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All Journal International Journal of Remote Sensing and Earth Sciences (IJReSES) Prosiding Seminar Nasional Official Statistics The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Dede Dirgahayu Domiri
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Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Social Sciences

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DEVELOPMENT OF LAND MOISTURE ESTIMATION MODEL USING MODIS INFRARED, THERMAL, AND EVI TO DETECT DROUGHT AT PADDY FIELD Dede Dirgahayu Domiri
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 1 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (448.216 KB) | DOI: 10.30536/j.ijreses.2013.v10.a1842

Abstract

The drought phenomena often occurs in summer season at paddy field of Java island. The drought phenomena causes decrease in rice production. This research was aimed to develop a model of land  moisture (LM) estimation  at  agricultural field,  especially  for  paddy  field  based  on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data which has seven reflectance and two thermal bands. The method used in this study included data correction, advance processing of MODIS data  (land indices  transformation),  extraction  of  land  indices  value  at  location  of  field  survey,  and regression  analysis  to  make  the  best  model  of  land  moisture  estimation. The  result  showed that reflectance of 2nd channel (NIR) and rasio of Enhanced Vegetation Index (EVI) with Land Surface Temperature (LST) had high correlation with surface soil moisture (% weight) at 0 – 20 cm depth with formula: LM = 15.9*EVI/LST – 0.934*R2 – 16.8 (SE=9.6%; R2 =76.2%). Based on the model, land  moisture  was  derived  spatially at the  agricultural field,  especially at paddy  field to  detect  andmonitor drought events. Information of land moisture can be used as an indicator to detect drought condition and early growing season of paddy crop 
PEMANFAATAN ENSEMBLE LEARNING DAN PENGINDERAAN JAUH UNTUK PENGKLASIFIKASIAN JENIS LAHAN PADI Arif Handoyo Marsuhandi; Agus Mohamad Soleh; Hari Wijayanto; Dede Dirgahayu Domiri
Seminar Nasional Official Statistics Vol 2019 No 1 (2019): Seminar Nasional Official Statistics 2019
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (294.368 KB) | DOI: 10.34123/semnasoffstat.v2019i1.247

Abstract

Pertanian adalah bidang yang sangat penting di Indonesia, sektor ini di tahun 2017 mencatat penyerapan tenaga kerja sebanyak 29.68% dari total seluruh pekerja (BPS, 2018), namun pentingnya sektor pertanian ini berbanding terbalik dengan data pertanian yang tersedia. Tahun 1998 Badan Pusat Statistik (BPS) bersama Japan International Cooperation Agency (JICA) telah mengisyaratkan overestimasi luas panen sekitar 17,07 persen. Ketidakuratan data pertanian ini kemudian diperbaiki pada tahun 2018 melalui kerjasama para stakeholder dengan menyusun suatu metodologi baru dalam menghitung luas lahan yang diberi nama kerangka sampel area. Selain metodologi yang sudah diperbarui, kemajuan teknologi dan teknik analisis di bidang ilmu pengetahuan juga mendukung perbaikan data pertanian. Citra satelit dan teknik klasifikasi menggunakan ensemble learning dapat dimanfaatkan dalam mengklasifikasikan jenis lahan padi. Pada penelitian ini digunakan citra satelit yang berasal dari United States Geological Survey (USGS) yaitu Landsat 8 dan teknik klasifikasi ensemble learning. Citra satelit dimanfaatkan untuk mengekstrak indeks vegetatif dari koordinat koordinat yang diteliti, sedangkan ensemble learning yang digunakan dalam penelitian ini yaitu Random Forest dan Boosting. Hasil pengolahan data menunjukkan Random Forest memiliki akurasi yang lebih tinggi daripada Boosting yaitu dengan nilai 76,52 untuk Random Forest dan 75,60 untuk Boosting. Keunggulan Random Forest terhadap Boosting tidak hanya dari sisi tingkat akurasi saja namun juga dari kestabilan model yang dibentuk.
IDENTIFICATION OF LAND SURFACE TEMPERATURE DISTRIBUTION OF GEOTHERMAL AREA IN UNGARAN MOUNT BY USING LANDSAT 8 IMAGERY Udhi C. Nugroho; Dede Dirgahayu Domiri
International Journal of Remote Sensing and Earth Sciences Vol. 12 No. 2 (2015)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2015.v12.a2708

Abstract

Indonesia located at the confluence of Eurasian tectonic plate, Australian tectonic plate and the Pacific tectonic plate. Therefore, Indonesia has big geothermal potential. One of the areas that has geothermal potential is Ungaran Mount. Remote sensing technology can have a role in geothermal exploration activity to map the distribution of land surface temperatures associated with geothermal manifestations. The advantages of remote sensing are able to get information without having to go directly to the field with a large area, and it takes quick, so that the information can be used as an initial reference exploration activities. This study aimed to obtain the distribution of land surface temperature as a regional analysis of geothermal potential. The method of this research was a correlation of brightness temperature (BT) Landsat 8 with land surface temperature (LST) MODIS. The results of correlation analysis showed the R2 value was equal to 0.87, it shows that between BT Landsat 8 and LST MODIS has a very high correlation. Based on Landsat 8 LST imagery correction, the average of fumarole temperature and hot spring is 240C. Fumarole and hot spring are located in dense vegetation land which has average temperature around 26.90C. Land surface temperature Landsat 8 can not be directly used to identify geothermal potential, especially in the dense vegetation area, due to the existence of dense vegetation which can absorb heat energy released by geothermal surface feature.
DEVELOPMENT OF LAND MOISTURE ESTIMATION MODEL USING MODIS INFRARED, THERMAL, AND EVI TO DETECT DROUGHT AT PADDY FIELD Dede Dirgahayu Domiri
International Journal of Remote Sensing and Earth Sciences Vol. 10 No. 1 (2013)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2013.v10.a1842

Abstract

The drought phenomena often occurs in summer season at paddy field of Java island. The drought phenomena causes decrease in rice production. This research was aimed to develop a model of land moisture (LM) estimation at agricultural field, especially for paddy field based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data which has seven reflectance and two thermal bands. The method used in this study included data correction, advance processing of MODIS data (land indices transformation), extraction of land indices value at location of field survey, and regression analysis to make the best model of land moisture estimation. The result showed that reflectance of 2nd channel (NIR) and rasio of Enhanced Vegetation Index (EVI) with Land Surface Temperature (LST) had high correlation with surface soil moisture (% weight) at 0 – 20 cm depth with formula: LM = 15.9*EVI/LST – 0.934*R2 – 16.8 (SE=9.6%; R2 =76.2%). Based on the model, land moisture was derived spatially at the agricultural field, especially at paddy field to detect andmonitor drought events. Information of land moisture can be used as an indicator to detect drought condition and early growing season of paddy crop
Co-Authors Agus Mohamad Soleh Arif Handoyo Marsuhandi Hari Wijayanto Udhi C. Nugroho