skip to main content

Pola Spasial Perubahan Tutupan Lahan/Penggunaan Lahan Menggunakan Google Earth Engine di Kabupaten Majalengka

*Adrian Adrian  -  Institut Pertanian Bogor, Indonesia
Widiatmaka Widiatmaka scopus  -  Institut Pertanian Bogor, Indonesia
Khursatul Munibah scopus  -  Institut Pertanian Bogor, Indonesia
Irman Firmansyah orcid scopus  -  System Dynamics Center, Indonesia

Citation Format:
Abstract
Pembangunan fisik di suatu wilayah memerlukan lahan, seperti sektor perumahan, pertanian, industri, pertambangan, serta transportasi. Pertambahan jumlah penduduk akan berimplikasi terhadap meningkatkan kebutuhan akan ruang yang menyebabkan perubahan Land Use Land Cover (LULC) di suatu wilayah. Kabupaten Majalengka merupakan bagian dari pengembangan Kawasan Segitiga Rebana (Cirebon-Patimban-Kertajati) yang telah direncanakan dan ditetapkan menjadi kawasan ekonomi khusus (KEK). Penelitian ini bertujuan untuk menganalisis LULC perubahan di Kabupaten Majalengka (2011-2021) menggunakan data citra Sentinel 2A selama 10 tahun (2011-2021) diperoleh dari Google Earth Engine (GEE). Klasifikasi LULC menggunakan machine learning dengan pendekatan random forest dipadu dengan Analisa Normalized Difference Built-Up Index (NDBI), Normalized Difference Water Index (NDWI) dan peta lahan baku sawah untuk menghasilkan peta tutupan lahan.  Hasil pengolahan citra yang menghasilkan peta penggunaan lahan menggunakan alogaritma smile-Random Forest pada platform GEE dipadu dengan Analisa NDWI dan NDBI mengahasilkan peta tutupan lahan yang akurat dengan nilai OA sebesar 98.81% dan kappa sebesar 95.91%. Penurunan luasan lahan pertanian (sawah, ladang) di Kabupaten Majalengka mengalami penyusutan seluas 4457,36 ha dalam kurun waktu sepuluh tahun (2011-2021). Kelebihan Platform GEE dimana menyediakan akses cepat dan mudah ke berbagai data citra satelit tanpa harus mengunduh atau menyimpan data secara lokal.
Fulltext View|Download
Keywords: Google Earth Engine, Land Use Changes, KEK Rebana, Sawah
Funding: LPDP (lembaga Pengelola Dana Pendidikan

Article Metrics:

  1. 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. https://doi.org/10.1109/JSTARS.2020.3021052
  2. Arikunto. (200). Teknik Sampel. In Prosedur Penelitian Suatu Pendekatan Praktik-Revisi ke X
  3. Bappenas. (2018). Pedoman Pelaksanaan Intervensi Penurunan Stunting Terintegrasi di Kabupaten/Kota. Rencana Aksi Nasional Dalam Rangka Penurunan Stunting: Rembuk Stunting
  4. BPS. (2018). Kabupaten Majalengka dalam angka tahun 2018. In BPS Kabupaten Majalengka. Badan Pusat Statistik
  5. Carrasco, L., O’Neil, A. W., Daniel Morton, R., & Rowland, C. S. (2019). Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine. Remote Sensing, 11(3). https://doi.org/10.3390/rs11030288
  6. Duan, Q., Tan, M., Guo, Y., Wang, X., & Xin, L. (2019). Understanding the Spatial Distribution of Urban Forests in China Using Sentinel-2 images with Google Earth Engine. Forests, 10(9). https://doi.org/10.3390/f10090729
  7. Gandharum, L., Hartono, D. M., Karsidi, A., & Ahmad, M. (2022). Monitoring Urban Expansion and Loss of Agriculture on the North Coast of West Java Province, Indonesia, Using Google Earth Engine and Intensity Analysis. Scientific World Journal, 2022(Sdg 2). https://doi.org/10.1155/2022/3123788
  8. Gao, B. C. (1996). NDWI - A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
  9. Gómez, J. A., Patiño, J. E., Duque, J. C., & Passos, S. (2020). Spatiotemporal Modeling of Urban Growth Using Machine Learning. Remote Sensing, 12(1). https://doi.org/10.3390/rs12010109
  10. Gomiero, T. (2016). Soil Degradation, Land Scarcity and Food Security: Reviewing a Complex Challenge. In Sustainability (Switzerland). 8(3). https://doi.org/10.3390/su8030281
  11. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
  12. Halmy, M. W. A., Gessler, P. E., Hicke, J. A., & Salem, B. B. (2015). Land Use/Land Cover Change Detection and Prediction in the North-Western Coastal Desert of Egypt Using Markov-CA. Applied Geography, 63, 101–112. https://doi.org/10.1016/j.apgeog.2015.06.015
  13. Undang Undang Republik Indonesia No. 41 Tahun 2009 Tentang Perlindungan Lahan Pertanian Pangan Berkelanjutan, Pub. L. No. 41, 49 (2009). https://peraturan.go.id/id/uu-no-41-tahun-2009
  14. Jetz, W., Rahbek, C., & Lichstein, J. W. (2005). Local and Global Approaches to Spatial Data Analysis in Ecology. In Global Ecology and Biogeography. 14(1). pp. 97–98. https://doi.org/10.1111/j.1466-822X.2004.00129.x
  15. Kempler, S., & Mathews, T. (2017). Earth Science Data Analytics: Definitions, Techniques and Skills. Data Science Journal. https://doi.org/10.5334/dsj-2017-006
  16. Lichtenberg, E., & Ding, C. (2008). Assessing farmland protection policy in China. Land Use Policy, 25(1), 59–68. https://doi.org/10.1016/j.landusepol.2006.01.005
  17. Piao, Y., Jeong, S., Park, S., & Lee, D. (2021). Analysis of Land Use and Land Cover Change Using Time-Series Data and Random Forest in North Korea. Remote Sensing, 13(17). https://doi.org/10.3390/rs13173501
  18. Rana, V. K., & Venkata Suryanarayana, T. M. (2020). Performance Evaluation of MLE, RF and SVM Classification Algorithms for Watershed Scale Land Use/Land Cover Mapping Using Sentinel 2 Bands. Remote Sensing Applications: Society and Environment, 19. https://doi.org/10.1016/j.rsase.2020.100351
  19. Roziqin, A., & Kusumawati, I. (2017). Analisis Pola Permukiman Menggunakan Data Penginderaan Jauh di Pulau Batam. IRONS: 8th Industrial Research Workshop and National Seminar Politeknik Negeri Bandung, 52–58
  20. Sari, N. M., & Kushardono, D. (2019). Analisis Dampak Pembangunan Infrastruktur Bandara Internasional Jawa Barat Terhadap Alih Fungsi Lahan Pertanian Melalui Citra Satelit Resolusi Tinggi. Jurnal Geografi, 11(2), 146–162. https://doi.org/10.24114/jg.v11i2.13470
  21. Sellars, S., Nguyen, P., Chu, W., Gao, X., Hsu, K. L., & Sorooshian, S. (2013). Computational Earth Science: Big Data Transformed Into Insight. Eos (United States). https://doi.org/10.1002/2013EO320001
  22. Singha, M., Dong, J., Zhang, G., & Xiao, X. (2019). High Resolution Paddy Rice Maps in Cloud-Prone Bangladesh and Northeast India Using Sentinel-1 Data. Scientific Data, 6(1). https://doi.org/10.1038/s41597-019-0036-3
  23. Somantri, L., Ridwana, R., & Himayah, S. (2021). Land Value Analysis in the Suburban of Bandung and Agricultural Land Availability Impact. IOP Conference Series: Earth and Environmental Science, 683(1). https://doi.org/10.1088/1755-1315/683/1/012088
  24. Sutanto. (1987). Prinsip Dasar Penginderaan Jauh. Panduan Aplikasi Penginderaan Jauh Tingkat Dasar
  25. Vigneshwaran, S., & Vasantha Kumar, S. (2018). Extraction of Built-Up Area Using High Resolution Sentinel-2A and Google Satellite Imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(4/W9), 165–169. https://doi.org/10.5194/isprs-archives-XLII-4-W9-165-2018
  26. Widiatmaka, W., Ambarwulan, W. A., Tambunan, R. P., Nugroho, Y. A., Suprajaka, S., Nurwadjedi, N., & Santoso, P. B. K. (2014). Land Use Planning of Paddy Field Using Geographic Information System and Land evaluation in West Lombok, Indonesia. Indonesian Journal of Geography. https://doi.org/10.22146/ijg.5004
  27. Xie, Y., Mei, Y., Guangjin, T., & Xuerong, X. (2005). Socio-Economic Driving Forces of Arable Land Conversion: A Case Study of Wuxian City, China. Global Environmental Change, 15(3), 238–252. https://doi.org/10.1016/j.gloenvcha.2005.03.002
  28. Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and Cloud Computing: Innovation Opportunities and Challenges. In International Journal of Digital Earth. 10(1), pp. 13–53. https://doi.org/10.1080/17538947.2016.1239771

Last update:

No citation recorded.

Last update: 2024-05-25 22:33:51

No citation recorded.