Integrating Remote Sensing Data and Energy Balance Modeling for Detection of Drought and Its Publication in the Internet (Case Study of Karawang District, West Java)

INTEGRATING REMOTE SENSING DATA AND ENERGY BALANCE MODELING FOR DETECTION OF DROUGHT AND

ITS PUBLICATION IN THE INTERNET (Case Study of Karawang District, West Java)

ADI WITONO

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY 2008

STATEMENT

Hereby I, Adi Witono, do declare that this thesis entitled “Integrating Remote Sensing Data and Energy Balance Modeling for Detection of Drought and Its Publication in the Internet (Case Study of Karawang District, West Java)” is my own work and has not been submitted in any form for another degree or diploma programs (course) to any University or other institution. The content of the thesis has been examined by the advising committee and the external examiner.

Bogor, June 2008

ABSTRACT

ADI WITONO (2008). Integrating Remote Sensing Data and Energy Balance Modeling for Detection of Drought and Its Publication in the Internet (Case Study of Karawang District, West Java). Under the supervision of TANIA JUNE and IWAN SETIAWAN.

Drought detection based on evaporative fraction (EF) in Karawang district has been done by using remote sensing and energy balance modeling. EF indicates how much of the available energy is used for evapotranspiration, that is, for transpiration of the vegetation and evaporation of the soil and EF will be close to one (no water stress). As long as moisture is available, energy will be used for its evaporation. With little or no moisture left, all available energy will be directed into the sensible heat flux and EF will approach zero (serious water stress). The objectives of this research are to analyze energy balance and evaporative fraction as drought indicator in Karawang district; to asses the potential use of remote sensing capability in identifying the surface cover parameter of image and visualization using Remote Sensing and Geographic Information System and to develop a web-based GIS application to visualize and disseminate research results. The core processes of workflows involved in this research are image pre processing, data processing and analysis, and developing web-based GIS. Potential drought in Karawang district is low either in the wet season or dry season. Web based GIS drought indicator in Karawang district has been tested by sample end user. This process was done to see comments from the users after seeing and using this system.

SUMMARY

ADI WITONO (2008). Integrating Remote Sensing Data and Energy Balance Modeling for Detection of Drought and Its Publication in the Internet (Case Study of Karawang District, West Java). Under the supervision of TANIA JUNE and IWAN SETIAWAN.

The remote sensing, especially satellite remote sensing, can observe land, ocean and atmosphere from the space. In most applications, remote sensing data are converted into physical parameters or indices. The quantitative and qualitative analysis based on the same criteria can be implemented by using remote sensing data. Moreover, most of remote sensing data are processed as images, and then area-based information can be derived. Area-based information is quite effective to drought indicator.

Drought detection based on evaporative fraction (EF) in Karawang district has been done by using remote sensing and energy balance modeling. EF indicates how much of the available energy is used for evapotranspiration, that is, for transpiration of the vegetation and evaporation of the soil and EF will be close to one (no water stress). As long as moisture is available, energy will be used for its evaporation. With little or no moisture left, all available energy will be directed into the sensible heat flux and EF will approach zero (serious water stress).

The objectives of this research are to analyze energy balance and evaporative fraction as drought indicator in Karawang district; to asses the potential use of remote sensing capability in identifying the surface cover parameter of image and visualization using Remote Sensing and Geographic Information System and to develop a web-based GIS application to visualize and disseminate research results.

The core processes of workflows involved in this research are image pre processing, data processing and analysis, and development web GIS. Image pre processing stages consists of selecting, cropping, radiometric and geometric correction. Data processing and analysis to estimate energy balance has been performed using daily meteorological data from a surface station and daily remote sensing data from Landsat. Development web GIS is process to publish the spatial information of drought indicator.

Evaporative fraction (EF) values used to determine area of drought vulnerability range from 0 – 0.4. The analysis results cover all areas of Karawang i.e. irrigated and non irrigated lands. Data of paddy filed areas obtained from the Department of Agriculture for Karawang district and after being overlaid between paddy fields as irrigated land and drought vulnerability area showed that the total area indicating drought vulnerability for irrigated land in June 2003, June 2004, July 2004, July 2005, August 2005 and October 2006 are 640 ha, 524 ha, 572 ha, 21 ha, 126 ha and 41,893 ha, respectively.

This research studied the implementation of a web-based GIS drought indicator in Karawang. Web-based GIS has been tested by sample end user. This process was done to see comments from the users after seeing and using this system.

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INTEGRATING REMOTE SENSING DATA AND ENERGY BALANCE MODELING FOR DETECTION OF DROUGHT AND

ITS PUBLICATION IN THE INTERNET (Case Study of Karawang District, West Java)

ADI WITONO

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

MASTER OF SCIENCE IN INFORMATION TECHNOLOGY FOR NATURAL RESOURCES MANAGEMENT

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY June 2008

Research Title : Integrating Remote Sensing Data and Energy Balance Modeling for Detection of Drought and Its Publication in the Internet (Case Study of Karawang District, West Java) Student Name : Adi Witono

Student ID : G051050101

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

Approved by, Advisory Board

Dr. Ir. Tania June, M.Sc Ir. Iwan Setiawan, PM

Supervisor Co-Supervisor

Endorsed by,

Program Coordinator Dean of Graduate School

Dr. Ir. Hartrisari Hardjomidjojo, DEA Prof. Dr. Ir. Khairil A. Notodiputro, MS

Date of Examination: Date of Graduation: April 18, 2008

ACKNOWLEDGMENT

First of all I thanks to my God, my Family who has once more granted me success in my studies. I pay my heartfelt love and tribute to my beloved wife, Hetti Kurniasih who together with you waited patiently. I dedicate all my success to you.

Further, I would like to express my gratitude and sincerer appreciation to the following that contributed to my studies and success in one way or the other:

1. The LAPAN Institution thought the Fellowship Program for granting me fellowship to study in MIT Biotrop IPB Bogor Indonesia;

2. Dr. Tania June, M.Sc my research supervisors for all her valuable guidance and usefull advise during my MSc proposal preparation and MSc thesis writing;

3. Ir. Iwan Setiawan PM, my research coo-supervisors for all his valuable guidance and useful advice during my MSc proposal preparation and MSc thesis writing;

4. Prof. Dr. Ir. Hidayat Pawitan,M.Sc as examiner of this thesis for his positive ideas and inputs.

5. Special thanks go to all the members of lecturers and staff in MIT Biotrop IPB Bogor;

6. Finally, I thank my fellow MSc in IT for NRM students-2005 for the wonderful student working relations we shared together. I will always value and treasure all the memories and laughter we shared together in class, computer cluster and cafeteria.

CURRICULUM VITAE

The author was born in Banyuwangi, East Java on December 29 1973, Indonesia. He is the youngest son of Tumijo and Suminah. He finished his Elementary, Junior, and High school in Government School, Banyuwangi. He received him undergraduate diploma from IPB, Faculty of Mathematic and Natural Sciences in field of Agrometeorology in 1999. Since 2003 to present, he worked for National Institute of Aeronautic and Space (LAPAN) Indonesia.

In 2005, he received a scholarship to study Master Science in Information Technology for Natural Resource Management in Bogor Agricultural University and received his Master degree in 2008.

TABLE OF CONTENTS

TABLE OF CONTENTS ……… LIST OF TABLES ……….………...……… LIST OF FIGURES ………..

ix xi xii

I. INTRODUCTION ………...

1.1 Background ……….

1.2 Objectives...………..

1.3 Scope ...………...

II. LITERATURE REVIEW ………

2.1 Drought Indicator ………..…………..

2.1.1 Albedo ……….

2.1.2 Surface Temperature ………... 2.1.3 Normalized Different Vegetation Index ……….. 2.1.4 Energy Balance ………... 2.2 Application of Remote Sensing and GIS for Drought Prediction ... 2.3 Web Publication ………...………...

III. RESEARCH METHODOLOGY ………

3.1 Time and Location ……….. 3.2 Data and Sources Properties ………...

3.3 Required Tools ………

3.4 Methodology ………...

3.4.1 Image Pre-Processing ……….. 3.4.1.1 Selecting and Cropping Data ………...…. 3.4.1.2 Radiometric and Geometric Correction ……… 3.4.2 Data Processing and Analysis ………. 3.4.3 Energy Balance Modeling..……….. 3.4.4 Development of Web GIS..……….

1 1 4 4 6 6 9 12 13 14 21 23 25 25 25 25 26 27 27 27 27 27 28 IV. RESULT AND DISCUSSION …..………..

4.1 Energy Balance ………...… 4.1.1 Existing Land Use ………...

4.1.2 Albedo ……….

31 31 31 31

4.1.3 Surface Temperature ………...

4.1.4 NDVI ………...

4.1.5 Wind Velocity ………. 4.1.6 Air Temperature ……….. 4.1.7 Solar Radiation ……… 4.1.8 Net Radiation ……….. 4.1.9 Soil Heat Flux ………. 4.1.10 Sensible Heat Flux ……….. 4.1.11 Evapotranspiration .………. 4.1.12 Evaporative Fraction ………... 4.1.13 Irrigation ………. ………... 4.1.14 Drought Indicator ……… 4.2 Web Development .……… 4.2.1 Database Management ……… 4.2.2 Code Constructing ………...

4.2.3 Web View ………

4.2.4 Spatial Information from Web GIS ………. 4.2.5 Data Editing ……… 4.2.6 Other Capability ……….. V. CONCLUSION AND RECOMMENDATION ...………

5.1 Conclusion ………..…………

5.2 Recommendation………...………... REFERENCES ………... 33 35 36 38 40 41 42 43 45 46 48 50 57 57 58 58 59 60 61 62 62 62 63

LIST OF TABLES

No. Caption Page

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Landsat-7 ETM+ postcalibration dynamic ranges………... Earth-sun distance in astronomical units ………...………. Mean solar exoatmospheric irradiances ……….. TM thermal band calibration constants ………... The existing land use of Karawang district ………. Albedo from Karawang district ………... Surface temperature in Karawang district………... NDVI in Karawang district……….. Total net radiation in Karawang district ……….…… Area of energy for soil heat flux ………. Total area of evaporative fraction (EF) in Karawang district ……... Discharge of water in Walahar dam (m3/dt) and volume (m3) ……... Acquisition data base on condition of season ………. Drought vulnerability data in Karawang district ………. Prediction of drought vulnerability ………... Schedule for planting season, prediction of harvest and watering irrigation ……….. Paddy field area in Karawang district ……….

10 11 11 12 31 33 33 35 42 42 46 48 51 54 54 55 56

LIST OF FIGURES

No. Caption Page

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

The area of study in Karawang district, West Java province

Spatial analysis functions ……… Flow diagram of research methodology ……….. Flow diagram of energy balance modeling ………. Schema to build and publish information ………... Distribution of albedo in Karawang district ……… Distribution of surface temperature in Karawang district …………... Distribution of NDVI in Karawang district………. Distribution of wind velocity in Karawang district………. Distribution of air temperature in Karawang district………... Distribution of net radiation (Wm-2) in Karawang district ………... Distribution energy for soil heat flux in Karawang district ………… Distribution energy for sensible heat flux in Karawang district…….. Distribution energy for evapotranspiration in Karawang district…… Distribution evaporative fraction (EF) in Karawang district………... Irrigation network map ………… ………. ………. Monthly average rainfall pattern in Karawang district period from 1971 to 2000 ……… Paddy field area in Karawang district (Source: Department of Agriculture) ………. Physical database using phpMyAdmin environment ……….. Main interface of drought indicator at Karawang district ……..……. Main page of web-based GIS drought indicator ………. Editing page to database in the web system ……… The screen shot of add comment page of web system………. The screen shot of add news page of web system………

5 23 26 28 28 32 34 36 38 40 41 43 44 46 47 49 51 53 58 59 59 60 61 61

I.

INTRODUCTION

1.1. Background

Droughts are normal recurring climatic phenomena that vary in space, time, and intensity. They may affect people and agriculture at local scales for short periods or cover broad regions or have impacts that are felt for years (Brown, et. al., 2002). Traditional methods of drought monitoring rely on rainfall data, which are limited in the network of stations and incomplete climate data, often inaccurate caused by human error or unworking instrumentation. Most importantly, data in near real time either spatially or temporally is available but not accessible. In this sense, remote sensing technology has greatly enhanced our ability to monitor and manage the natural resources, especially in the areas real time spatial and temporal data are recorded continuously and has been used extensively for water resources management (Runtunuwu, 2005). Remote sensing data can be used in regional monitoring and management in two main ways. The first is to monitor changes in land cover type and condition. This land cover type (forest, crop, grassland, etc) and condition (green or dry) as well as the temporal series of greening of crops can be monitored using a variety of satellite borne instruments (such as Landsat TM, ASTER, SPOT and AVHRR) at a range of spatial and temporal scales. The second use involves converting the remotely sensed data to physical measurements of the earth and using them to derive environmental parameters. This may lead to estimates for parameters of the cover (such as leaf area index or LAI, cover fraction and reflectivity) as well as geophysical parameters such as the surface radiometric temperature and albedo (Jupp, et. al., 1998). The dynamic nature of droughts causes challenges in

agricultural planning, predicting of drought occurrence, monitoring, and providing relief to drought-stricken areas. Because of the variability and significant multiple impacts of droughts, we need to improve the available tools to capture their spatial and temporal dimensions (Brown, et. al., 2002).

Despite the restricted availability of long time series of adequate remote sensing data, their advantage of high spatial resolution, together with a satisfying sampling rate of relevant surface parameters, should be taken into consideration for drought monitoring. In order to derive spatially resolved information on the water stress of vegetation, different data source have to be combined. In general, remote sensing data provide a high spatial resolution, but few physical parameters, while point data from surface measurements show high accuracy, but not spatially resolved. Thematic data like landuse classification give qualitative information of the surface cover, but remain static in time.

In the present study, the moisture status of the land surface is monitored using the daily evolution of the evaporative fraction (EF). Among the various flux ratios using energy balance modeling, EF has received special interest. EF is defined as the part of the available energy used for evapotranspiration. This quantity is an indicator of the moisture status of the land surface, mainly consisting of natural vegetation and agriculture.

Water irrigation for agriculture in Karawang is mainly from Jatiluhur, and have problem of water distribution during drought season. The height of water level of Jatiluhur dam on November 15, 2006 is 81.59 meter. Normal condition is usually indicated by water level of 89.81-92.06 meter (Pikiran Rakyat, 2006). Water volume of Jatiluhur dam on January 24, 2007 is below normal level. Consequences, only 50% or 115,000 hectare from 230,000 hectare of irrigated

paddy fields in Karawang, Subang, Bekasi and Indramayu area get supply of water. In Karawang target for paddy field area is 101,830 hectare, but only 47,637 hectare is being realized. It occurred due to lack of water supply for irrigation from Jatiluhur dam (Kompas, 2007). The water supply from Jatiluhur dam for irrigation is very limited since the allocation of water supply is divided for industry and domestic use in Jakarta, Bekasi, and Karawang.

In environmental analysis, spatial data is one of the important factors that need to be considered. Many researches, decision-makers, planners, and corporate executives use this data to help them in making some critical decisions. One of the common tools is geographic information system (GIS) which runs on PC’s or workstations used to store, to analyze, and display multiple layers of geographic information. The unique advantage of GIS is the ability to relate locational map data or spatial data to relevant non-locational data, which is called attribute. Remote sensing information produced by satellite must be interpreted before used in a GIS.

In GIS environment, the expansion of computer network, including internet and World Wide Web (www), creates a new opportunity to develop a system for distribution of spatial information. Web-based geographical data services involved management of spatial and non-spatial data introduced to publish information of drought indictor. The development of a web-based system by integrating GIS and DBMS could serve two crucial purposes. Firstly, it could allow user to operate the system without having to grapple with the underlying intricacies of GIS and DBMS technology. Secondly, it could allow sharing of information and technical expertise among a wide range of users. The rapid

growth of the Internet and World Wide Web (WWW) provides highly customized, accessible, and interactive sources of information.

The purpose of this research is to detect drought indicator using energy balance and evaporative fraction with combine climate and remote sensing data in Karawang district, West Java. Furthermore develop a web-based GIS application to visualize and disseminate research results.

1.2. Objectives

The research objectives are:

1. to analyze energy balance and evaporative fraction such as drought indicator in Karawang district;

2. to asses the potential use of remote sensing capability in identifying the surface cover parameter of image and visualization using Remote Sensing and Geographic Information System;

3. to develop a web-based GIS application to visualize and disseminate research results.

1.3. Scope

Research site is Karawang district, which is geographically located between 107o 05’ 11’’ - 107o 38’ 32’’ East and 05o 55’ 58’’ - 06o 38’ 28’’ South. The research area is dominated by paddy fields and unirrigated land. Karawang district covers an area of 1,737.30 km2 consisting of 30 sub districts. Administratively, Karawang district is bounded by Java Sea in the northern part, Subang district in the eastern part, Bekasi districts in the western part and Bogor and Purwakarta districts in the southern part. Figure 1 shows Karawang district, West Java Province.

II.

LITERATURE REVIEW

2.1 Drought Indicator

Drought indicator is divided in three types

(

Voght, et. al., 1999). First, is meteorological drought indicators based on meteorological parameters as recorded at meteorological stations. An example is the Standardized Precipitation Index (SPI). The SPI is a statistical indicator evaluating the lack or surplus of precipitation during a given period of time as a function of a long term normal precipitation to be expected during that period. It is calculated using a continuous, long term (more than 30 years) series of historic monthly precipitation records. A moving window is selected (1, 3, 6, 12, 24 months, depending on the purpose of the analysis) and new series generated. Because rainfall is not normally distributed for aggregation periods of less than 12 months a gamma distribution is fitted to the frequency distribution. The SPI for a given rainfall amount is then given by the precipitation deviation from the mean of an equivalent normally distributed probability distribution function with a zero mean and standard deviation of one resulting in units of standard deviation. This is an advantage since the SPI is normalized so that wetter and drier climates can be represented in the same way. In addition, wet periods can be monitored as well.

Second, is satellite based indicators calculated from satellite derived surface parameter. Examples are various vegetation indices such as the Normalized Difference Vegetation Index (NDVI), the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI). Vegetation indices can be efficient indicators of water stress in relatively homogenous terrain. However, in more heterogeneous regions their interpretation becomes more difficult. The VCI

is an indicator of the status of the vegetation cover as a function of the NDVI minimum and maximum encountered for a given ecosystem over many years. It normalizes the NDVI (or any other vegetation index) and allows for a comparison of different ecosystem. It is an attempt to separate the short-term climatic signal from long term ecological signal and in this sense it is a better indicator of water stress condition than the NDVI. The significance of the VCI is strongly related to the relation between the vegetation index and the vitality of the vegetation cover under investigation. In addition, it depends on the number and quality of images available for the calculation of the absolute minimum and maximum. The Temperature Condition Index (TCI) is an equivalent indicator based on the surface skin temperature derived from Landsat data. Both, the VCI and the TCI are dimensionless and vary between the values of zero and one. Zero indicates the worst condition ever encountered over the period of available images, one indicates the best condition encountered during the same period of time. If the covered period includes dry and wet years and under the assumption that the vegetation condition is mainly related to the water availability, these indicators have a high potential for monitoring water stress.

Third, a process base indicator is the result of modeling of energy and matter transfer between the atmosphere and the surface. An example is evaporative fraction, EF. EF is defined as the part of the available energy used for evapotranspiration, i.e., the latent heat flux. This quantity is regarded as an indicative of the moisture status of the surface cover.

The remote sensing, especially satellite remote sensing, can observe land, ocean and atmosphere from the space. In most applications, remote sensing data are converted into physical parameters or indices. The quantitative and qualitative

analysis based on the same criteria can be implemented by using remote sensing data. Moreover, most of remote sensing data are processed as images, and then area-based information can be derived. Area-based information is quite effective to drought indicator.

Monitoring by satellite remote sensing techniques is most appropriate for detecting the status of soil moisture, evapotranspiration, crop growth, land cover type and drought. Using a surface observation station network and remote sensing techniques, the development and spread of drought conditions can be monitored in a routine and cost-effective manner. A method based on energy balance was developed to estimate evapotranspiration (ET) and evaporative fraction (EF) to monitor drought using Landsat-7 ETM+ digital images and meteorological data The Landsat-7 ETM+ data were used to compute reflectance and temperature after atmospheric correction. ET was estimated by combining remotely sensed reflected solar radiation and surface temperature with ground station meteorological data to calculate net radiation and sensible flux.

In principle, remote sensing can detect the electromagnetic wave radiated or reflected from atmosphere, land or ocean. However, due to limitation in budget, we focus on the usage of medium resolution data (Landsat-7 ETM) which are available free of charge through the Internet. Landsat-7 ETM+ images were used for the analysis in the present research. Especially, those images were used for the extraction of drought indicator. Three parameters were selected, i.e. albedo, Normalized Difference Vegetation Index (NDVI), and land surface temperature (Ts). In the following subsection, methods to derive parameters are described.

2.1.1 Albedo (α)

Land surface albedo is a physical parameter that describes the optical reflectance of the land surface. Albedo is commonly defined as the reflectance of a surface integrated with respect to both wavelength (usually between 0.3 µm and 3.0 µm) and angle (i.e. for all directions within the hemisphere above the surface). Examples of the albedo applications include global and regional climatic models for computing the surface energy balance. Three types of albedos, i.e. total-shortwave, total-visible and total-near-infrared albedos, are available. The procedure to estimate albedo from digital number (DN) value is described as follows:

Firstly, according to USGS (2002) DN value was converted into radiance L by applying equation (1). A digital number or DN is the value stored within a pixel or cell of an image. Typically, the DN of the pixel represents the amount of light reflected back to the satellite/sensor. However, this is dependent upon the type of data stored in the image. Digital data acquired from satellites are provided to the user in the form of quantified and calibrated values (QCal) for individual picture elements (pixels). These post-calibration QCal values are in units of digital numbers, which have a full range of 8 bits.

(

)

(

)(

min min

min max

min

max _{QCal QCal L}

QCal QCal

L L

L − +

− −

=

)

..………..………..(1)

where Lmin and Lmax are the spectral radiances for each band at digital numbers 0

or 1 and 255, QCal is calibrated and quantified scaled radiance values in digital numbers 0-255, QCalmin and QCalmax are the minimum and maximum quantized

calibrated pixel value. Values for Lmax and Lmin vary for each of the Landsat

published for a given sensor are expressed in units of watts per square meter per steradian per micrometer (W m-2 ster-1 µm-1). Table 1 shows Lmin and Lmaxfor all

bands of Landsat 7 ETM+, or Lmin, Lmax, QCalmin and QCalmax available in the

metadata of data.

Table 1. Landsat-7 ETM+ postcalibration dynamic ranges. Unit of Lmin and Lmax are W m-2ster-1µm-1

High Gain Band

Lmin Lmax

1 -6.2 191.6

2 -6.4 196.5

3 -5.0 152.9

4 -5.1 241.1

5 -1.0 31.06

6 0.0 12.65

7 -0.35 10.80

8 -4.7 158.3

Source: USGS (2002)

Radiance was converted into reflectance. According to USGS (2002) the reflectance can be calculated from Landsat -7 data using equation as follows:

θ π

ρ

λ λ

cos

2

ESUN d L _i

i = ………..………(2) where,

i

ρ = Effective at satellite planetary reflectance composed the combined surface and atmospheric reflectance of the earth (unitless).

i

L_λ = Spectral radiance at the sensor's aperture (W m-2ster-1µm-1)

d= Earth-Sun distance in astronomical units from nautical handbook (Table 2)

λ

ESUN = Meansolar exoatmospheric irradiances (Table 3)

Table 2. Earth-sun distance in astronomical units

Julian

Day Distance

Julian

Day Distance

Julian

Day Distance

Julian

Day Distance

Julian

Day Distance

1 .9832 74 .9945 152 1.0140 227 1.0128 305 .9925 15 .9836 91 .9993 166 1.0158 242 1.0092 319 .9892 32 .9853 106 1.0033 182 1.0167 258 1.0057 335 .9860 46 .9878 121 1.0076 196 1.0165 274 1.0011 349 .9843 60 .9909 135 1.0109 213 1.0149 288 .9972 365 .9833 Source: USGS (2002)

The astronomical unit, the AU, is a unit of distance equal approximately to the average distance between the earth and sun. More precisely stated, one astronomical unit is approximately the value of the semi major axis of the orbit of the earth. (For the purists, the AU is actually a tiny bit less than the semi major axis.) This represents a distance of about 93 million miles or 150 million kilometers.

Table 3. Meansolar exoatmospheric irradiances Band Wm-2 m-1

1 1969.000 2 1840.000 3 1551.000 4 1044.000 5 225.700 7 82.07 8 1368.000 Source: USGS (2002)

Finally, albedo was estimated for Landsat-7 ETM+ using equation as follows:

3 2

1 0.317 0.240

443 .

0 ρ ρ ρ

α_VIS = + + ………..……….(3) 003 . 0 116 . 0 212 . 0 693 .

0 ₄ + ₅ + ₇ −

= ρ ρ ρ

α_NIR ………..………...(4)

where αvis, αnir and αsw denote total visible (0.4-0.7 µm), near-infrared (0.7-2.5 µm)

and shortwave albedo (0.25-2.5 µm) for ETM+, respectively. ρn denotes

reflectance of band n (band 1-5 and7). 2.1.2 Surface Temperature (Ts)

Thermal band data (band 6) from Landsat ETM+ can also be converted from spectral radiance to effective at-satellite temperature. According to USGS (2002) surface temperature can be calculated from Landsat data divided two steps. First step calculation brightness temperature with equation:

⎥ ⎦ ⎤ ⎢ ⎣ ⎡ + = 1 ln ) ( 1 2 λ L K K K

T ………..……… (6)

where,

T(K) = Effective at satellite temperature in Kelvin K1= calibration constant 1 in Wm-2ster-1µm-1

K2= calibration constant 2 in Kelvin

Lλ= Spectral Radiance in Wm-2ster-1μm-1

DN=Digital number of each channel (0-255)

Table 4 shows calibration constants K1 and K2 for Landsat TM and ETM+.

Table 4. TM thermal band calibration constants

Units Wm-2ster-1μm-1 Kelvin

Constant K1 K2

Landsat 5 TM 607.76 1260.56 Landsat 7 ETM+ 666.09 1282.71 Source: USGS (2002)

Second step is to calculate a kinetic temperature. The effective satellite temperature values T(K) are referred to a black body. Therefore, corrections for

spectral emissivity ε are necessary. The kinetic temperature is the variable needed for subjects like heat transfer, because it is the translational kinetic energy which leads to energy transfer from a hot area (larger kinetic temperature, higher molecular speeds) to a cold area (lower molecular speeds) in direct collisional transfer. Kinetic temperature is derived from emissivity correction with equation as follows:

Tkinetic K

T_{( )}=ε0.25 _{...……….……….………...(7)}

where ε is emissivity constant (vegetation 0.95, non vegetation 0.92 and water body 0.98).

Last step is to calculate a land surface temperature with equation below: 16 . 273 − =Tkinetic Ts ………..………(8) where Ts is surface temperature (oC).

2.1.3 Normalized Different Vegetation Index (NDVI)

NDVI has been widely recognized useful for the studies of the land biosphere characteristics and dynamics at regional to global scales. NDVI is more sensitive to chlorophyll and less contaminated by atmospheric water vapor. NDVI is obtained through calculation of reflectance’s of the red and near infrared (NIR), expressed as the following equations.

RED NIR RED NIR NDVI ρ ρ ρ ρ + −

= ………..……….(9) where ρRED and ρNIR denote reflectance’s at red band and NIR band, respectively.

For Landsat TM/ETM+, band 3 and 4 are used.

Vegetation indices (VI) are commonly used to calculate and map vegetation characteristics. The NDVI has been the most widely used method to explore spatial and temporal variation in vegetation properties. The index value

has a range from -1.0 to 1.0, in which higher index values are associated with higher level of healthy vegetation.

2.1.4 Energy Balance

Solar radiation is the largest energy source and is able to change large quantities of liquid water into water vapor. The potential amount of radiation that can reach the evaporating surface is determined by its location and time of the year. Due to differences in the position of the sun, the potential radiation differs at various latitudes and in different seasons. The actual solar radiation reaching the evaporating surface depends on the turbidity of the atmosphere and the presence of clouds which reflect and absorb major parts of the radiation. When assessing the effect of solar radiation on evapotranspiration, one should also bear in mind that not all available energy is used to vaporize water. Part of the solar energy is used to heat up the atmosphere and the soil profile.

A simple modeling methodology rooted in climatology – called ‘energy balance’ modeling – is available to study the role of land cover energy consumption rates. Energy balance refers to the physical fact that energy cannot be created nor destroyed so that the solar and longwave radiation energy received by a land cover layer during any time interval must exactly equal, or ‘balance,’ the energy gained by that layer minus that is lost from the layer during the same time interval. The physical equations that describe these gains and losses are widely used in climate studies.

The radiation coming from the sun can be split into longwave and shortwave. The longwave radiation does heat particular ground features that will eventually be released after a certain amount of time. The shortwave radiation is

instantly reflected by ground features according to their albedo characteristics.

The components of the net radiation (Rn) have been derived as follows (W/m2):

out out in

in Rl Rs Rl Rs

Rn= + − − ………...………....(10) The equation above is the energy budget concept of the surface at noon. This equation explained that the net radiation is the accumulation from incoming shortwave radiation (Rsin) plus incoming longwave radiation (Rlin) minus

outgoing shortwave radiation (Rsout) and outgoing longwave radiation (Rlout).

This rather simple system of radiation balance is considering the ground elements as a layer of a given height, responding uniformly to a radiation stimulus. This concept has two direct advantages, the first one is to simplify inlayer structural ground element interactions, and the second one is that it is very well fitting the ideal description of a satellite remote sensing and its ground sampling unit: the pixel.

The net radiation is the difference between incoming and outgoing radiation of both short and longwave lengths. It is the balance between the energy absorbed, reflected and emitted by the earth's surface or the difference between the incoming net shortwave and the net outgoing longwave radiation. Rn is normally positive during the daytime and negative during the nighttime. The total daily value for Rn is almost always positive over a period of 24 hours, except in extreme conditions at high latitudes.

The Energy Balance partitioning is summarized at an instant time t (at the time of satellite overpass) by the following equation:

E H G

where Rn is the net radiation emitted from the Earth surface (W/m2), G is the soil

heat flux (W/m2), H is the sensible heat flux (W/m2), λE is the latent heat flux, being the energy necessary to vaporize water (W/m2).

The equation to calculate the net radiation is given by the following equation:

(

4 16 . 273 ) 1 ( − + − +

= Rs Rl Ts

Rn α εσ

)

………...………..(12) where α is the albedo, Rs is the downward solar radiation, Rl is the downward longwave radiation, ε is the emissivity of the surface, σ is the Stefan-Bolzmann constant, and Ts is the surface temperature.

If the solar radiation, Rs, is not measured, it can be calculated with the Angstrom formula which relates solar radiation to extra terrestrial radiation and relative sunshine duration: Ra N n bs as Rs ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ₊ = ………..……….(13)

Depending on atmospheric conditions (humidity, dust) and solar declination (latitude and month), the Angstrom values as and bs will vary. Where no actual solar radiation data are available and no calibration has been carried out for improved as and bs parameters, the values as = 0.25 and bs = 0.50 are

recommended. The extraterrestrial radiation, Ra, and N is the daylight hours or maximum possible duration of sunshine. The actual duration of sunshine, n, is recorded with a Campbell Stokes sunshine recorder.Ra is extraterrestrial radiation constitute function from altitude, time angle, zenith angle and sun declination angle depending on date.

The calculation of the clear-sky radiation, Rs, when n = N, for as and bs the data is not available, is required for computing net shortwave radiation.

(

z

)

Ra

Rs= _0.75+_2*10−5 _{………..……….(14)}

where z is station elevation above sea level.

The rate of longwave energy emission is proportional to the absolute temperature of the surface raised to the fourth power. This relation is expressed quantitatively by the Stefan-Boltzmann law. The net energy flux leaving the earth's surface is, however, less than that emitted and given by the Stefan-Boltzmann law due to the absorption and downward radiation from the sky.

(

)

4

(

2

)

17 . 0 1 7 . 0 * 16 .

273 N

Ta

Rl =εσ + + ………...…………(15) where Ta is air temperature from climate station and N is percentage of cloud for satellite data.

Generally lateral fluxes are not considered when dealing with remote sensing images because of their spatial cover capturing the instantaneous energy balance system. Even when transforming the energy balance components for a daily extrapolation of the values, lateral exchanges between pixels are found either in one pixel or in the neighboring ones, extrapolation does not expose lateral values yet encompasses them. The instantaneous soil heat flux (G) is approximated by fraction on the net radiation, as a function of the NDVI (Normalized Difference Vegetation Index). The soil heat flux is the energy that is utilized in heating the soil. G is positive when the soil is warming and negative when the soil is cooling. Although the soil heat flux is small compared to Rn and may often be ignored, the amount of energy gained or lost by the soil in this process should theoretically be subtracted or added to Rn when estimating evapotranspiration.

where, ) 0.98NDVI -(1 ) 007 . 0

(0.0038+ α 4

=Ts Rn

G

………..…..………….(16) NDVI = Normalized Different Vegetation Index (determined from Landsat) Heat flow into the soil, is driven by a thermal gradient in the uppermost topsoil. It is a conduction flux through the soil matrix. This gradient varies with the state of the vegetation covering the soil that is influencing the light interception by the soil surface. The radiative heating of the topmost layer is then directly modifying the surface temperature and thermal gradient in the top layer.

The sensible heat flux (H) is a convection flux through the atmosphere layers, coming from the surface skin boundary layer with the topmost soil/vegetation layer. The sensible heat flux has been estimated from the difference between radiometric surface temperature (Ts) and surface-measured air temperature (Ta), and the formulation of a bulk aerodynamic resistance (W/m2).

The difference between the known radiometric surface temperature and the unknown aerodynamic surface temperature, which actually should be applied, is referred to the following formulation of aerodynamic resistance.

) (

273 900

2 Ts Ta

U Ta

H −

+

=γ λ ………..………..………...(17) where,

Ts = Surface temperature (° C) (Landsat) Ta = Air temperature (° C)

γ = Psychometrics constants ( kPa °C-1)

= Wave lenght of radiation emission (11.5 µm) U2 = Wind velocity at 2 m above ground surface (m s-1)

P P

c_{p 3}

10 * 665 . 0 − = = ελ

γ (FAO, 1998)…...………..……….(18) where,

P=Atmospheric pressure (kPa)

λ= Latent heat of vaporization (2.45 MJ kg-1)

cp = Specific heat at constant pressure, 1.013 10-3 (MJ kg-1 °C-1)

ε = Ratio molecular weight of water vapour/dry air = 0.622

The specific heat at constant pressure is the amount of energy required to increase the temperature of a unit mass of air by one degree at constant pressure. Its value depends on the composition of the air, i.e., on its humidity. For average atmospheric conditions a value cp = 1.013 10-3 MJ kg-1 °C-1 can be used. As an

average atmospheric pressure is used for each location (Equation 19), the psychrometric constant is kept constant for each location.

26 . 5 293 006 . 0 293 3 . 101 ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ − = z

P (FAO, 1998)…...………..……….(19)

where,

z = Elevation above sea level (m)

Wind speeds measured at different heights above the soil surface are different. Surface friction tends to slow down wind passing over it. Wind speed is slowest at the surface and increases with height. For this reason anemometers are placed at a chosen standard height, i.e., 10 m in meteorology and 2 or 3 m in agrometeorology. For the calculation of evapotranspiration, wind speed measured at 2 m above the surface is required. To adjust wind speed data obtained from instruments placed at elevations other than the standard height of 2m, a

logarithmic wind speed profile may be used for measurements above a short grassed surface:

(

67.8 5.42

)

ln

87 . 4

2 = ₋

z U

U _z (FAO, 1998)……….…(20) where,

U2 = Wind velocity at 2 m above ground surface (m s-1)

Uz = Wind velocity from measurements at above ground surface (m s-1)

z = Height of measurement above ground surface (m)

The energy necessary to vaporize water under given atmospheric conditions is especially ruled by the resistance to vaporization parameter. The latent heat flux (λE) is energy that used to evaporate water. The evapotranspiration process is determined by the amount of energy available to vaporize water.

)

(Rn G H

E= − −

λ ………..………..(21) where,

λE = Energy for Evapotranspiration (W/m2) Rn = Net Radiation (W/m2)

H = Energy for Sensible Heat Flux (W/m2)

G = Energy for Soil Heat Flux (W/m2)

Finally, the evaporative fraction (EF) is expressed as:

G Rn

E EF

−

= λ ………..………...(22) EF indicates how much of the available energy is used for evapotranspiration, that is, for transpiration of the vegetation and evaporation of the soil and EF will be close to one (no water stress). As long as moisture is available, energy will be used for its evaporation. With little or no moisture left, all available energy will

be directed into the sensible heat flux and EF will approach zero (serious water stress).

The above mentioned methods, however, require quite complex models to construct the case-specific algorithms which make direct use of remote measurements of spectral radiances. Presently a lot of effort is concentrated into increasing the accuracy of radiant fluxes, even if surface albedo can easily be estimated by common sensors (enabling the calculation of the shortwave net radiation), it takes more specific sensors to estimate the longwave component of the radiation balance. Surface albedo and temperature can also be the basis for estimates of the upwelling components, while the downwelling components are based on meteorological data. Soil heat flux can be estimated by the ratio G/Rn through spectral indices or by semi empirical equation including Rn, the surface albedo, the surface temperature, NDVI and the area average surface albedo. Even if the net radiation and soil heat flux parts of the energy balance equation are relatively well known and estimated from a remote sensing point of view, the remote sensing of the sensible heat flux especially its most critical parameter is still limited.

2.2 Application of Remote Sensing and GIS for Drought Prediction

Remote sensing is the science (and to some extent, art) of acquiring information about the earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information. In much of remote sensing, the process involves an interaction between incident radiation and the targets of interest. This is exemplified by the use of imaging systems where the following seven elements

are involved. Note, however that remote sensing also involves the sensing of emitted energy and the use of non-imaging sensors.

Computer based systems are used to store and manipulate geographic information (Aronoff, 1991). A computer system is capable of holding and using data describing places on the earth’s surface. Any of various software applications, running on PCs or workstations, that store, analyzes, and displays multiple layers of geographic information (Lang, 1998).

Many types of information that are needed in natural drought management are important such as map, satellite imagery, GPS data, climate data, etc. Many of these data have different projection and coordinate system, and need to be brought to a common map basis in order to superimpose them (Westen, 2002). Remote sensing and GIS provide a historical database from which drought map may be generated, indicating which areas are potentially dangerous.

When drought occurs, the speed of information collection from air and space borne platforms and the possibility of information dissemination with a corresponding swiftness make it possible to monitor the occurrence of the drought. Simultaneously, GIS may be used to integrate satellite data with other relevant data (such as climate data), useful in combination with Global Positioning System (GPS) and classification of the spatial analysis functionality (selection, manipulation, exploration, and confirmation). The interaction between the various functions is schematically summarized in Figure 2 (Anselin, 1998).

Figure 2. Spatial analysis functions 2.3 Web Publication

The greatest problem for decision makers, resource managers and user who handle information is accessing the right information at the real time. Internet is a global, public collection of individual networks that is operated by private organizations, universities, and government agencies (Maududie, et. al., 2002). GIS takes advantages of the internet as a media of GIS dissemination, so that the GIS data can be accessed by different computers (or servers) from anywhere across the internet. To be able to access and share remote GIS data, the system requires high interoperability (Peng, et. al., 2003). The internet and web-based GIS is an effective medium for publication of spatial data. Web-web-based GIS is not a single technology, there are many combined components to build web based GIS, such as software and hardware. These technologies are related to web based GIS including Object-Oriented Language, GIS package and language,

HTML and web scripting, and the theories about GIS. It is important to define the different forms of web-based GIS, before selecting a specific location for any given application.

One type of web-based GIS applications is Map Generators. Map generators use a web-based browser form. The user enters specifications of drought event such as location, thematic layers and symbols on the form. The form is passed to the web server. A gateway at the web server passes the request to a GIS server. For instance, the gateway could pass the request in the form of AML to an Arc/INFO server. The Arc/INFO server generates a graphic file, which is converted to a GIF image. The GIF image is sent back to the client and viewed using native browser capability. The advantage of map generators is creating custom maps on the fly. Disadvantages include lack of access to the raw spatial data, typically at a slower speed, limited predefined user choices, and involved setup.

III.

RESEARCH METHODOLOGY

3.1 Time and Location

The research was conducted from February until June 2007 at MIT-BIOTROP covering Karawang District, West Java. Image processing and analysis of satellite data have been conducted from March to May 2007 at the National Institute of Aeronautics and Space (LAPAN).

3.2 Data and Sources Properties

1. Remote sensing data (Landsat-7 ETM+) obtained from LAPAN between 2003 until 2006 (series data per year for wet or dry session).

2. Vectors data (administration from Bakosurtanal and irrigation network from Local Government) and DEM-SRTM from USGS.

3. Climatic data from BMG (series data rainfall 1971-2000, air temperature and wind velocity 1995 – 2002) and from PJT-II Jatiluhur (series data rainfall 2004-2006).

4. Field data (coordinate position, land cover and land use checking from classification Landsat ETM+ between actual conditions in the field).

5. Statistical data (drought vulnerability, paddy field, discharge and volume of water).

3.3 Required Tools

The hardware tools used for this research consist of Personal Computer (Pentium IV with Processor 1.8 GB, RAM 526 MB, Hard disk 60 GB), LaserJet 2500 Printer, and hand held Global Positioning System (GPS) device, while the softwares consist of ER Mapper 7, Arc View 3.3, and Microsoft Office 2003.

The softwares used to publish information through Web GIS are:

- MS4W (Map Server for Windows). MS4W, a free software package, that is Apache for web server application i.e., Map Server; this software is used to develop web based GIS application. With the use of PHP script, map server script can process the spatial database and non-spatial database

- MySQL 4.0.18 is a software used to store and manage all related data. This is a free software for database application

- Macromedia Dreamweaver MX 2004 is used to develop web pages. 3.4 Methodology

Data flow diagram of this research methodology is described in Figure 3. The following individual components will be explained in detail.

Map Data RS Data Field Data Climatic Data

Selecting and Cropping data Radiometric & Geometric Correction NDVI Albedo Surface Temperature Land Cover Classification Emisivity Constantan Short Radiation Wind Velocity Air Temperature Extraterrestrial Radiation Topography, Irrigation, Paddy Field DEM Interpolation Energy Balance Model Evaporative Fraction EF Map Overlay Spatial Information of Water Stress for Drought Indicator

Statistic Data

Drought Location Paddy Field Debit & Volume Water

Web GIS INTERNET Data Collection

Map Data RS Data Field Data Climatic Data

Selecting and Cropping data Radiometric & Geometric Correction NDVI Albedo Surface Temperature Land Cover Classification Emisivity Constantan Short Radiation Wind Velocity Air Temperature Extraterrestrial Radiation Topography, Irrigation, Paddy Field DEM Interpolation Energy Balance Model Evaporative Fraction EF Map Overlay Spatial Information of Water Stress for Drought Indicator

Statistic Data

Drought Location Paddy Field Debit & Volume Water

Web GIS INTERNET Data Collection

3.4.1 Image Pre-Processing

3.4.1.1 Selecting and Cropping Data

The aim of selecting data is to get the free cloud-cover image data. Furthermore, to get the efficiency in image processing, the selected data are cropped for the study area.

3.4.1.2 Radiometric and Geometric Correction

Radiometric and geometric errors are the most common types of error encountered in remotely sensed imagery. To remove or minimize the error the following should be done:

1. Radiometric correction, using histogram adjustment

2. Geometric correction is the basic operation in geometric rectification which covers collecting ground control point (GCP), transformation and resampling. The topographic map from Bakosurtanal of Karawang district with the scale of 1:25.000 is used to correct the image data.

3.4.2 Data Processing and Analysis

Satellite imagery data is analyzed using ER Mapper 7 and vector data is processed using ArcView 3.3. Field data are collected from surveys and position of the location is taken by using GPS. Field data is used to verify result of land cover. Result analysis is thematic images of evaporative fraction from 2003 until 2006.

3.4.3 Energy Balance Modeling

Energy balance estimation has been performed using daily meteorological data from surface station and daily remote sensing data from Landsat. The flow diagram modeling of energy balance is presented in Figure 4.

Remote Sensing Data

Climate Data

NDVI Ts α Ta U2 Rs

Net Radiation (Rn)

( )4

16 . 273 ) 1 ( − + − +

= Rs Rl Ts

Rn α εσ

Net Radiation (Rn)

( )4

16 . 273 ) 1 ( − + − +

= Rs Rl Ts

Rn α εσ

Sensible Heat Flux (H)

) ( 273 900

2Ts Ta

U Ta

H −

+

=γ λ

Sensible Heat Flux (H)

) ( 273 900

2Ts Ta

U Ta

H −

+

=γ λ

Soil Heat Flux (G)

) 0.98NDVI -(1 ) 007 . 0

(0.0038α α2 4

α +

=Ts

Rn G

Soil Heat Flux (G)

) 0.98NDVI -(1 ) 007 . 0

(0.0038α α2 4

α +

=Ts

Rn

G Energy Evapotranspiration (λE)

) (Rn G H E= − −

λ

Energy Evapotranspiration (λE)

) (Rn G H E= − −

λ

Evaporative Fraction (EF)

G Rn E EF − = λ

Evaporative Fraction (EF)

G Rn E EF − = λ

Figure 4. Flow diagram of energy balance modeling 3.4.4 Development of Web GIS

In this research we also develop a web GIS to publish the spatial information of energy balance (Figure 5). Developing web GIS is divided into three major steps. First, is the design data information of drought indicator. Second, is building web page based on drought information. The last step is publishing the spatial drought information through the internet.

The raw materials for information system are data input, i.e. the representation of facts or concepts that becomes usable when processed. Data is required to produce information which is needed by users. There are conceptual, logical and physical database designs. Conceptual design focuses on what data should be stored in the database, while the function model deals with how the data is processed. To put this in the context of the relational database, the data model is used to design the relational tables. The functional model is used to design the queries that will access and perform operations on those tables. Logical database design focuses on constructing a model of information used in an enterprise based on specific data model. Logical model explains the logic of database which is the process of constructing a model of information used in an enterprise based on specific data model, but independent of particular DBMS and other physical considerations. Physical design describes the physic of database visualization, which allows the designer to make decisions on how the database is to be implemented. Physical model describes the data physically where entity is set based on database software.

This phase building web page also applies to the programs which process the database. In the system development approach, the parallel activities could be observed. The designed web page of the system is divided into two parts.

1. Design of spatial modeling includes spatial data selection and spatial data manipulation.

2. Design of output includes map, graph and table.

The last step is implementation to improve the system. A standard web browser will be used as the web page. The browser connects to the server, which contains

spatial database. The server sends back a result of request to the client. An interface will be developed which allows user to query the spatial database. The result is returned to the user’s browser as an HTML file that contains the dataset in image format (usually .png/gif format).

IV.

RESULTS AND DISCUSSION

4.1. Energy Balance

The component is analyzed from Landsat-7 and climate data. Landsat-7 ETM+ image data used is from 2003 to 2006. Mosaic process is applied only to data 2006 to see land cover condition. The data actually was invalid since 2003 and a lot of striping data cannot be analyzed. Climate data are obtained from BMG (air temperature and wind velocity data acquisition 1995 to 2002) and from PJT-II Jatiluhur (rainfall data acquisition 2004 to 2006).

4.1.1. Existing Land Use

The exiting land use map is obtained by interpreting Landsat-7 ETM+ image in October 2006. The land use map consists of paddy field, industrial area, residential area, fish pond, vegetation cover, unirrigated land and water body. Table 5 shows Karawang district where 22% of the total area is paddy field area and 1.8% industrial area. Because in October 2006 usually start of the rainy season, but there was no rainfall observed. Based on data form BPS Karawang total area paddy field at 2006 is 82,285 ha. So existing paddy field detected is low and partly detected is unirrigated land. Data used is Landsat-7 ETM+ after mosaic process.

Table 5. The existing land use of Karawang district

No Land Use Area (ha) Area (%)

1 Paddy Field 42,424 22

2 Industry 3,476 2

3 Residential 10,282 5

4 Fish Pond 14,777 8

5 Vegetation 51,889 27

6 Unirrigated Land 69,891 36

7 Water 357 0

Total 193,097 100

4.1.2. Albedo

The albedo of an object is the extent to which it reflects light, defined as ratio of reflected to incident electromagnetic radiation. Its value depends on the frequency of radiation unqualified, it usually refers to same appropriate average across the spectrum of visible light. In general, the albedo depends on the

direction and direction distribution of incoming radiation. Around 20,000-30,000 ha area cannot be analyzed due to a striping data since 2003.

Acquisition: 6/3/2003 Acquisition: 4/18/2004 Acquisition: 6/5/2004

Acquisition: 7/23/2004 Acquisition: 4/21/2005 Acquisition: 7/10/2005

Acquisition: 8/11/2005 Acquisition: 10/1/2006 Legend Figure 6. Distribution of albedo in Karawang district

Table 6. Albedo from Karawang district

6/3/2003 4/18/2004 6/5/2004 7/23/2004 4/21/2005 7/10/2005 8/11/2005 10/1/2006

0-0.1 22,364 19,029 39,652 28,152 25,128 46,825 15,435 12,972

0.1-0.2 149,146 149,049 110,634 135,556 115,995 116,127 150,699 156,612

0.2-0.3 583 1,035 6,623 6,341 13,220 1,137 1,711 2,581

0.3-0.4 74 287 3,150 337 4,958 320 357 49

0.4-0.5 49 262 3,826 47 4,198 164 312 6

0.5-0.6 3 48 2,811 0 1,255 29 171 0

0.6-0.7 0 0 519 0 0 0 16

0.7-0.8 0 0 0 0 0 0 0 0

0.8-0.9 0 0 0 0 0 0 0 0

0.9-1 0 0 0 0 0 0 0 0

No Data 21,022 23,533 26,026 22,808 28,486 28,639 24,540 21,022

Total 193,241 193,241 193,241 193,241 193,241 193,241 193,241 193,241 Albedo/

Date

Area (ha)

0

Table 6 shows area of albedo range in Karawang district, and the distribution of albedo. The dominant albedo is 0.1-0.2, the indicator area are vegetation, residential, industrial, and unirrigated lands. Albedo with range of 0-0.1 is dominant in water body and fish pond areas. It indicaties that a lot of radiant reflected from surface is low and radiation absorbed is high.

4.1.3. Surface Temperature

Land surface temperature is one of the key parameters of land surface process, combining surface-atmosphere interaction and energy fluxes between the atmosphere and the ground. Table 7 shows the distribution of surface temperature at Karawang district. The value of land surface temperature ranges from 24-34 oC detected from thermal sensor of Landsat-7 ETM+ satellite. Based on Figure 7 the surface temperature value of October 01, 2006 is high.

Table 7. Surface temperature in Karawang district.

6/3/2003 4/18/2004 6/5/2004 7/23/2004 4/21/2005 7/10/2005 8/11/2005 10/1/2006

24-25 0 102 4,782 24,432 10,941 1,135 289 2

25-26 10 317 5,443 11,181 8,533 4,587 515 7

26-27 205 1,885 6,325 17,096 36,622 16,748 1,086 32

27-28 44,197 64,421 42,995 46,838 102,035 125,211 83,370 2,539 28-29 94,448 85,636 92,316 52,874 6,255 14,563 67,609 25,529 29-30 32,642 16,389 15,119 16,530 360 2,328 15,516 42,425

30-31 715 923 235 1,464 9 30 316 40,766

31-32 2 25 1 19 1 1 0 40,497

32-33 0 0 0 0 0 0 0 12,

33-34 0 0 0 0 0 0 0 8,

No Data 21,022 23,543 26,026 22,808 28,486 28,639 24,540 21,022 Total 193,241 193,231 193,241 193,241 193,241 193,241 193,241 193,241

TS/ Date Area (ha)

406 016

Acquisition: 6/3/2003 Acquisition: 4/18/2004 Acquisition: 6/5/2004

Acquisition: 7/23/2004 Acquisition: 4/21/2005 Acquisition: 7/10/2005

Acquisition: 8/11/2005 Acquisition: 10/1/2006 Legend (oC) Figure 7. Distribution of surface temperature in Karawang district

Distribution pattern of surface temperature shows higher surface temperature in residential and industrial areas than other areas, for example Landsat data of August 11, 2005. Surface temperatures in residential and industrial areas range between 29-31 oC, while the other land cover classifications are lower. Very significant condition is shown in October 1, 2006, where the land

surface is relatively dry because there was no rain for a successive time and limited distribution of water from Jatiluhur dam, consequently the surface temperature of non-vegetation is higher (29-34 oC ), while for vegetation area it is less than 29 oC.

4.1.4. NDVI

Based on Figure 8 the vegetation index number in April 2004 is higher than in June-July 2004, while the condition in 2005 showed that for the wet season it is lower than dry season (July-August). Furthermore, in 2003 the vegetation index is moderate especially in paddy field areas, while in 2006 the distribution is different with low vegetation index or mostly negative. Vegetation index area (Normalized Different Vegetation Index) is dominant at moderate level (0-0.6) except in October 2006 where the dominance is at low level (-0.6-0). Table 8. NDVI in Karawang district.

6/3/2003 4/18/2004 6/5/2004 7/23/2004 4/21/2005 7/10/2005 8/11/2005 10/1/2006

-1--0.8 0 5 0 1 33 12 0

-0.8--0.6 123 131 93 60 57 56 4 119

-0.6--0.4 9,545 6,881 8,884 10,385 6,716 7,044 5,215 5,208 -0.4--0.2 41,670 31,168 48,430 59,101 53,720 44,969 36,733 52,157 -0.2-0 42,765 38,734 46,870 40,907 59,219 37,240 48,599 70,431 0-0.2 32,341 37,598 41,678 34,672 29,609 30,227 45,068 30,470 0.2-0.4 42,600 53,387 19,602 24,113 14,692 39,125 32,745 13,427

0.4-0.6 2,730 1,591 1,149 924 190 5,478 1 393

0.6-0.8 0 0 0 0 0 0 0

0.8-1 0 214 508 269 518 452 336 11

No Data 21,467 23,533 26,026 22,808 28,486 28,639 24,540 21,022 Total 193,241 193,241 193,241 193,241 193,241 193,241 193,241 193,241

NDVI/ Date

Area (ha)

1

3

Acquisition: 7/23/2004 Acquisition: 4/21/2005 Acquisition: 7/10/2005

Acquisition: 8/11/2005 Acquisition: 10/1/2006 Legend Figure 8. Distribution of NDVI in Karawang district.

4.1.5. Wind Velocity

Wind velocity is obtained from West Java and Jakarta areas using spatial interpolation (kriging). The wind velocity data is obtained from 10 stations surrounding Karawang district. Temporal resolution data is monthly average from 1995 to 2002. Wind velocity variation from January until December indicates higher value in northern side of Karawang especially coastal area than southern side. Based on Beaufort scale from January to December wind velocity can be classified into high, moderate, and low wind velocities. High category is wind velocity more than 6 m/s, moderate 3-6 m/s and low, less than 3 m/s. High category is dominant in northern side, moderate in center Karawang and low in southern area based on from topographic condition of Karawang district.

Southern area of Karawang district is high land with air pressure higher than the northern area.

January February March

April May June

October November December Legend

Figure 9. Distribution of wind velocity (m/s) in Karawang district. 4.1.6. Air Temperature

Air temperature obtained by interpolation method from data collected around Karawang district shows different pattern and class for each month. Average temperature in February is less than 25 oC. This maybe due to the high temperature of Karawang district during the rainy season caused by very low radiation to land surface and much radiation is emitted by the clouds. Trend of temperature from January until December is relative similar, but temperature value is different. Northern area of Karawang is higher than southern area. The topography condition of Karawang determines temperature degree. North area land altitude is less than 25 meter, while south area is higher. In general, air temperatures in Karawang are around 23-28 oC.

January February March

April May June

October November December Legend

Figure 10. Distribution of air temperature ( oC ) in Karawang district. 4.1.7. Solar Radiation

Solar radiation (Rs) is the amount of radiation reaching a horizontal plane. Solar radiation is also referred to as shortwave radiation. For a cloudless day, Rs is roughly 75% of extra terrestrial radiation. On a cloudy day, the radiation is scattered in the atmosphere, but even with extremely dense cloud cover, about 25% of the extra terrestrial radiation may still reach the earth's surface mainly as diffuse sky radiation. Extra terrestrial radiation (Ra) is the solar radiation received at the top of the earth's atmosphere on a horizontal surface. If the sun is directly overhead, the angle of incidence is zero and the extraterrestrial radiation is 0.0820 MJ m-2 min-1. Solar radiation is also known as global radiation, meaning that it is the sum of direct shortwave radiation from the sun and diffuse sky radiation from all upward angles.

4.1.8. Net Radiation

The distribution net radiation showing on Figure 11 as follows:

Acquisition: 6/3/2003 Acquisition: 4/18/2004 Acquisition: 6/5/2004

Acquisition: 7/23/2004 Acquisition: 4/21/2005 Acquisition: 7/10/2005

Acquisition: 8/11/2005 Acquisition: 10/1/2006 Legend (Wm-2) Figure 11. Distribution of net radiation (Wm-2) in Karawang district

Net radiation is calculated based on position of latitude and time of Julian day. Total net radiation is shown in Table 9.

Table 9. Total net radiation in Karawang district

6/3/2003 4/18/2004 6/5/2004 7/23/2004 4/21/2005 7/10/2005 8/11/2005 10/1/2006

0-50 0 0 0 0 0 0 0 0

50-100 0 0 156 0 0 0 0 0

100-150 23 40 6,014 7 1,187 121 273 0

150-200 181 415 6,923 676 7,719 739 658 6

200-250 160,020 2,876 146,872 144,047 34,338 155,413 119,073 257 250-300 11,995 169,079 12,893 28,689 131,469 15,098 52,908 187,332

300-350 0 1,167 0 0 1,035 0 0 5,582

350-400 0 0 0 0 0 0 0 0

No Data 21,022 19,664 20,382 19,822 17,493 21,871 20,329 63 Total 193,241 193,241 193,241 193,241 193,241 193,241 193,241 193,241

Rn/ Date Area (ha)

In April Karawang district has net radiation between 250-300 Wm-2, in May-August 200-250 Wm-2 and October 250-300 Wm-2. In October almost all area receive net radiation around 250-300 Wm-2, or 187,000 hectare from total area of Karawang (193000 hectare). Geographically in Karawang district the influence on amount of the net radiation which reaches the land surface is not significant.

4.1.9. Soil Heat Flux

Soil heat flux distribution is shown in Figure 12. Based on Table 10 it is found that in Karawang district potential energy for soil heating is 30-40 Wm-2, except for the data of October 01, 2006 when total energy used to heat soil is about 40-50 Wm-2 for areas near water body or fish pond and for water body and fish pond it is about 30-40 Wm-2.

Table 10. Area of energy for soil heat flux

6/3/2003 4/18/2004 6/5/2004 7/23/2004 4/21/2005 7/10/2005 8/11/2005 10/1/2006

0-10 445 437 551 496 551 544 501 10

10-20 0 0 84 1,784 0 0 0 2

20-30 12,385 333 29,965 47,818 9,181 71,299 1,085 3

30-40 159,389 172,806 142,259 123,321 166,016 99,527 171,327 65,900

40-50 0 1 0 0 0 0 0 127,263

50-60 0 0 0 0 0 0 0 0

No Data 21,022 19,664 20,382 19,822 17,493 21,871 20,329 63 Total 193,241 193,241 193,241 193,241 193,241 193,241 193,241 193,241

Soil / Date

Acquisition: 6/3/2003 Acquisition: 4/18/2004 Acquisition: 6/5/2004

Acquisition: 7/23/2004 Acquisition: 4/21/2005 Acquisition: 7/10/2005

Acquisition: 8/11/2005 Acquisition: 10/1/2006 Legend (Wm-2)

Figure 12. Distribution energy for soil heat flux (Wm-2) in Karawang district 4.1.10. Sensible Heat Flux

Distribution energy used to heat air or sensible heat flux is shown in Figure 13. Sensible heat flux ranges from 50-100 Wm-2 and for several areas are

about 100-150 Wm-2. Except data in October 01, 2006 where total energy used is higher than the other data about 100-200 Wm-2. The dry months before October 2006 is long i.e. about 4-5 months, therefore the total energy used to heat air is high.

Acquisition: 6/3/2003 Acquisition: 4/18/2004 Acquisition: 6/5/2004

Acquisition: 7/23/2004 Acquisition: 4/21/2005 Acquisition: 7/10/2005

Acquisition: 8/11/2005 Acquisition: 10/1/2006 Legend (Wm-2) Figure 13. Distribution energy for sensible heat flux in Karawang district

4.1.11. Evapotranspiration

The total energy used for evapotranspiration ranged from 100-200 Wm-2. Figure 14 presents distribution of energy use for evapotranspiration in Karawang district for vegetation or non vegetation cover. Based on October 2006 data, the distribution is different from other data, because part of Karawang area uses energy about 50-100 Wm-2. It shows that amount of water content in soil or vegetation is low than total energy needed for evaporation. Land cover such as water body, fish pond and cloud needs higher energy about 200-250 Wm-2. It indicates that an area covers with water or cloud needs more energy for evaporation. Furthermore, it will decrease total energy used for soil and air heating.

Acquisition: 6/3/2003 Acquisition: 4/18/2004 Acquisition: 6/5/2004

Acquisition: 8/11/2005 Acquisition: 10/1/2006 Legend (Wm-2) Figure 14. Distribution energy for evapotranspiration in Karawang district 4.1.12. Evaporative Fraction

Evaporative fraction constitutes index value from ratio amount of energy used for evapotranspiration with amount of net radiation minus soil heat flux. EF value is near or equal one indicating this area is not potential of drought. It is caused by amount of energy focused to evapotranspiration process like transpiration and evaporation. The water balance in soil and vegetation is sufficient for transpiration process of crops and evaporation of the land. This drought indication occurs if amount of energy used for evapotranspiration is low, because soil and vegetation water content is little. EF value is near or equal zero indicating this area is potential of drought.

Table 11. Total area of evaporative fraction (EF) in Karawang district

6/3/2003 4/18/2004 6/5/2004 7/23/2004 4/21/2005 7/10/2005 8/11/2005 10/1/2006

0-0.1 0 2 0 9 0 0 2 2,980

0.1-0.2 7 6 2 107 0 0 8 12,371

0.2-0.3 103 120 54 744 6 32 114 26,028

0.3-0.4 2,636 2,423 1,018 2,105 18 290 1,105 35,673

0.4-0.5 23,141 16,663 8,322 5,404 197 1,725 5,842 35,257 0.5-0.6 46,567 72,356 48,117 16,599 6,880 4,687 16,207 29,567 0.6-0.7 57,529 62,839 61,347 30,174 56,934 10,089 37,320 26,077 0.7-0.8 34,590 17,777 36,808 28,655 77,267 35,343 54,107 19,811 0.8-0.9 7,571 1,231 10,130 25,554 18,953 67,100 51,531 5,180

0.9-1.0 75 0 7,062 64,069 15,493 52,103 6,677 233

No Data 21,022 19,825 20,382 19,822 17,493 21,871 20,329 63 Total 193,241 193,241 193,241 193,241 193,241 193,241 193,241 193,241

Acquisition: 6/3/2003 Acquisition: 4/18/2004 Acquisition: 6/5/2004

Acquisition: 7/23/2004 Acquisition: 4/21/2005 Acquisition: 7/10/2005

Acquisition: 8/11/2005 Acquisition: 10/1/2006 Legend

Figure 15. Distribution of evaporative fraction (EF) in Karawang district

Table 11 and Figure 15 show the distribution of evaporative fraction. The dry or wet seasons in 2003-2005 have low potential of drought, because there is enough water for evapotranspiration. Water source can be supplied by rain and

irrigation. On the contrary in 2006 there is a potential for drought in several locations of Karawang.

4.1.13. Irrigation

The source for water irrigation from PJT II Jatiluhur is divided into 3 locations that is Curug dam for southern and eastern areas and Walahar dam for northern area. Northern area receives the much water supply from irrigation. The water debit in Walahar dam during period January-September 2006 is shown in Table 12 and for irrigation network is shown in Figure 16.

Table 12. Discharge of water in Walahar dam (m3/s) and Volume (m3)

Period

Water Use (Q1)

Available Water

(Q2)

Water volume of Q1

Water volume of Q2

1-15 January 90.48 257.10 117,268,300.80 333,202,636.80

16-31 January 65.52 290.36 84,916,593.00 376,311,420.00

1-15 February 57.00 163.90 73,868,630.40 212,414,486.40

16-28 February 63.86 183.13 82,764,952.62 237,341,763.69

1-15 March 59.86 125.88 77,582,348.31 163,140,579.69

16-31 March 61.24 69.36 79,369,921.11 89,884,777.85

1-15 April 76.75 92.04 99,466,006.15 119,285,036.31

16-30 April 82.19 103.91 106,522,825.85 134,673,341.54

1-15 May 75.24 76.28 97,515,725.54 98,864,363.08

16-31 May 76.92 81.40 99,690,831.00 105,488,730.00

1-15 June 84.01 87.67 108,870,480.00 113,623,171.20

16-30 June 66.82 67.24 86,599,411.20 87,141,657.60

1-15 July 75.61 79.59 97,985,289.60 103,154,083.20

16-31 July 71.80 78.22 93,055,149.00 101,372,472.00

1-15 August 53.20 57.56 68,953,248.00 74,595,686.40

16-31 August 54.46 54.49 70,581,196.80 70,625,347.20

1-15 September 40.16 64.67 52,045,027.20 83,809,641.60

16-30 September 48.15 91.61 62,396,092.80 118,732,694.40

The water of Walahar dam is distributed to irrigation network in Karawang district. This area belongs to Division 2 of PJT-II Jatiluhur. Based on the data

mentioned above, available water supply is higher than water utilized January to September 2006.

North Tarum Networks

West Tarum Networks

East Tarum Networks

4.1.14. Drought Indicator

Rain is the main source of water in the land for lives of crop, animal, and human. Decrease of land water can be identified by condition of surface cover and open surface. Based on evaporation fraction as indicator of drought level it could be concluded that actual rainfall data for Karawang district differed from time to time. For dry months the accumulation of rainfall is 100 mm or less per month. Actual occurrence of rainfall in several locations in Karawang is shown in Figure 17. Monthly average data from 1971 until 2000 are used. Monthly average rainfall data for 30 years indicate that the dry season in Karawang district occurs from May to September and rainy season from October to April.

Rainfall in Batujaya

0 50 100 150 200 250 300 350 400 450 500 550

1 2 3 4 5 6 7 8 9 10 11 12

Month R a in fa ll ( m m )

Rainfall in Karaw ang

0 50 100 150 200 250 300 350 400

1 2 3 4 5 6 7 8 9 10 11 12

Month R a in fa ll ( m m )

Rainfall in Jatisari 0 50 100 150 200 250 300

1 2 3 4 5 6 7 8 9 10 11 12

Month Ra in fa ll ( m m )

Figure 17. Monthly average rainfall pattern in Karawang district period from 1971 to 2000 (Source: BMG)

According to the rainfall pattern the Landsat data used in this research can be grouped based on season as shown in Table 13.

Table 13. Acquisition data base on condition of season

Date Season

3-Jun-2003 Dry Season 18-Apr-2004 Wet Season

5-Jun-2004 Dry Season 23-Jul-2004 Dry Season 21-Apr-2005 Wet Season

10-Jul-2005 Dry Season 11-Aug-2005 Dry Season 1-Oct-2006 Dry Season

Based on average rainfall condition the irrigation capacity decreases as shown, the decrease in ability of soil to save water, the leaves vegetation become yellowish adjusting to the long dry months. The decrease of irrigated water is correlated with increase of wide areas of drought vulnerability in Karawang district. Nevertheless, irrigation water supply from PJT II Jatiluhur especially in division II Karawang provides enough water and decreases drought potential areas.

Result analysis for drought vulnerability in Karawang district by using satellite image Landsat ETM 7+ indicates that in the dry month only a small area is vulnerable of drought. Relatively wider areas of drought potential is detected at the beginning of rain season i.e. acquisition Landsat image in October 1, 2006.

Evaporative fraction (EF) values used to determine area of drought vulnerability range from 0 – 0.4.

Landsat data June 3, 2003 analysis indicates potential of drought vulnerability is 2,746 Ha. Water availability in soil for June is adequate. Indicator of water availability can be seen by land cover condition with relatively green vegetation index and high energy used for evaporation that indicates water for evapotranspiration from soil and crop is available in the soil, only a small area in Karawang is potential of drought, which means most energy is used for sensible heat flux.

The same result is shown from Landsat image analysis for 2004 and 2005. In 2004 there is increase of drought vulnerability from 1,074 Ha in June to 2,965 Ha in July. The same trend is observed for analysis in 2005 where increased area of drought is from 322 Ha in July to 1,229 Ha in August. Similar increase of drought vulnerability area is shown for result analysis in 2006. The analysis is based on data acquisition at the start of the rainy season, but climate conditions i.e. delay of the start of rainy season i.e. October is still the dry season. About 77,057 Ha areas in Karawang indicated drought vulnerability.

The analysis results cover all areas of Karawang i.e. irrigated and non irrigated lands. Data of paddy filed areas obtained from the Department of Agriculture for Karawang district and after being overlaid between paddy fields as irrigated land and drought vulnerability area showed that the total area indicating drought vulnerability for irrigated land in June 2003, June 2004, July 2004, July 2005, August 2005 and October 2006 are 640 ha, 524 ha, 572 ha, 21 ha, 126 ha and 41,893 ha, respectively.

Figure 18. Paddy field areas in Karawang District (Source: Department of Agriculture)

Based on statistical data as shown in Table 14 it indicates that area of drought vulnerability which could not be recovered by irrigation water from 2003 until 2006 has increased from 915 Ha in 2003 to 1,142 Ha in 2006.

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INTEGRATING REMOTE SENSING DATA AND ENERGY BALANCE MODELING FOR DETECTION OF DROUGHT AND

ITS PUBLICATION IN THE INTERNET (Case Study of Karawang District, West Java)

ADI WITONO

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

MASTER OF SCIENCE IN INFORMATION TECHNOLOGY FOR NATURAL RESOURCES MANAGEMENT

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY June 2008

Research Title : Integrating Remote Sensing Data and Energy Balance Modeling for Detection of Drought and Its Publication in the Internet (Case Study of Karawang District, West Java)

Student Name : Adi Witono

Student ID : G051050101

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

Approved by, Advisory Board

Dr. Ir. Tania June, M.Sc Ir. Iwan Setiawan, PM

Supervisor Co-Supervisor

Endorsed by,

Program Coordinator Dean of Graduate School

Dr. Ir. Hartrisari Hardjomidjojo, DEA Prof. Dr. Ir. Khairil A. Notodiputro, MS

Date of Examination: Date of Graduation:

ACKNOWLEDGMENT

First of all I thanks to my God, my Family who has once more granted me success in my studies. I pay my heartfelt love and tribute to my beloved wife, Hetti Kurniasih who together with you waited patiently. I dedicate all my success to you.

Further, I would like to express my gratitude and sincerer appreciation to the following that contributed to my studies and success in one way or the other:

1. The LAPAN Institution thought the Fellowship Program for granting me fellowship to study in MIT Biotrop IPB Bogor Indonesia;

2. Dr. Tania June, M.Sc my research supervisors for all her valuable guidance and usefull advise during my MSc proposal preparation and MSc thesis writing;

3. Ir. Iwan Setiawan PM, my research coo-supervisors for all his valuable guidance and useful advice during my MSc proposal preparation and MSc thesis writing;

4. Prof. Dr. Ir. Hidayat Pawitan,M.Sc as examiner of this thesis for his positive ideas and inputs.

5. Special thanks go to all the members of lecturers and staff in MIT Biotrop IPB Bogor;

6. Finally, I thank my fellow MSc in IT for NRM students-2005 for the wonderful student working relations we shared together. I will always value and treasure all the memories and laughter we shared together in class, computer cluster and cafeteria.

CURRICULUM VITAE

The author was born in Banyuwangi, East Java on December 29 1973, Indonesia. He is the youngest son of Tumijo and Suminah. He finished his Elementary, Junior, and High school in Government School, Banyuwangi. He received him undergraduate diploma from IPB, Faculty of Mathematic and Natural Sciences in field of Agrometeorology in 1999. Since 2003 to present, he worked for National Institute of Aeronautic and Space (LAPAN) Indonesia.

In 2005, he received a scholarship to study Master Science in Information Technology for Natural Resource Management in Bogor Agricultural University and received his Master degree in 2008.