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All Journal Jurnal Ilmiah Widya International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Dede Dirgahayu
Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, Sekolah Pascasarjana Institut Pertanian Bogor
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Other

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PEMANTAUAN KEJADIAN BANJIR LAHAN SAWAH MENGGUNAKAN DATA PENGINDERAAN JAUH Moderate Resolution Imaging Spectroradiometer (MODIS) DI PROVINSI JAWA TIMUR DAN BALI Zubaidah, Any; Dirgahayu, Dede; Pasaribu, Junita Monika
Jurnal Ilmiah Widya Vol 1 No 1 (2013)
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah III Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (155.366 KB)

Abstract

This paper discussed about the utilization of MODIS and TRMM satellite data to monitor flood in paddy field. The purpose of this research is to improve the quality of provision spatial information of flood prone area in the paddy field in East Java and Bali Province which can be done periodically (monthly) based on remote sensing data. Data used in this research is Terra/Aqua MODIS (Moderate Resolution Imaging Spectoradiometer) on November and December 2011 on 8 daily period, rainfall data which is obtained from TRMM data in the same period on November and December 2011, standard extensive field and administration map of East Java and Bali province. The method which is used in this research is to combine EVI (Enhance Vegetation Index) with rainfall data in the same period in order to obtain flood prone area, which is classified into 5 classes, namely non flood, mild, moderate, heavy, and very heavy.
PENGINDERAAN JAUH UNTUK PEMANTAUAN KEKERINGAN LAHAN SAWAH Zubaidah, Any; Dirgahayu, Dede; Pasaribu, Junita Monika
Jurnal Ilmiah Widya Vol 2 No 1 (2014)
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah III Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (107.758 KB)

Abstract

Information of drough condition, especially in the paddy field is much needed in order to manage food availability in a certain area. Drough monitoring in paddy field can be generated by using MODIS and TRMM data. The purpose of this paper is to show spatial information of drough prone condition in the paddy field in East Java Province especially on July – September 2011. Data used in this paper is Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) and TRMM data in the same period on July – September 2011, standard extensive field and administration map of East Java Province. The method which is used in this paper is combining EVI (Enhance Vegetation Index) with LST (Land Surface Temperature) to obtain ETP (Potential Evapotranspiration) and make Meteorologist and Agronomist Drough parameter. Furthermore, processing of reflectance data was done to calculate Hydrologist Drough parameter. After that, this drough condition was classified into five class, namely non dry, mild, moderate, heavy and puso (crop failure).
OPTIMIZATION OF RICE FIELD CLASSIFICATION MODEL BASED ON THRESHOLD INDEX OF MULTITEMPORAL LANDSAT IMAGES Dirgahayu, Dede; Parsa, Made; Harini, Sri; Kurhardono, Dony
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 17, No 1 (2020)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2020.v17.a3333

Abstract

The development of rice land classification models in 2018 has shown that the phenology-based threshold of rice crops from the multi-temporal Landsat image index can be used to classify rice fields relatively well. The weakness of the models was the limitations of the research area, which was confined to the Subang region, West Java, so it is was deemed necessary to conduct further research in other areas. The objective of this study is to obtain optimal parameters of classification model of rice and land based on multi-temporal Landsat image indexes. The study was conducted in several districts of rice production centers in South Sulawesi and West Java (besides Subang). The threshold method was employed for the Landsat Image Enhanced Vegetation Index (EVI). Classification accuracy was calculated in two stages, the first using detailed scale reference information on rice field base, and the second using field data (from a survey). Based on the results of the analysis conducted on several models, the highest accuracy is generated by the three index parameter models (EVI_min, EVI_max, and EVI_range) and adjustable threshold with 94.8% overall accuracy. Therefore this model was acceptable for used for nationally rice fields mapping.
CLASSIFICATION OF RICE-PLANT GROWTH PHASE USING SUPERVISED RANDOM FOREST METHOD BASED ON LANDSAT-8 MULTITEMPORAL DATA Triscowati, Dwi Wahyu; Sartono, Bagus; Kurnia, Anang; Dirgahayu, Dede; Wijayanto, Arie Wahyu
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 16, No 2 (2019)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3217

Abstract

Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.
ANALISIS SPASIAL KONVERSI LAHAN SAWAH DI KABUPATEN BEKASI (STUDI KASUS DI KECAMATAN CIBITUNG DAN TAMBUN) Dirgahayu, Dede
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 1 No. 1 (2004)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v1i1.3095

Abstract

The result of spatial analysis indicates that there has been position sidetrack of paddy field concentration (SM=Spatial Mean)from the year 1996 until the year 2000 to the North East direction as far as 691 m in Cibitung district and 481 m in Tambun district. The SM movement is away from the center of social economic activity of the community whether in yhe local area (district) or the regional area (City, Regency). Land conversion of paddy field into non-agricultural land that mostly occur are as a residential and industrial area. Land conversion has also occured in Tambun of paddy - then more settlement type about 105.2 Ha and 154.6 Ha in Cibitung. Land conversion of paddy - then more industry type has occured in Cibitung about 486.1 Ha and 87.9 Ha in Tambun. Paddy field conversion that occurs in the research location has taken place in Highly Suitable (S1) land, and has high productivity because it taken place in the area High Accessibility towards main road and center of the district.
MODEL BAHAYA BANJIR MENGGUNAKAN DATA PENGINDERAAN JAUH DI KABUPATEN SAMPANG Haryani, Nanik Suryo; Zubaidah, Any; Dirgahayu, Dede; Yulianto, Hidayat Fajar; Pasaribu, Junita
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 9 No. 1 (2012)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v9i1.3259

Abstract

Flood is the first biggest disaster in Indonesia, as stated by the National Disaster Management Agency (BNPB) in the BNPB’s natural disaster data of year 2000 to 2009. Considering the flood has the significant impact of causing the casualties and material losses, it is necessary to study on it. One of useful data for studying the flood is remote sensing data. The advantage of good historical data makes it possible to see the changes of cover/land use from year to year in a region. The extensive area coverage of remote sensing data allows it to view and analyze in a comprehensive manner. The method of the study of flood hazard models is using multiple variables, where each variable has a class of criteria. Determination of the weight of each flood variable by using the Composite Mapping Analysis. The results of this study shows the main cause of flooding in the District of Sampang is that most of the land system in the cities are the combined estuary and swamp plain, forming a low land and is triggered by the torrential rain. The model of flood hazard maps produced by variable weighting floods with a multi criteria analysis method which is function of rainfall, landuse, slope, land system and elevation.
UJI MODEL FASE PERTUMBUHAN PADI BERBASIS CITRA MODIS MULTIWAKTU DI PULAU LOMBOK Parsa, I Made; Dirgahayu, Dede; Manalu, Johannes; Carolita, Ita; KH, Wawan
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 1 (2017)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.pjpdcd.2017.v14.a2621

Abstract

Model testing is a step that must be done before operational activities. This testing aimed to test rice growth phase models based on MODIS in Lombok using multitemporal LANDSAT imagery and field data. This study was carried out by the method of analysis and evaluation in several stages, these are: evaluation of accuracy by multitemporal Landsat 8 image analysis, then evaluation by using field data, and analysis of growth phase information to calculate model consistency. The accuracy of growth phase model was calculated using Confusion Matrix. The results of stage I analysis for phase of April 30 and July 19 showed the accuracy of the model is 58-59%, while the evaluation of stage II for phase of period July 19 with survey data indicated that the overall accuracy is 53%. However, the results of model consistency analysis show that the resulting phase of the smoothed MODIS imagery shows a consistent pattern as well as the EVI pattern of rice plants with an 86% accuracy, but not for pattern data without smoothing. This testing give conclusion is the model is good, but for operational MODIS input data must be smoothed first before index value extraction.
PENGEMBANGAN METODE KLASIFIKASI LAHAN SAWAH BERBASIS INDEKS CITRA LANDSAT MULTIWAKTU Parsa, Made; Dirgahayu, Dede; Harini, Sri
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i1.3349

Abstract

Research on the development of a paddy field classification model based on Landsat remote sensing images aims to obtain a rapid classification of paddy field models. This study uses input multitemporal Landsat images (path/row 122/064) in 2017. The research was conducted in Subang regency, which is one of the center of West Java rice production. The method used in this study is the threshold method for the multi-temporal Landsat image index. As a reference, detailed scale spatial information on paddy fields base is used which is supplemented with data from field surveys using drones. First, an atmospheric correction of Landsat images was carried out using DOS (Dark Object Subtraction) Method, then transformation image to several indices: Enhance vegetation Index (EVI), Normal Difference Water Index (NDWI), and Normal Difference bare Index (NDBI) was carried out. For cloudy images, the index is filled with interpolation techniques from the index value before and after. The next step is smoothing index and statistical analysis to obtain minimum, maximum, mean, median, range (maximum - minimum), EVI_planting, EVI_harvesting, mean_planting-harvesting, mean_vegetative, mean_generative, NDWI_planting, NDWI_harvesting, NDBI_planting, and NDBI_harvesting. Classification accuracy is calculated by using the confusion matrix technique using detailed scale spatial information references. Based on the analysis and test of accuracy that has been done on several models, the highest accuracy is generated by the 1.5 stdev threshold model four index parameters (EVI_min, EVI_Max, EVI_range, and EVI_mean) with an accuracy of 86.56% and a kappa value of 0.716.
EVALUASI REHABILITASI LAHAN KRITIS BERDASARKAN TREND NDVI LANDSAT-8 (Studi Kasus: DAS Serayu Hulu) Kartika, Tatik; Dirgahayu, Dede; Sari, Inggit Lolita; Parsa, I Made; Carolita, Ita
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 16 No. 2 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v16i2.3353

Abstract

The use of remote sensing in vegetation monitoring has been widely applied, including vegetation density monitoring. However, the use to evaluate rehabilitation program on critical land is still limited. Evaluation of forest cover and land rehabilitation activities become important due to the increase of critical land. The current method to evaluate the land condition is conducted by ground check at the rehabilitation site held at the end of the year after the initial implementation of the rehabilitation program until the third year. This method requires a lot of time, labour, and money. Based on the standard regulation to evaluate the rehabilitation program, the program is successful if 90% the new vegetation planted can grows until the third year. Therefore, this research uses an effective and efficient method for evaluating land rehabilitation programs using remote sensing data by understanding vegetation conditions and their densities using multi-temporal analysis for large areas. A multi-temporal Landsat-8 images from 2015-2018 will be used to analyze the Normalized Difference Vegetation Index (NDVI) trend in the time-based sequence method using spatial analysis. The results show that the non-forest area in Serayu Hulu Watershed consist of non-critical land, moderate critical land, critical land, and severe ciritical land. According to the ground check and NDVI trend analysis, the rehabilitation in non-critical land of the non-forest area was generally unsuccessful due to the area rehabilitation plant were harvested before the rehabilitation evaluation time ended. On the otherhand, on critical land; moderate critical land; and severe critical land of the non-forest area, the success of rehabilitation program was indicated by the achievement of the NDVI threshold value at 0.4660; 0.4947. 0.4916, respectively.
Co-Authors Anang Kurnia Any Zubaidah Bagus Sartono I Made Parsa Inggit Lolita Sari, Inggit Lolita Ita Carolita, Ita Johannes Manalu, Johannes Junita Monika Pasaribu KH, Wawan Kurhardono, Dony Nanik Suryo Haryani Parsa, Made Pasaribu, Junita Sri Harini Tatik Kartika, Tatik Triscowati, Dwi Wahyu Yulianto, Hidayat Fajar