter et al. 2009) which is estimated as a product of time varying surface solar irradiance and EVI from the MODIS satellite, with a constant light utilization efficiency term that is modified by time varying stress scalar terms for temperature and moisture effects. As documented in Potter (1999), the monthly NPP flux, defined as net fixation of CO2 by vegetation, is computed

in NASA–CASA on the basis of light-use efficiency (Monteith 1972). Monthly production of plant biomass is estimated as a product of time-varying surface solar irradiance (Sr) (Kistler et al. 2001), and EVI from the MODIS satellite (Huete et al., 2002), and a constant light utilization efficiency term (emax) that is modified by time-varying stress scalar terms for temperature (T) and moisture (W) effects. The equation to estimate the NPP is defined below.

NPP = Sr EVI emax Tscalar Wscalar

Where,

NPP = Net Primary Production (gC m-2 year-1) Sr = Solar irradiance (W m-1)

EVI = Enhanced Vegetation Index from MODIS emax = Constant Light Utilization Efficiency Term Tscalar = Optimal temperature for plant production

Wscalar = Monthly water deficit

The emax term is set uniformly at 0.39 gC MJ−1 PAR, a value that derives from calibration of predicted annual NPP to previous field estimates (Potter et al. 1993). Tscalar is computed with reference to derivation of optimal temperatures

(Topt) for plant production. Topt setting varied by latitude and longitude, ranging

from near 0°C in the Arctic to the middle thirties in low-latitude deserts. Wscalar is

estimated from monthly water deficits, based on a comparison of moisture supply (precipitation and stored soil water) to potential evapotranspiration (PET).

The PAR values are actually restricted to just a portion of electromagnetic spectrum from 0.4 to 0.7 micrometers ( m) which is comparable to the range of

light of human eye can see. Therefore, this value was assumed to be approximately 0.5 of the incoming solar radiation (Rasib et al. 2008) and it was used for this research.

Tscalar is estimated using the equation developed for the terrestrial ecosystem

model (Raich et al. 1991). The equation for Tscalar is:

Tscalar = [ (T – Tmax) (T – Tmin) ] / [ (T – Tmax) (T – Tmin) – (T – Topt)2 ]

where T is the observed temperature (oC) and Tmin, Tmax, and Topt are minimum,

maximum, and optimal temperature for photosynthesis with a value of 20oC, 40oC, and 30oC respectively.

Wscalar is the effect of water deficit on plant photosynthesis, and is

estimated as a function of rainfall, run off, groundwater reserves and potential evapotranspiration. The equation for Wscalar is defined below:

Wscalar = 0.5 + [ 0.5 * (EET / PET) ]

where EET and PET are estimated and potential evapotranspiration, where Wscalar

ranged between 0.5 (dry) to 1 (wet). Therefore, the function in Wscalar can be

described as below:

PPT > PET => EET = PET PPT < PET => EET = PPT

where PPT is the total precipitation. Water run-off and groundwater reserves are ignored and where PET is calculated based on Priestley and Taylor (1972) with the equation:

where Rn is the net-radiation (MJ m-2 month-1), G is the heat flux at ground level

assumed to be 0, γ is the constant psychometric with a value of about 66 Pa K-1. and α are the latent heat of evaporation and the empirical factor of with values 2.5 MJ Kg-1 and 1.26 respectively. Δ is calculated using the following mathematical equation:

Δ = 2504 * exp [(17.27 * T) / (T + 237.2)] / (T + 237.3)2

where Δ is the slope vapour pressure curve (KPa oC-1) and T the air temperature (oC).

3.4.5 Validation with the ground check

The result of the calculation should be validated with the ground check. Validation of the NPP is an essential step in establishing its utility; however, validation is challenging because of a variety of scaling issues (Morisette et al.

2002, Turner et al. 2004). Site-level validation of MODIS NPP has been more limited because of the logistical constraints of measuring NPP and scaling it to the size of a MODIS grid cell (Turner et al. 2004, 2005). These efforts have likewise found site-specific differences in the degree of agreement between ground-based and MODIS-based NPP estimates. The MODIS NPP algorithm requires the computation of autotrophic respiration (Ra) based on inputs of leaf area index (LAI) and temperature, along with look-up table values for constants and the base rate of respiration (Running et al. 2000). Specific problems with the Ra component of NPP have been identified in some cases (Turner et al. 2005).

It is widely known that tower flux measurements of Net Ecosystem Production (NEP) can be used for model validation at the small site scale. Nevertheless, we have not included comparisons of tower-based NEP to NASA-CASA modeled NEP in this island study, because tower eddy flux estimates are not designed to represent large-scale (e.g., 8 km) NEP fluxes that we model with NASA-CASA.

NEP is named ecosystem carbon sink (positive value) or carbon source (negative value). It is a key characteristic to measure ecosystem carbon uptake or

release. Theoretically, when an ecosystem matures, e.g. climax, it is in equilibrium with the climate and soil environment, so the carbon uptake and release are balanced, and NEP approximates to zero. But if the environmental conditions, such as climate, changed, the ecosystem carbon budgets were not balanced.

In any year over the past nine years, net ecosystem production can be very large in one location but very small or negative in another location because of the spatial heterogeneity of vegetation, soils and climate. Locations with large positive annual NEP are often those that receive a high amount of precipitation. In contrast, locations with negative NEP are often those that receive little precipitation. Year-to-year changes in spatial pattern of NEP were most probably caused by changes in the spatial pattern of precipitation, which can be changed dramatically by the El Nino events (Vörösmarty et al. 1996).

For this research, different samples from different areas for ground check were considered. The first area was related to the place where the highest value of NPP is found. The second area was a place where the lowest value of NPP is found. For that, we checked on the ground what kind of features exists exactly in the area, and how can we come up with a conclusion based on what we found in the area with our system.

A sample for water body has been taken on the Mahakam River which is the largest river in East Kalimantan, Indonesia, with a catchment area of approximately 77,100 km2. The catchment lies between 2_˚N to 1_˚S latitude and 113_˚E to 118_˚E longitude. The river originates in Cemaru (Van Bemmelen 1949) from where it flows south-eastwards, meeting the River Kedang Pahu at the city of Muara Pahu. From there, the river flows eastward through the Mahakam lakes region, which is a flat tropical lowland area surrounded by peat land. Thirty shallow lakes are situated in this area, which are connected to the Mahakam through small channels.

The samples for forests have been taken in different areas. However, some parts of the very deep forest one was in Tandilang Forest, located in the village called Sulang’ai, Sub-district of Batang Alai Timur, Hulu Sungai Tengah Regency, in South Kalimantan Province.

Over the past decade, the government of Indonesia has drained some peat swamp forests of the island of Borneo for conversion to agricultural land. The dry years of 1997-8 and 2002-3 saw huge fires in the peat swamp forests. A study for the European Space Agency found that the peat swamp forests are a significant carbon sink for the planet, and that the fires of 1997-8 may have released up to 2.5 billion tonnes, and the 2002-3 fires between 200 million to 1 billion tonnes, of carbon into the atmosphere. Much of the emissions from peatlands in Borneo are due to changes in their hydrological regime, caused by drainage from nearby plantations (particularly oil palm). Peatland conservation and rehabilitation are more efficient undertakings than reducing deforestation (in terms of claiming carbon credits from REDD initiatives), due to the much larger reduced emissions achievable per unit area and the much lower opportunity costs involved (Mathai 2009). Indonesia contributes 50 percent of tropical peat swamps and 10 percent of dry land in the world. The ground check for peatland was done in a vast peatland area called “Jl Asang Permai Gambut”.

Deeper analysis in Indonesia suggests that oil palm development might be a cover for something more lucrative: logging. Recently much has been made about the conversion of Asia's biodiversity rainforests for oil-palm cultivation. Environmental organizations have warned that by eating foods that use palm oil as an ingredient, Western consumers are directly fueling the destruction of orangutan habitat and sensitive ecosystems. However, oil-palm plantations now cover millions of hectares across Malaysia, Indonesia, and Thailand.

Palm oil became the world's number one fruit crop, trouncing its nearest competitor, the humble banana because of its crop's unparalleled productivity. Palm oil is the most productive oil seed in the world. A single hectare of oil palm may yield 5,000 kilograms of crude oil, or nearly 6,000 liters of crude. The ground check for palm oil was done at the “Kebun Kelapa Sawit Pelaihari”, a palm oil plantation owned by PT. Perkebunan Nusantara XIII (PERSERO).

Mining sector in South Kalimantan Province is dominated by oil, natural gas and coal, but oil and natural gas is inclined to have decreased, coal precisely have very fast increasing amount. Coal has been blamed as a major contributor to greenhouse gas emissions. The South Kalimantan Province is an area with

abundant deposits of coal and contributes 16.36 % to the national coal stock. Coal mining is a profitable business. It creates employment, generates value, and improves the foreign investment of a country or region. However, coal mining has its disadvantages including not to mention the impact to the environment. Since it was impossible to go through the mining area, we took the points as close as the mining area as possible and we also took some points from some remaining areas and conditions of previous mining activities.

For residential area, the ground check was done in Pangkalan Bun, capital of the Kotawaringin Barat regency, in the western part of Central Kalimantan. This is the entry point to reach Tanjung Puting Park in the southern part, and the Dayak villages in the north of Central Kalimantan Province.

IV. RESULTS

4.1 Enhanced Vegetation Index (EVI)

The enhanced vegetation index (EVI) was developed to optimize the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences. In this study, the Enhanced Vegetation Index data were obtained from MODIS. Low EVI is sparsely vegetated land, high EVI is densely vegetated land, and EVI where values are equal to or below zero are assumed to be typically caused by water bodies.

EVI 0.4 0.42 0.44 0.46 0.48 0.5 0.52 0.54 0.56 Ja n -0 1 Ju l-0 1 Ja n -0 2 Ju l-0 2 Ja n -0 3 Ju l-0 3 Ja n -0 4 Ju l-0 4 Ja n -0 5 Ju l-0 5 Ja n -0 6 Ju l-0 6 Ja n -0 7 Ju l-0 7 Ja n -0 8 Ju l-0 8 Ja n -0 9 Ju l-0 9 Ja n -1 0 Ju l-1 0 EVI

Figure 3 Time series of monthly mean of EVI in Kalimantan

Figure 3 illustrates changes in the average value of EVI in Kalimantan between 2001 and 2010. In October 2005, the amount of EVI reached its highest amount with about 0.55, while the lowest amount was in October 2006, around 0.46. However, during this 10-year period, there were a lot of changes to the proportion of EVI. In the beginning of 2001, the value was about 0.51 then 0.55 in March 2001, but a dramatic fall was noticed in November 2002 (around 0.48). A big variation was seen until July 2005: the EVI varied between 0.49 and 0.53. On the other hand, a small variation was noticed over the next five years (2006-2010). The value of EVI reached its peak in March 2006, September 2007, March 2008, and September 2010 with a value of 0.53, 0.52, 0.53, and 0.52 respectively. However, it decreased rapidly to 0.46 by the end of 2010. Overall, Figure 3 shows how big is the variation of EVI during the first 5-year period and the last 2 years.

4.1.1 Climatology process and Anomalies of EVI

a. Monthly average for 10 years

0.4 0.42 0.44 0.46 0.48 0.5 0.52 0.54 0.56 Ja n -0 1 Ju l-0 1 Ja n -0 2 Ju l-0 2 Ja n -0 3 Ju l-0 3 Ja n -0 4 Ju l-0 4 Ja n -0 5 Ju l-0 5 Ja n -0 6 Ju l-0 6 Ja n -0 7 Ju l-0 7 Ja n -0 8 Ju l-0 8 Ja n -0 9 Ju l-0 9 Ja n -1 0 Ju l-1 0 EVI CP

Figure 4 Time series of monthly mean of EVI and monthly average of 10-year period

Figure 4 compares the monthly mean of EVI and the Climatology Process in Kalimantan for 10 years. As can be seen in Figure 4, the proportion of EVI varied between around 0.46 to 0.55 while the monthly average of 10 years was around 0.49 to 0.54. In the beginning of 2001, the monthly average of 10 years was by far lower than the proportion of EVI: it was only about 0.52 while the EVI was about 0.54. However, at mid-2001, the amount of EVI declined dramatically to almost 0.50 which was almost similar to the monthly average of 10 years. During the 10-year period, the proportion of EVI varied a lot. In general, the value was more than 0.50 except at the end of 2002 and 2006, from the middle of 2009 until mid-2010, and by the end of 2010. The monthly average of 10 years for EVI, on the other hand, showed a small variation. Even if there are many upward and downward trends happening throughout the period, the value was not lower than 0.49. Overall, Figure 4 showed that the proportion of EVI was much more stable from 2001 to mid 2009 except in October 2006 and was quite low from mid-2009 to mid-2010.

b. Anomalies AN -0.06 -0.04 -0.02 0 0.02 0.04 0.06 J a n-01 Ju l-0 1 J a n-02 Ju l-0 2 J a n-03 Ju l-0 3 J a n-04 Ju l-0 4 J a n-05 Ju l-0 5 J a n-06 Ju l-0 6 J a n-07 Ju l-0 7 J a n-08 Ju l-0 8 J a n-09 Ju l-0 9 J a n-10 Ju l-1 0 AN

Figure 5 Time series of anomalies for EVI from 2001 to 2010

Figure 5 shows the anomalies for the EVI in Kalimantan from 2001 to 2010. It is clear that the values varied between -0.04 and 0.05 during this 10-year period. In October 2005, it reached its peak with a value of 0.05, whereas in October 2006, it had its lowest value at -0.04. For the other years, the variation was around -0.03 to 0.02. During the first 5 years, all values are positive except in September to December 2002, April 2003, and August 2004 to June 2005, where the variation of the anomalies showed a downward trend. Similarly a downward trend was also noticed over the last 5-year period. However, some of the values are above 0 in February to June 2006, January and May 2007, September 2007 to March 2008, August to December 2008, February and April 2009, and September to November 2010 before a rapid decline by the end of 2010 with a value of -0.03. Overall, Figure 5 indicates that there was a difference between the variations of the values of the anomalies of EVI from 2001 to 2005, and from 2006 to 2010.

4.1.2 Spatial distribution of Enhanced Vegetation Index

Enhanced Vegetation Index (EVI) data were obtained from the MODIS sensor. It corrects for some distortions in the reflected light caused by the particles in the air as well as the ground cover below the vegetation. The EVI data product also does not become saturated as easily as NDVI when viewing rainforests and other area of the Earth with large amounts of chlorophyll. The EVI data are designed to provide consistent, spatial and temporal comparisons of vegetation conditions, and it offers

the potential for regional analysis and systematic and effective monitoring of the forest area.

The EVI values were resulted from 10-year estimation. The values vary between -0.2 as minimum to 1 as maximum. The EVI values equal to or below zero were assumed to be typically caused by water bodies. The low EVI values were sparsely vegetated land meanwhile the high EVI values were densely vegetated land. Estimation of EVI values over Kalimantan terrestrial from 2001 to 2010 are shown in the Table 1.

Table 1 The general pattern of monthly EVI in Kalimantan

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Min -0.19 -0.16 -0.17 -0.19 -0.18 -0.19 -0.19 -0.19 -0.20 -0.20 -0.19 -0.19

2001 Mean 0.51 0.52 0.55 0.52 0.50 0.51 0.52 0.52 0.52 0.50 0.50 0.50

Max 0.96 0.97 0.96 0.93 0.99 0.96 0.97 1.00 0.96 1.00 0.97 0.96

Min -0.19 -0.15 -0.19 -0.19 -0.16 -0.18 -0.20 -0.19 -0.18 -0.20 -0.19 -0.20

2002 Mean 0.50 0.51 0.53 0.52 0.51 0.51 0.50 0.53 0.50 0.48 0.49 0.49

Max 0.98 1.00 1.00 0.98 0.96 0.98 0.99 0.98 0.99 1.00 0.95 0.98

Min -0.20 -0.17 -0.17 -0.20 -0.17 -0.20 -0.20 -0.19 -0.19 -0.18 -0.20 -0.20

2003 Mean 0.52 0.52 0.52 0.50 0.51 0.52 0.52 0.52 0.52 0.50 0.51 0.51

Max 1.00 0.98 0.96 0.99 0.95 0.98 0.96 0.98 0.99 1.00 1.00 1.00

Min -0.18 -0.15 -0.16 -0.16 -0.19 -0.19 -0.13 -0.19 -0.18 -0.18 -0.20 -0.17

2004 Mean 0.51 0.53 0.53 0.53 0.52 0.51 0.51 0.51 0.49 0.49 0.50 0.49

Max 1.00 0.96 0.99 1.00 0.99 0.98 1.00 0.96 0.99 0.94 1.00 1.00

Min -0.20 -0.19 -0.17 -0.19 -0.18 -0.19 -0.15 -0.17 -0.14 -0.19 -0.18 -0.17

2005 Mean 0.50 0.50 0.50 0.50 0.49 0.49 0.51 0.52 0.52 0.55 0.53 0.52

Max 1.00 1.00 0.96 0.99 0.97 0.96 0.94 1.00 1.00 1.00 0.96 1.00

Min -0.17 -0.19 -0.19 -0.16 -0.13 -0.18 -0.16 -0.20 -0.20 -0.20 -0.20 -0.17

2006 Mean 0.50 0.52 0.53 0.52 0.51 0.50 0.51 0.51 0.50 0.46 0.47 0.49

Max 0.98 0.99 0.94 0.95 0.98 0.99 0.94 0.97 0.97 1.00 0.99 1.00

Min -0.18 -0.17 -0.19 -0.18 -0.14 -0.15 -0.15 -0.18 -0.17 -0.19 -0.17 -0.18

2007 Mean 0.51 0.49 0.51 0.50 0.51 0.50 0.51 0.51 0.53 0.52 0.50 0.50

Max 1.00 1.00 0.98 0.98 0.97 1.00 0.97 1.00 0.93 1.00 0.98 1.00

Min -0.16 -0.17 -0.17 -0.19 -0.15 -0.19 -0.19 -0.19 -0.17 -0.19 -0.19 -0.19

2008 Mean 0.50 0.52 0.53 0.50 0.50 0.50 0.50 0.51 0.51 0.50 0.49 0.51

Max 0.98 0.99 0.99 0.99 0.96 0.94 0.98 1.00 1.00 0.98 1.00 1.00

Min -0.19 -0.20 -0.16 -0.17 -0.19 -0.20 -0.20 -0.19 -0.20 -0.20 -0.18 -0.19

2009 Mean 0.50 0.52 0.50 0.51 0.50 0.50 0.50 0.48 0.49 0.46 0.48 0.49

Max 1.00 0.97 0.98 0.99 0.95 1.00 0.99 1.00 1.00 1.00 1.00 0.98

Min -0.18 -0.20 -0.19 -0.18 -0.20 -0.20 -0.20 -0.19 -0.19 -0.20 -0.19 -0.20

2010 Mean 0.48 0.49 0.50 0.49 0.48 0.50 0.49 0.51 0.52 0.51 0.50 0.47

Max 0.99 0.99 0.99 0.99 0.99 0.99 1.00 1.00 0.97 1.00 0.99 1.00

The Table 1 illustrates the general pattern of monthly EVI in Kalimantan. It is clear that the minimal value during the 10-year period for the Min was in July and

September of 2001 and 2009 with a value of about -0.20; while the maximal value for the Min was in May and July 2004 with a value of about -0.12. The Mean had its highest value in October 2005 with about 0.55; and its lowest amount in October 2006 with 0.45. For the Max, the October 2005 proportion about 1 was the highest throughout the 10-year period; whereas the September 2007 with about 0.92 was the lowest.

Figure 6 shows the spatial distribution of EVI over Kalimantan in 2001, 2005, 2006, and 2010.

Mean EVI 2001 Mean EVI 2005

Mean EVI 2006 Mean EVI 2010

Figure 6 Yearly mean of EVI in the year 2001, 2005, 2006, and 2010

4.2 Temperature

The temperature data used for this study was from NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) at monthly value. Below is the monthly average of temperature in Kalimantan from 2001 to 2010.

TEMP 24 24.5 25 25.5 26 26.5 27 J a n-01 J a n-02 J a n-03 J a n-04 J a n-05 J a n-06 J a n-07 J a n-08 J a n-09 J a n-10 Time

o C TEMP

Figure 7 Monthly average temperature over Kalimantan from 2001 to 2010

Figure 7 gives information about changes of the monthly average temperature in Kalimantan between 2001 and 2010. The monthly average temperature in Kalimantan ranged between 25.04°C to 26.38°C except in May 2010 where it reached 26.72°C. The pattern of monthly average temperature showed that monthly average temperature in Kalimantan reached its highest temperature value during May and June. It is evident that the value of monthly average temperature in Kalimantan decreased from November to February for every year from about 25.7°C to 25.1°C. Similarly in July of every year, it fell slightly from about 25.9°C to 25.4°C; and for the other period of the year especially from February to May, the figure showed an upward trend with a value of about 25.05°C to 26.3°C and reached its peak in May or June. The monthly average temperature had its highest value in May 2010 and its lowest value in January 2009 with a value of 26.72°C and 25.04°C respectively. In general, each year variation’s trend was almost the same over the 10-year period from 2001 to 2010.

Table 2 reveals the general pattern of monthly temperature in Kalimantan from 2001 to 2010. It is showed that for the Min, the highest value was in May 2010 at 22.68 oC; while the lowest proportion was in August 2004 at just 20.74 oC.

Table 2 The general pattern of monthly Temperature in Kalimantan

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Min 21.87 21.93 22.04 22.08 22.04 21.55 21.30 21.28 21.52 21.87 21.62 21.43

2001 Mean 25.50 25.37 25.79 26.15 26.38 26.09 25.81 25.93 25.77 26.00 25.83 25.39

Max 27.92 27.79 28.04 28.80 29.39 29.22 29.13 29.42 28.48 28.34 28.23 27.87

Min 21.58 21.52 21.80 21.95 22.07 21.66 21.16 20.92 21.19 21.53 21.71 21.79

2002 Mean 25.22 25.06 25.44 25.94 26.27 26.05 25.90 25.69 25.54 25.79 25.91 25.90

Max 28.03 27.88 28.09 28.37 29.20 29.32 29.50 29.16 28.58 28.28 28.37 28.59

Min 21.72 21.57 21.75 22.11 21.77 21.46 21.26 21.40 21.43 21.82 21.68 21.68

2003 Mean 25.48 25.24 25.54 26.08 26.26 25.81 25.70 25.80 25.86 25.91 25.78 25.39

Max 28.17 27.98 28.06 28.54 29.38 28.89 28.86 29.14 29.00 28.43 28.08 27.79

Min 21.68 21.64 21.99 22.10 21.92 21.11 21.26 20.74 21.30 21.37 21.67 21.69

2004 Mean 25.18 25.10 25.60 26.17 26.30 25.96 25.45 25.54 25.43 25.66 25.71 25.50

Max 27.87 27.86 28.03 28.76 29.45 29.67 28.49 29.28 28.27 28.24 28.19 28.01

Min 21.61 21.68 21.85 22.10 22.26 21.88 21.71 21.41 21.52 21.86 21.84 21.86

2005 Mean 25.06 25.31 25.54 25.93 26.33 26.36 25.98 25.85 26.00 26.02 25.86 25.68

Max 27.69 27.96 28.32 28.38 29.23 29.64 29.21 29.04 29.10 28.45 28.12 28.29

Min 21.81 22.02 21.94 22.04 21.99 21.70 21.58 21.25 21.45 21.78 21.66 22.17

2006 Mean 25.21 25.38 25.56 25.87 26.10 25.87 25.93 25.79 25.63 25.75 25.70 25.90

Max 27.71 27.75 27.89 28.27 28.97 28.91 29.25 29.16 28.41 28.20 28.26 28.23

Min 22.06 21.90 22.03 22.28 22.21 22.08 21.62 21.38 21.52 21.86 21.60 21.96

2007 Mean 25.44 25.13 25.55 25.91 26.14 26.14 25.80 25.62 25.74 25.76 25.45 25.50

Max 28.02 27.54 27.75 28.24 28.79 29.23 29.03 28.86 28.62 28.09 27.54 28.03

Min 21.64 21.73 21.78 22.08 21.88 21.81 21.43 21.52 21.79 21.93 22.04 21.97

2008 Mean 25.18 25.06 25.20 25.78 25.95 25.79 25.47 25.52 25.86 25.87 25.89 25.59

Max 27.45 27.57 27.43 28.11 28.61 28.66 28.47 28.30 28.71 28.41 28.09 28.00

Min 21.60 21.83 21.82 22.06 22.20 21.65 21.49 21.70 21.84 21.92 21.91 21.88

2009 Mean 25.05 25.18 25.55 26.21 26.34 26.15 25.95 25.90 26.04 25.88 25.90 25.55

Max 27.43 27.60 27.70 28.78 29.01 29.43 29.24 28.79 28.82 28.24 28.38 27.97

Min 21.65 22.07 22.27 22.59 22.68 22.13 21.92 21.78 21.78 22.01 21.84 21.67

2010 Mean 25.40 25.58 25.83 26.39 26.73 26.37 26.01 25.95 25.96 26.08 25.84 25.39

Max 27.84 28.07 28.17 28.76 29.43 29.45 28.87 28.84 28.66 28.55 28.22 27.80

The value of Mean, on the other hand, reached its highest proportion in May 2010 and its lowest proportion in January 2009 with about 26.72 oC and 25.04 oC respectively. The Max, however, had its maximal temperature in June 2004 with 29.67 oC and its minimal temperature in February 2008 with 27.43 oC.

4.3 Precipitation

The Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its associated latent heating. TRMM was successfully launched on November 27, at 4:27 PM (EST) from the Tanegashima SpaceCenter in Japan. The rainfall

measuring instruments on the TRMM satellite include the Precipitation Radar (PR), an electronically scanning radar operating at 13.8 GHz; TRMM Microwave Image (TMI), a nine-channel passive microwave radiometer; and Visible and Infrared Scanner (VIRS), a five-channel visible/infrared radiometer. TRMM data was used in order to derive the precipitation.

PRE

0 50 100 150 200 250 300 350 400

Jan-01

Jan-0 2

Ja n-03

Jan-04

Ja n-05

Jan-06

Jan-0 7

Jan-0 8

Jan-09

Jan-1 0

Time

mm PRE

Figure 8 Monthly average precipitation over Kalimantan from 2001 to 2010

The Figure 8 reveals the monthly average precipitation in Kalimantan over 10-year period from 2001 to 2010. Monthly Average precipitation varied from 44 mm to 376 mm per month. The highest precipitation was found during the months of December and January. At the beginning of each year, the value of precipitation was above 250 mm; while it is less than 200 mm in the middle of each year especially in July. The monthly average precipitation reached its peak in January 2009 where the value was about 376 mm, but a dramatic fall at 107 mm followed it in September 2009. In August 2004, the monthly average precipitation had its lowest amount with about 44 mm, but generally the graph showed an upward trend in September 2004 with an amount of 204 mm.

Table 3 shows the general pattern of monthly precipitation in Kalimantan for 10 years from 2001 to 2010. As can be seen in the Table 2, the Min had its maximal proportion mostly during the months of October, November, and December with about 105 mm, 120 mm, and 111 mm respectively; while it had its minimal proportion generally in February, March and July to August of every year.

Table 3 The general pattern of monthly Precipitation in Kalimantan (in mm)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Min 60.62 6.46 28.70 34.89 14.40 23.46 0.00 0.00 26.48 39.57 67.26 76.58

2001 Mean 312.99 240.08 203.34 235.42 120.75 192.70 104.18 115.19 199.64 242.11 296.87 260.20

Max 909.80 629.68 620.03 624.07 411.91 461.07 366.12 532.63 554.19 607.85 692.10 703.77

Min 1.31 0.00 0.00 0.00 11.90 44.52 0.00 0.00 0.00 0.00 46.86 15.47

2002 Mean 251.42 136.93 193.20 158.08 158.55 210.07 81.80 130.08 125.57 153.17 293.87 211.62

Max 1131.90 714.34 601.09 406.66 365.66 591.09 375.36 452.29 622.73 439.16 746.24 542.41

Min 4.56 0.00 0.00 0.00 34.72 1.11 0.00 0.00 0.00 34.91 16.14 84.89

2003 Mean 293.20 207.89 214.25 192.17 179.12 158.86 166.07 141.54 171.30 275.31 258.85 347.18

Max 940.51 955.88 590.70 589.10 429.31 519.78 464.94 434.24 470.60 700.64 713.49 829.43

Min 0.00 0.11 0.29 0.00 35.25 9.35 0.00 0.00 0.00 0.00 3.36 24.71

2004 Mean 267.63 157.19 196.50 186.48 231.17 127.15 202.61 44.33 204.50 153.52 279.56 356.18

Max 1311.40 467.46 517.46 659.59 484.00 487.54 477.92 280.57 513.08 484.97 648.65 918.64

Min 0.96 0.00 0.00 0.00 17.25 4.49 10.96 0.03 0.66 55.05 78.17 48.92

2005 Mean 198.37 175.88 190.76 175.39 201.44 179.78 168.08 139.99 144.62 274.25 265.02 288.76

Max 715.15 720.43 613.71 621.53 501.48 729.06 503.67 503.85 418.68 564.64 602.90 729.46

Min 19.34 0.56 3.75 13.67 24.10 46.26 0.00 0.00 0.00 0.00 16.62 35.99

2006 Mean 235.77 266.64 171.98 213.01 224.75 251.71 102.22 124.25 182.15 129.44 187.76 318.17

Max 622.39 844.95 493.21 525.84 485.88 612.34 799.72 481.72 665.63 566.11 544.59 917.24

Min 91.52 0.00 2.99 6.06 25.62 47.57 1.22 0.15 0.15 51.17 62.06 60.21

2007 Mean 326.00 228.94 180.34 230.03 226.60 300.55 266.86 174.00 198.14 258.62 274.70 363.90

Max 805.74 644.23 617.45 633.31 789.07 703.80 656.57 462.25 474.56 643.33 713.17 864.15

Min 36.78 16.67 21.51 31.41 22.33 38.91 3.48 13.01 3.05 23.34 120.24 90.24

2008 Mean 224.41 263.50 305.85 231.25 179.95 231.40 238.94 227.71 227.90 327.73 325.10 354.97

Max 697.64 1010.10 919.09 645.08 486.35 477.18 617.37 541.30 553.68 712.78 678.55 967.70

Min 20.70 3.60 27.18 21.75 35.08 3.72 5.28 0.00 0.00 6.52 89.91 33.82

2009 Mean 376.20 244.48 238.76 237.98 160.21 128.21 143.32 134.39 107.91 249.38 340.38 299.37

Max 1246.10 816.32 528.75 699.50 373.09 443.84 730.99 415.61 411.63 864.66 878.04 674.35

Min 11.73 0.00 0.00 3.06 6.68 43.18 81.82 78.42 93.66 105.18 85.77 111.56

2010 Mean 284.53 183.28 205.40 244.91 246.18 277.59 331.85 277.72 321.88 312.20 307.16 316.26

Max 794.42 605.44 661.48 873.52 571.74 731.01 716.21 717.07 699.63 761.63 675.70 767.57

For the Mean, it varied from 44 mm which occurred in August 2004 to 376 mm in January 2009. As far as the Max is concerned, it was noticed that the maximal amount was in January 2002, January 2009, and January 2004 with a value more than 1000 mm; whereas the minimal amount was in May 2004 with about 280 mm.

4.4 Fraction of Absorbed Photosynthetically Active Radiation (FPAR)

MODIS FPAR/LAI is the Fraction of Absorbed Photosynthetically Active radiation that a plant canopy absorbs for photosynthesis and growth in the 0.4 – 0.7nm spectral range. FPAR is expressed as a unitless fraction of the incoming

55 -3.43892 114.842681 10/24/2011 16:04 31 South Kalimantan 56 -3.442924 114.829729 10/24/2011 16:08 40 South Kalimantan 57 -3.448571 114.803219 10/24/2011 16:14 27 South Kalimantan 58 -3.448496 114.780999 10/24/2011 16:18 24 South Kalimantan 59 -3.437848 114.754382 10/24/2011 16:26 28 South Kalimantan 60 -3.43789 114.754378 10/24/2011 16:27 19 South Kalimantan 61 -3.437915 114.75436 10/24/2011 16:28 23 South Kalimantan 397 -1.25961 116.899576 10/5/2011 20:28 26 East Kalimantan 398 0.471366 117.485142 10/7/2011 8:31 38 East Kalimantan 399 0.453099 117.483686 10/7/2011 8:37 75 East Kalimantan 400 0.437486 117.475877 10/7/2011 8:42 86 East Kalimantan 401 0.429499 117.472332 10/7/2011 8:44 51 East Kalimantan 402 0.374581 117.473333 10/7/2011 8:58 54 East Kalimantan 403 0.374131 117.473385 10/7/2011 8:58 53 East Kalimantan 404 0.325496 117.481993 10/7/2011 9:06 68 East Kalimantan 405 0.265948 117.474269 10/7/2011 9:15 62 East Kalimantan 406 0.237998 117.475332 10/7/2011 9:24 16 East Kalimantan 407 0.232395 117.46853 10/7/2011 9:26 21 East Kalimantan 408 0.14096 117.428736 10/7/2011 9:44 50 East Kalimantan 409 0.096206 117.388267 10/7/2011 10:14 81 East Kalimantan 410 0.081838 117.37918 10/7/2011 10:17 57 East Kalimantan 411 0.030013 117.353431 10/7/2011 10:26 63 East Kalimantan 412 -0.046849 117.364051 10/7/2011 10:37 94 East Kalimantan 413 -0.136158 117.338493 10/7/2011 10:53 80 East Kalimantan 414 -0.204681 117.297703 10/7/2011 11:08 72 East Kalimantan 415 -0.300406 117.271627 10/7/2011 11:27 60 East Kalimantan 416 -0.372246 117.265062 10/7/2011 11:41 32 East Kalimantan 417 -0.41549 117.235987 10/7/2011 11:50 58 East Kalimantan 418 -0.452982 117.199585 10/7/2011 12:00 45 East Kalimantan 419 -0.461439 117.186129 10/7/2011 12:03 17 East Kalimantan 420 -0.456153 117.168382 10/7/2011 12:07 12 East Kalimantan 421 -0.46986 117.138635 10/7/2011 12:19 17 East Kalimantan 422 -0.500452 117.127047 10/7/2011 13:27 10 East Kalimantan 423 -0.519579 117.117931 10/7/2011 13:37 10 East Kalimantan 424 -0.622103 117.098011 10/7/2011 13:58 80 East Kalimantan 425 -0.761121 117.046675 10/7/2011 14:34 96 East Kalimantan 426 -0.762532 117.042065 10/7/2011 14:35 94 East Kalimantan 427 -0.77317 117.032708 10/7/2011 14:38 113 East Kalimantan 428 -0.793105 117.024385 10/7/2011 14:43 130 East Kalimantan 429 -0.854802 117.010003 10/7/2011 14:54 134 East Kalimantan 430 -1.079356 116.926427 10/7/2011 15:22 53 East Kalimantan 431 -1.235554 116.839515 10/7/2011 16:02 45 East Kalimantan 432 -1.254536 116.838366 10/7/2011 16:41 24 East Kalimantan 433 -1.277059 116.837592 10/7/2011 17:09 47 East Kalimantan 434 -1.276851 116.83736 10/8/2011 11:22 18 East Kalimantan 435 -1.260429 116.901274 10/8/2011 11:45 20 East Kalimantan 436 -1.262055 116.90123 10/8/2011 12:52 0 East Kalimantan 437 -1.262055 116.90123 10/8/2011 12:52 0 East Kalimantan 438 -1.262055 116.90123 10/8/2011 12:52 0 East Kalimantan 439 -3.437465 114.754383 10/20/2011 18:01 33 South Kalimantan 440 -3.442879 114.824774 10/20/2011 18:26 38 South Kalimantan 441 -3.397407 114.662815 10/20/2011 18:55 15 South Kalimantan 442 -3.354081 114.629527 10/20/2011 19:03 6 South Kalimantan 443 -3.339313 114.618841 10/20/2011 20:17 26 South Kalimantan

444 -3.339379 114.618892 10/21/2011 7:16 25 South Kalimantan 445 -3.382464 114.655652 10/21/2011 7:31 14 South Kalimantan 446 -3.382451 114.655658 10/21/2011 7:32 11 South Kalimantan 447 -3.384234 114.652805 10/21/2011 7:36 18 South Kalimantan 448 -3.420571 114.680456 10/21/2011 7:41 15 South Kalimantan 449 -3.426764 114.685138 10/21/2011 7:43 17 South Kalimantan 450 -3.428005 114.686045 10/21/2011 7:43 18 South Kalimantan 451 -3.441029 114.743907 10/21/2011 7:52 25 South Kalimantan 452 -3.443328 114.754035 10/21/2011 7:53 31 South Kalimantan 453 -3.44771 114.776225 10/21/2011 7:56 38 South Kalimantan 454 -3.442666 114.821185 10/21/2011 8:03 31 South Kalimantan 455 -3.442916 114.832201 10/21/2011 8:06 43 South Kalimantan 456 -3.442811 114.847774 10/21/2011 8:08 47 South Kalimantan 457 -3.443462 114.84796 10/21/2011 8:09 47 South Kalimantan 458 -3.437331 114.848362 10/21/2011 8:10 38 South Kalimantan 459 -3.427157 114.84929 10/21/2011 8:12 21 South Kalimantan 460 -3.407946 114.84812 10/21/2011 8:16 22 South Kalimantan 461 -3.381707 114.890133 10/21/2011 8:26 21 South Kalimantan 462 -3.374478 114.901075 10/21/2011 8:28 21 South Kalimantan 463 -3.341765 114.947025 10/21/2011 8:35 25 South Kalimantan 464 -3.339294 114.948389 10/21/2011 8:36 27 South Kalimantan 465 -3.311811 115.003731 10/21/2011 8:47 30 South Kalimantan 466 -3.305879 115.007556 10/21/2011 8:48 30 South Kalimantan 467 -3.287499 115.024003 10/21/2011 8:51 45 South Kalimantan 468 -3.254905 115.03892 10/21/2011 8:59 55 South Kalimantan 469 -3.17573 115.078318 10/21/2011 9:19 33 South Kalimantan 470 -3.159162 115.088259 10/21/2011 9:23 36 South Kalimantan 471 -3.15523 115.086 10/21/2011 9:26 28 South Kalimantan 472 -3.154851 115.086931 10/21/2011 9:27 28 South Kalimantan 473 -3.155624 115.087609 10/21/2011 9:29 28 South Kalimantan 474 -3.112553 115.081338 10/21/2011 9:46 38 South Kalimantan 475 -3.097686 115.086678 10/21/2011 9:49 32 South Kalimantan 476 -3.077194 115.100852 10/21/2011 9:52 35 South Kalimantan 477 -3.029141 115.124219 10/21/2011 10:01 34 South Kalimantan 478 -2.963084 115.149511 10/21/2011 10:13 30 South Kalimantan 479 -2.92587 115.169441 10/21/2011 10:22 24 South Kalimantan 480 -2.913197 115.193442 10/21/2011 10:26 21 South Kalimantan 481 -2.872362 115.231948 10/21/2011 10:35 33 South Kalimantan 482 -2.857143 115.240702 10/21/2011 10:38 24 South Kalimantan 483 -2.84936 115.244537 10/21/2011 10:42 29 South Kalimantan 484 -2.811533 115.257264 10/21/2011 12:04 37 South Kalimantan 485 -2.786244 115.265717 10/21/2011 12:09 19 South Kalimantan 486 -2.777282 115.263424 10/21/2011 12:11 21 South Kalimantan 487 -2.733657 115.285679 10/21/2011 12:24 22 South Kalimantan 488 -2.646085 115.331641 10/21/2011 13:10 24 South Kalimantan 489 -2.622224 115.343572 10/21/2011 13:14 20 South Kalimantan 490 -2.553557 115.418147 10/21/2011 13:37 31 South Kalimantan 491 -2.547748 115.44831 10/21/2011 14:06 33 South Kalimantan 492 -2.549514 115.459849 10/21/2011 14:16 39 South Kalimantan 493 -2.551402 115.486156 10/21/2011 14:30 50 South Kalimantan 494 -2.545887 115.493693 10/21/2011 14:53 50 South Kalimantan 495 -2.546016 115.493445 10/21/2011 14:55 53 South Kalimantan 496 -2.546106 115.503946 10/21/2011 14:59 45 South Kalimantan 497 -2.543006 115.508983 10/21/2011 15:04 39 South Kalimantan

498 -2.532431 115.53552 10/21/2011 15:18 63 South Kalimantan 499 -2.538122 115.536605 10/21/2011 15:23 74 South Kalimantan 500 -2.538282 115.536745 10/21/2011 15:25 72 South Kalimantan 501 -2.546846 115.538103 10/21/2011 15:31 67 South Kalimantan 502 -2.56744 115.537318 10/21/2011 15:42 81 South Kalimantan 503 -2.566844 115.536489 10/21/2011 15:47 80 South Kalimantan 504 -2.566721 115.536148 10/21/2011 15:55 0 South Kalimantan 505 -2.566821 115.536485 10/21/2011 16:01 77 South Kalimantan 506 -2.575587 115.538992 10/21/2011 16:10 56 South Kalimantan 507 -2.577285 115.540193 10/21/2011 16:11 62 South Kalimantan 508 -2.559644 115.544444 10/21/2011 16:37 62 South Kalimantan 510 -2.559662 115.544487 10/21/2011 16:56 69 South Kalimantan 511 -3.442553 114.825793 10/22/2011 7:30 28 South Kalimantan 512 -3.442566 114.747855 10/22/2011 8:13 22 South Kalimantan 513 -3.44507 114.702051 10/22/2011 8:59 23 South Kalimantan 514 -3.479063 114.707229 10/22/2011 9:05 18 South Kalimantan 515 -3.511164 114.713314 10/22/2011 9:10 16 South Kalimantan 516 -3.522954 114.717059 10/22/2011 9:12 19 South Kalimantan 517 -3.560784 114.739601 10/22/2011 9:20 37 South Kalimantan 518 -3.574648 114.72678 10/22/2011 9:23 37 South Kalimantan 519 -3.617781 114.70195 10/22/2011 9:31 18 South Kalimantan 520 -3.629751 114.703153 10/22/2011 9:36 22 South Kalimantan 521 -3.633604 114.703465 10/22/2011 9:39 20 South Kalimantan 522 -3.645077 114.699375 10/22/2011 9:54 35 South Kalimantan 523 -3.649929 114.693262 10/22/2011 9:56 36 South Kalimantan 524 -3.655698 114.685092 10/22/2011 9:59 25 South Kalimantan 525 -3.658303 114.677139 10/22/2011 10:01 22 South Kalimantan 526 -3.654902 114.67114 10/22/2011 10:04 32 South Kalimantan 527 -3.639846 114.645067 10/22/2011 10:13 16 South Kalimantan 528 -3.639785 114.641382 10/22/2011 10:14 17 South Kalimantan 529 -3.640458 114.63853 10/22/2011 10:17 16 South Kalimantan 530 -3.640457 114.638528 10/22/2011 10:17 17 South Kalimantan 531 -3.63971 114.638408 10/22/2011 10:20 23 South Kalimantan 532 -3.639853 114.645641 10/22/2011 10:24 16 South Kalimantan 533 -3.648173 114.663803 10/22/2011 10:30 29 South Kalimantan 534 -3.65594 114.684716 10/22/2011 10:37 21 South Kalimantan 535 -3.695362 114.730962 10/22/2011 10:53 38 South Kalimantan 536 -3.711205 114.748875 10/22/2011 10:57 58 South Kalimantan 537 -3.713708 114.752196 10/22/2011 10:57 58 South Kalimantan 538 -3.716239 114.751523 10/22/2011 10:59 86 South Kalimantan 539 -3.716747 114.751269 10/22/2011 11:06 96 South Kalimantan 540 -3.716756 114.751286 10/22/2011 11:08 98 South Kalimantan 541 -3.709654 114.746411 10/22/2011 11:23 52 South Kalimantan 542 -3.712634 114.750849 10/22/2011 11:24 49 South Kalimantan 543 -3.731072 114.760636 10/22/2011 11:28 21 South Kalimantan 544 -3.730041 114.766949 10/22/2011 11:32 22 South Kalimantan 545 -3.729559 114.769956 10/22/2011 11:34 22 South Kalimantan 546 -3.727382 114.76162 10/22/2011 11:38 30 South Kalimantan 547 -3.730633 114.763083 10/22/2011 11:41 24 South Kalimantan 548 -3.730722 114.763001 10/22/2011 11:42 25 South Kalimantan 549 -3.731563 114.757732 10/22/2011 11:44 20 South Kalimantan 550 -3.764348 114.76854 10/22/2011 11:53 28 South Kalimantan 551 -3.765341 114.768916 10/22/2011 11:56 29 South Kalimantan 552 -3.790471 114.776631 10/22/2011 13:24 23 South Kalimantan

553 -3.790347 114.775942 10/22/2011 13:31 23 South Kalimantan 554 -3.802263 114.76796 10/22/2011 13:35 27 South Kalimantan 555 -3.801788 114.763685 10/22/2011 13:38 17 South Kalimantan 556 -3.801364 114.763687 10/22/2011 14:05 27 South Kalimantan 557 -3.789989 114.776112 10/22/2011 14:24 28 South Kalimantan 558 -3.84491 114.670218 10/22/2011 14:57 19 South Kalimantan 559 -3.836107 114.67263 10/22/2011 15:12 31 South Kalimantan 560 -3.835555 114.672976 10/22/2011 15:15 35 South Kalimantan 561 -3.837264 114.67369 10/22/2011 15:28 45 South Kalimantan 562 -3.837519 114.677265 10/22/2011 15:29 31 South Kalimantan 563 -3.839853 114.682127 10/22/2011 15:32 32 South Kalimantan 564 -3.842474 114.680471 10/22/2011 15:34 26 South Kalimantan 565 -3.883862 114.651524 10/22/2011 15:52 4 South Kalimantan 566 -3.880082 114.618963 10/22/2011 15:57 7 South Kalimantan 567 -3.876271 114.614806 10/22/2011 15:58 4 South Kalimantan 568 -3.869435 114.610145 10/22/2011 15:59 10 South Kalimantan 569 -3.86853 114.609588 10/22/2011 16:03 7 South Kalimantan 570 -3.868099 114.60887 10/22/2011 16:06 9 South Kalimantan 571 -3.868027 114.608585 10/22/2011 16:11 8 South Kalimantan 572 -3.869513 114.610146 10/22/2011 16:29 16 South Kalimantan 573 -3.869485 114.610124 10/22/2011 16:31 18 South Kalimantan 574 -3.860474 114.674539 10/22/2011 16:43 26 South Kalimantan 575 -3.828554 114.710004 10/22/2011 16:50 22 South Kalimantan 576 -3.817578 114.733226 10/22/2011 16:55 33 South Kalimantan 577 -3.816106 114.733313 10/22/2011 16:56 31 South Kalimantan 578 -3.812086 114.732563 10/22/2011 16:58 31 South Kalimantan 579 -3.809712 114.733167 10/22/2011 17:00 32 South Kalimantan 580 -3.806344 114.73447 10/22/2011 17:03 28 South Kalimantan 581 -3.799078 114.770497 10/22/2011 18:49 38 South Kalimantan 582 -3.862254 114.795344 10/23/2011 9:12 72 South Kalimantan 583 -3.873648 114.800252 10/23/2011 9:15 57 South Kalimantan 584 -3.879572 114.804246 10/23/2011 9:16 48 South Kalimantan 585 -3.885147 114.804554 10/23/2011 9:17 48 South Kalimantan 586 -3.899609 114.808039 10/23/2011 9:20 58 South Kalimantan 587 -3.911549 114.800085 10/23/2011 9:25 52 South Kalimantan 588 -3.912085 114.800847 10/23/2011 9:27 42 South Kalimantan 589 -3.91183 114.819474 10/23/2011 9:35 58 South Kalimantan 590 -3.913877 114.823357 10/23/2011 9:35 53 South Kalimantan 591 -3.923149 114.834491 10/23/2011 9:37 48 South Kalimantan 592 -3.923475 114.835108 10/23/2011 9:37 48 South Kalimantan 593 -3.933327 114.843405 10/23/2011 9:39 33 South Kalimantan 594 -3.939547 114.843178 10/23/2011 9:40 31 South Kalimantan 595 -3.940942 114.843066 10/23/2011 9:41 32 South Kalimantan 596 -3.946728 114.842853 10/23/2011 9:44 39 South Kalimantan 597 -3.958805 114.852433 10/23/2011 9:46 40 South Kalimantan 598 -3.965159 114.894264 10/23/2011 9:53 46 South Kalimantan 599 -3.980528 114.935768 10/23/2011 10:01 20 South Kalimantan 600 -3.973942 114.949258 10/23/2011 10:03 24 South Kalimantan 601 -3.971648 114.98683 10/23/2011 10:08 30 South Kalimantan 602 -3.971318 114.98933 10/23/2011 10:08 32 South Kalimantan 603 -3.942637 115.051927 10/23/2011 10:16 35 South Kalimantan 604 -3.934674 115.064036 10/23/2011 10:18 25 South Kalimantan

605 -3.916958 115.075968 10/23/2011 10:21 23 South Kalimantan 606 -3.897489 115.093117 10/23/2011 10:25 25 South Kalimantan

607 -3.89478 115.107066 10/23/2011 10:29 35 South Kalimantan 608 -3.893991 115.116627 10/23/2011 10:30 25 South Kalimantan 609 -3.89388 115.116977 10/23/2011 10:30 25 South Kalimantan 610 -3.890482 115.131807 10/23/2011 10:32 28 South Kalimantan 611 -3.886089 115.15523 10/23/2011 10:35 43 South Kalimantan 612 -3.878723 115.188777 10/23/2011 10:40 30 South Kalimantan 613 -3.878526 115.192951 10/23/2011 10:41 27 South Kalimantan 614 -3.856226 115.208869 10/23/2011 10:45 23 South Kalimantan 615 -3.85528 115.245216 10/23/2011 10:52 19 South Kalimantan 616 -3.856194 115.248652 10/23/2011 10:52 21 South Kalimantan 617 -3.853996 115.257605 10/23/2011 10:54 34 South Kalimantan 618 -3.85588 115.273678 10/23/2011 10:56 17 South Kalimantan 619 -3.843421 115.31679 10/23/2011 11:02 20 South Kalimantan 620 -3.840203 115.328998 10/23/2011 11:04 22 South Kalimantan 621 -3.812881 115.347033 10/23/2011 11:09 36 South Kalimantan 622 -3.810011 115.343923 10/23/2011 11:14 50 South Kalimantan 623 -3.806951 115.347952 10/23/2011 11:17 41 South Kalimantan 624 -3.804705 115.356035 10/23/2011 11:20 35 South Kalimantan 625 -3.80168 115.362335 10/23/2011 11:21 49 South Kalimantan 626 -3.784679 115.388517 10/23/2011 11:27 29 South Kalimantan 627 -3.781549 115.393532 10/23/2011 11:28 27 South Kalimantan 628 -3.779506 115.396112 10/23/2011 11:28 22 South Kalimantan 629 -3.778322 115.406222 10/23/2011 11:31 20 South Kalimantan 630 -3.783192 115.391478 10/23/2011 11:38 32 South Kalimantan 631 -3.797746 115.367 10/23/2011 12:52 61 South Kalimantan 632 -3.807834 115.350297 10/23/2011 12:55 40 South Kalimantan 633 -3.840325 115.330738 10/23/2011 13:00 22 South Kalimantan 634 -3.84581 115.287873 10/23/2011 13:07 32 South Kalimantan 635 -3.852032 115.277634 10/23/2011 13:08 24 South Kalimantan 636 -3.853283 115.265824 10/23/2011 13:10 29 South Kalimantan 637 -3.855323 115.254963 10/23/2011 13:11 28 South Kalimantan 638 -3.856109 115.248986 10/23/2011 13:13 19 South Kalimantan 639 -3.855225 115.245164 10/23/2011 13:13 15 South Kalimantan 640 -3.856292 115.208958 10/23/2011 13:20 17 South Kalimantan 641 -3.886661 115.17688 10/23/2011 13:28 35 South Kalimantan 642 -3.889853 115.159175 10/23/2011 13:32 38 South Kalimantan 643 -3.936972 115.060495 10/23/2011 13:49 19 South Kalimantan 644 -3.944678 115.048793 10/23/2011 13:51 22 South Kalimantan 645 -3.964141 115.02051 10/23/2011 13:54 31 South Kalimantan 646 -3.97133 115.014148 10/23/2011 13:55 18 South Kalimantan 647 -3.969957 115.003064 10/23/2011 13:57 22 South Kalimantan 648 -3.970679 114.993982 10/23/2011 13:58 23 South Kalimantan 649 -3.971478 114.98773 10/23/2011 13:58 28 South Kalimantan 650 -3.972417 114.981135 10/23/2011 13:59 16 South Kalimantan 651 -3.980522 114.935784 10/23/2011 14:05 13 South Kalimantan 652 -3.975172 114.866394 10/23/2011 14:17 43 South Kalimantan 653 -3.964569 114.855265 10/23/2011 14:20 43 South Kalimantan 654 -3.959317 114.853122 10/23/2011 14:20 36 South Kalimantan 655 -3.958516 114.852146 10/23/2011 14:21 36 South Kalimantan 656 -3.955288 114.844836 10/23/2011 14:22 43 South Kalimantan 658 -3.933607 114.843331 10/23/2011 14:24 28 South Kalimantan 659 -3.92587 114.837975 10/23/2011 14:25 34 South Kalimantan

660 -3.923347 114.834929 10/23/2011 14:26 41 South Kalimantan 661 -3.91184 114.820797 10/23/2011 14:28 45 South Kalimantan

662 -3.887208 114.806567 10/23/2011 14:33 35 South Kalimantan 663 -3.873477 114.800128 10/23/2011 14:36 52 South Kalimantan 664 -3.859767 114.796246 10/23/2011 14:38 62 South Kalimantan 665 -3.855943 114.797492 10/23/2011 14:39 52 South Kalimantan 666 -3.840411 114.789855 10/23/2011 14:43 52 South Kalimantan 667 -3.840021 114.789504 10/23/2011 14:43 50 South Kalimantan 668 -3.778166 114.775437 10/23/2011 15:23 19 South Kalimantan 669 -3.736236 114.759425 10/23/2011 15:31 16 South Kalimantan 670 -3.729913 114.76772 10/23/2011 15:34 27 South Kalimantan 671 -3.729192 114.772464 10/23/2011 15:36 32 South Kalimantan 672 -3.727124 114.77664 10/23/2011 15:37 32 South Kalimantan 673 -3.723126 114.77719 10/23/2011 15:39 23 South Kalimantan 674 -3.722268 114.777681 10/23/2011 15:41 33 South Kalimantan 675 -3.722691 114.778504 10/23/2011 15:43 28 South Kalimantan 676 -3.72274 114.778471 10/23/2011 15:44 25 South Kalimantan 677 -3.723679 114.777123 10/23/2011 15:52 28 South Kalimantan 678 -3.726373 114.777582 10/23/2011 15:54 22 South Kalimantan 679 -3.732409 114.752246 10/23/2011 16:00 32 South Kalimantan 680 -3.73155 114.757281 10/23/2011 16:05 24 South Kalimantan 681 -3.749696 114.76491 10/23/2011 16:09 21 South Kalimantan 682 -3.764098 114.769532 10/23/2011 16:12 31 South Kalimantan 683 -3.790641 114.775588 10/23/2011 19:13 19 South Kalimantan 684 -3.710793 114.747235 10/24/2011 8:30 56 South Kalimantan 685 -3.635917 114.703705 10/24/2011 8:43 13 South Kalimantan 686 -3.626842 114.702817 10/24/2011 8:44 8 South Kalimantan 687 -3.622848 114.702439 10/24/2011 8:44 12 South Kalimantan 688 -3.577938 114.724208 10/24/2011 8:52 22 South Kalimantan 689 -3.560778 114.739566 10/24/2011 8:55 32 South Kalimantan 690 -3.540826 114.726399 10/24/2011 8:58 22 South Kalimantan 691 -3.522615 114.716687 10/24/2011 9:01 17 South Kalimantan 692 -3.514772 114.713952 10/24/2011 9:02 12 South Kalimantan 693 -3.51055 114.713126 10/24/2011 9:07 4 South Kalimantan 694 -3.492001 114.709609 10/24/2011 9:11 3 South Kalimantan 695 -3.491717 114.709555 10/24/2011 9:11 3 South Kalimantan 696 -3.486806 114.708623 10/24/2011 9:11 4 South Kalimantan 697 -3.456697 114.702935 10/24/2011 9:15 14 South Kalimantan 698 -3.446449 114.700977 10/24/2011 9:17 49 South Kalimantan 699 -3.435788 114.691572 10/24/2011 9:19 20 South Kalimantan 700 -3.426298 114.684454 10/24/2011 9:20 19 South Kalimantan 701 -3.341674 114.620211 10/24/2011 9:36 15 South Kalimantan 702 -3.298024 114.604867 10/24/2011 9:52 16 South Kalimantan 703 -3.296608 114.603594 10/24/2011 9:53 14 South Kalimantan 704 -3.28942 114.590305 10/24/2011 9:58 19 South Kalimantan 705 -3.277283 114.59001 10/24/2011 10:02 19 South Kalimantan 706 -3.216829 114.560902 10/24/2011 10:26 33 South Kalimantan 707 -3.217073 114.561149 10/24/2011 10:35 34 South Kalimantan 708 -2.708261 111.64831 11/7/2011 11:13 3 Central Kalimantan 709 -2.684307 111.631118 11/7/2011 11:20 29 Central Kalimantan 710 -2.6765 111.631631 11/7/2011 11:21 22 Central Kalimantan 711 -2.676121 111.629678 11/7/2011 11:23 16 Central Kalimantan 712 -2.713214 111.644748 11/8/2011 10:04 18 Central Kalimantan 713 -2.713415 111.645009 11/8/2011 10:11 39 Central Kalimantan