Nanin Anggraini, Nanin
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PEMANFAATAN DATA SATELIT UNTUK ANALISIS POTENSI GENANGAN DAN DAMPAK KERUSAKAN AKIBAT KENAIKAN MUKA AIR LAUT (APPLICATION OF SATTELITE DATA TO ANALYZE INUNDATION POTENTIAL AND THE IMPACT OF SEA LEVEL RISE) Anggraini, Nanin; Trisakti, Bambang; Soesilo, Tri Edhi Budhi
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 9 No. 2 Desember 2012
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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Abstract

Meningkatnya volume air laut menyebabkan kenaikan muka air laut yang mengancam keberadaan pulau-pulau kecil dan wilayah pesisir seperti pesisir Jakarta Utara. Selain karena kenaikan muka air laut, Jakarta Utara juga terancam oleh fenomena penurunan permukaan tanah. Penelitian ini bertujuan untuk memprediksi kenaikan muka air laut pada tahun 2030 dan dampaknya terhadap wilayah pesisir. Prediksi total tinggi muka air laut diperoleh berdasarkan data pasang surut (pasut), penurunan permukaan tanah, dan kenaikan muka air laut skenario B2 dari IPCC. Wilayah pesisir yang berpotensi tergenang karena kenaikan muka air laut diprediksi dengan data DEM SRTM X-C resolusi spasial 30 m. Analisis dampak kerusakan dilakukan dengan cara overlay antara potensi genangan dengan penggunaan lahan dari data QuickBird. Hasil memperlihatkan bahwa, prediksi total tinggi muka air laut tahun 2030 akibat pasut adalah 2,88 m, penurunan permukaan tanah 2,28 m, dan skenario IPCC 1,29 m, sehingga tinggi muka air laut rencana adalah 6,45 m. Jenis penggunaan lahan yang berpotensi rusak akibat tergenang didominasi oleh permukiman sebesar 1045 ha dan industri 563 ha. Permukiman yang berpotensi tergenang paling luas berada di Kecamatan Penjaringan dengan luas 523 ha dan wilayah industri berada di Kecamatan Cilincing dengan luas 171 ha. Kata kunci: DEM SRTM X-C band, Kenaikan muka air laut, Penurunan permukaan tanah, skenario B2 SRES IPCC, QuickBird
MONITORING OF DROUGHT-VULNERABLE AREA IN JAVA ISLAND, INDONESIA USING SATELLITE REMOTE-SENSING DATA Roswintiarti, Orbita; Sofan, Parwati; Anggraini, Nanin
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol 8 (2011)
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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Abstract

The impact of climatic variability and climate change is of great importance in Indonesia. Monitoring this impact, furthermore, is essential to the preparedness of the regions in dealing with drought-vulnerable conditions. In this study, satellite remote sensing data were used for monitoring drought in Java island, Indonesia. Monthly rainfall data from Tropical Rainfall Measuring Mission (TRMM) data were used to derive the Standardized Precipitation Index (SPI). The Moderate Resolution Imaging  Spectroradiometer (MODIS) onboard the Terra and Aqua satellites was used for calculating the Enhanced Vegetative Index (EVI) and Land Surface Temperature (LST). EVI and LST were then converted to the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), which are useful indices for the estimation of vegetation moisture and thermal conditions, respectively. Vegetation Health Index (VHI) was calculated using the VCI and TCI to represent the overall vegetation health. The analysis was carried out during the El Niño/Southern Oscillation (ENSO) of June to August 2009. From the SPI analysis, of the vegetation moisture condition has gradually developed in the East Java province in June 2009. Meanwhile, from the TCI maps it is found that the vegetative stress (TCI < 36) due to the thermal condition of vegetation was built up in the West Java province in June 2009. Meanwhile, frm the TCI maps it is found that the vegetative stress (TCI < 36) due to the thermal condition of vegetation was built up in the West Java province in June 2009. Hence, the overall vegetative health in Java island obtained from the VHI maps shows that the moderate vegetative drought (VHI < 36) started to develop in July 2009.Keywords: Java island, TRMM, EVI, SPI, VCI, TCI, VHI   
ESTIMASI BATIMETRI DARI DATA SPOT 7 STUDI KASUS PERAIRAN GILI MATRA NUSA TENGGARA BARAT Setiawan, Kuncoro Teguh; Manessa, Masita Dwi Mandini; Winarso, Gathot; Anggraini, Nanin; Girrastowo, Gigih; Astriningrum, Wikanti; Herianto, Herianto; Rosid, Syamsu; Supardjo, A. Harsono
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 15 No. 2 Desember 2018
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (618.853 KB) | DOI: 10.30536/j.pjpdcd.2018.v15.a3008

Abstract

Indonesia merupakan negara kepulauan dengan ribuan pulau besar dan kecil yang memliki perairan laut dangkal. Salah satu informasi yang dibutuhkan dari pulau-pulau tersebut adalah peta batimetri khususnya diperairan laut dangkal. Informasi tersebut masih sangat terbatas pada skala yang besar untuk skala yang lebih detil masih sangat terbatas. Untuk menyelesaikan permasalahan tersebut dibutuhkan teknogi penginderaan jauh. Salah satu pemanfaatan teknologi penginderaan jauh adalah untuk menghasilkan informasi batimetri. Banyak metode yang dapat digunakan untuk menghasilkan informasi batimetri dengan teknologi tersebut. Metode yang digunakan dalam penelitian ini adalah metode regresi linier berganda (MLR) yang dikembangkan oleh Lyzenga, 2006. Data yang akan di gunakan adalah citra satelit SPOT 7 di Perairan Laut Dangkal Gili Trawangan, Gili Meno dan Gili Air Pulau Lombok Provinsi Nusa Tenggara Barat. Metode penentuan batimetri tersebut dilakukan pada data kedalaman insitu dengan melakukan dua modifikasi yaitu yang pertama dengan tidak memperhatikan jenis objek habitat dasar dan yang kedua memperhatikan objek habitat dasar karang, lamun, makroalga dan substrat.Hasil dari penelitian ini memberikan korelasi R2 yang meningkat dari 0,721 menjadi 0,786 serta penuruanan nilai kesalahan RMSE dari 3,3 meter menjadi 2,9 meter.
KAJIAN DAMPAK PERUBAHAN IKLIM TERHADAP KEBAKARAN HUTAN DAN DEFORESTASI DI PROVINSI KALIMANTAN BARAT Anggraini, Nanin; Trisakti, Bambang
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol 8 (2011)
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

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Abstract

Increasing or decreasing of rainfall intensity, due to the climate change, affects the enviroment condition in many Indonesia areas. For instance: low rainfall intensity causes high number of forest fire occurrence in Kalimantan Island. The impact of climate change is studied by analyizing the correlation among rainfall intensity, number of forest fire occurrence and forest area change in West Kalimantan Province. The rainfall is extracted using Tropical Rainfall Measurement Mission (TRMM) data for 2001- 2008. The number of forest fire occurrence is identified by the number of hotspot extracted from thermal sensor of satellite data MODIS for 2001 - 2008. The forest area is calculated from MODIS data for 2003, 2005, 2007 and 2009. Pixel which has Normalized Difference Vegetation Index (NDVI) value more than 0,7 along a year round is assumed as forest pixel. The NDVI value is obtained by doing training sample in forest area. The result shows that the rainfall has slightly upward trend in  limantan. The rainfall has negatif correlation with the number of hotspot. When the rainfall was the lowest and the number of hotspot was the highest in 2004, the forest area between 2003 and 2005 decreased (deforestation) significantly. On the other hand, then the rainfall was high and the hotspot was low in 2008, no decreasing in forest area otherwise we found the increasing of forest area. It is probably due to reforestation and expansion of plantation area (such as oil palm).Keywords: Rainfall, Climate change, Forest area, Hotspot, NDVI
MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA Winarso, Gathot; Vetrita, Yenni; Purwanto, Anang D.; Anggraini, Nanin; Darmawan, Soni; Yuwono, Doddy M.
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 12, No 2 (2015)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (624.791 KB) | DOI: 10.30536/j.ijreses.2015.v12.a2687

Abstract

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.
ANALISIS PERUBAHAN GARIS PANTAI UJUNG PANGKAH DENGAN MENGGUNAKAN METODE EDGE DETECTION DAN NORMALIZED DIFFERENCE WATER INDEX (UJUNG PANGKAH SHORELINE CHANGE ANALYSIS USING EDGE DETECTION METHOD AND NORMALIZED DIFFERENCE WATER INDEX) Anggraini, Nanin; Marpaung, Sartono; Hartuti, Maryani
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 14 No. 2 Desember 2017
Publisher : Indonesian National Institute of Aeronautics and Space (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1711.126 KB) | DOI: 10.30536/j.pjpdcd.1017.v14.a2545

Abstract

Besides to the effects from tidal, coastline position changed due to abrasion and accretion. Therefore, it is necessary to detect the position of coastline, one of them by utilizing Landsat data by using edge detection and NDWI filter. Edge detection is a mathematical method that aims to identify a point on a digital image based on the brightness level. Edge detection is used because it is very good to present the appearance of a very varied object on the image so it can be distinguished easily. NDWI is able to separate land and water clearly, making it easier for coastline analysis. This study aimed to detect coastline changes in Ujung Pangkah of Gresik Regency caused by accretion and abrasion using edge detection and NDWI filters on temporal Landsat data (2000 and 2015). The data used in this research was Landsat 7 in 2000 and Landsat 8 in 2015. The results showed that the coastline of Ujung Pangkah Gresik underwent many changes due to accretion and abrasion. The accretion area reached 11,35 km2 and abrasion 5,19 km2 within 15 year period. Abstrak Selain akibat adanya pasang surut, posisi garis pantai berubah akibat adanya abrasi dan akresi. Oleh karena itu diperlukan adanya deteksi posisi garis pantai, salah satunya dengan memanfaatkan data Landsat dengan menggunakan filter edge detection dan NDWI. Edge detection adalah suatu metode matematika yang bertujuan untuk mengidentifikasi suatu titik pada gambar digital berdasarkan tingkat kecerahan. Filter edge detection digunakan karena sangat baik untuk menyajikan penampakan obyek yang sangat bervariasi pada citra sehingga dapat dibedakan dengan mudah. NDWI mampu memisahkan antara daratan dan perairan dengan jelas sehingga memudahkan untuk analisis garis pantai. Penelitian ini bertujuan untuk deteksi perubahan garis pantai di Ujung Pangkah Kabupaten Gresik yang disebabkan oleh adanya akresi dan abrasi dengan menggunakan filter edge detection dan NDWI pada data Landsat temporal (tahun 2000 dan 2015). Data yang digunakan pada penelitian ini adalah citra Landsat 7 tahun 2000 dan Landsat 8 tahun 2015. Hasil penelitian menunjukkan bahwa garis pantai di Ujung Pangkah Gresik banyak mengalami perubahan akibat adanya akresi dan abrasi. Luas akresi mencapai 11,35 km2 dan abrasi 5,19 km2 dalam periode waktu 15 tahun.
The Examination of The Satellite Image-Based Growth Curve Model Within Mangrove Forest Jaya, I Nengah Surati; Saleh, Muhammad Buce; Noventasari, Dwi; Santi, Nitya Ade; Anggraini, Nanin; Sutrisno, Dewayany; Yuxing, Zhang; Xuenjun, Wang; Qian, Liu
Jurnal Manajemen Hutan Tropika Vol 25, No 1 (2019)
Publisher : Institut Pertanian Bogor (IPB University)

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Developing growth curve for forest and environmental management is a crucial activity in forestry planning. This paper describes a proposed technique for developing a growth curve based on the SPOT 6 satellite imageries. The most critical step in developing a model is on pre-processing the images, particularly during performing the radiometric correction such as reducing the thin cloud. The pre-processing includes geometric correction, radiometric correction with image regression, and index calculation, while the processing technique include training area selection, growth curve development, and selection. The study found that the image regression offered good correction to the haze-distorted digital number.  The corrected digital number was successfully implemented to evaluate the most accurate growth-curve for predicting mangrove.  Of the four growth curve models, i.e., Standard classical, Richards, Gompertz, and Weibull models, it was found that the Richards is the most accurate model in predicting the mean annual increment and current annual increment.  The study concluded that the growth curve model developed using high-resolution satellite image provides comparable accuracy compared to the terrestrial method.  The model derived using remote sensing has about 9.16% standard of error, better than those from terrestrial data with 15.45% standard of error.