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Fakultas Geografi, Universitas Gadjah Mada

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Klasifikasi Pohon Keputusan untuk Kajian Perubahan Penggunaan Lahan Kota Semarang Menggunakan Citra Landsat TM/ETM+

Majalah Geografi Indonesia Vol 23, No 2 (2009): September 2009
Publisher : Fakultas Geografi, Universitas Gadjah Mada

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Abstract

ABSTRAK Kota Semarang masih berkembang pesat. Dengan jumlah penduduk sekitar 1.434.025 jiwa (BPS, 2006) yang tinggal di kota, kota ini bisa disebut kota metropolitan. Pertumbuhan penduduk Kota Semarang sejak tahun 1994 ketika ekspansi ke 16 daerah kabupaten menunjukkan perbaikan. Kondisi ini menyebabkan kebutuhan lahan yang lebih tinggi, sehingga konversi lahan pertanian menjadi nonpertanian akan meningkat. Untuk yang terakhir, data dari jarak jauh-merasakan memainkan peran penting yang memberikan informasi terbaru untuk penggunaan lahan. Hal ini harus didukung oleh canggih metodologi pengolahan gambar seperti otomatis klasifikasi spektral. Penelitian ini mencoba untuk membandingkan dua algoritma klasifikasi Landsat TM digital / ETM + adalah classifier kemungkinan dan keputusan pohon maksimum, akurasi tertinggi berikutnya digunakan untuk studi perubahan penggunaan lahan di Kota Semarang. Penggunaan lahan klasifikasi yang diterapkan memiliki berbeda dua-tahap detail untuk skala 1: 250.000 (tingkat I) dan 1: 100.000 (level II). Hasil ini pada penelitian ini menunjukkan bahwa penggunaan lahan peta klasifikasi pohon keputusan pada akurasi keseluruhan dan Kappa Indeks lebih tinggi dari penggunaan lahan peta hasil maximun klasifikasi kemungkinan dan penggunaan lahan klasifikasi tingkat I memiliki akurasi yang lebih baik daripada penggunaan lahan klasifikasi tingkat II. Akurasi tingkat I klasifikasi di peta tahun 1994, untuk klasifikasi kemungkinan maksimum yang diperoleh adalah 54,14% yang memiliki indeks Kappa adalah 0,4822, dan akurasi untuk klasifikasi pohon keputusan adalah 66,34% dengan indeks Kappa 0,6256. Akurasi peta tahun 2002 untuk klasifikasi kemungkinan maksimum yang diperoleh adalah 75,12% yang memiliki indeks Kappa 0713, dan keputusan klasifikasi pohon akurasi 81,46% yang memiliki indeks Kappa 0787. Pada peta tahun 2006 untuk klasifikasi kemungkinan maksimum yang diperoleh adalah akurasi keseluruhan 78,05% yang memiliki indeks Kappa 0,7641 dan keputusan klasifikasi pohon akurasi 82,45% yang memiliki indeks Kappa 0805. Perubahan penggunaan lahan di Kota Semarang menginstruksikan turunnya perkebunan dan lahan pertanian dan meningkatnya penyelesaian dan industri. ABSTRACT The  Semarang  City  is  still  growing  rapidly.   With  total  population  of approximately 1,434,025 people (BPS, 2006) who lived in the city, this city can be called a metropolitan city. Growth of Semarang City population since 1994  when expansion into 16 district areas showed improvement. This condition caused the need of  land higher, so that the conversion of agricultural into nonagricultural land will increased. For the latter, remotely-sensed data plays an important role which provide updated information for land use. This is must be supported by the advanced of image processing methodology such as automated   spectral  classification. This study attempted to compare two classification algorithm of digital Landsat TM/ETM+ is the maximum likelihood and decision tree classifier, the next highest accuracy used for the study of land use change in the Semarang City. Land use classification which was applied has different two-stage of the detail for scale of 1 : 250.000 (level I)  and 1 : 100.000 (level II). This  result  on  this  study indicate  that  the  landuse  map  of  decision  tree classification  on overall accuracy and Kappa Index was higher than landuse map of result maximun likelihood classification and land use classification of level I   have accuration which better than land use classification of level II. The accuracy of level  I classification at map year 1994, for maximum likelihood classification obtained is 54,14%  that  have  Kappa  index  is  0,4822,  and  the  accuracy  for  decision  tree classification is 66,34% with Kappa index 0,6256. The accuracy of map year 2002 for maximum likelihood classification obtained is 75,12% that have Kappa index 0,713, and for decision tree classification accuration of 81,46% that have Kappa index 0,787. At map year 2006 for maximum likelihood classification obtained  is overall accuration of 78,05% that have Kappa index 0,7641 and for decision tree classification accuration of 82,45% that have Kappa index 0,805. Change of land use in Semarang City instruct the  descent  of  plantation  and  agricultural  land and increasing  of  settlement  and industrial.

Aplikasi ALOS PALSAR Full Polarimetric Untuk Pemetaan Penutup Lahan Di Sebagian Kabupaten Sleman

PROMINE Vol 6 No 1 (2018): PROMINE
Publisher : Jurusan Teknik Pertambangan, Fakultas Teknik, Universitas Bangka Belitung

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Abstract

The simplest way to interpret polarimetric imagery for land cover classification is to use visualinterpretation methods. The existence of interpretations key as a tool for visual interpretation becomesimportant when different interpreters can produce different results. The quality of the results of theinterpretation of land cover is then determined by the quality of the interpretation tool, in this case, thekey to the interpretation of land cover. The purpose of this study was to make the key to land coverclass interpretation in the Full Polarimetric ALOS PALSAR image, then the interpretation key wasused for reference in making land cover maps and measuring the accuracy of the results of the visualinterpretation. The image used in this study consisted of HH, VV, HV and VH bands. The location ofthe study was in parts of Sleman District. The analysis is done visually by on-screen digitizing onALOS Palsar composite HH + VV HV + VH HH-HV image, which is then interpreted key. The truetest is done by means of the overall accuracy test and Kappa. Visually, ALOS PALSAR imagery isable to distinguish 12 land cover classes in the research area, namely built land, rice fields, mixedgardens, moorlands, salak garden, grass, forest, shrubs, open land, airports, water bodies and lavawith 83% Overall accuracy, and 78% Kappa accuracy.

Pemetaan Spasiotemporal Pola Ekspansi Perkotaan Memanfaatan Data Landsat Multitemporal Di Perkotaan Metropolitan Semarang

PROMINE Vol 6 No 1 (2018): PROMINE
Publisher : Jurusan Teknik Pertambangan Fakultas Teknik Universitas Bangka Belitung

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Abstract

Utilization of multitemporal remote sensing data among others can be used to determine the pattern ofchanges in urban expansion. One of the most important types of cities in urban systems is themetropolitan urban area that covers several districts and cities. This is because the region generallyacts as the capital of the country, the provincial capital, and the center of economic activities that arenational or strategic. Understanding urban expansion at metropolitan urban levels is important forexpanding knowledge in times of urban growth and its impact on the environment. Aims in this studyare: (1) utilization of multitemporal Landsat data for mapping urban expansion patterns, (2) knowingthe effectiveness of object-based classification for mapping of urban settlements and (3)spatiotemporal urban expansion pattern analysis in Semarang metropolitan city. This study utilizingLandsat TM, ETM + and OLI image data to map urban settlement land cover using object-basedclassification with Random Forest algorithm. Next, quantifying the typology of urban expansion andcompare the spatiotemporal pattern of urban expansion during 2005-2015 on the results of land covermapping. This research has found that (1) object-based classification with Random Forest algorithmis quite effective in terms of time of work to map urban settlement cover on Landsat digital data havingmedium spatial resolution; (2) Metropolitan City of Semarang is experiencing rapid and massivedevelopment and has a very varied spatiotemporal pattern; (3) Size of the city affect the pattern ofurban expansion, followed by rapid expansion of the region, smaller cities tend to develop withleapfrogging/ outlying models.