Pemetaan Perubahan Lahan Sawah Kabupaten Sukabumi Menggunakan Google Earth Engine

Widia Siska; Widiatmaka Widiatmaka; Yudi Setiawan; Setyono Hari Adi

doi:10.14710/tataloka.24.1.74-83

DOI: https://doi.org/10.14710/tataloka.24.1.74-83

Pemetaan Perubahan Lahan Sawah Kabupaten Sukabumi Menggunakan Google Earth Engine

*Widia Siska - Institut Pertanian Bogor, Indonesia

Widiatmaka Widiatmaka - Institut Pertanian Bogor, Indonesia

Yudi Setiawan - Institut Pertanian Bogor, Indonesia

Setyono Hari Adi - Balai Penelitian Agroklimat dan Hidrologi, Indonesia

under http://creativecommons.org/licenses/by-nc-sa/4.0/.

BibTex Citation Data :

@article{tataloka10714,
    author = {Widia Siska and Widiatmaka Widiatmaka and Yudi Setiawan and Setyono Adi},
    title = {Pemetaan Perubahan Lahan Sawah Kabupaten Sukabumi Menggunakan Google Earth Engine},
    journal = {TATALOKA},
  volume = {24},
    number = {1},
    year = {2022},
    keywords = {Google Earth Engine, Kabupaten Sukabumi, Sawah},
    abstract = { Google Earth Engine  (GEE) merupakan layanan pemrosesan geospasial yang telah banyak digunakan di berbagai bidang pemetaan. Tujuan penelitian ini adalah  identifikasi perubahan lahan sawah Kabupaten Sukabumi menggunakan  GEE . Data citra landsat 5 dan landsat 8 yang digunakan di GEE merupakan data citra yang telah di  pre-process  dan terkoreksi. Klasifikasi penggunaan/tutupan lahan dibedakan menjadi 6 kelas yaitu sawah, badan air, pemukiman, bervegetasi, hutan dan tanah terbuka. Sampel acak penggunaan lahan dibuat sebanyak 394 titik di GEE menggunakan  poin  dan  rectangular . Klasifikasi penggunaan lahan dianilisis menggunakan metode  Random Forest  (RF). Penilaian akurasi dihitung menggunakan  confusion Matrix,  sedangkan   validasi lapang dilakukan dengan menggunakan metode  stratified random sampling .    Uji akurasi analisis tutupan lahan tahun 2020 dengan  confusion Matrix  menghasilkan nilai  Overall Accuracy (OA)  0,94 dan nilai  kappa   0,91; tahun 2015 dengan nilai OA 0,93 dan nilai  Kappa  0,91; sedangkan tahun 2010 memiliki nilai  OA  0.96 dan nilai  Kappa  0.94. Hasil analisis ini menunjukkan bahwa luas lahan sawah Kabupaten Sukabumi mengalami penyusutan seluas 10,317.27  ha dalam kurun waktu sepuluh tahun (2010-2020). Klasifikasi penggunaan lahan menggunakan GEE dapat menghasilkan peta dengan akurasi tinggi dengan OA >85%, serta dapat mempersingkat waktu analisis.},
   issn = {2356-0266},   pages = {74--83}  doi = {10.14710/tataloka.24.1.74-83},
    url = {https://ejournal2.undip.ac.id/index.php/tataloka/article/view/10714}
}

Citation Format:

Abstract

Google Earth Engine (GEE) merupakan layanan pemrosesan geospasial yang telah banyak digunakan di berbagai bidang pemetaan. Tujuan penelitian ini adalah identifikasi perubahan lahan sawah Kabupaten Sukabumi menggunakan GEE. Data citra landsat 5 dan landsat 8 yang digunakan di GEE merupakan data citra yang telah di pre-process dan terkoreksi. Klasifikasi penggunaan/tutupan lahan dibedakan menjadi 6 kelas yaitu sawah, badan air, pemukiman, bervegetasi, hutan dan tanah terbuka. Sampel acak penggunaan lahan dibuat sebanyak 394 titik di GEE menggunakan poin dan rectangular. Klasifikasi penggunaan lahan dianilisis menggunakan metode Random Forest (RF). Penilaian akurasi dihitung menggunakan confusion Matrix, sedangkan validasi lapang dilakukan dengan menggunakan metode stratified random sampling. Uji akurasi analisis tutupan lahan tahun 2020 dengan confusion Matrix menghasilkan nilai Overall Accuracy (OA) 0,94 dan nilai kappa 0,91; tahun 2015 dengan nilai OA 0,93 dan nilai Kappa 0,91; sedangkan tahun 2010 memiliki nilai OA 0.96 dan nilai Kappa 0.94. Hasil analisis ini menunjukkan bahwa luas lahan sawah Kabupaten Sukabumi mengalami penyusutan seluas 10,317.27 ha dalam kurun waktu sepuluh tahun (2010-2020). Klasifikasi penggunaan lahan menggunakan GEE dapat menghasilkan peta dengan akurasi tinggi dengan OA >85%, serta dapat mempersingkat waktu analisis.

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Keywords: Google Earth Engine, Kabupaten Sukabumi, Sawah

Funding: IPB

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Language : EN

In Vol 24, No 1 (2022): Volume 24 No. 1, February 2022

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