DETEKSI PERKEMBANGAN LAHAN TERBANGUN KOTA GORONTALO BERDASARKAN CITRA LAST (LANDSAT, ASTER, & SENTINEL-2A) (Detection of the built-up area development in Gorontalo City Based on LAST (Landsat, ASTER, & Sentinel-2A) Imagery)

Arthur Gani Koto, Ivan Taslim

Abstract


Abstract – Built-up area is easily found in urban areas which is the most land use compared to other land use. The development of the built-up area has also increased with increasing population and increasing economic activity. Most of the population activities in the form of economy, services, trade, offices, education, health, and entertainment facilities that are centralized in urban areas have caused the availability of non-built-up area to shrink further. Detection of the built-up area can be assessed from remote sensing data using urban indices, multispectral classification (supervised and unsupervised classification), and spectral bands. This study aims to detect the built-up area based on multisensor and multitemporal imagery. Landsat 5 TM, Landsat 8, ASTER, and Sentinel-2B (LAST) images were used in this study. Digital image processing is performed on each image using the guided classification method support vector machine (SVM) algorithm. Four classes of land cover were taken, namely built-up area, vegetation, bare land, and water bodies. Samples of built-up area classes were taken as many as 31 random sampling points spread over the study area. Validation tests were carried out for each image based on the ground check. Results of the study showed that the development of the built-up area was directed to the north and the difference in the extent of information on the built-up area due to differences in spatial resolution.

Keywords: built-up area, landsat, aster, sentinel, supervised classification, gorontalo


Abstrak –Lahan terbangun mudah ditemukan di wilayah perkotaan yang merupakan penggunaan lahan paling banyak dibandingkan penggunaan lahan lainnya. Perkembangan lahan terbangun turut meningkat seiring pertambahan jumlah penduduk dan peningkatan aktivitas ekonomi. Sebagian besar aktivitas penduduk berupa ekonomi, jasa, perdagangan, perkantoran, pendidikan, kesehatan, dan sarana hiburan yang terpusat di wilayah perkotaan menyebabkan ketersediaan lahan non-terbangun kian menyusut pula. Deteksi lahan terbangun dapat dikaji dari data penginderaan jauh menggunakan indeks perkotaan (urban index), klasifikasi multispektral (supervised and unsupervised classification), dan saluran spektral (spectral bands). Penelitian ini bertujuan mendeteksi lahan terbangun berdasarkan citra multis-sensor dan multi-temporal. Citra landsat 5 TM, landsat 8, ASTER, dan sentinel-2B (LAST) digunakan dalam penelitian ini. Pengolahan citra digital dilakukan pada masing-masing citra yang menggunakan metode klasifikasi terbimbing algoritma support vector machine (SVM). Sebanyak empat kelas tutupan lahan diambil, yaitu lahan terbangun, vegetasi, lahan terbuka dan tubuh air. Sampel kelas lahan terbangun diambil sebanyak 31 titik secara random sampling yang tersebar di wilayah penelitian. Uji validasi dilakukan untuk masing-masing citra berdasarkan ground check. Hasil penelitian menunjukkan bahwa perkembangan lahan terbangun mengarah ke utara, dan perbedaan luasan informasi lahan terbangun yang disebabkan perbedaan resolusi spasial.

Kata kunci: lahan terbangun, landsat, aster, sentinel, klasifikasi terbimbing, gorontalo

References


As-syakur, A. R., Adnyana, I. W., Arthana, I. W., & Nuarsa, I. (2012). Enhanced Built-Up and Bareness Index (EBBI) for Mapping Built-Up and Bare Land in an Urban Area. Remote Sensing, 4, 2957-2970.

Bhaskaran, S., Paramananda, S., & Ramnarayan, M. (2010). Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Applied Geography, 30, 650-665.

Bhatti, S. S., & Tripathi, N. K. (2014). Built-up area extraction using Landsat 8 OLI imagery. GIScience & Remote Sensing, 4, 445-467.

Chen, Y., Shi, P., Fung, T., Wang, J., & Li, X. (2007). Object-oriented classification for urban land cover mapping with ASTER imagery. International Journal of Remote Sensing, 28(20), 4645–4651.

Congedo, L. (2013) Semi-Automatic Classification Plugin for QGIS. [pdf] Rome: Sapienza University. Available at:

Congedo, L., Munafo’, M., & Macchi, S. (2013). Investigating the Relationship between Land Cover and Vulnerability to Climate Change in Dar es Salaam. Rome: Sapienza University. Retrieved from http://www.planning4adaptation.eu/Docs/papers/08_NWP-DoM_for_LCC_in_Dar_using_Landsat_Imagery.pdf.

Daldjoeni, N. (2014). Geografi Kota dan desa. Yogyakarta: Penerbit Ombak.

Jieli, C., Manchun, L., Chenglei, S., & Wei, H. (2010). Extract Residential Areas Automatically by New Built up Index. Theme Paper for the 18th International Conference on Geoinformatics (pp. 1-5). IEEE.

Kaimaris, D., & Patias, P. (2016). Identification and area measurement of the built-up area with the built-up index (BUI). International journal of advanced remote sensing and GIS, 5(6), 1844-1858.

Kota Gorontalo Dalam Angka. (2016). Gorontalo: Badan Pusat Statistik Kota Gorontalo.

Koto, A. G. (2013). Pemanfaatan Teknologi Penginderaan Jauh dan SIG Untuk Evaluasi Lahan Kering di Kabupaten Bantaeng, Sulawesi Selatan. Universitas Gadjah Mada, Fakultas Geografi. Yogyakarta: Universitas Gadjah Mada.

Landsat 7 Users Handbook. (n.d.).

Landsat 8 Users Handbook, Versi 1.0. (2015). U.S. Geological Survey.

Lefebvre , A., Sannier , C., & Corpetti, T. (2016). Monitoring Urban Areas with Sentinel-2A Data Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree. Remote Sensing, 8, 606-626.

Maryati, S., Sune, N., & Sutarno. (2015). Analisis spasial arah perkembangan Kota Gorontalo menggunakan citra landsat multitemporal. Simposium Nasional Sains Geoinformasi IV, 442-445.

Pesaresi, M., Corbane, C., Julea , A., Florczyk , A. J., Syrris, V., & Soille, P. (2016). Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas. Remote Sensing, 8, 299-316.

SENTINEL-2 Users Handbook. (2015).

Xu, H., Huang, S., & Zhang, T. (2013). Built-up land mapping capabilities of the ASTER and Landsat ETM+ sensors in coastal areas of southeastern China. Advances in Space Research, 52, 1437–1449.

Zhang, J., Li, P., Wang, J. (2014). Urban Built-Up Area Extraction from Landsat TM/ETM+ Images Using Spectral Information and Multivariate Texture. Remote Sensing, 6, 7339-7359.


DOI: http://dx.doi.org/10.31314/jsig.v1i2.177

Article metrics

Abstract views : 421 | views : 208

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 JURNAL SAINS INFORMASI GEOGRAFIS



Lisensi Creative Commons
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional. ISSN (online): 2614-1671

 

Indexing Site :

 

Web
Analytics JSIG StatCounter