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All Journal Jurnal Ilmiah Widya International Journal of Remote Sensing and Earth Sciences (IJReSES)
Dede Dirgahayu
Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, Sekolah Pascasarjana Institut Pertanian Bogor
Earth & Planetary Sciences 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.
Co-Authors Anang Kurnia Any Zubaidah Bagus Sartono Junita Monika Pasaribu Kurhardono, Dony Parsa, Made Sri Harini Triscowati, Dwi Wahyu