Application of remote sensing and Google Earth Engine to create an agricultural land use status quo mapping in 2023 in Hoa Vang district, Da Nang city

Keywords

bản đồ
Hòa Vang
nông nghiệp
sử dụng đất
viễn thám map
Hoa Vang
agriculture
landuse
remote sensing

Abstract

Hoa Vang is an agricultural district of Da Nang City. Therefore, the management and utilization of agricultural land are among its essential tasks. This study aims to utilize Sentinel 2 imagery to create a current status of agricultural land use map in Hoa Vang District in 2023 by using Google Earth Engine. The research employs the Random Forest method for image prediction, assesses reliability using the Kappa index and the overall accuracy. The calculated Kappa index achieves a value of 0.85, and the overall accuracy is 0.87. These results demonstrate that the agricultural land use map established through Sentinel 2 imagery is highly reliable. The study reveals that agricultural land covers an area of 61,204 hectares, accounting for 83.5% of the district's natural area. Forestry land has the most significant area, with 52,438 hectares, representing 71.5% of the district's total area. Rice cultivation land (3,484.1 hectares) and perennial crop cultivation land (3,348 hectares) have equivalent areas, accounting for approximately 9.5%. Other annual crop cultivation land has the smallest area within the agricultural land category, with 1,933.9 hectares, representing 2.6% of the district's area. The research findings serve as reference material for managing and utilizing agricultural land in Hoa Vang District, Da Nang City.

https://doi.org/10.26459/hueunijard.v133i3B.7443

References

  1. FAO (1997), Integrated approach to the planning and management of land resources, Progress Report.
  2. Franklin, S. E. & Wulder, M. A. (2002), Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas, Progress in Physical Geography: Earth and Environment, 26, 173–205.
  3. Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A. & Rahman, A. (2020), Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review, Remote Sensing, 12, 1135.
  4. Sentinel, E. (2004), Missions-Sentinel Online. ESA: Paris, France.
  5. Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y. & Ranagalage, M. (2020), Sentinel-2 Data for Land Cover/Use Mapping: A Review, Remote Sensing, 12, 2291.
  6. 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.
  7. Bruzzone, L., Bovolo, F., Paris, C., Solano-Correa, Y. T., Zanetti, M. & Fernández-Prieto, D. (2017), Analysis of multitemporal Sentinel-2 images in the framework of the ESA Scientific Exploitation of Operational Missions, 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), IEEE.
  8. Xu, Y., Yu, L., Feng, D., Peng, D., Li, C., Huang, X., Lu, H. & Gong, P. (2019), Comparisons of three recent moderate resolution African land cover datasets: CGLS-LC100, ESA-S2-LC20, and FROM-GLC-Africa30, International Journal of Remote Sensing, 40, 6185–6202.
  9. Chi, M., Plaza, A., Benediktsson, J. A., Sun, Z., Shen, J. & Zhu, Y. (2016), Big data for remote sensing: Challenges and opportunities, Proceedings of the IEEE, 104, 2207–2219.
  10. 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.
  11. Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S. & Brisco, B. (2020), Google Earth Engine for geo-big data applications: A meta-analysis and systematic review, ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170.
  12. Ủy ban nhân dân huyện Hòa Vang (2023), Báo cáo thuyết minh tổng hợp kế hoạch sử dụng đất năm 2023 huyện Hòa Vang, thành phố Đà Nẵng.
  13. Immitzer, M., Vuolo, F. & Atzberger, C. (2016), First experience with Sentinel-2 data for crop and tree species classifications in central Europe, Remote sensing, 8, 166.
  14. Novelli, A., Aguilar, M. A., Nemmaoui, A., Aguilar, F. J. & Tarantino, E. (2016), Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain), International journal of applied earth observation and geoinformation, 52, 403–411.
  15. Vuolo, F., Neuwirth, M., Immitzer, M., Atzberger, C. & Ng, W.-T. (2018), How much does multi-temporal Sentinel-2 data improve crop type classification?, International journal of applied earth observation and geoinformation, 72, 122–130.
  16. Copernicus (2023), S2A_MSIL1C_20230507T030521_N0509_R075_T48PZC_20230507T052914.SAFE, Available from: https://browser.dataspace.copernicus.eu.
  17. Oshiro, T. M., Perez, P. S. & Baranauskas, J. A. (2012), How Many Trees in a Random Forest? (2012), Machine Learning and Data Mining in Pattern Recognition, Berlin, Heidelberg: Springer Berlin Heidelberg, 7376.
  18. Belgiu, M. & Drăguţ, L. (2016), Random forest in remote sensing: A review of applications and future directions, ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.
  19. Tsutsumida, N. & Comber, A. J. (2015), Measures of spatio-temporal accuracy for time series land cover data, International Journal of Applied Earth Observation and Geoinformation, 41, 46–55.
  20. Maryantika, N. & Lin, C. (2017), Exploring changes of land use and mangrove distribution in the economic area of Sidoarjo District, East Java using multi-temporal Landsat images, Information Processing in Agriculture, 4(4), 321–332.
  21. Islami, F. A., Tarigan, S. D., Wahjunie, E. D. & Dasanto, B. D. (2022), Accuracy Assessment of Land Use Change Analysis Using Google Earth in Sadar Watershed Mojokerto Regency, IOP Conference Series: Earth and Environmental Science, 950, 012091.