DOI: https://doi.org/10.14710/geoplanning.8.2.75-84

Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and Random Forest Classifier Using Sentinel-2 Multitemporal Satellite Imagery

*Nurwita Mustika Sari  -  Pusat Riset Aplikasi Penginderaan Jauh LAPAN, , Indonesia
R. Rokhmatuloh  -  Department of Geography, Faculty of Mathematics and Natural Science, Indonesia University, Indonesia
Masita Dwi Mandini Manessa  -  Department of Geography, Faculty of Mathematics and Natural Science, Indonesia University, Indonesia

Citation Format:
Abstract

The existence of vegetation in an area has an important role to maintain the carrying capacity of the environment and create a comfortable environment as a place to live. In an effort to create a sustainable environment, there are various pressures on vegetation that cause a decrease in vegetation area. Economic activity, population growth and other anthropogenic activities trigger the dynamics of vegetation cover in an area that causes land cover changes from vegetation to non-vegetation. Majalengka Regency as one of the areas with intensive regional physical development in line with the operation of BIJB Kertajati and the Cipali toll road became the study area in this research. This study aims to monitor the dynamics of vegetation cover with the proposed method namely the integration of the OBIA and Random Forest classifier using multi temporal Sentinel-2 satellite imagery. The results show that there is a decrease in the area of vegetation in the research area as much as 4,329.6 hectares to non-vegetation areas in the period 2016-2020. The vegetation area in 2020 is 84,716.07 hectares and non-vegetation area is 35,708 hectares. Thus, there has been a decrease in the percentage of vegetation area from 73.94% in 2016 to 70.35% in 2020, meanwhile for non-vegetation areas there has been an increase from 26.06% in 2016 to 29.65% in 2020.

Fulltext View|Download
Keywords: Vegetation cover; vegetation dynamics; Sentinel-2; OBIA; Random Forest
Funding: Remote Sensing Application Center LAPAN, BRIN; Lembaga Pengelola Dana Pendidikan (LPDP) Kementerian Keuangan Republik Indonesia

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
Development of Desertification Indicators for Desertification Monitoring from Landsat Images Using Python Programming The Spatial Model of Paddy Productivity Based on Environmental Vulnerability in Each Phase of Paddy Planting Prospective Mapping of Land Cover and Land Use in The Classified Forest of The Upper Alibori Based on Satellite Imagery More recent articles
Most cited articles
SATELLITE-DERIVED BATHYMETRY USING RANDOM FOREST ALGORITHM AND WORLDVIEW-2 IMAGERY THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA TRANSFORMASI HUNIAN DENGAN PERSPEKTIF SPASIAL DAN TATANAN BUDAYA: KOMPARASI PERMUKIMAN KUMUH BANG BUA, THAILAND DAN KAMPUNG NAGA, INDONESIA SPATIAL EXPLICIT MODELING TO UNDERSTAND THE DYNAMICS OF LANDUSE SWITCH USING OPEN SOURCE SATELLITE DATA EVALUASI KESESUAIAN LOKASI DAN JANGKAUAN PELAYANAN SEKOLAH MENENGAH UMUM DI KECAMATAN KEBAKKRAMAT KABUPATEN KARANGANYAR More cited articles

  1. Bhatnagar, S., Gill, L., Regan, S., Naughton, O., Johnston, P., Waldren, S., & Ghosh, B. (2020). Mapping Vegetation Communities Inside Wetlands Using Sentinel-2 Imagery In Ireland. International Journal of Applied Earth Observation and Geoinformation, 88, 102083. [https://doi.org/10.1016/j.jag.2020.102083">Crossref]

  2. Blaschke, T., Lang, S., Lorup, E., Strobl, J., & Zeil, P. (2000). Object-Oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications. EnviroInfo 2000: Umweltinformatik ’00 Umweltinformation Für Planung, Politik Und Öffentlichkeit Copyright 2000 Metropolis Verlag, Marburg, ISBN: 3-89518-307-5.

  3. Cao, Y., Wang, Y., Li, G., & Fang, X. (2019). Vegetation Response to Urban Landscape Spatial Pattern Change in the Yangtze River Delta, China. Sustainability, 12(1), 68. [https://doi.org/10.3390/su12010068">Crossref]

  4. Chen, W., Huang, H., Dong, J., Zhang, Y., Tian, Y., & Yang, Z. (2018). Social functional mapping of urban green space using remote sensing and social sensing data. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 436–452. [https://doi.org/10.1016/j.isprsjprs.2018.10.010">Crossref]

  5. Direktorat Jenderal Pengendalian Perubahan Iklim KLHK. (2020). Tentang Perubahan Iklim. Knowledge Centre Perubahan Iklim. 

  6. Fonge, B. A., Tabot, P. T., Bakia, M.-A., & Awah, C. C. (2019). Patterns of land-use change and current vegetation status in peri-urban forest reserves: The case of the Barombi Mbo Forest Reserve, Cameroon. Geology, Ecology, and Landscapes, 3(2), 104–113. [https://doi.org/10.1080/24749508.2018.1508981">Crossref]

  7. Fu, B., Wang, Y., Campbell, A., Li, Y., Zhang, B., Yin, S., Xing, Z., Jin, X. (2017). Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data. Ecological Indicators, 73, 105–117. [https://doi.org/10.1016/j.ecolind.2016.09.029">Crossref]

  8. Guha, S., & Govil, H. (2020). An assessment on the relationship between land surface temperature and normalized difference vegetation index. Environment, Development and Sustainability. [https://doi.org/10.1007/s10668-020-00657-6">Crossref]

  9. Gui, X., Wang, L., Yao, R., Yu, D., & Li, C. (2019). Investigating the urbanization process and its impact on vegetation change and urban heat island in Wuhan, China. Environmental Science and Pollution Research, 26(30), 30808–30825. [https://doi.org/10.1007/s11356-019-06273-w">Crossref]

  10. Kavzoglu, T., & Tonbul, H. (2017). A comparative study of segmentation quality for multi-resolution segmentation and watershed transform. 2017 8th International Conference on Recent Advances in Space Technologies (RAST), 113–117. Istanbul, Turkey: IEEE. [https://doi.org/10.1109/RAST.2017.8002984">Crossref]

  11. Kowalski, K., Senf, C., Hostert, P., & Pflugmacher, D. (2020). Characterizing spring phenology of temperate broadleaf forests using Landsat and Sentinel-2 time series. International Journal of Applied Earth Observation and Geoinformation, 92, 102172. [https://doi.org/10.1016/j.jag.2020.102172">Crossref]

  12. Labib, S. M., & Harris, A. (2018). The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method. European Journal of Remote Sensing, 51(1), 231–240. [https://doi.org/10.1080/22797254.2017.1419441">Crossref]

  13. Lu, L., Weng, Q., Guo, H., Feng, S., & Li, Q. (2019). Assessment of urban environmental change using multi-source remote sensing time series (2000–2016): A comparative analysis in selected megacities in Eurasia. Science of The Total Environment, 684, 567–577. [https://doi.org/10.1016/j.scitotenv.2019.05.344">Crossref]

  14. Luciano, A. C. dos S., Picoli, M. C. A., Rocha, J. V., Duft, D. G., Lamparelli, R. A. C., Leal, M. R. L. V., & Le Maire, G. (2019). A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm. International Journal of Applied Earth Observation and Geoinformation, 80, 127–136. [https://doi.org/10.1016/j.jag.2019.04.013">Crossref]

  15. Mukhoriyah, M., Sari, N. M., Sharika, M., & Hanifati, L. N. (2019). Identifikasi Ketersediaan Ruang Terbuka Hijau Kecamatan Kramat Jati Kodya Jakarta Timur Menggunakan Citra Pleiades. Jurnal Planologi, 16(2), 158. [https://doi.org/10.30659/jpsa.v16i2.5005">Crossref]

  16. Naipospos, B., Rahmah, U. A., & Muthia, A. (2020). Partnership scheme for BIJB Kertajati West Java International Airport. IOP Conference Series: Earth and Environmental Science, 592, 012020. [https://doi.org/10.1088/1755-1315/592/1/012020">Crossref]

  17. Nguyen, U., Glenn, E. P., Dang, T. D., & Pham, L. T. H. (2019). Mapping vegetation types in semi-arid riparian regions using random forest and object-based image approach: A case study of the Colorado River Ecosystem, Grand Canyon, Arizona. Ecological Informatics, 50, 43–50. [https://doi.org/10.1016/j.ecoinf.2018.12.006">Crossref]

  18. Nse, O. U., Okolie, C. J., & Nse, V. O. (2020). Dynamics of Land Cover, Land Surface Temperature and NDVI in Uyo Capital City, Nigeria. Scientific African, e00599. [https://doi.org/10.1016/j.sciaf.2020.e00599">Crossref]

  19. Priyanta, M. (2015). Pembaruan dan Harmonisasi Peraturan Perundang- undangan Bidang Lingkungan dan Penataan Ruang Menuju Pembangunan Berkelanjutan. Hasanuddin Law Review, 1(3), 337–349.

  20. Puissant, A., Rougier, S., & Stumpf, A. (2014). Object-oriented mapping of urban trees using Random Forest classifiers. International Journal of Applied Earth Observation and Geoinformation, 26, 235–245. [https://doi.org/10.1016/j.jag.2013.07.002">Crossref]

  21. Referowska-Chodak, E. (2019). Pressures and Threats to Nature Related to Human Activities in European Urban and Suburban Forests. Forests, 10(9), 765. [https://doi.org/10.3390/f10090765">Crossref]

  22. Sari, N. M., & Kushardono, D. (2016). Quality Analysis of Single Tree Object with Obia and Vegetation Index from Lapan Surveillance Aircraft Multispectral Data in Urban Area. Geoplanning: Journal of Geomatics and Planning, 3(2), 93. [https://doi.org/10.14710/geoplanning.3.2.93-106">Crossref]

  23. 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">Crossref]

  24. Sathyakumar, V., Ramsankaran, R., & Bardhan, R. (2020). Geospatial approach for assessing spatiotemporal dynamics of urban green space distribution among neighbourhoods: A demonstration in Mumbai. Urban Forestry & Urban Greening, 48, 126585. [https://doi.org/10.1016/j.ufug.2020.126585">Crossref]

  25. Schug, F., Frantz, D., Okujeni, A., van der Linden, S., & Hostert, P. (2020). Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data. Remote Sensing of Environment, 246, 111810. [https://doi.org/10.1016/j.rse.2020.111810">Crossref]

  26. Syafarudin, A., & Mulyana, B. (2019). Formulation Strategy of PT. Bandara Internasional Jawa Barat Bandung Indonesia, Kertajati in Business Aerocity (Aerotropolis). International Review of Management and Marketing, 9(3), 106–111. [https://doi.org/10.32479/irmm.8113">Crossref]

  27. Tjahjono, T., & Yuliawati, E. (2017). Bandar Udara Internasional Jawa Barat (BIJB) dan Potensi Kertajati Sebagai Aerocity. WARTA ARDHIA, 43(1), 43. [https://doi.org/10.25104/wa.v43i1.274.43-50">Crossref]

  28. Uca Avci, Z. D., Karaman, M., Ozelkan, E., Kumral, M., & Budakoglu, M. (2014). OBIA based hierarchical image classification for industrial lake water. Science of The Total Environment, 487, 565–573. [https://doi.org/10.1016/j.scitotenv.2014.04.048">Crossref]

  29. Wolch, J. R., Byrne, J., & Newell, J. P. (2014). Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough.’ Landscape and Urban Planning, 125, 234–244. [https://doi.org/10.1016/j.landurbplan.2014.01.017">Crossref]

  30. Wu, J., Wang, Y., Qiu, S., & Peng, J. (2019). Using the modified i-Tree Eco model to quantify air pollution removal by urban vegetation. Science of The Total Environment, 688, 673–683. [https://doi.org/10.1016/j.scitotenv.2019.05.437">Crossref]

  31. Yang, G., Zhao, Y., Xing, H., Fu, Y., Liu, G., Kang, X., & Mai, X. (2020). Understanding the changes in spatial fairness of urban greenery using time-series remote sensing images: A case study of Guangdong-Hong Kong-Macao Greater Bay. Science of The Total Environment, 715, 136763. [https://doi.org/10.1016/j.scitotenv.2020.136763">Crossref]


Last update:

  1. Modern approaches to the preparation of maps of Arctic vegetation

    I. A. Lavrinenko. Geobotanical mapping, 2023. doi: 10.31111/geobotmap/2023.14

  2. Industrial Development-Induced Gentrification

    Imam Buchori, Abdurrahman Zaki, Pangi Pang, Anang Wahyu Sejati, Yudi Basuki, Angrenggani Pramitasari. International Review for Spatial Planning and Sustainable Development, 11 (1), 2023. doi: 10.14246/irspsd.11.1_276

  3. Impacts of rapid urbanization on spatial dynamics of land use–based carbon emission and surface temperature changes in the Semarang Metropolitan Region, Indonesia

    Dimas Danar Dewa, Imam Buchori. Environmental Monitoring and Assessment, 195 (2), 2023. doi: 10.1007/s10661-022-10839-6

  4. mCLESS: The Multi-Class Least-Error Square Sum for Interpretable Classification

    Hwan Hee Park, Seung Heon Lee, Hwamog Kim, Seongjai Kim. 2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE), 2023. doi: 10.1109/CSCE60160.2023.00016

Last update: 2024-04-18 08:55:31

No citation recorded.