Ekstraksi Permukiman dari Kombinasi Citra Sentinel-2 dan Sentinel-1 dengan Pendekatan Object-Based Image Analysis

https://doi.org/10.22146/jgise.91380

Dias Eramudadi(1*), Catur Aries Rokhmana(2)

(1) Departemen Teknik Geodesi, Fakultas Teknik, Universitas Gadjah Mada
(2) Departemen Teknik Geodesi, Fakultas Teknik, Universitas Gadjah Mada
(*) Corresponding Author

Abstract


Informasi yang akurat dan termutakhir mengenai data spasial permukiman skala menengah dibutuhkan berbagai bidang seperti agenda pembangunan berkelanjutan, perubahan iklim dan pengurangan risiko bencana. Namun, ekstraksi informasi spasial untuk permukiman selalu menjadi tantangan karena heterogenitas spasial permukiman yang kompleks. Penelitian ini bertujuan mengekstraksi permukiman dari kombinasi citra Sentinel-2 dan Sentinel-1 menggunakan metode Object-Based Image Analysis (OBIA) dengan platform Google Earth Engine (GEE). Dataset ekstraksi permukiman dibentuk dari 33 fitur kombinasi saluran spektral, indeks spektral dan tekstur. Sementara itu, dataset segmentasi terdiri dari kombinasi indeks spektral UI–NDVI-MNDWI hasil perhitungan Optimum Index Factor (OIF). Segmentasi diproses menggunakan Simple Non-Iterative Clustering (SNIC) dan diklasifikasi dengan algoritma Random Forest (RF). Secara visual, hasil ekstraksi permukiman menunjukkan pola distribusi yang konsisten dengan permukiman peta Rupabumi Indonesia (RBI) skala 1:25000, tetapi memiliki karakter geometri yang berbeda. Oleh karena itu, hasil ekstraksi permukiman belum dapat digunakan secara langsung sebagai data masukan pembuatan peta RBI skala menengah, tetapi dapat dimanfaatkan sebagai panduan digitasi dan mendukung kontrol kualitas. Selain itu, nilai penting fitur dalam klasifikasi RF juga dianalisis dengan hasil fitur polarisasi VV memiliki kontribusi paling tinggi. Uji akurasi menghasilkan nilai overall accuracy dan F-score sebesar 92%. Hasil ini menunjukkan bahwa model klasifikasi dan metode OBIA di GEE mampu menghasilkan data ekstraksi permukiman dengan akurasi yang tinggi di wilayah dengan landskap yang beragam.

Keywords


Ekstraksi Permukiman, OBIA, Sentinel-2, Sentinel-1, GEE

Full Text:

PDF


References

Achanta, R., & Süsstrunk, S. (2017). Superpixels and Polygons using Simple Non-Iterative Clustering. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua(Ic), 4895–4904. https://doi.org/10.1109/CVPR.2017.520

Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052

Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Queiroz Feitosa, R., van der Meer, F., van der Werff, H., van Coillie, F., & Tiede, D. (2014). Geographic Object-Based Image Analysis - Towards A New Paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180–191. https://doi.org/10.1016/j.isprsjprs.2013.09.014

Cheng, J., Sun, G., Zhang, A., Fu, H., Jiao, Z., & Yao, Y. (2021). Synergetic Use of Descending and Ascending SAR With Optical Data for Impervious Surface Mapping. International Geoscience and Remote Sensing Symposium (IGARSS), 2021-July, 4272–4275. https://doi.org/10.1109/IGARSS47720.2021.9553144

Congalton, R. G. (1991). A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, 37(1), 35–46. https://doi.org/10.1016/0034-4257(91)90048-B

Corbane, C., Lemoine, G., Pesaresi, M., Kemper, T., Syrris, V., & Ferri, S. (2018). Enhanced Automatic Detection of Human Settlements using Sentinel-1 Interferometric Coherence. International Journal of Remote Sensing, 39(3), 842–853. https://doi.org/10.1080/01431161.2017.1392642

Csillik, O. (2017). Fast Segmentation and Classification of Very High Resolution Remote Sensing Data using SLIC Superpixels. Remote Sensing, 9(3). https://doi.org/10.3390/rs9030243

Devi,N. S. & Santosa, P. B. (2022). Analisis Geospasial Perubahan Ruang Terbuka Hijau Wilayah Kota Purwokerto dari Tahun 2013 sampai 2020. Journal of Geospatial Information Science and Engineering, Vol. 5 No. 2 (2022). https://doi.org/10.22146/jgise.74620

ESA. (2015). Sentinel-2 User Handbook. In ESA Standard Document.

Firozjaei, M. K., Sedighi, A., Kiavarz, M., Qureshi, S., Haase, D., & Alavipanah, S. K. (2019). Automated Built-up Extraction Index: A New Technique for Mapping Surface Built-up Areas Using LANDSAT 8 OLI Imagery. Remote Sensing, 11(17). https://doi.org/10.3390/rs11171966

Foody, G. M. (2002). Status of Land Cover Classification Accuracy Assessment. Remote Sensing of Environment, 80, 185–201. https://doi.org/https://doi.org/10.1016/S0034-4257(01)00295-4

Gao, B.-C. (1996). NDWI - A Normalized Difference Water Index forRemote Sensing of Vegetation Liquid Water From Space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/https://doi.org/10.1016/S0034-4257(96)00067-3

Haralick, R. M., Dinstein, I., & Shanmugam, K. (1973). Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6), 610–621. https://doi.org/10.1109/TSMC.1973.4309314

Hossain, M. D., & Chen, D. (2019). Segmentation for Object-Based Image Analysis (OBIA): A Review of Algorithms and Challenges from Remote Sensing Perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150(November 2018), 115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.009

Huete, A. R. (1988). Comparative Studies on IFAT, ELISA & DAT for Serodiagnosis of Visceral Leishmaniasis in Bangladesh. Remote Sensing of Environment, 25(1), 295–309. https://doi.org/https://doi.org/10.1016/0034-4257(88)90106-X

Ji, H., Li, X., Wei, X., Liu, W., Zhang, L., & Wang, L. (2020). Mapping 10-m Resolution Rural Settlements using Multi-Source Remote Sensing Datasets with The Google Earth Engine Platform. Remote Sensing, 12(17), 1–23. https://doi.org/10.3390/rs12172832

Juniati, E. (2018). 2D Semantic Labeling Penutup Lahan di Area Urban dengan Analisis Berbasis Objek Dari Foto Udara dan LiDAR. Universitas Gadjah Mada.

Kete, S. C. R., Suprihatin, Tarigan, S. D., & Effendi, H. (2019). Land Use Classification Based on Object and Pixel using Landsat 8 OLI in Kendari City, Southeast Sulawesi Province, Indonesia. IOP Conference Series: Earth and Environmental Science, 284(1). https://doi.org/10.1088/1755-1315/284/1/012019

Kpienbaareh, D., Sun, X., Wang, J., Luginaah, I., Kerr, R. B., Lupafya, E., & Dakishoni, L. (2021). Crop Type and Land Cover Mapping in Northern Malawi Using The Inegration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data. Remote Sensing, 13(4), 1–21. https://doi.org/10.3390/rs13040700

Kushardono, D. (2012). Klasifikasi Spasial Penutup Lahan Dengan Data Sar Dual- Polarisasi Menggunakan Normalized Difference Polarization Index Dan Fitur Keruangan Dari Matrik Kookurensi (Spatial Land Cover Classification Using Dual-Polarization Sar Data Based on Normalized Diff. Jurnal Penginderaan Jauh, 9(1), 12–24.

Matarira, D., Mutanga, O., & Naidu, M. (2022). Google Earth Engine for Informal Settlement Mapping : A Random Forest Classification Using Spectral and Textural Information. Remote Sensing, 14(20). https://doi.org/10.3390/rs14205130

Matarira, D., Mutanga, O., Naidu, M., & Vizzari, M. (2023). Object-Based Informal Settlement Mapping in Google Earth Engine Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data. Land, 12, 1–17. https://doi.org/https://doi.org/10.3390/land12010099

Rudiastuti, A. W., Farda, N. M., & Ramdani, D. (2021). Mapping Built-Up Land & Settlements : A Comparison of Machine Learning Algorithms. Proceedings Volume 12082, Seventh Geoinformation Science Symposium 2021. https://doi.org/10.1117/12.2619493

Rudiastuti, A. W., Lumban-Gaol, Y., Silalahi, F. E. S., Prihanto, Y., & Pranowo, W. S. (2022). Implementing Random Forest Algorithm in GEE: Separation and Transferability on Built-Up Area in Central Java, Indonesia. International Journal of Informatics Visualization, 6, 74–82. https://doi.org/http://dx.doi.org/10.30630/joiv.6.1.873

Santosa, P. B. (2016). Evaluation of satellite image correction methods caused by differential terrain illumination. Jurnal Forum Geografi. Vol. 30, No. 1 (2016). https://doi.org/10.23917/forgeo.v30i1.1768

Tassi, A., Gigante, D., Modica, G., & Martino, L. Di. (2021). Pixel- vs . Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest : The Case Study of Maiella National Park. Remote Sensing, 13. https://doi.org/10.3390/rs13122299

Teluguntla, P., Thenkabail, P., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., Yadav, K., & Huete, A. (2018). A 30-m Landsat-Derived Cropland Extent Product of Australia and China using Random Forest Machine Learning Algorithm on Google Earth Engine Cloud Computing Platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144(February), 325–340. https://doi.org/10.1016/j.isprsjprs.2018.07.017

Vergni, L., Vinci, A., Todisco, F., Santaga, F. S., & Vizzari, M. (2021). Comparing Sentinel-1, Sentinel-2, and Landsat-8 Data in the Early Recognition of Irrigated Areas in Central Italy. Journal of Agricultural Engineering, 52(4), 43–53. https://doi.org/10.4081/JAE.2021.1265

Vizzari, M. (2022). PlanetScope, Sentinel-2, and Sentinel-1 Data Integration for Object-Based Land Cover Classification in Google Earth Engine. Remote Sensing, 14, 1–19. https://doi.org/https://doi.org/10.3390/rs14112628

Wang, L., Gong, P., Ying, Q., Yang, Z., Cheng, X., & Ran, Q. (2010). Settlement Extraction in The North China Plain using Landsat and Beijing-1 Multispectral Data with An Improved Watershed Segmentation Algorithm. International Journal of Remote Sensing, 31(6), 1411–1426. https://doi.org/10.1080/01431160903475332

Widyaningrum, E., Perdana, A. P., Andari, R., Mayasari, R., & Damayanti, A. P. (2021). Penggunaan Citra Satelit Sentinel-2 Dan Spot 6-7 Dengan Kompilasi Data Keruangan Untuk Pemutakhiran Peta Dasar. Elipsoida, 04(02), 100–108. https://doi.org/https://doi.org/10.14710/elipsoida.2021.13874

Xu, H. (2006). Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179

Zurqani, H. A., Post, C. J., Mikhailova, E. A., & Allen, J. S. (2019). Mapping Urbanization Trends in a Forested Landscape Using Google Earth Engine. Remote Sensing in Earth Systems Sciences, 2(4), 173–182. https://doi.org/10.1007/s41976-019-00020-y



DOI: https://doi.org/10.22146/jgise.91380

Article Metrics

Abstract views : 553 | views : 783

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

JGISE is indexed by: Google Scholar Dimensions Crossref Garuda DOAJ
Sinta WorldCat OneSearch Base

Journal of Geospatial Information Science and Engineering (JGISE) ISSN: 2623-1182 (Online) Email: jgise.ft@ugm.ac.id The Contents of this website is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.