Analisis Korelasi Perubahan Garis Pantai Terhadap Luasan Mangrove Di Wilayah Pesisir Pantai Semarang

Indonesia is a country with the largest mangrove area in the world. According to the FAO (2007) Indonesia had a mangrove area of 3,062,300 million hectares in 2005, which represented 19% of the total area of mangrove forest in the whole world. As time passes, mangrove forests experience changes in the area. The changes occur naturally by mangrove and the environment, as well as a result of human intervention. Other cases that may occur is caused by the changing coast line, decreasing the area of the mangrove forests. Therefore, the monitoring of shore line and mangrove area changes with remote sensing method is needed to control the surrounding ecosystem degradation.In this study, the data is studied with two methods. The shore line changes are studied using BILKO method that can separate between land and water surface while the mangrove area changes are studied using composite band and supervised classification method. Both methods will be correlated to obtain results. The result is a quantitative, visual and statistical count.Spatial data analysis results obtained in the period 2012-2013 in the area of Semarang reported that there have been changes in the coast line with abrasion area of 60,66 hectares and accretion area of 21,99 hectares, in 2013-2014, the abrasion covered an area of 36,21 hectares and accretion 23,91 hectares. The mangrove area experienced a reduction in 2012-2013, reaching 145,75 hectares and in year 2013-2014 the degradation reached 198,17 hectares.Statistical analysis result showed that the correlation value is 0,766 > 0,05, meaning that the correlation between the shoreline changes to the mangrove area changes have a strong connection and the significance value is of 0,445 > 0,05, meaning that the changes to the shore line aren't significant to the changes of the mangrove area

Analisis Penggunaan Ndvi Dan Bsi Untuk Identifikasi Tutupan Lahan Pada Citra Landsat 8 (Studi Kasus : Wilayah Kota Semarang, Jawa Tengah)

Land is one of the important natural resources that are needed by living things both animals, plants and humans to stand, as a place of life and activities of life as well as to meet their needs. Land and human beings have a very complex relationship and closely with each other which can not be separated. So that people can meet their needs as optimally as possible, the natural resources requires the processing, preservation and protection. In this study, carried out the identification of land cover in the Landsat 8 May 29, 2015 acquisition of the city of Semarang. The method used is the analysis of NDVI and combination of NDVI BSI which later developed land cover classes consist of five classes including water, barren, settlements, rice fields and vegetation classification results are then compared with reference Maximum Likelihood classification.Results from this study to the level of accuracy obtained NDVI classification results amounted to 49.43% with the user\u27s accuracy for the class of water by 76.15%, barren by 12.60%, settlements by 85.37%, rice fields by 25.44% and vegetation by 65.55%. As for the combination of NDVI BSI classification results obtained by 60.14% accuracy level with the user\u27s accuracy for the class of water by 77.03%, barren by 8.07%, settlements by 82.47%, rice fields by 39.48% and vegetation by 65, 88%

Analisis Pengaruh Fenomena El Nino dan La Nina terhadap Curah Hujan Tahun 1998 - 2016 Menggunakan Indikator Oni (Oceanic Nino Index) (Studi Kasus : Provinsi Jawa Barat)

Perubahan iklim dalam rentang 10 tahun terakhir membawa Perubahan yang sangat drastis di permukaan bumi. Beberapa pengaruh iklim ini salah satunya anomali suhu udara yang mencolok seperti fenomena El Nino dan La Nina atau yang lebih dikenal dengan fenomena ENSO (El Nino Southern Oscillation). Fenomena ENSO merupakan suatu kondisi permukaan laut di wilayah Samudera Pasifik mengalami kenaikan atau penurunan suhu permukaan laut sehingga menyebabkan adanya pergeseran musim di wilayah Indonesia. Pergeseran musim yang terjadi karena fenomena ENSO juga berpengaruh besar terhadap produksi pangan dan komoditas pertanian yang lain.Pada penelitian ini, metode pengolahan data penelitian menggunakan bahasa pemograman untuk mengolah data SST dan data curah hujan dari tahun 1998 sampai tahun 2016. Data yang digunakan berupa data suhu permukaan laut yang berasal dari satelit NOAA yaitu SST Reynolds (Sea Surface Temperature) serta data curah hujan harian yang berasal dari satelit TRMM. Pembuatan indeks ONI pada penelitian ini menggunakan data SST bulanan yang telah dikonversikan kemudian dikelompokkan kedalam masing-masing kelas. Masing-masing kelas mempunyai nilai kurang dari -0,5 yaitu keadaan La Nina, lebih dari 0,5 yaitu keadaan El Nino dan nilai diantara -0,5 sampai 0,5 yaitu keadaan normal. Pengujian data menggunakan analisis pola spasial curah hujan dan suhu permukaan laut yang dipengaruhi oleh fenomena El Nino dan La Nina.Hasil penelitian berupa peta sebaran SST dan curah hujan secara musiman untuk mengetahui pengaruh dari fenomena El Nino dan La Nina di wilayah Jawa Barat. Fenomena El Nino dan La Nina di Laut Jawa terjadi pada bulan Agustus sampai bulan Februari. Pada saat El Nino, nilai suhu permukaan laut (SST) 27ᵒC -28ᵒC dengan rata-rata 27,71ᵒC sedangkan untuk intensitas curah hujannya yaitu 1,0mm/hr-2,0mm/hr dengan rata-rata 1,63mm/hr. Pada saat La Nina, nilai suhu permukaan laut (SST) 29ᵒC-30ᵒC dengan rata-rata 29,06ᵒC sedangkan intensitas curah hujannya yaitu 9,0mm/hr-10mm/hr dengan rata-rata 9,74mm/hr. Korelasi antara curah hujan dan SST sebesar 0,413 yang menyatakan hubungan yang cukup kuat antar parameter. Sehingga dapat disimpulkan, kenaikan SST saat La Nina mempengaruhi kenaikan intensitas curah hujan sedangkan untuk penurunan SST saat El Nino mempengaruhi penurunan intensitas curah hujan

Optimalisasi Parameter Segmentasi Berbasis Algoritma Multiresolusi Untuk Identifikasi Kawasan Industri Antara Citra Satelit Landsat Dan Alos Palsar ( Studi Kasus : Kecamatan Tugu Dan Genuk, Kota Semarang)

The classification methods development in remote sensing in order to get accurate and precision informations has been enormously evolved. One of data classification method envolving is the object based classification. This data classification has had several advantages on object especially in separating shape segments in precisely and accurately. In this research, in order to identify an industrial area using the object based classification, we have already used the multi-resolution segmentation algorithm. Also we had had a value of segmentation parameters optimization to reach the best classification results. This research has used a Landsat 7 ETM+ and ALOS PALSAR images which is located in Genuk and Tugu sub-district.To identify industry area, this process divided into two processes, first process is segmentation and the second process is classification. In segmentation process, both of the satellite imageries are processed by using multiresolution segmentation algorithm. There are three segmentation parameters in this algorithm as follows a scale, a shape, and a compactness. Furthermore, the process of classification is to classify the segmentation products that are segmentated into an each class. It will be form a two land use class as like an industry and a non-industry area.From segmentation proccess, it was resulted an optimal value for Landsat 7ETM+ imagery in Genuk sub-district around 30 in scale parameter, 0.1 in shape parameter and 0.3 in compactness parameter. While the optimal value in Tugu sub-district around 25 in scale parameter, 0.5 in shape parameters, and 0.3 for compactness parameters.For ALOS PALSAR imagery, it was resulted an optimal value in Genuk Sub-district around 25 in scale parameter, 0.5 in shape parameter and 0.5 in compactness parameter.For Tugu Sub-district, it has obtained around 27 in scale parameter, 0.5 in shape parameter and 0.5 for compactness parameter. The accuration test has obtained using a confution matrix from both of sattelite imagery which is get an overall accuracy value around 100 percents

Analisis Perbandingan Kepadatan Pemukiman Menggunakan Klasifikasi Supervised Dan Segmentasi (Studi Kasus: Kota Bandung)

Settlements are residential area consisting of more than one housing unit which is very important in human life. Nowadays, vacant area and green area are decreasing because of the higher density of settlements, especially in big cities such as Bandung.Remote sensing technology and geographic information system (GIS) can help obtaining the dense settlement information by analyzing the image of high resolution Quickbird. In this study, the method used to analyze is segmentation and supervised classification.Information of the dense settlement wide area in Bandung in 2012 is obtained using segmentation method and supervised classification. The result of both methods is compared. The Result of dense settlement area of Bandung in 2012 using segmentation method is 115,949,487.2 m², while according to the result of supervised classification is 117,233,067.02 m². Based on the accuracy test of segmentation method, it is 100% whereas supervised classification is 98,418%. In statistical test point of view, both of the methods have a remarkably strong correlation and the same direction with the value of 0.998, with the hypothesis that the wide of dense settlements area obtained from segmentation method was different with supervised classification method was significantly different with

Identifikasi Kawasan Upwelling Berdasarkan Variabilitas Klorofil-a, Suhu Permukaan Laut Dari Data Citra Aqua Modis Tahun 2003-2015 Dan Arus (Studi Kasus: Perairan Nusa Tenggara Timur)

The existence of Indonesian sea which is widely and strategic becoming Indonesia as the world maritime axis that is why Indonesia have potential sea natural resources, one of them is East Nusa Tenggara (NTT) where have great fish potential. A lot of fish potential is related to fitoplankton existence which can know from chlorophyll-a and sea surface temperature by remote sensing with using Aqua MODIS imagery and also complemented with the wind vector and wind velocity from QuickScat Imagery in NTT sea.The data processing method in this research was used IDL to process sea surface temperature, chlorophyll-a and tide from 2013 until 2015, therefore sea surface temperature distribution spatial pattern, chlorophyll-a, vector and speed of wind to identify upwelling phenomenon in NTT sea which proven a lot of nutrition and a lot of fitoplankton as natural feed for fish therefore it can get impact for rising fish productivity in NTT sea. The test of data is required by analyzed distribution of clorophyll-a, Sea Surface Temperature (SST) and wind to fish potential area in NTT sea.This research product are sea surface temperature distribution map and tide map, based on climatology this is to know cause and effect of upwelling in NTT sea. Upwelling phenomenon in NTT sea is happened on May until december. When the upwelling happens, chlorophyll-a value is about 0,223 until 0,413 mg/m3with the average is 0,329 mg/m3, the highest distribution of chlorophyll-a is on September. Sea surface temperature value distribution between 26,768-28,689 ⁰C with the average is 27,548 ⁰C, and The lowest distribution is on August and wid speed when upwelling happens is about 3,654-5,351 m/s with the average is 4,715 m/s, The highest wind speed is on July. Therefore, it makes upwelling time late in NTT sea