UTILIZATION EOS PLATFORM AS CLOUD-BASED GIS TO ANALYZE VEGETATION GREENNESS IN CIREBON REGENCY, INDONESIA

Authors

  • Moh. Dede Program Studi Magister Ilmu Lingkungan, Sekolah Pascasarjana, Universitas Padjadjaran
  • Millary Agung Widiawaty Departemen Pendidikan Geografi, FPIPS, Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.30818/jitu.3.1.3257

Keywords:

Cirebon, EOS platform, remote sensing, vegetation greenness.

Abstract

Cloud-Based GIS development has been increasing rapidly since the need for big computing for online spatial data. Besides Google Earth Engine, there is actually another Cloud-Based GIS with similar features namely EOS Platform. This study aims to determine the EOS Platform utilization as a Cloud-Based GIS to Analyze Vegetation Greenness in Cirebon Regency, Indonesia. The selection of research location based on the various phenomenon of development in the Cirebon Regency. Vegetation greenness analysis using the NDVI algorithm which available on EOS Processing and Landsat series images are obtained from Land Viewer. Changes in vegetation greenness were analyzed descriptively from NDVI values in two periods at each pixel in the same location. The results of the analysis with the EOS Platform show a decreasing vegetation greenness in the western and peri-urban areas caused by LULC changes. From this analysis, it is proven that EOS Platform can be used for effective and efficient satellite image processing. Even so, some EOS Platform products with BETA version status still show some obstacles related to integration between products.

Author Biography

  • Moh. Dede, Program Studi Magister Ilmu Lingkungan, Sekolah Pascasarjana, Universitas Padjadjaran
    Mahasiswa Magister Ilmu Lingkungan, Konsentrasi Manajemen Sumber Daya Alam dan Lingkungan. Minat penelitian pada bidang sistem informasi geografis,  open data, spatial big data.

References

M. A. Widiawaty, Mari Mengenal Sains Informasi Geografis. Bandung: Aria Mandiri Group, 2019.

M. Dede, M. A. Widiawaty, I. Setiawan, and N. Huda. (2019). Integrasi AHP dan sistem informasi geografis untuk analisis potensi kerawanan illegal fishing di Indonesia. Jurnal Sains Informasi Geografi, 2 (2), pp. 8-17.

C. Doldirina. (2015). Open data and earth observations: the case of opening up access to and use of earth observation data through the global earth observation system of systems. JIPITEC, 6, pp. 73-85.

H. Sajjad and P. Kumar. Future challenges and perspective of remote sensing technology: potential and future trends. In Applications and Challenges of Geospatial Technology. Basel: Springer Nature Switzerland AG, 2019.

M. Jha and V. Chowdary. (2007). Challenges of using remote sensing and gis in developing nations. Hydrogeology Journal, 15, pp. 197-200.

M. Dede, M. A. Widiawaty, G. P. Pramulatsih, A. Ismail, A. Ati and H. Murtianto. (2019). Integration of participatory mapping, crowdsourcing and geographic information system in flood disaster management (case study Ciledug Lor, Cirebon). Journal of Information Technology and Its Utilization, 2 (2), pp. 44-47.

J. O'Brien, J. O. Wallgrün, J. Detwiler and A. Murdoch. (2020). GEOG 489: Advanced Python Programming for GIS. [Online]. Available https://www.e-education.psu.edu/geog489/node/1405.

O. Mutanga and L. Kumar. (2019). Google Earth Engine applications. Remote Sensing, 11, 591.

J. Rapinski, M. Bednarczyk and D. Zinkiewicz. (2019). JupyTEP ide as an online tool for earth observation data processing. Remote Sensing, 11, 1973.

M. M. Ar-Rahiem, M. R. Fakhlevi and M. I. Hekmatyar, M. I. Analisis Fenomena Pulau Panas Perkotaan Kota Bandung Menggunakan Google Earth Engine. In Prosiding Seminar Nasional Penginderaan Jauh Tahun 2019. Jakarta, 2019.

J. Lawhead, Learning Geospatial Analysis with Python. Birmingham: Packt Publishing Ltd., 2019.

EOS. (2020). EOS Platform. [Online]. Available https://eos.com/platform/.

L. Kumar and O. Mutanga. (2018). Google Earth Engine applications since inception: usage, trends, and potential. Remote Sensing, 10, 1509.

M. Dede, G. P. Pramulatsih, M. A. Widiawaty, Y. R. Ramadhan and A. Ati. (2019). Dinamika suhu permukaan dan kerapatan vegetasi di Kota Cirebon. Jurnal Meteorologi Klimatologi dan Geofisika, 6 (1), pp. 23-31.

S. Arifin. Penyajian dan visualisasi citra ortho satelit landsat produk Indonesia’s National Carbon Accounting System (INCAS) dalam Google Earth. In Prosiding Seminar Nasional Teknologi Informasi dan Komunikasi. Yogyakarta, 2013.

U. Upendi. (2017). Geographic information system produksi energi dan pertambangan Kabupaten Musi Banyuasin. Jurnal Nasional Teknologi dan Sistem Informasi, 3 (3), pp.360-369.

W. Nurdian, M. Dede, M. A. Widiawaty, Y. R. Ramadhan and Y. Purnama. Pemanfaatan sensor Mikro DHT11-Arduino untuk monitoring suhu dan kelembaban udara. In Prosiding Seminar Nasional Tahunan Ilmu Lingkungan Universitas Padjadjaran. Bandung, 2019.

J. A. Sobrino, J. C. Jiménez-Muñoz and L. Paolini. (2004). Land surface temperature retrieval from Landsat TM 5. Remote Sensing of Environment, 90 (4), pp. 434-440.

B. Govaerts and N. Verhulst, The Normalized Difference Vegetation Index (NDVI) GreenSeeker™ Handheld Sensor: Toward the Integrated Evaluation of Crop Management. Mexico City: CIMMYT, 2010.

J. Navarro. First experiences with Google Earth Engine. In Proceeding of 3rd International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2017). Porto, 2017.

G. Donchyts. (2019). Google Earth Engine Workshop. [Online] Available: http://www.jerico-ri.eu/download/summer%20school%20-%20the%20netherlands/Genna%20Donchyts%20-%20GEE%20Training.pdf.

M. A. Widiawaty, A. Ismail, M. Dede and Nurhanifah. (2020). Modeling LULC dynamic using geographic information system and Markov-CA. Geosfera Indonesia, 4: 1-15.

M. Dede, M. A. Widiawaty, Nurhanifah, A. Ismail, A. R. P. Artati, A. Ati and Y. R. Ramadhan. (2020). Estimasi perubahan kualitas udara berbasis citra satelit penginderaan jauh di sekitar PLTU Cirebon. Jambura Geoscience Review, 2 (2).

C. D. Michaelis and D. P. Ames. Web Feature Service (WFS) and Web Map Service (WMS). In Encyclopedia of GIS. New York: Springer, 2017.

U. Gandhi. (2019). Bekerja dengan Data WMS. [Online]. Available: http://www.qgistutorials.com/id/docs/working_with_wms.html.

C. K. N. C. K. Mohd and F. Shahbodin. (2015). Personalized learning environment: alpha testing, beta testing & user acceptance test. Procedia - Social and Behavioral Sciences, 195, pp. 837-843.

Downloads

Published

2020-08-26

Issue

Vol. 3 No. 1 (2020)

Section

Artikel

License

The proposed policy for journals that offer open access

Authors who publish with this journal agree to the following terms:

  1. Copyright on any article is retained by the author(s).
  2. Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
  3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
  5. The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License

How to Cite

UTILIZATION EOS PLATFORM AS CLOUD-BASED GIS TO ANALYZE VEGETATION GREENNESS IN CIREBON REGENCY, INDONESIA. (2020). Journal of Information Technology and Its Utilization, 3(1), 1-4. https://doi.org/10.30818/jitu.3.1.3257