Rice Crop Spatial Distribution And Production Estimation Using Modis Evi (Case Study Of Karawang, Subang, And Indramayu Regency)

RICE CROP SPATIAL DISTRIBUTION AND

PRODUCTION ESTIMATION USING MODIS EVI

(case study of Karawang, Subang, and Indramayu Regency)

JAROT MULYO SEMEDI

GRADUATE SCHOOL

BOGOR AGRICULTURE UNIVERSITY

BOGOR

STATEMENT

I, Jarot Mulyo Semedi, hereby stated that this thesis entitled:

RICE CROP SPATIAL DISTRIBUTION AND

PRODUCTION ESTIMATION USING MODIS EVI

(case study of Karawang, Subang, and Indramayu Regency)

is a result of my own work under the supervision of advisory board during the period of May 2011 until July 2012 and that it has not been published ever. The content of this thesis has been examined by the advisory board and external examiner.

Bogor, July 2012

Jarot Mulyo Semedi

ABSTRACT

JAROT MULYO SEMEDI. RICE CROP SPATIAL DISTRIBUTION AND PRODUCTION ESTIMATION USING MODIS EVI (case study of Karawang, Subang, and Indramayu Regency). Under supervision of VINCENTIUS P. SIREGAR and HARTANTO SANJAYA.

Rice in Indonesia generally is harvested twice a year (80-90 growing days) in well irrigated areas and harvested once a year (100-130 growing days) in non-irrigated areas. Changes in plant phenology are considered to be a most sensitive indicator of plant responses to climate change observable on remotely sensed images. Vegetation Index (VI) is used to diagnose and predict crop behavior over time by using multi temporal Enhanced Vegetation Index (EVI) from MODIS imageries to estimate crop areas. The EVI will take full advantage of MODIS measurement capabilities to correct for various distortions in the reflected light caused by the particles in the air as well as the ground cover below the vegetation. The classified EVI were compared with crop statistic of the research area to detect which group of classification has the rice growth pattern using regression analysis. Both estimation on rice field area and production from MODIS EVI were under estimated compared to BPS data with error value of 7,38% for rice field area and 10, 36% for rice productivity. The temporal pattern of MODIS EVI images for two groups of classification (Group D and F) shows normal pattern of rice growth since 2008 through 2010, while different pattern showed by Group G, H and J. The last three classes shows normal growing pattern in 2008 and 2009, but in 2010 show disturbed pattern with many variation of EVI value through time and it was confirmed with the decreasing production number in BPS data. The production estimation of rice was very depends on the variety of rice being planted in the research area. The errors of rice production calculation can be minimized by identifying the areas of each rice varieties

ABSTRAK

JAROT MULYO SEMEDI. DISTRIBUSI SPASIAL TANAMAN PADI DAN ESTIMASI PRODUKSI DENGAN MENGGUNAKAN CITRA MODIS EVI (Study Kasus Kabupaten Karawang, Subang, dan Indramayu). Dibawah bimbingan VINCENTIUS P. SIREGAR dan HARTANTO SANJAYA.

Padi di Indonesia pada umumnya dapat dipanen sebanyak dua kali dalam satu tahun (untuk masa tanam 80-90 hari) pada perwahan yang memilliki irigasi dan satu kali dalam satu tahun pada persawahan tadah hujan (untuk masa tanam 100-130 hari). Perubahan pada fenologi tanaman merupakan indikator yang cukup sensitif terhadap adanya perubahan iklim, dan hal tersebut dapat diamati dengan teknologi penginderaan jauh. Indeks Vegetasi (IV) dapat digunakan untuk menggambarkan fenologi dari tanaman dengan menggunakan formula Enhanced Vegetation Index (EVI) dari sensor MODIS yang di rekam secara temporal sehingga dapat juga digunakan untuk memperkirakan luas lahan sawah. EVI memiliki kelebihan dibanding Indeks Vegetasi yang lain dengan mampu mengurangi distorsi yang disebabkan oleh ganguan atmosfir yang disebabkan oleh uap air. Data temporal EVI yang sudah diklasifikasi kemudian dibandingkan dengan data statistik pertanian menggunakan analisis regresi untuk menentukan kelas EVI yang mewakili pola pertumbuhan tanaman padi. Hasil dari perkiraan luas tanaman padi menggunakan data MODIS EVI menunjukkan bias sebesar 7,38% lebih kecil dibanding dengan data dari BPS dan perkiraan untuk produktivitas padi menghasilkan bias sebesar 10,36% lebih sedikit dari data BPS. Dari lima kelas EVI yang menunjukkan tutupan lahan sawah, terdapat dua kelas (Grup D dan F) yang menunjukkan pola pertumbuhan padi yang normal dari tahun 2008 hingga 2010. Sedangkan untuk tiga grup lainnya (Grup G, H dan J) hanya menunjukkan pola normal pada rentang waktu 2008 hingga 2009 dan pada tahun 2010 terdapat pola pertumbuhan yang tidak normal. Fenomena ini juga divalidasi dengan data BPS yang menurun pada tahun 2010. Hasil dari perkiraan produksi padi sangat bergantung terhadap varietaas padi yang ditanam di wilayah penelitian. Dengan adanya informasi varietas padi yang lebih detil akan mengurangi bias pada perkiraan produksi padi.

SUMMARY

JAROT MULYO SEMEDI. RICE CROP SPATIAL DISTRIBUTION AND PRODUCTION ESTIMATION USING MODIS EVI (case study of Karawang, Subang, and Indramayu Regency). Under supervision of Vincentius P. Siregar and Hartanto Sanjaya.

Indonesia is one of the rice producers in the world and most of the rice fields is located in Java Island which have the productivity of 60% of nearly 4 x 106 ton of rice (Amien et al., 1996). Rice in Indonesia generally is harvested twice a year (80-90 growing days) in well irrigated areas and harvested once a year (100-130 growing days) in non-irrigated areas (Indonesian Agency for Agricultural Research and Development, 2009). In Indonesia rice usually planted in the beginning of rainy season in October - December then to be harvested in February (Directorate General of Food Crops, 2009).

Optical satellite remote sensing provides a viable means to meet the requirement of improved regional-scale detection and mapping of rice fields. Changes in plant phenology are considered to be a most sensitive indicator of plant responses to climate change observable on remotely sensed images (Linderholm, 2006). Vegetation Indices (VI) has proved particularly popular in the monitoring and characterization of plant cover. VI is used to diagnose and predict crop behavior over time and has been implemented by Potgieter et al. (2007) and Xiao et al. (2005) by using multi temporal Enhanced Vegetation Index (EVI) from MODIS imageries to estimate crop areas. The EVI will take full advantage of MODIS measurement capabilities to correct for various 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 (Earth Observatory, 2009). It is becoming evident that studies of the growing season of land vegetation have become an important scientific issue for research into global climate change. The objectives of this research are to obtain the spatial distribution of rice fields in Karawang, Subang, and Indramayu Regency as national rice barn and to calculate estimation of rice production using medium resolution MODIS satellite imagery.

The main data used for this research is MODIS EVI imagery with the resolution of 250m by 250m with the acquisition date ranging from 1 January 2008 to 31 December 2010. The landuse map and rice production statistic were acquired from Badan Informasi Geospasial (BIG/formerly known as BAKOSURTANAL) and Badan Pusat Statistik (BPS) that will be used to validate the spatial distribution and production estimation of rice field in the research area.

The first technique to do is to change the 16-day of EVI MODIS image projection from sinusoidal projection into geographic projection, and the results were used to do next process. All of the 69 EVI MODIS satellite data ranging from January 2008 to December 2010 then were stacked to create an image with 69 bands that will make it able to be analyzed as multi temporal image of 16 days interval of EVI data. Next of the process is to subset to 69 bands EVI image with administrative boundary of Karawang, Subang, and Indramayu Regency and resulting EVI image of research area.

Unsupervised classification of 69 layers of MODIS EVI image was used as the first step to detect rice field in the research area. The signature file from the classified image was used to determine the temporal pattern (phenology) of rice. The result then validated by rice field map derived from landuse map of BIG. Once the area of rice field calculated from the image, the estimation of production can be calculated by multiplying the area by rice productivity. Rice productivity was identified by recognizing the dominant rice species that planted in the research location. Rice calendar data is used to identify the growing stages of rice in Karawang, Subang, and Indramayu Regency. The combination between rice calendar data and Landuse map of West Java Province will provide the information of rice growing season in each area of west java or in other word we could regionalized growing season of rice in research area.

45 classes are selected based on the divergence separability which can explain the patterns of EVI behavior from 2008 to 2010 with the interval of sixteen days. For further analysis, a process of averaging the data annually is being done, as well as the grouping the classes with similar pattern. The crop statistics of research location were attained in tabular format which consists of the number of yield in tons for each Kecamatan of the Regency. The analogue crop area data reported in hectares was entered into Microsoft Excel used in the data processing for this study as an agricultural parameter to map rice distribution and calculate the area estimation.

By using multiple step-wise regression of EVI value of the 19 Classes data and crop production based on BPS data, rice field phenology pattern were shown by 5 out of 19 classes. Each of the EVI classes that shows the phenological pattern of rice in research area then were examine identify the growing season for each location to determine the crop calendar.

The temporal EVI images show the difference of planting date in research location. From the EVI temporal pattern, the planting and harvesting date were rotating starting from the south area towards north of the area. The type of rice field in the research location were dominated by irrigated rice field which causing that the planting and harvesting date moving towards north through time were the irrigation water started from the south region.

Each EVI classes then were examined to determine the growing season of rice for each class as a base for generating a crop calendar. Growing seasons of rice were varied according MODIS EVI temporal pattern. The variations ranging from 96-128 days and for each class there is difference days of planting pattern ranging from 16-48 days where the changes are moving backwards according to number of classes.

The result from regression between classified image and rice production were able to detect the area of rice within 250 by 250m pixel with R2 of 0,89. Based on the regression result, the estimation rice field area can be calculated by summing the area of Group Class.

The dominant rice variety that being planted in the research area based on Balai Besar Padi – Ministry of Agriculture are Ciherang, IR64, and Cilamaya Munjul. The productivity of rice variety in the research area is ranging from 5 to 8 tons/Ha. To estimate the total production of rice in the research area, the total rice field area then multiplied by the average of rice productivity (in this case is 6,5

tons/Ha). The result of estimation of production is 3.696.573,09 Tons for Karawang, Subang, and Indramayu Regencies.

Based on the pattern of temporal MODIS EVI images for Group A, Group B, and Group C, the growing season of rice shows normal pattern of rice growth since 2008 through 2010. Different pattern showed by Group D and Group E. The last two classes shows normal growing pattern in 2008 and 2009, but in 2010 show disturbed pattern with many variation of EVI value through time.

Based on the result of the research, it can be concluded that MODIS EVI with 250m by 250m resolution able to view spatial distribution of rice field in Karawang, Subang, and Indramayu Regency. The estimation of rice field area based on MODIS EVI result were under estimate compare to the rice field area based on landuse map from government data (BPS), but the errors only 7,38% for rice field area and 10,36% for rice production. The 250m by 250m image resolution also able to described rice phenology and its rotating growing season over the research area. This resolution also prove that MODIS EVI able to identify rice field and age more detailed compared by previous research using NOAA imagery which has 1km by 1km spatial resolution. Based on the temporal analysis, the rotation of growing season was started from the southern part of research area and moving towards north. The rotation of growing season show difference from east to west because of the research area has different irrigation river system which gave nearly the same schedule. Indramayu Regency has an early 16-48 days early planting of rice compared to Karawang and Subang Regency. The production estimation of rice was very depends on the variety of rice being planted in the research area. The errors of rice production calculation can be minimized by identifying the areas of each rice varieties.

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RICE CROP SPATIAL DISTRIBUTION AND

PRODUCTION ESTIMATION USING MODIS EVI

(case study of Karawang, Subang, and Indramayu Regency)

JAROT MULYO SEMEDI

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

GRADUATE SCHOOL

BOGOR AGRICULTURE UNIVERSITY

BOGOR

Research Title : RICE CROP SPATIAL DISTRIBUTION AND PRODUCTION ESTIMATION USING MODIS EVI (case study of Karawang, Subang, and Indramayu Regency)

Name : Jarot Mulyo Semedi

Student ID : G051080111

Study Program : Master of Science in IT for Natural Resource Management

Approved by, Advisory Board

Signed Signed

Dr. Vincentius P. Siregar, DEA Hartanto Sanjaya, S.Si, M.Sc

Supervisor Co-Supervisor

Endorsed by,

Program Coordinator Dean of The Graduate School

Signed Signed

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

Date of Examination: Date of Graduation: July 25th, 2012

ACKNOWLEDGEMENT

Alhamdulillah, thank you to The Almighty Allah SWT for all the blessing for me during my study in MIT and finished my thesis entitled Rice Crop Spatial Distribution and Production Estimation (case study of Karawang, Subang, and Indramayu Regency). This thesis was written as a requirement to complete the master program at Master of Science in Information Technology for Natural Resources Management (MIT), Bogor Agricultural University. I would like to express my gratitude to:

1. Department of Geography, Faculty of Mathematic and Natural Science, University of Indonesia and also Center for Applied Geography Research for giving financial support for my master study,

2. Dr. Vincentius P. Siregar, DEA and Hartanto Sanjaya, S.Si, M.Sc for supervising, giving inputs, and guiding my research. Also thank you for Dr. Ir. Hartrisari Hardjomidjojo, DEA as external examiner for giving inputs and suggestions to improve my thesis report,

3. My deep appreciation for my family for giving support during my master study from the beginning until the end, especially for my beloved wife Sandra Wulandari,

4. MIT Coordinator, lecturers, and all MIT staff for giving knowledge and helps during my study,

5. All MIT Students and alumni, especially for 2008 and 2009 class. Thank you for all your friendships,

6. All parties who cannot be mentioned one by one. Thank you for the support.

I realize that this thesis report is still far from perfection; hence we need all inputs and suggestions to improve it. Hopefully this thesis can be useful especially in the use of remote sensing application for agriculture related to food security.

Bogor, August 2012

CURRICULUM VITAE

Jarot Mulyo Semedi was born in Semarang, Central Java on May 20th, 1981. He was the fourth child of Soewarno Hadi and Surtinah. He graduate from Undergraduate Degree in Geography in University of Indonesia in 2005, since then He was promoted as assistant lecturer at Geography Department, University of Indonesia with subject majoring Remote Sensing and GIS until present day. In 2007, He joined the Center for Applied Geography Research as Assistant Researcher and has done some research relating to spatial analysis until present day. He was granted for financial support to continue master study at Master of Science in Information Technology for Natural Resources Management (MIT), Bogor Agricultural University in 2008 by Department of Geography.

TABLE OF CONTENTS

TABLE OF CONTENTS ... i LIST OF FIGURES ... iii LIST OF TABLES ... iii LIST OF APPENDIX ... iii 1 INTRODUCTION ... 1

1.1 Background ... 1 1.2 Objectives ... 3

2 LITERATURE REVIEW ... 5

2.1 Vegetation Indices ... 5 2.2 MODIS Satellite Data ... 7 2.3 Time Series Analysis of Rice Crop ... 8 2.4 Related Research ... 11

3 METHODOLOGY ... 15

3.1 Study Area ... 15 3.2 Data Source ... 17 3.3 Methodology ... 17 3.3.1 Data Collection ... 17 3.3.2 Processing MODIS Data ... 20 3.3.3 Rice Calendar Regionalization ... 21 3.4 Regression Analysis ... 22

4 RESULT AND DISCUSSION ... 25

4.1 Temporal MODIS Processing ... 25 4.2 Data Extraction ... 26 4.3 Rice Detection ... 29 4.4 Rice Planting Rotation ... 33

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5 CONCLUSION AND RECOMMENDATION ... 41

5.1 Conclusion ... 41 5.2 Recommendation... 41

REFFERENCE ... 43 APPENDIX ... 46

iii

LIST OF FIGURES

Figure 2-1. Principles of Vegetation Indices ... 6 Figure 2-2. Rice Phenology ... 9 Figure 2-3. Vegetative and Generative phase of rice based on MODIS EVI ... 10 Figure 2-4. Profile of Vegetative phase based on MODIS EVI (Domiri, 2005) ... 10 Figure 2-5. Profile of Generative phase based on MODIS EVI (Domiri, 2005)... 11 Figure 3-1. Location of the research area (show in blue color)... 16 Figure 3-2. Flowchart of the research ... 19 Figure 4-1. MODIS Scene clipped to research location ... 25 Figure 4-2. Classification divergence statistic ... 26 Figure 4-3. Result from unsupervised classification of 45 class EVI ... 27 Figure 4-4. 19 class of EVI supervised grouping ... 28 Figure 4-5. Rice crop spatial distribution ... 31 Figure 4-6. Time series EVI images planting and harvesting time ... 34 Figure 4-7. Time Series of Rice Age Based on EVI Value ... 35 Figure 4-8. Phenology pattern of rice in 2008-2010 ... 36 Figure 4-9. Irrigation Region of Karawang, Subang, and Indramayu Regency .... 37 Figure 4-10. MODIS EVI Pattern of Group D and Group F... 38 Figure 4-11. MODIS EVI Pattern of Group G, Group H, and Group J ... 38 Figure 4-12. Decreasing rice production ... 39

LIST OF TABLES

Table 3-1. Rice Production in West Java year 2007 (BPS, 2008) ... 15 Table 4-1. Derived groups of EVI grouping based on similarity pattern ... 28 Table 4-2. Multiple linear regressions result ... 29 Table 4-3. Rice field area estimation ... 30 Table 4-4. Crop statistic from BPS ... 32 Table 4-5. Growing Seasons of Rice of MODIS EVI Classes ... 36

LIST OF APPENDIX

Appendix 1. EVI Class of Rice Field... 46 Appendix 2. Dominant Rice Variety ... 48

1

INTRODUCTION

1.1 Background

Research and studies in food security theme become important nowadays because of the increasing of population in trough out the world and the lack of agriculture land to support food as a basic need for the living. The increasing of population in the world will give effect on the decreasing of agriculture land for food. Humans tend to convert agriculture and forest land in their neighborhood to become settlement and did not consider the effect on future generation. Decreasing GSL results in low yields production because of the short period between planting and harvest time. On the other hand increasing GSL gives the opportunity for multiple cropping times. However, this also depends on water availability (Linderholm, 2006).

Rice in Indonesia is becoming the primary food for the people of Indonesia since the government announced that the country were able to self-supporting rice for the people in the 1980’s. Indonesia is one of the rice producer in the world and most of the rice fields is located in Java Island which have the productivity of 60% of nearly 4 x 106 ton of rice (Amien et al., 1996). Rice in Indonesia generally is harvested twice a year (80-90 growing days) in well irrigated areas and harvested once a year (100-130 growing days) in non-irrigated areas (Indonesian Agency for Agricultural Research and Development, 2009). The need of sufficient water to grow makes rice plantation vulnerable to drought and flood (UNCTAD, 2009). In Indonesia rice usually planted in the beginning of rainy season in October - December then to be harvested in February (Directorate General of Food Crops, 2009).

The decreasing of agriculture land in Java Island gives contribution to the decreasing rice production of Indonesia. In 2011 Indonesia were consider as four biggest rice importers in the world along with Nigeria, Philippines, and Saudi Arabia. The unpredictable climate can also cause the spreading of pest that could disturb rice plantation growth. This condition gives the need for government to develop a monitoring system for rice production and distribution.

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Along with the advance of remote sensing technology in the world today, the need to identify, measure and monitor rice crop are becoming urgent related to food security. Remote sensing technology has a various specification regarding on spatial resolution and temporal resolution with plenty of sensors that able to scan earth surface in many ways. Satellite sensor that mostly use for earth surface detection is optical satellite sensor. Optical satellite remote sensing provides a viable means to meet the requirement of improved regional-scale detection and mapping of rice fields. A new generation of advanced optical sensors, including the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, and VEGETATION (VGT) onboard the SPOT-4 satellite, provide additional shortwave infrared bands that are sensitive to vegetation moisture and soil water (Xiao et al., 2005).

Temporal analysis becomes an alternative in vegetation detection using satellite imagery compared to single time analysis that need very high spatial and spectral resolution to differentiate vegetation species. High frequency of satellite revisiting time gives new approach in identifying vegetation by its growth behavior. The frequent satellite temporal imagery or commonly known as hyper temporal satellite imagery allow us to differentiate vegetation based on its growth stages or phenology. Changes in plant phenology are considered to be a most sensitive indicator of plant responses to climate change observable on remotely sensed images (Linderholm, 2006). Various empirical methods to rapidly process large amounts of remote sensing data and extract the desired information have been proposed. Among these, Vegetation Indices (VI) has proved particularly popular in the monitoring and characterization of plant cover. VI is used to diagnose and predict crop behavior over time and has been implemented by Potgieter et al. (2007) and Xiao et al. (2005) by using multi temporal Enhanced Vegetation Index (EVI) from MODIS imageries to estimate crop areas. The EVI will take full advantage of MODIS measurement capabilities to correct for various 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 (Earth Observatory, 2009).

3

It is becoming evident that studies of the growing season of land vegetation have become an important scientific issue for research into global climate change because of its ability to describe vegetation or crops growth behavior. This study monitors the temporal profiles of MODIS-based vegetation indices of rice crop to detect the growing season change in Java Island that could be threatened by changing climate.

The use of MODIS satellite imagery in this research tries to enhance the previous research of Satellite Assessment of Rice in Indonesia (SARI) Project in 1998 which produce rice phenology using National Oceanic and Atmospheric Administration (NOAA) satellite imagery.

1.2 Objectives

The objectives of this research are to obtain the spatial distribution of rice fields in Karawang, Subang, and Indramayu Regency as national rice barn and to calculate the estimation of rice production using medium resolution MODIS satellite imagery.

2

LITERATURE REVIEW

2.1 Vegetation Indices

Various empirical methods to rapidly process large amounts of remote sensing data and extract the desired information have been proposed. Among these, vegetation indices have proved particularly popular in the monitoring and characterization of plant cover. Such indices capitalize on the strong spectral reflectance gradient exhibited by live green vegetation around 0.7 µm.

Vegetation indices constitute a simple and convenient approach to extract useful information from satellite remote sensing data, provided they are designed to address the needs of specific applications and take advantage of the characteristics of particular instruments. The Landsat and SPOT systems participated in crop control and inventories of various kinds. Landsat Thematic Mapper (TM) 4 band and Multispectral Scanner (MSS) 6 and 7 bands (and SPOT Band 3) are the most sensitive for detecting IR reflectance from plant cells (modified by water content). TM Band 3 and MSS Band 5 (and SPOT Band 2), which measure reflectance in the visible red, provide data on the influence of light-absorbing chlorophyll. Ratio images using these bands help to quantify the amount of vegetation, as biomass, involved in signature responses. Furthermore, the measurement scale has the desirable property ranging from -1 to 1 with zero representing the approximate value of no vegetation; thus negative values represent non-vegetated surfaces (Oindo and Skidmore, 2002). It should be mentioned that in this report the vegetation index range are projected in to screen representation from 0 to 255 using linear stretching method in order to get the integer value of vegetation index and simpler to be analyzes using remote sensing software.

Of the several Vegetation Indexes (VI) developed so far, the most commonly used is the Normalized Difference Vegetation Index (NDVI), which indicates chlorophyll activity and is calculated from (Near IR band - Red band) divided by (Near IR band + Red band). Temporal NDVI profiles in particular are widely used in studying vegetation phonologies, especially those of crops.

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The Enhanced Vegetation Index (EVI) is often employed as an alternative to NDVI because it is less sensitive to these limitations, but requires information on reflectance in the blue wavelengths, which is not available on some satellites and is difficult to extract from broadband radiation measurements. A few remote sensing studies have explored the combination of blue, red, and near infrared (NIR) bands for development of improved vegetation indices that are related to vegetation greenness (Huete et al. 1997 and Gobron et al. 2000). The main requirement of a vegetation indices is to combine the chlorophyll absorbing red spectral region with spectral region with the non absorbing, leaf reflectance signal in the near – infrared (NIR) to provide a consistent and robust m measure of area-averaged canopy photosynthetic capacity.

The enhanced vegetation index (EVI) is an ‘optimized’ vegetation index with improved sensitivity in high biomass regions and an improved vegetation monitoring characteristic via a decoupling of the canopy background signal and a reduction in atmospheric influences, and it is calculated from (Huete et al., 1997).

EVI = Enhanced vegetation index

G = Gain factor (=2.5)

NIR = Near infrared and ρRed–ρBlue is the red/blue reflectance

C1 C2 = Atmospheric resistance red and blue correction coefficients (1 and 6.0)

L = The canopy background brightness correction factor is 7.5 Figure 2-1. Principles of Vegetation Indices

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Based on Xiao (2004), the advanced optical sensor in EVI have additional spectral bands (e.g. blue and shortwave infrared), making it possible to develop time series data improved vegetation indices. In this concept, MODIS imagery was chosen for its particular advantages over Landsat in terms of temporal and spatial resolution. Landsat TM & MSS have a temporal resolution of 16 days and a spatial resolution of 30m (for TM) and 80m (for MSS). MODIS has a temporal resolution of acquiring daily imagery in its orbital period.

2.2 MODIS Satellite Data

Moderate Resolution Imaging Spectroradiometer (MODIS) is a remote sensing sensor onboard EOS Terra and EOS Aqua satellite. 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. The high frequency of MODIS sensor viewing earth surface gives the ability for user to study the dynamic process on the earth surface in medium resolution. The high frequency of temporal satellite data or commonly known as hyper-temporal satellite data also able to understand earth system models and to predict global phenomena that dynamically happens on lands, oceans, and lower atmospheres.

In this study, the daily MODIS data were acquired and calculate the EVI value for each day. Because of the research location located at the tropical area, cloud cover becomes a problem in remote sensing especially for daily remote sensing data like MODIS. By taking the average value of EVI for a certain day interval, cloud cover can be removed but it reduced the temporal resolution. The method on how to process MODIS imagery will be discussed more on the next chapter.

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2.3 Time Series Analysis of Rice Crop

Time Series Analysis (TSA) can produce and analysis many input images, providing both spatial and temporal outputs. Long temporal sequence of regularly acquired data (Hyper temporal image data), such as EVI time series, have been used for monitoring anomalies, drought, vegetation phonology, Land cover characteristics and to estimate crop yields. Crops exhibit distinctive behaviors that are captured by temporal patterns of EVI which have strong periodic characteristics in a year, so cropland can be distinguished from other vegetation types through analysis of their respective phonologies especially those of crops captured by the EVI-profiles. Detecting and monitoring changes that relate to ecosystem processes are still not fully quantified or understand (Bates et al., 2008). It requires among others the integration of spatial and temporal aspects (Beck et al., 2008) where GIS data with more than 30 years of imagery available, play a key role as data source of spatial and temporal information (Schmidt and Skidmore, 2003).

The growing season of rice are divided into eight phases where each phase have different time span and also depends on crop species. Figure 2-2 shows the time span of rice growth for early variety and late variety. Before the rice seed being planted, rice field needed to be flooded. Once the seed planted or at germination phase, it will take 25-30 days to reach tiller initiation phase and the rice leaf starting to grow. The leaf color then turning from yellow into green in the early tillering phase and mid tillering phase. The growth will reach maximum at the panicle initiation where for early variety rice it will take 55-60 days from germination phase and for late variety it will take 65-75 days. The next phase is the flowering phase, where rice plantation started to grow flowers and the leaf color started to turn into yellow. For the early variety rice, this phase were reach in 85-100 days after germination phase and for the late variety rice it needs 100-115 days. The last phase is harvesting, where rice is already developed and ready to be harvested. The early variety rice need 130-145 days after germination phase to be harvested, while for the late variety it needs 140-165 days.

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Based on the rice growth phases, the green color that produced by rice leaf were at the panicle initiation phase. This phase will be detected by the EVI value as the highest value or in the other word is where the crop has the greenest color. The EVI value will decrease when the rice plant started to flowering and then harvested.

Figure 2-2. Rice Phenology

In general, the phenological pattern of rice has an almost symmetrical bell shape. The vegetative growth stage will correlated with the increasing EVI value until it reaches the maximum value in between 55 – 65 Day After Planting (DAP). It should be understand that the peak of the bell shape pattern is not the phase where rice are ready to be harvested, but the phonological pattern were based on EVI value or based on green index of rice. The peak on the phenological pattern means that the EVI value reaches maximum and the objects shows very green color from it leafs. In rice phenology, the greenest leaf color happens in the panicle initiation phase before the rice start to flowering.

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Figure 2-3. Profile of Vegetative and Generative phase of rice based on MODIS EVI (Domiri, 2005) The profile shown in figure 2-3 were divided into two model by Domiri (2005), the vegetative phase and generative phase.

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Figure 2-5. Profile of Generative phase based on MODIS EVI (Domiri, 2005)

2.4 Related Research

Research on rice has long carried out by researchers because of its relation to food security, especially in Indonesia. As one of agricultural country in the world, most of Indonesian people always consume rice for their main course. The objective of researches was focusing on estimating rice production to fulfill the people needs of main food. The use of remote sensing technology is already common in agriculture research because of its ability to scan the earth surface remotely. The combination of remote sensing data and temporal analysis becomes a good combination in predicting the agriculture plantation behavior over time.

High frequency of visiting time of satellite or commonly known by hyper-temporal satellite data are able to detect earths land cover behavior over time. In 2007, Ramoelo did a research in Portugal that try to map land cover changes by using hiper-temporal satellite imagery at a country level. The method used can play a crucial role in characterizing and quantifying the land cover changes in terms of “conversions” and “modifications” useful to study habitats of species

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over a period of time. The NDVI derived land cover can be one of the imperative inputs for species distribution modeling. The research showed that in order to reduce the problem of co linearity in species distribution predictor variables, NDVI derived land cover can be used as a proxy for precipitation and digital elevation model (DEM) derived parameter in Portugal. Finally, NDVI derived land cover data has good overall accuracy (85%) which it makes suitable for use as an input for many natural resource applications, such as herpetofauna studies as well as various environmental monitoring and planning applications.

One of the researches that try to predict rice production is Satellite Assessment of Rice in Indonesia (SARI) Project that was initiated in 1997 by BPPT and Ministry of Agriculture in Indonesia. The project was trying to develop a monitoring system of rice in Indonesia using satellite imagery with optical and SAR sensors. A rice-mapping method using SAR sensor has been established that is based on the temporal change of the backscatter - the so-called ˝change index˝ - at the field scale. The value of this change index depends on the points during the growing cycle at which the SAR data are acquired, i.e. whether the change is over the growing season when the backscatter is increasing, or between harvesting and the beginning of the next cycle when the backscatter is decreasing. In order to apply this method, these changes need to be accurately quantified. This is made difficult because of speckle, a well-known effect in SAR imagery that gives a noisy aspect to images and introduces errors in the measurements of the backscatter. A rice-field mapping algorithm has therefore been developed that enables one to reduce speckle, derive a suitable change index, and finally separate rice and non rice areas. For the optical sensor, the project uses 1 km by 1 km NOAA imagery and using its NDVI to detect rice growth at coarse scale because of the ability of NOAA satellite to scan earth surface twice a day. The temporal NDVI were able to shows rice crop growth stage over time.

In 2005, Domiri from LAPAN also did a research related to rice detection using satellite imagery. Domiri’s research objective is to observe and detect the age of rice using MODIS satellite and produce rice growth equation model that can estimate rice age in vegetative phase and rice age in generative phase. The use

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of MODIS satellite imagery gives the advantages of higher spatial resolution (250-500m) compared to NOAA satellite imagery.

This research tries to enhance the previous research related to rice crop identification and monitoring. The use of time series MODIS EVI in this research gives more detailed information in term of spatial resolution and the ability to describe the rice planting rotation in the research area. Spatial resolution of MODIS imagery used in this research is 250m by 250m pixel size which is higher than NOAA spatial resolution of 1km by 1km pixel size and gives the ability for MODIS imagery to identify land cover better than NOAA imagery. The use of EVI rather than NDVI in this research also to improve the result in identifying vegetation because EVI has an improved sensitivity in high biomass regions and an improved vegetation monitoring characteristic.

3

METHODOLOGY

3.1 Study Area

The study area of this research is in Karawang, Subang, and Indramayu Regency, West Java Province, where natural resources are abundant in the province. West Java in the year 2007 has 374.850 ha of technical irrigation rice field, while the rice with semi-technical irrigation has 128.465 ha, and area of rice with non-technical irrigation has 435.913 ha. In 2007, these rice fields produced 9.952.990tons of rice where most of the rice was produced in Karawang, Subang, and Indramayu Regency where the total rice production for 3 regencies reaches 3.057.042 tons or 30,7% out of West Java total rice production (see table 3-1).

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Figure 3-1. Location of the research area (show in blue color)

Karawang, Subang, and Indramayu Regency located at the north coast of West Java Province (see figure 3-1). The low land north coast of Java Island is suitable for rice plantation because of its geological condition and the existence of water irrigation from river dam. The alluvial plane that produced by volcanic mountains that exist in the middle part of Java Island were spread along the northern and southern coast of the island and creating a low land of fertile land for agriculture.

The low land of north coast of Java Island not only suitable for agriculture, but this area also becomes the settlement concentration of Java Island. This condition will generate problems in land demand for settlements and for agriculture. Because of Karawang, Subang, and Indramayu were already consider as national rice barn and rice became the primary regional income, the agriculture land especially rice would not be converted to settlements but because of the increasing of population in the area, agriculture land cannot be increased in term of field area. The production of rice in Karawang, Subang, and Indramayu Regency only depends on the variety of rice that has short growing season.

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3.2 Data Source

This research will use TERRA MODIS satellite data. The data satellite will be collect by daily since January 2008 up to December 2010. The data were available for free download from Land Processes Distributed Active Archive Center (LPDAAC) (http://lpdaac.usgs.gov) or from the Warehouse Inventory Search Tool(WIST) (https://wist.echo.nasa.gov).

The spatial resolution of MODIS satellite data used in this research is 250m by 250m pixel size. This resolution of MODIS data expected to be able to differentiate rice field and non rice field landcover in Karawang, Subang, and Indramayu Regency. The 250m by 250m MODIS also suitable to view land cover differentiation within small scale resolution map of the research area.

The image acquisition of MODIS data, image processing and image analyzing will be conducted in computer laboratory.

3.3 Methodology

3.3.1 Data Collection

As stated in previous point, the research study will use Terra MODIS satellite imagery data. The acquisition of MODIS data will download with free of charge from specific website. One requirement to make MODIS data available is the availability of high speed internet connection to download the data.

All the data are available freely from the Land Processes Distributed Active Archive Center (LPDAAC) (http://lpdaac.usgs.gov) or from the Warehouse Inventory Search Tool(WIST) (https://wist.echo.nasa.gov). Because heavy clouds may affect the quality of the images, the 16-day composite surface reflectance images might have more opportunities to avoid the impact of clouds than the daily collected ones. The changes within every 16-day can be ignored and the temporal coverage frequency is fit for the seasonal change detection. Therefore, this study used the 16-day composite surface reflectance images to overcome the cloud problem. Each pixel in the 16-day composite images is the best one in quality within every 16 day on the basis of high observation coverage, low view angle, the absence of clouds, cloud shadows and aerosols

(http://modis-18

land.gsfc.nasa.gov/surfrad.htm), so the selected date might be different for different pixels.

The general of methodology which will be conduct in this research study i.e. pre-processing or acquisition of MODIS data, image processing and the expected result as show in Figure 3.2.

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EVI Time Series

Jan 2008 – Dec 2010

EVI Time Series Geographic Projection

Layer Stacking Re-Projected to Geographic Projection

EVI with 69 Layers

Administrative boundary AOI

Subseting

EVI of AOI

Unsupervised classification

Classified Image of EVI

RICE PRODUCTION ESTIMATION AND CROP CALENDAR

Rice field from RBI Map & Rice Productivity from BPS Temporal Analysis

Supervised classification & Regression Analysis

RICE SPATIAL DISTRIBUTION AND AREA ESTIMATION

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3.3.2 Processing MODIS Data

In order to process MODIS data, there are several remote sensing technique was applied to get the imagery become useable. Vegetation indices were used to enhance the features of the objects of interest in the study. As the blue band is sensitive to atmospheric conditions, it is used to adjust the reflectance in the red band as a function of the reflectance in EVI.

Cloud covers become a problem where a research using MODIS data conducted in tropical area like Indonesia. The best way to remove cloud cover and get a clear image data is to make composite of imagery with different dates for the same location. In this research, the daily MODIS EVI data were composited for each 16 days and resulting of 69 EVI image temporal data ranging from January 2008 to December 2010 with interval of 16 days. The next technique to do is to change the 16-day of EVI MODIS image projection from sinusoidal projection into geographic projection in order to get the common projection that can be overlaid with other data, and the results were used to do next process. All of the 69 EVI MODIS satellite data ranging from January 2008 to December 2010 then were stacked to create an image with 69 bands that will make it able to be analyzed as multi temporal image of 16 days interval of EVI data. Next of the process is to subset to 69 bands EVI image with administrative boundary of Karawang, Subang, and Indramayu Regency and resulting multi temporal EVI image of research area. Once the image was stacked and subsets in to research area, then the image need to be classified to identify the land cover of the research area. In this research the classification process were done in two steps, first is using unsupervised classification method then continued with supervised classification based on temporal behavior of land cover.

The iterative self-organized unsupervised clustering algorithm (ISODATA) of the ERDAS imagines software was used to derive spectral classes from 69 EVI image data layers. The ISODATA procedure is iterative in that it repeatedly performs an entire classification (outputting a thematic raster layer) and recalculates statistics. Self-organizing refers to the way in which it locates clusters with minimum user input. The ISODATA clustering method uses spectral distance, as in the sequential method, but iteratively classifies the pixels, redefines

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the criteria for each class, and classifies again, so that the spectral distance patterns in the data gradually emerge (ERDAS, 1997).

It starts from arbitrary cluster means. In each successive clustering, the means of clusters are shifted. A cluster is a group of pixels (classes) with similar spectral characteristics. The ISODATA utility repeats the clustering of pixels in the image, until either a maximum number of set iterations has been performed (50), or a maximum coverage threshold is reached (set to 1.0). Performing an unsupervised classification is simpler than a supervised classification, because the cluster signatures are automatically generated by the ISODATA algorithm. The user must predetermine the number of iterations and number of resulting clusters (classes). In this research, separate ISODATA runs were carried out to define 5 to 50 classes with interval of 5.

In each run the desired number of classes is produced by the ISODATA clustering Algorithm. The divergence statistical measure of distance (ERDAS, 2003) between defined cluster signatures by run was used to compare the various runs. The best run with a clear distinguished peak in the divergence separability was selected for further study.

After the ISODATA clustering was performed, the image will have result on many types of land cover on research location. The result from ISODATA still gives a coarse classification and still has too many classes; this is why supervised classification still needs to be performed to enhance the result. The supervised classification was done by identifying phenology similarity between classes. Classes that have similar phenology pattern then were combined into one group and so on until each of the groups shows different phenology pattern.

3.3.3 Rice Calendar Regionalization

Rice calendar data is used to identify the growing stages of rice in Karawang, Subang, and Indramayu Regency. The combination between rice calendar data and Landuse map of West Java Province will provide the information of rice growing season in each area of west java or in other word we could regionalized growing season of rice in research area.

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In this research, growing season of rice will be identified by the MODIS EVI value of rice field that shows the green leaf of rice plantations. Temporal MODIS EVI data that show the greenest index of rice plantation will give the picture of rice plantation age over the research area and the multi temporal pattern of MODIS EVI then were analyzed to see the shifting EVI value in research area to determine the rice growing season (start and end) in several location of research area.

3.4 Regression Analysis

The multiple linear regression analysis was used to generate the regression equations. Assuming that variables and crop area statistics were independent of each other and that crop area is a linear combination of multiple predictors, the multiple linear regressions of the districts were carried out using the selected predictors by the stepwise regression.

Multiple linear regressions are used, when y is considered a function of more than one (independent) x variables. Stepwise regression removes and adds variables to the regression model for the purpose of identifying a useful subset of the predictors.

The main approaches are:

- Forward selection, which involves starting with no variables in the model, trying out the variables one by one and including them if they are 'statistically significant'.

- Backward elimination, which involves starting with all candidate variables and testing them one by one for statistical significance, deleting any that are not significant.

The statistical and spatial data pre-processing, provided a data set in tabular format of the EVI classes. The classes were as the predictors with the consolidated crop area statistic as a response variable. The multiple stepwise regression analysis was used to select variables that were significant distributors of crop area. The predictors were subjected to a no constant stepwise linear regression in order to eliminate variables that were not significant to the regression. The elimination

23

was carried out iteratively first entering the variable that explains the most variance in the data, until no more variables could be eliminated. Selected variables that yielded a negative coefficient value though significant were eliminated since they seemed to suggest that negative crop area is existent. The selected variables were then used to run the multiple linear regressions for the different crop areas. Only significant predictors of crop area were included in the multiple linear regression models. The results of the multiple linear regressions were coefficients that had to be spatially distributed in the form of value maps.

4

RESULT AND DISCUSSION

4.1 Temporal MODIS Processing

The 16 days interval of MODIS EVI composite ranging from January 2008 to December 2010 are compiled and stacked using image processing software and resulting one image consisting of 69 layers. To focus more on the study area, the resulting image is clipped by the area of interest which is the three regencies in West Java: Karawang, Subang, and Indramayu (see figure 4.1). Unsupervised classifications were carried out to generate a map with a pre-defined number of classes. Unsupervised indicates that no additional data were used or expert’s guidance applied, to influence the classification approach.

Figure 4-1. MODIS Scene clipped to research location

Regarding the resultant graphs from the divergence separability, 45 classes were chosen from the high average and low minimum separability. The result shows that based on the image spectral characteristic, land cover in the research area were best differentiated using 45 classes of land cover classification.

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Figure 4-2. Classification divergence statistic

4.2 Data Extraction

45 classes are selected based on the divergence separability which can explain the patterns of EVI behavior from 2008 to 2010 with the interval of sixteen days (see figure 4-2). For further analysis, a process of averaging the data annually is being done, as well as the grouping the classes with similar pattern (see figure 4-3). The crop statistics of research location were attained in tabular format which consists of the number of yield in hectare for each one of the regency. The analogue crop area data reported in hectares was entered into Microsoft Excel used in the data processing for this study as an agricultural parameter to distribute crop area.

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Figure 4-3. Result from unsupervised classification of 45 class EVI

The supervised classification was done by grouping temporal pattern that have similar behavior of phenology. When the first classification using ISODATA clustering carried out, the process was based on image spectral characteristic differentiation. Meanwhile on the second classification process, it was done based on temporal behavior (phenology) differentiation of the 45 classes in order to get smaller class. Out of 45 classes, 19 classes were derived from supervised classification process which able to differentiate land cover based on spectral characteristic and temporal characteristic.

The grouping process of EVI classes needs to be done carefully because there are vegetations that have similar pattern throughout a year. Not all classes can be grouped with other, some classes has its own temporal characteristic or phenology so that cannot be grouped with other and stand alone as one group. The result from the classification can be seen in Table 4-1 and figure 4-4 which shows the grouping result of EVI classes and later will be used for identifying rice spatial distribution.

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Table 4-1. Derived groups of EVI grouping based on similarity pattern

No Group EVI Classes No Group EVI Classes

1 A Class 1, 4 11 K Class 21

2 B Class 2 12 L Class 24

3 C Class 3 13 M Class 25, 26, 27

4 D Class 5 14 N Class 28, 29, 35

5 E Class 6 15 O Class 30, 31, 32, 34

6 F Class 7, 8, 9 16 P Class 33

7 G Class 10, 11, 12 17 Q Class 36, 39, 42

8 H Class 13, 14, 15 18 R Class 37, 38, 40, 41, 44

9 I Class 16, 18, 22, 23 19 S Class 43, 45

10 J Class 17, 19, 20

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4.3 Rice Detection

To identify pixel value that represent rice field, multiple linear regression was conducted by comparing the number of pixel in each classification with rice crop area derived from BPS data. Multiple linear regressions on the data based on 19 classes EVI grouping and crop statistic from BPS give result R2 of 0,89 which described that the result shows good correlation between crop area and EVI Group D, Group F, Group G, Group H, and Group J (see table 4-2). The 5 out of 19 class resulted from the regression process then were considered as rice field for next calculation to estimate the rice production in Karawang, Subang, and Indramayu Regency.

Table 4-2. Multiple linear regressions result

Regression Statistics

Multiple R 0.945829 R Square 0.894593 Adjusted R

Square 0.868266 Standard

Error 2202.428 Observations 59 ANOVA

df SS MS F Significance F

Regression 5 2.22E+09 4.45E+08 91.65966 7.69E-25 Residual 54 2.62E+08 4850690

Total 59 2.49E+09

Coefficients

Standard

Error t Stat P-value Lower 95% Upper 95%

Lower 95.0%

Upper 95.0%

Intercept 0 #N/A #N/A #N/A #N/A #N/A #N/A #N/A

Group G 1.034164 0.179695 5.755102 4.2E-07 0.673897 1.394431 0.673897 1.394431 Group D 2.233164 0.334074 6.684648 1.34E-08 1.563387 2.902941 1.563387 2.902941 Group H 1.160912 0.194857 5.95777 1.99E-07 0.770248 1.551576 0.770248 1.551576 Group F 1.013324 0.151267 6.698927 1.27E-08 0.710053 1.316596 0.710053 1.316596 Group J 0.544629 0.150224 3.625456 0.000639 0.243449 0.845809 0.243449 0.845809

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Based on the regression result, the spatial distribution and the estimated area of rice field in the research area can be identified by using formula:

Rice Area (Y) = Area (Group D + Group F + Group G + Group H + Group J)

By summarizing the EVI Group EVI Group D, Group F, Group G, Group H, and Group J, the estimation of rice field can be measured and compared with the existing data from BPS for validation (see table 4-3 and figure 4-5). A bias between estimated area and BPS data can occurred in this process because MODIS EVI data has the spatial resolution of 250m by 250m pixel size, meanwhile the real condition of rice field can be smaller or bigger than 250m by 250m.

Table 4-3. Rice field area estimation

GROUP Area (Ha)

D 19.603,9805

F 73.686,4218

G 86.752,9034

H 65.791,9433

J 38.516,5273

Total Rice Field Area 284.351,7763

The harvested areas from BPS data then were compared with the area estimation result from image data. The data from BPS seems to be larger from the estimation from image data because BPS data measured the harvested area which means that when the rice plantations were harvested twice a year then the area also multiplied by two (most of rice variety planted in the research area were harvested twice a year). To compare the area from BPS data with the result from image data, the estimation area need to be multiplied by 2 times of harvesting which gives the result of 568.703,5526 Ha of estimated harvested area. The error was under estimated compared to BPS data with the difference of 7,38% for rice field area.

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Figure 4-5. Rice crop spatial distribution

Once the area of rice field estimated based on statistic regression, to estimate the rice production information on the rice variety that being planted in the research area are needed. Because most of the rice field in the research area were own by individual and farmers are allowed to plant any type of rice variety on their field, this research only use the dominant rice variety that being planted as the parameter to estimate rice production. The selection of rice variety that being planted by farmers usually coming from the ability of certain rice variety to have high productivity and short growing seasons and also stands for certain pest. The dominant rice variety that being planted in the research area based on information from Balai Besar Padi – Ministry of Agriculture are Ciherang, IR64, and Cilamaya Munjul. The variety of rice and its productivity become the base in estimating the rice productivity. The productivity of Ciherang, IR64, and Cilamaya Munjul are ranging from 5 to 8 tons/Ha. To estimate the total production of rice in the research area, the total rice field area then multiplied by the average of rice productivity (in this case is 6,5 tons/Ha) and harvesting time in

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one year which shown by two bell shape phenology pattern in each EVI groups. The estimation of rice production can be seen in the calculation below:

Rice Production (Ton) = 284.351,7763 Ha * 6,5 Tons/Ha * 2 time harvesting

= 3.696.573,09 Tons

To validate the rice field area and rice production estimation, the result was compared to the existing data of rice production of 2008-2010 collected from BPS (see table 4-4). The statistic data for Karawang Regency was not complete because there was no data about Karawang Regency in Figures year 2010, so the comparison was done using 2009 data. Based on the comparison, the estimation result was under estimate compare to the crop statistic. The error of the estimation are 10,36% for rice production.

Table 4-4. Crop statistic from BPS

REGENCY

2008 2009 2010

HARVESTED AREA (Ha) PRODUCTION (TON) HARVESTED AREA (Ha) PRODUCTION (TON) HARVESTED AREA (Ha) PRODUCTION (TON)

KARAWANG 194,536.00 1,255,118.00 195,670.00 1,362,357.00 NO DATA NO DATA SUBANG 172,447.00 1,091,612.00 185,209.00 1,128,353.00 174,337.00 959,533.00 INDRAMAYU 190,090.00 1,229,476.75 229,784.00 1,588,866.12 239,698.00 1,557,552.30

TOTAL 557,073.00 3,576,206.75 610,663.00 4,079,576.12 414,035.00 2,517,085.30

The estimation results from the calculation were under estimate for both area and production of rice. This condition was caused by the resolution of MODIS imagery that covers 250m by 250m for one pixel. The coarse resolution of MODIS imagery cannot identify rice field smaller than 250m by 250m area and causing that some of the rice fields were not detected and gives less result compared with BPS data. On the other hands, the error value for rice field area were less than 10% which means that more than 90% pixel were able to identify rice field. The high error on production estimation which gives value of 10,36% were mainly caused by the identification of rice variety that being planted in the

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research area. This research only considers three types of rice variety which is dominant in the research area, meanwhile in the field farmers not only planted Ciherang, Cilamaya Munjul, and IR64 rice variety.

4.4 Rice Planting Rotation

The temporal EVI images show the difference of planting date in research location. From the EVI temporal pattern, the planting and harvesting date were rotating starting from the south area towards north of the area (see figure 4-6). Figure 4-6 shows the EVI value in research area where the white color represent the panicle initiation phase of rice plantation where rice leaf is at the greenest color phase. Through the time white color are shifting from south area towards north area or in other word that the planting rotation were started from the south and then shifting to the north. In figure 4-7, rice ages in research area were able to be determined based on EVI value through time. Rice age were identified by growing phases of rice plantation that consist of pre-flooding phase, germination phase, tillering phase, panicle initiation phase, flowering phase, and harvesting phase. The figure also describes the planting rotation of rice through time shifting from south towards north. The type of rice field in the research location were dominated by irrigated rice field which causing that the planting and harvesting date moving towards north through time were the irrigation water started from the south region.

Each EVI classes then were examined to determine the growing season of rice for each class as a base for generating a crop calendar. Growing seasons of rice were varied according MODIS EVI temporal pattern. The variations ranging from 96-128 days and for each class there is a rotating days of planting pattern ranging from 16-48 days where the changes are moving backwards according to number of classes (see figure 4-8 and table 4-5).

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Figure 4-6. Time series EVI images showing the movement of planting and harvesting time (red line showing the boundary of rice field and non-rice field)

35

36

Figure 4-8. Phenology pattern of rice in 2008-2010

Table 4-5. Growing Seasons of Rice of MODIS EVI Classes

G

roup

Growing Season (GS) Days 2008 GS 1 2008 GS 2 2009 GS 1 2009 GS 2 2010 GS 1 2010 GS 2

D 18 Feb – 9 Jun 12 Aug – 16 Nov 4 Feb – 12 Jun 15 Aug – 28 Nov 8 Mar – 15 Jun 2 Aug – 8 Dec

F 1 Jan – 8 May 9 Jun – 13 Sep 19 Jan – 25 Apr 12 Jun – 2 Oct 6 Jan – 14 May 15 Jun – 22 Nov

G (2007) – 22 Apr 24 May – 12 Aug 18 Dec 08 – 24 Mar 11 May – 15 Aug 6 Jan – 28 Apr 15 Jun – 3 Sep

H (2007) – 21 Mar 22 Apr – 27 Jul 16 Nov 08 - 8 Mar 25 Apr – 30 Jul 21 Dec 09 – 12 Apr 14 May – 2 Aug

J (2007) – 5 Mar 6 Apr – 11 Jul 2 Dec 08 – 8 Mar 9 Apr – 28 Jun 6 Jan – 27 Mar 28 Apr – 17 Jul

The planting rotation pattern from south to north area is caused by the water irrigation system that irrigates the rice field in Karawang, Subang, and Indramayu Regency. The water flow of irrigation systems in the research area were started from the south of research area where two dams located and become the water source for irrigation. The research location were irrigated by two river system; Citarum river system with Jatiluhur Dam that irrigate Karawang, Subang, and west part of Indramayu Regency, and Cimanuk-Cisanggarug river system with Jatigede Dam that irrigate most of Indramayu Regency (see figure 4-9). The irrigation systems in Karawang, Subang, and Indramayu Regency become the

37

most important variable in the growing of rice and related to rice crop production. Most of the rice variety that planted in the northern coast of Java Island is very dependent to water irrigation. That is why if there is a problem within water distribution for rice, the production will decrease because of many rice fields cannot be harvested.

Figure 4-9. Irrigation Region of Karawang, Subang, and Indramayu Regency

From the growing season table 4-5 and the spatial distribution of rice field shows that Indramayu Regency has early planting rotation for each growing season (showed by Group J). Meanwhile Karawang and Subang Regency started 16-48 days later which is showed by Group H.

Based on the pattern of temporal MODIS EVI images for Group D and Group F, the growing season of rice shows normal pattern of rice growth since 2008 through 2010. Different pattern showed by Group G, Group H, and group J. The last three classes shows normal growing pattern in 2008 and 2009, but in 2010 show disturbed pattern with many variation of EVI value through time (see figure 4-10 and figure 4-11). The disturbed phenology pattern shows in time range

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of day 945 until day 1089 or in calendar date starting August 2010 until December 2010.

Figure 4-10. MODIS EVI Pattern of Group D and Group F

Figure 4-11. MODIS EVI Pattern of Group G, Group H, and Group J

The crop statistic from BPS in table 4-4 shows that rice productions in 2010 were decreasing compare to 2009 production (see figure 4-12). In Subang Regency the rice production decreases from 1.128.353 Tons in 2009 becoming 959.533 Tons in 2010. While in Indramayu Regency, rice production decreases from 1.588.866,12 Tons in 2009 becoming 1.557.552,3 Tons in 2010.

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Figure 4-12. Decreasing rice production -

500,000 1,000,000 1,500,000 2,000,000

KARAWANG SUBANG INDRAMAYU

2008

2009

5

CONCLUSION AND RECOMMENDATION

5.1 Conclusion

Based on the result of the research, it can be concluded that MODIS EVI with 250m by 250m resolution able to view spatial distribution of rice field in Karawang, Subang, and Indramayu Regency. The estimation of rice field area based on MODIS EVI result were under estimate compare to the rice field area based on landuse map from government data (BPS), but the errors only 7,38% for rice field area and 10,36% for rice production. The 250m by 250m image resolution also able to described rice phenology and its rotating growing season over the research area. This resolution also prove that MODIS EVI able to identify rice field and age more detailed compared by previous research using NOAA imagery which has 1km by 1km spatial resolution. Based on the temporal analysis, the rotation of growing season was started from the southern part of research area and moving towards north of the research area. The rotation of growing season show difference from east to west because of the research area has different irrigation river system which gave nearly the same schedule. Indramayu Regency has an early 16-48 days early planting of rice compared to Karawang and Subang Regency. The production estimation of rice was very depends on the variety of rice being planted in the research area. The errors of rice production calculation can be minimized by identifying the areas of each rice varieties.

5.2 Recommendation

This research has shown that hyper temporal satellite imagery able to described spatial distribution of rice in the research area as an alternative for crop monitoring. For further research there are some recommendations to enhance the analysis on crop analysis:

 The study recommends comparing with various vegetation index (VI) in order to have comparison which VI that able to describe vegetation phenology best.

 The temporal analysis also able to deliver landcover change through VI changes over time by identifying vegetation phenology that grows in the research area.

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46

APPENDIX

48

Appendix 2. Dominant Rice Variety Ciherang

Varietas Padi - Varietas Padi Sawah

Asal persilangan : IR18349-53-1-3-1-3/IR19661-131-3-1//IR19661-131-3-1///IR64 ////IR64

Kelompok : Padi Sawah

Nomor Seleksi : S3383-1d-Pn-41--3-1

Golongan : Cere

Umur tanaman : 116 - 125 hari

Bentuk tanaman : Tegak

Tinggi tanaman : 107 - 115 cm Anakan produktif : 14 - 17 batang

Warna kaki : Hijau

Warna batang : Hijau

Warna telinga daun : Putih

Warna lidah daun :

Warna daun : Hijau

Permukaan daun : Kasar pada sebelah b

Posisi daun : Tegak

Daun bendera : Tegak"

Bentuk gabah : Panjang ramping

Warna gabah : Kuning bersih

Kerontokan : Sedang

Kerebahan : Sedang

Tekstur nasi : Pulen

Kadar amilosa : 23 %

Bobot 1000 butir : 27-28 gram Rata-rata produksi : 5 - 8,5 t/ha Potensi hasil : 5 - 8,5 t/ha Ketahanan terhadap

Hama

: Tahan terhadap wereng coklat biotipe 2 dan 3

Ketahanan terhadap penyakit

: Tahan terhadap bakteri hawar daun (HDB) strain III dan IV

Anjuran : Cocok ditanam pada musim hujan dan kemarau

dengan ketinggian di bawah 500 m dpl.

Pemulia :

49

Teknisi : Tarjat T, Z. A. Simanullang,., E. Sumadi dan Aan A

Di lepas tahun : 2000

IR 64

Varietas Padi - Varietas Padi Sawah Asal persilangan : IR5657/IR2061

Kelompok : Padi Sawah

Nomor Seleksi : IR18348-36-3-3

Golongan : Cere

Umur tanaman : 115 hari Bentuk tanaman : Tegak Tinggi tanaman : 85 cm Anakan produktif : 25 batang

Warna kaki : Hijau

Warna batang : Hijau

Warna telinga daun : Tidak berwarna Warna lidah daun :

Warna daun : Hijau

Permukaan daun : Kasar

Posisi daun : Tegak

Daun bendera : Tegak" Bentuk gabah : Kuning bersih

Warna gabah : Ramping, panjang

Kerontokan : Tahan

Kerebahan : Tahan

Tekstur nasi : Pulen

Kadar amilosa : 27 %

Bobot 1000 butir : 24,1 gram Rata-rata produksi : 5,0 t/ha Potensi hasil : 5,0 t/ha Ketahanan terhadap

Hama

: Tahan wereng coklat biotipe 1, 2 dan wereng hijau

Ketahanan terhadap penyakit

: Agak tahan bakteri busuk hawar daun (Xanthomonas oryzae) - Tahan kerdil rumput

Anjuran : Baik ditanam untuk i sawah irigasi dataran rendah di Jawa Timur Cukup baik untuk padi rawa/pasang surut

50

Pemulia :

Peneliti :

Teknisi :

Di lepas tahun : 1986

Cilamaya Munjul

Varietas Padi - Varietas Padi Sawah Asal persilangan : Pelita I-1/B2388

Kelompok : Padi Sawah

Nomor Seleksi :

Golongan : Cere (indica), kadang-kadang berbulu Umur tanaman : 126 - 130 hari

Bentuk tanaman : Tegak

Tinggi tanaman : 90 - 105 cm

Anakan produktif : Banyak (15-20 batang)

Warna kaki : Hijau

Warna batang : Hijau

Warna telinga daun : Hijau

Warna lidah daun :

Warna daun :

Permukaan daun : Kasar

Posisi daun : Tegak

Daun bendera : Tegak"

Bentuk gabah : Bulat besar

Warna gabah : Kuning bersih

Kerontokan : Agak tahan

Kerebahan : Tahan

Tekstur nasi : Pulen

Kadar amilosa : 21 %

Bobot 1000 butir : 26-27 gram Rata-rata produksi : 5 - 6 t/ha Potensi hasil : 5 - 6 t/ha Ketahanan terhadap

Hama

: Wereng coklat biotipe 1 dan 2

Ketahanan terhadap penyakit

:

51

Anjuran :

Pemulia :

Peneliti :

Teknisi

: Susanto Tw., Z. Harahap, Asep Abdie, Dadan S., Naz

APPENDIX

Appendix 2. Dominant Rice Variety Ciherang

Varietas Padi - Varietas Padi Sawah

Asal persilangan : IR18349-53-1-3-1-3/IR19661-131-3-1//IR19661-131-3-1///IR64 ////IR64

Kelompok : Padi Sawah

Nomor Seleksi : S3383-1d-Pn-41--3-1

Golongan : Cere

Umur tanaman : 116 - 125 hari Bentuk tanaman : Tegak

Tinggi tanaman : 107 - 115 cm Anakan produktif : 14 - 17 batang

Warna kaki : Hijau

Warna batang : Hijau Warna telinga daun : Putih Warna lidah daun :

Warna daun : Hijau

Permukaan daun : Kasar pada sebelah b

Posisi daun : Tegak

Daun bendera : Tegak"

Bentuk gabah : Panjang ramping Warna gabah : Kuning bersih

Kerontokan : Sedang

Kerebahan : Sedang

Tekstur nasi : Pulen Kadar amilosa : 23 % Bobot 1000 butir : 27-28 gram Rata-rata produksi : 5 - 8,5 t/ha Potensi hasil : 5 - 8,5 t/ha Ketahanan terhadap

Hama

: Tahan terhadap wereng coklat biotipe 2 dan 3 Ketahanan terhadap

penyakit

: Tahan terhadap bakteri hawar daun (HDB) strain III dan IV

Anjuran : Cocok ditanam pada musim hujan dan kemarau dengan ketinggian di bawah 500 m dpl.

Pemulia :

Teknisi : Tarjat T, Z. A. Simanullang,., E. Sumadi dan Aan A Di lepas tahun : 2000

IR 64

Varietas Padi - Varietas Padi Sawah Asal persilangan : IR5657/IR2061

Kelompok : Padi Sawah

Nomor Seleksi : IR18348-36-3-3

Golongan : Cere

Umur tanaman : 115 hari Bentuk tanaman : Tegak Tinggi tanaman : 85 cm Anakan produktif : 25 batang Warna kaki : Hijau Warna batang : Hijau

Warna telinga daun : Tidak berwarna Warna lidah daun :

Warna daun : Hijau Permukaan daun : Kasar Posisi daun : Tegak Daun bendera : Tegak" Bentuk gabah : Kuning bersih Warna gabah : Ramping, panjang Kerontokan : Tahan

Kerebahan : Tahan

Tekstur nasi : Pulen Kadar amilosa : 27 % Bobot 1000 butir : 24,1 gram Rata-rata produksi : 5,0 t/ha Potensi hasil : 5,0 t/ha Ketahanan terhadap

Hama

: Tahan wereng coklat biotipe 1, 2 dan wereng hijau Ketahanan terhadap

penyakit

: Agak tahan bakteri busuk hawar daun (Xanthomonas oryzae) - Tahan kerdil rumput

Anjuran : Baik ditanam untuk i sawah irigasi dataran rendah di Jawa Timur Cukup baik untuk padi rawa/pasang surut

Pemulia :

Peneliti :

Teknisi :

Di lepas tahun : 1986

Cilamaya Munjul

Varietas Padi - Varietas Padi Sawah Asal persilangan : Pelita I-1/B2388

Kelompok : Padi Sawah

Nomor Seleksi :

Golongan : Cere (indica), kadang-kadang berbulu Umur tanaman : 126 - 130 hari

Bentuk tanaman : Tegak Tinggi tanaman : 90 - 105 cm

Anakan produktif : Banyak (15-20 batang)

Warna kaki : Hijau

Warna batang : Hijau Warna telinga daun : Hijau Warna lidah daun :

Warna daun :

Permukaan daun : Kasar

Posisi daun : Tegak

Daun bendera : Tegak" Bentuk gabah : Bulat besar Warna gabah : Kuning bersih Kerontokan : Agak tahan

Kerebahan : Tahan

Tekstur nasi : Pulen Kadar amilosa : 21 % Bobot 1000 butir : 26-27 gram Rata-rata produksi : 5 - 6 t/ha Potensi hasil : 5 - 6 t/ha Ketahanan terhadap

Hama

: Wereng coklat biotipe 1 dan 2 Ketahanan terhadap

penyakit

:

Anjuran :

Pemulia :

Peneliti :

Teknisi

: Susanto Tw., Z. Harahap, Asep Abdie, Dadan S., Naz