Green Open Space and Barren Land Mapping for Flood Mitigation in Jakarta, the Capital of Indonesia

High levels of rainfall, tidal flooding, land subsidence, intensified urban development, scarce barren land and a shortage of green open spaces (GOS) are contributing factors to the persistent flooding in Jakarta. Therefore, this study was conducted to map the GOS, built-up, and barren land in the city in order to calculate the biopore infiltration hole (LRB) potential for water infiltration as part of Jakarta's flood mitigation efforts using the Landsat 8 operational land imager (OLI). The Landsat data acquired on September 11, 2019, with path/row 122/064 were processed using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method for the radiometric correction, and geometric correction with a root mean square error (RMSE) of 7.57 meters. Moreover, the normalized difference vegetation index (NDVI) was applied to classify the GOS, the normalized difference built-up index (NDBI) for the built-up areas, and the normalized difference barren land index (NDBaI) for barren land areas which were further confirmed using NDBI to distinguish them from the built-up areas. It is also important to note that the LRB potential was calculated by adding the GOS and barren land, dividing the result by the ideal land area multiplied by the ideal number of holes. The results showed that the GOS, built-up area, and barren land were 8.34%, 85.29%, and 2.48%, respectively. Furthermore, the LRB potential through the optimization of GOS and barren land was found to be 70.06 km2 and produced 16,816,248 LRB (18.27% of total needed). The realization of this value is expected to reduce the potential inundation in Jakarta by 15.6%

DISTRIBUSI LOGAM BERAT PADA SEDIMEN DI PERAIRAN MUARA DAN LAUT PROPINSI JAMBI

Keberadaan logam berat pada sedimen dapat menjadi polutan apabila konsentrasinya melebihi ambang batas yang ditentukan. Tujuan dari penelitian ini untuk mengkaji konsentrasi logam berat pada sedimen di perairan sungai dan laut di Propinsi Jambi. Tiga sampel sedimen di perairan sungai dan sepuluh sampel sedimen di perairan laut diambil untuk dilakukan analisis laboratorium. Analisis logam berat yang dilakukan di laboratorium meliputi Arsenic (As), Cadmium (Cd), Total Chromium (Cr), Nickel (Ni), Mercury (Hg), Selenium (Se), Zinc (Zn), Copper (Cu), Lead (Pb) dan Cobalt (Co).  Hasil analisis laboratorium menunjukkan bahwa Arsenic, Cadmium, Mercury dan Selenium tidak terdeteksi pada sedimen di perairan laut dan sungai. Daerah penelitian terdeteksi tercemar oleh Cobalt (Co) baik di muara sungai dengan konsentrasi 23-25 mg/kg maupun di peraian laut dengan konsentrasi 21-31 mg/kg. Sementara area dekat dengan muara sungai tercemar Cuprum (Cu) dengan konsentrasi 68 mg/kg dan sedikit tercemar  Nickel (Ni) dengan konsentrasi 14 mg/kg, dan Chromium (Cr) dengan konsentrasi 19 mg/kg. [Title : DISTRIBUTION OF HEAVY METAL IN SEDIMENT AT COASTAL AREA JAMBI PROVINCE] The presence of heavy metals in sediments can be a pollutant when its concentration exceeds a specified threshold. The objective of this study is to analyze the concentration of heavy metals in the river and marine sediments in the Jambi Province. Three samples of river sediments and ten samples of marine sediments was taken for laboratory analysis. Analysis of heavy metals were conducted in the laboratory include arsenic (As), Cadmium (Cd), Total Chromium (Cr), Nickel (Ni), Mercury (Hg), Selenium (Se), Zinc (Zn), Copper (Cu), Lead (Pb) and Cobalt (Co). Result of laboratory analysis indicates that Arsenic, Cadmium, Mercury and Selenium were not detected in sediments in the sea water and the mouth of rivers. Research area detected tainted by Cobalt (Co) with concentration 23-25 mg/kg in area near the mouth of the river and 23-25 mg/kg in sea water. While the area near the mouth of the river highly polluted by Cuprum (Cu) with concentration 68 mg/kg and slightly polluted by Nickel (Ni) with concentration 14 mg/kg and Chromium (Cr) with concentration 19 mg/kg.

Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport

MONITORING OF MANGROVE GROWTH AND COASTAL CHANGES ON THE NORTH COAST OF BREBES, CENTRAL JAVA, USING LANDSAT DATA

Severe abrasion occurred in the coastal area of Brebes Regency, Central Java between 1985 and 1995. Since 1997, mangroves have been planted around the location as a measure intended to prevent further abrasion. Between 1996 and 2018, monitoring has been carried out to assess coastal change in the area and the growth and development of the mangroves. This study aims to monitor mangrove growth and its impact on coastal area changes on the north coast of Brebes, Central Java Province using Landsat series data, which has previously proven suitable for wetland studies including mangrove growth and change. Monitoring of mangrove growth was analysed using the normalised difference vegetation index (NDVI) and the green normalised difference vegetation index (GNDVI) of the Landsat data, while the coastal change was analysed based on the overlaying of shoreline maps. Visual field observations of WorldView 2 images were conducted to validate the NDVI and GNDVI results. It was identified from these data that the mangroves had developed well during the monitoring period. The NDVI results showed that the total mangrove area increased between 1996 and 2018 about 9.82 km2, while the GNDVI showed an increase of 3.20 km2. Analysis of coastal changes showed that the accretion area about 9.17 km2 from 1996 to 2018, while the abrasion being dominant to the west of the Pemali River delta about 4.81 km2. It is expected that the results of this study could be used by government and local communities in taking further preventative actions and for sustainable development planning for coastal areas

IDENTIFIKASI POTENSI REMBESAN MIKRO DI LAPANGAN MIGAS MELALUI DETEKSI MINERAL LEMPUNG MENGGUNAKAN CITRA LANDSAT 8 OLI/TIRS, STUDI KASUS LAPANGAN MIGAS CEKUNGAN JAWA BARAT BAGIAN UTARA

Clay minerals in the oil and gas field have changed with an increase of the quantities in the middle of the oil and gas field and reduction in the edges. This reduction is the effect of micro seepage from oil and gas from the subsurface. The aims of the research is to identify the potential oil and gas seepage through clay mineral mapping. The data used where Landsat 8 OLI/TIRS with recording dated September 25, 2015. The method used in the mapping of clay minerals using the ratio of 1.55-1.75 µm (Short Wave Infrared 1) and 2.08-2.35 µm (Short Wave Infrared 2). The result of Landsat 8 OLI/TIRS data processing shows the potential of anomalies in edges of the oil and gas field. The anomaly is a change in the index value of clay minerals that tend to be lower with values 1.0 to 1.5 than the middle of oil and gas field with values 1.5 to 2.0. The potential pattern of the anomaly follows the border of the oil and gas field. Field surveys show that oil and gas field based on grain size analysis is dominated by clay-sized soil. The dominant clay minerals from X-Ray Diffraction analysis are smectite (56%) and kaolinite (6%).ABSTRAKMineral lempung di lapangan migas mengalami perubahan dengan terjadinya peningkatan kandungannya pada tengah lapangan migas dan pengurangan di tepinya. Pengurangan ini merupakan efek adanya rembesan mikro dari migas yang berasal dari bawah permukaan. Kajian ini bertujuan untuk mengidentifikasi adanya potensi rembesan migas melalui pemetaan mineral lempung. Adapun data yang digunakan adalah Landsat 8 OLI/TIRS dengan perekaman tanggal 25 September 2015. Metode yang digunakan pada pemetaan mineral lempung menggunakan perbandingan panjang gelombang 1.55-1.75 µm (Short Wave Infrared 1) dengan 2.08-2.35 µm (Short Wave Infrared 2). Hasil pengolahan data Landsat 8 OLI/TIRS menunjukkan adanya potensi anomali di tepi lapangan migas. Anomali tersebut berupa perubahan nilai indeks mineral lempung yang cenderung lebih rendah yaitu dengan nilai 1,0 – 1,5 dibandingkan lokasi di tengah lapangan yaitu dengan nilai 1,5 – 2,0.  Pola potensi anomali tersebut mengikuti batas tepi lapangan migas. Survei lapangan menunjukkan bahwa pada lapangan migas berdasarkan analisis ukuran butir didominasi oleh tanah berukuran lempung. Adapun mineral lempung yang dominan dari hasil analisis XRD berupa smektit (56%) dan terdapat kaolinit (6%)

Soil Moisture Mapping at Paddy Field in Indramayu Residence Using Landsat 8 OLI/TIRS

Drought monitoring is important for the paddy planting planning. Remote sensing is one tool can be used for it. Paddy field monitoring based on the soil moisture gives much knowledge related to the water content in the soil. Soil moisture analysis in this study is using Normalized Different Water Index (NDWI), Linear Soil Moisture (LSM), and Tasseled Cap. Soil moisture change could explain based on calculation results of NDWI, Linear Soil Moisture (LSM), and Tasseled Cap Transformation (TCT). Based on the results has explained that the driest year occurs in 2015 and June 2016 has a higher soil moisture. Comparison with the radar shows that the results of soil moisture analysis with Landsat was effective can be used with results relatively close to the radar results

ANALISIS KANAL-KANAL LANDSAT 8 OLI UNTUK PEMETAAN BATIMETRI DI SEKITAR PULAU PUTRI, KOTA BATAM

Batimetri mempunyai peran penting dalam perencanaan wilayah pesisir sehingga pemetaan batimetri dangkal sangat diperlukan. Penginderaan jauh merupakan salah satu metode yang efisien, mudah dan murah untuk pemetaan tersebut. Penelitian ini bertujuan untuk menganalisis kanal-kanal terbaik pada Landsat 8 OLI untuk memetakan batimetri dan kedalaman optimum yang dapat dipetakan sehingga dapat digunakan sebagai rujukan dalam memanfaatkan data penginderaan jauh untuk pemetaan tersebut. Lokasi kajian dilakukan di pulau Putri, Kota Batam, Provinsi Kepulauan Riau. Analisis regresi linear menunjukkan kanal tunggal terbaik untuk pemetaan batimetri adalah kanal hijau (kanal 3), diikuti oleh kanal merah (kanal 4) dan kanal inframerah dekat (kanal 5). Namun pemetaan batimetri dengan kombinasi kanal menghasilkan koefisien determinasi yang lebih baik. Analisis best subset menunjukkan pemetaan batimetri pada kedalaman  0 – 20 m menggunakan kanal 2, 3, 5, dan 6 dengan koefisien determinasi (R2) 85,4%; kedalaman 0 – 25 m menggunakan kanal 1, 3, 5, 6, dan 7 dengan R2 75%; dan pemetaan kedalaman 0 – 50 m  menggunakan kanal 1, 3, dan 4 dengan R2 49,1%. Hasil pemetaan batimetri menggunakan Landsat 8 OLI secara umum lebih efektif dan mempunyai akurasi tinggi pada kedalaman 0 – 20 m dan semakin berkurang kemampuannya pada kondisi perairan yang semakin dalam.Kata kunci: Batimetri, Landsat 8 OLI, kanal, algoritma. Bathymetry has an important role in planning coastal areas so that mapping of shallow bathymetry is needed. Remote sensing is one of the efficient, easy and inexpensive methods for mapping. This study aims to analyze the best channels in Landsat 8 OLI for mapping bathymetry and optimum depth that can be mapped so that it can be used as a reference in utilizing remote sensing data for mapping. The location of the study was conducted on the  Putri island, Batam City, Riau Islands Province. Linear regression analysis shows the best single channel for bathymetry mapping is the green channel (channel 3), followed by the red channel (channel 4) and the near infrared channel (channel 5). But bathymetry mapping with channel combinations produces a better coefficient of determination. Best subset analysis shows bathymetry mapping at depths of 0-20 m using channels 2, 3, 5, and 6 with a coefficient of determination (R2) of 85.4%; depth of 0 - 25 m using channels 1, 3, 5, 6, and 7 with R2 75%; and mapping depth 0 - 50 m using channels 1, 3, and 4 with R2 49.1%. The results of bathymetry mapping using Landsat 8 OLI are generally more effective and have a high accuracy at a depth of 0-20 m and are increasingly reduced in conditions of deeper water conditions. Keywords: Bathymetry, Landsat 8 OLI, Band, Algorithm

Identifikasi kondisi terkini semburan lumpur Sidoharjo dari Citra penginderaan jauh

Hal. 27-35 : ilus.; 28 c