GOOGLE EARTH ENGINE & SENTINEL-2 MULTISPECTRAL INSTRUMENT: INTEGRASI DATA SPATIO-TEMPORAL UNTUK MEMETAKAN LUCC MENGGUNAKAN ALGORITMA RANDOM FOREST
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Published
May 31, 2022
Main Article Content
Abstract
Google Earth Engine (GEE) is a mapping platform that has various geospatial
analysis capabilities on a global scale. Many applications have been carried out
in various fields such as LUCC monitoring, but are still lacking in Indonesia,
especially in the city of Bogor. The phenomenon of LUCC (land use - land cover
change) is one of the phenomena of changes in the earth's surface that has a
major impact on humans and is related to climate change, so this research was
carried out which resulted in the distribution of LUCC in the city of Bogor. This
study uses Sentinel-2-MSI imagery in the 2017 -2021 period and involves the
Random Forest algorithm classification method with a combination of indices
(NDVI-NDWI-NDBI). The results show that there is an average positive rate of
change for the types of land use changes such as water bodies (104.70%), build
up (0.11%), open land (34.55%) and except for agriculture (-11.29%) and
vegetation (-13.31%). LUCC in the study area causes agricultural areas to
experience land fragmentation due to settlement invasion so that it can be an
evaluation material for the local government in supporting sustainable
development.
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References
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Faichia, C., Tong, Z., Zhang, J., Liu, X., Kazuva, E., Ullah, K., & Al-Shaibah, B. (2020). Using RS data-based CA–markov model for dynamic simulation of
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Firman, T., Surbakti, I. M., Idroes, I. C., & Simarmata, H. A. (2011). Potential climate-change related vulnerabilities in Jakarta: Challenges and current status. Habitat International, 35(2), 372 - 378. https://doi.org/10.1016/j.habitatint.2010.11.011
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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
Gitelson, A. A., Kaufman, Y. J., Starkc, R., Rundquist, D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80, 76-87. https://doi.org/10.1016/S0034-4257(01)00289-9
Hegazy, I. H., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment. 4(1), 117 - 124. https://doi.org/10.1016/j.ijsbe.2015.02.005
Huete, A. R. 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25, 295-309. https://doi.org/10.1016/0034-4257(88)90106-X
Jiang, P., Cheng, L., Li, M., Zhao, R., & Duan, Y. (2015). Impacts of LUCC on soil properties in the riparian zones of desert oasis with remote sensing data:
a case study of the middle Heihe River basin, China. Science of the Total Environment,506,259-271. https://doi.org/10.1016/j.scitotenv.2014.11.004
Jones, P. (2017). Formalizing the informal: Understanding the position of informal settlements and slums in sustainable urbanization policies and strategies in Bandung, Indonesia. Sustainability, 9(8), 1436. https://doi.org/10.3390/su9081436
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Kim, C. (2016). Land use classification and land use change analysis using satellite images in Lombok Island, Indonesia. Forest Science and Technology, 12(4), 183-191. https://doi.org/10.1080/21580103.2016.1147498
Kumar, L., & Mutanga, O., (2018). Google earth engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1509.
https://doi.org/10.3390/rs10101509
Mutanga, O., & Kumar, L.. (2019). Google earth engine applications. Remote Sensing, 11(5), 591. https://doi.org/10.3390/rs11050591
Parente, L., Mesquita, V., Miziara, F., Baumann, L., & Ferreira, L. (2019). Assessing the pasturelands and livestock dynamics in Brazil, from 1985 to
2017: a novel approach based on high spatial resolution imagery and google earth engine cloud computing. Remote Sensing of Environment, 232, 1 - 11.
https://doi.org/10.1016/j.rse.2019.111301
Rad, A. M., Kreitler, J. and Sadegh, M. (2021) ‘Augmented Normalized Difference Water Index for Improved Surface Water Monitoring’, Environmental Modelling and Software. https://doi.org/10.1016/j.envsoft.2021.105030
Pauleit, S., Pribadi, D. O., & Abo El Wafa, H. (2019). Peri-urban agriculture: lessons learnt from Jakarta and Addis Ababa. Field Actions Science
Reports. The journal of field actions, (Special Issue 20), 18-25.
Pham, V. C., Pham, T. T. H., Tong, T. H. A., Nguyen, T. T. H., & Pham, N. H. (2014). The conversion of agricultural land in the peri-urban areas of Hanoi
(Vietnam): patterns in space and time. Journal of Land Use Science, 10(2), 224-242. https://doi.org/10.1080/1747423X.2014.884643
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(14), 2291. https://doi.org/10.3390/rs12142291
Pratomo, R. A., Samsura, D. & van der Krabben, E. 2020. Transformation of local people’s property rights induced by new town development (case studies in
Peri-Urban areas in Indonesia). Land, 9(7), 236. https://doi.org/10.3390/land9070236
Pribadi, D. O., & Pauleit, S. (2015). The dynamics of peri-urban agriculture during rapid urbanization of Jabodetabek Metropolitan Area. Land use policy, 48,
13-24. https://doi.org/10.1016/j.landusepol.2015.05.009
Pribadi, D. O., Zasada, I., Müller, K., & Pauleit, S. (2017). Multifunctional adaption of farmers as response to urban growth in the Jabodetabek Metropolitan Area, Indonesia. Journal of Rural Studies, (55), 100-111. https://doi.org/10.1016/j.jrurstud.2017.08.001
Pribadi, D. O., Vollmer, D., & Pauleit, S. (2018). Impact of peri-urban agriculture on runoff and soil erosion in the rapidly developing metropolitan area of
Jakarta, Indonesia. Regional Environmental Change, 18(7), 2129-2143. https://doi.org/10.1016/j.landusepol.2011.08.009
Rodríguez-Benito, C., Caballero, I., Nieto, K., & Navarro, G. (2021). Observation of maritime traffic interruption in Patagonia during the COVID-19
lockdown using copernicus sentinel-1 data and google earth engine. Remote Sensing, 13(6), 1119. https://doi.org/10.3390/rs13061119
Rondhi, M., Pratiwi, P. A., Handini, V. T., Sunartomo, A. F., & Budiman, S. A. (2018). Agricultural land conversion, land economic value, and sustainable
agriculture: A case study in East Java, Indonesia. Land, 7(4), 148. https://doi.org/10.3390/land7040148
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351, 309-317.
Rudiyanto, Minasny, B., Shah, R. M., Soh, N. C., Arif, C., & Setiawan, B. I. (2019). Automated near-real-time mapping and monitoring of rice extent,
cropping patterns, and growth stages in Southeast Asia using sentinel-1 time series on a google earth engine platform. Remote Sensing, 11(14), 1666.
https://doi.org/10.3390/rs11141666
Rustiadi, E., Pravitasari, A. E., Setiawan, Y., Mulya, S. P., Pribadi, D. O., & Tsutsumida, N. (2019). Impact of continuous Jakarta megacity urban expansion on the formation of the Jakarta-Bandung conurbation over the rice farm regions. Cities, 111, 103000. https://doi.org/10.1016/j.cities.2020.103000
Rustiadi, E., Pribadi, D. O., Pravitasari, A. E., Indraprahasta, G. S., & Iman, L. S. (2015). Jabodetabek megacity: from city development toward urban complex management system. in R. B. Singh (ed.), Urban Development Challenges, Risks and Resilience in Asian Mega Cities. 421-445). Tokyo, Jepang: Springer Nature Switzerland AG.
Sadek, M., & Li, X. (2019). Low-cost solution for assessment of urban flash flood impacts using Sentinel-2 satellite images and fuzzy analytic hierarchy
process: a case study of Ras Ghareb City, Egypt. Advances in Civil Engineering, 2019(23), 1 - 15. https://doi.org/10.1155/2019/2561215
Sakieh, Y., & Salmanmahiny, A. (2016). Performance assessment of geospatial simulation models of land-use change—a landscape metric-based approach.
Environmental Monitoring and Assessment, 188(3), 1 - 16.
Saputra, M. H., & Lee, H. S. (2019). Prediction of land use and land cover changes for north sumatra, indonesia, using an artificial-neural-network-based
cellular automaton. Sustainability, 11(11), 3024. https://doi.org/10.3390/su11113024
Sidhu, N., Pebesma, E., & Câmara, G. (2018). Using google earth engine to detect land cover change: Singapore as a use case. European Journal of Remote
Sensing, 51(1), 486-500. https://doi.org/10.1080/22797254.2018.1451782
Surya, B., Ahmad, D. N. A., Sakti, H. H., & Sahban, H. (2020). Land use change, spatial interaction, and sustainable development in the metropolitan urban
areas, South Sulawesi Province, Indonesia. Land, 9(3), 95. https://doi.org/10.3390/land9030095
Tarigan, A. K., Sagala, S., Samsura, D. A. A., Fiisabiilillah, D. F., Simarmata, H. A., & Nababan, M. (2016). Bandung City, Indonesia. Cities, (50), 100-110.
https://doi.org/10.1016/j.cities.2015.09.005
Xu, H. (2008) ‘A New Index for Delineating Built-Up Land Features in Satellite Imagery’, International Journal of Remote Sensing.
https://doi.org/10.1080/01431160802039957
Yu, W., Zang, S., Wu, C., Liu, W., & Na, X. (2011). Analyzing and modeling land use land cover change (LUCC) in The Daqing City, China. Applied Geography, 31(2), 600 - 608. https://doi.org/10.1016/j.apgeog.2010.11.019
Beaton, A., Whaley, R., Corston, K., & Kenny, F. (2019). Identifying historic river ice breakup timing using MODIS and google earth engine in support of operational flood monitoring in Northern Ontario. Remote Sensing of Environment, 224, 352 - 364. https://doi.org/10.1016/j.rse.2019.02.011
Bi, W., Weng, B., Yuan, Z., Ye, M., Zhang, C., Zhao, Y., Yan, D., & Xu, T., 2018. Evolution characteristics of surface water quality due to climate change and LUCC under scenario simulations: a case study in the Luanhe River Basin. International journal of environmental research and public health, 15(8), 724. https://doi.org/10.3390/ijerph15081724
BPS Bogor. 2021. Kota Bogor dalam angka tahun 2021. Bogor, Indonesia: Badan Pusat Statistik Kota Bogor.
BPS Jawa Barat 2021. Jawa Barat dalam angka tahun 2021. Bandung, Indonesia: Badan Pusat Statistik. Provinsi Jawa Barat.
Elhadary, Y. A. E., Samat, N., & Obeng-Odoom, F. (2013). Development at the peri-urban area and its impact on agricultural activities: An example from the Seberang Perai Region, Penang State, Malaysia. Agroecology and Sustainable Food Systems, 37(7), 834-856. https://doi.org/10.1080/21683565.2013.797950
Faichia, C., Tong, Z., Zhang, J., Liu, X., Kazuva, E., Ullah, K., & Al-Shaibah, B. (2020). Using RS data-based CA–markov model for dynamic simulation of
historical and future LUCC in Vientiane, Laos. Sustainability, 12(20), 8410. https://doi.org/10.3390/su12208410
Firman, T., Surbakti, I. M., Idroes, I. C., & Simarmata, H. A. (2011). Potential climate-change related vulnerabilities in Jakarta: Challenges and current status. Habitat International, 35(2), 372 - 378. https://doi.org/10.1016/j.habitatint.2010.11.011
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
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
Gitelson, A. A., Kaufman, Y. J., Starkc, R., Rundquist, D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80, 76-87. https://doi.org/10.1016/S0034-4257(01)00289-9
Hegazy, I. H., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment. 4(1), 117 - 124. https://doi.org/10.1016/j.ijsbe.2015.02.005
Huete, A. R. 1988. A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25, 295-309. https://doi.org/10.1016/0034-4257(88)90106-X
Jiang, P., Cheng, L., Li, M., Zhao, R., & Duan, Y. (2015). Impacts of LUCC on soil properties in the riparian zones of desert oasis with remote sensing data:
a case study of the middle Heihe River basin, China. Science of the Total Environment,506,259-271. https://doi.org/10.1016/j.scitotenv.2014.11.004
Jones, P. (2017). Formalizing the informal: Understanding the position of informal settlements and slums in sustainable urbanization policies and strategies in Bandung, Indonesia. Sustainability, 9(8), 1436. https://doi.org/10.3390/su9081436
Kaufman, Y. J., & Tenre, D. 1992. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience And Remote
Sensing, 30(2), 261-270. Kharisma, V., & Abe, N. (2020). Food insecurity and associated socioeconomic factors: Application of Rasch and Binary Logistic Models with household survey data in three megacities in Indonesia. Social Indicators Research, 148(2), 655-679.
Kim, C. (2016). Land use classification and land use change analysis using satellite images in Lombok Island, Indonesia. Forest Science and Technology, 12(4), 183-191. https://doi.org/10.1080/21580103.2016.1147498
Kumar, L., & Mutanga, O., (2018). Google earth engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1509.
https://doi.org/10.3390/rs10101509
Mutanga, O., & Kumar, L.. (2019). Google earth engine applications. Remote Sensing, 11(5), 591. https://doi.org/10.3390/rs11050591
Parente, L., Mesquita, V., Miziara, F., Baumann, L., & Ferreira, L. (2019). Assessing the pasturelands and livestock dynamics in Brazil, from 1985 to
2017: a novel approach based on high spatial resolution imagery and google earth engine cloud computing. Remote Sensing of Environment, 232, 1 - 11.
https://doi.org/10.1016/j.rse.2019.111301
Rad, A. M., Kreitler, J. and Sadegh, M. (2021) ‘Augmented Normalized Difference Water Index for Improved Surface Water Monitoring’, Environmental Modelling and Software. https://doi.org/10.1016/j.envsoft.2021.105030
Pauleit, S., Pribadi, D. O., & Abo El Wafa, H. (2019). Peri-urban agriculture: lessons learnt from Jakarta and Addis Ababa. Field Actions Science
Reports. The journal of field actions, (Special Issue 20), 18-25.
Pham, V. C., Pham, T. T. H., Tong, T. H. A., Nguyen, T. T. H., & Pham, N. H. (2014). The conversion of agricultural land in the peri-urban areas of Hanoi
(Vietnam): patterns in space and time. Journal of Land Use Science, 10(2), 224-242. https://doi.org/10.1080/1747423X.2014.884643
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(14), 2291. https://doi.org/10.3390/rs12142291
Pratomo, R. A., Samsura, D. & van der Krabben, E. 2020. Transformation of local people’s property rights induced by new town development (case studies in
Peri-Urban areas in Indonesia). Land, 9(7), 236. https://doi.org/10.3390/land9070236
Pribadi, D. O., & Pauleit, S. (2015). The dynamics of peri-urban agriculture during rapid urbanization of Jabodetabek Metropolitan Area. Land use policy, 48,
13-24. https://doi.org/10.1016/j.landusepol.2015.05.009
Pribadi, D. O., Zasada, I., Müller, K., & Pauleit, S. (2017). Multifunctional adaption of farmers as response to urban growth in the Jabodetabek Metropolitan Area, Indonesia. Journal of Rural Studies, (55), 100-111. https://doi.org/10.1016/j.jrurstud.2017.08.001
Pribadi, D. O., Vollmer, D., & Pauleit, S. (2018). Impact of peri-urban agriculture on runoff and soil erosion in the rapidly developing metropolitan area of
Jakarta, Indonesia. Regional Environmental Change, 18(7), 2129-2143. https://doi.org/10.1016/j.landusepol.2011.08.009
Rodríguez-Benito, C., Caballero, I., Nieto, K., & Navarro, G. (2021). Observation of maritime traffic interruption in Patagonia during the COVID-19
lockdown using copernicus sentinel-1 data and google earth engine. Remote Sensing, 13(6), 1119. https://doi.org/10.3390/rs13061119
Rondhi, M., Pratiwi, P. A., Handini, V. T., Sunartomo, A. F., & Budiman, S. A. (2018). Agricultural land conversion, land economic value, and sustainable
agriculture: A case study in East Java, Indonesia. Land, 7(4), 148. https://doi.org/10.3390/land7040148
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351, 309-317.
Rudiyanto, Minasny, B., Shah, R. M., Soh, N. C., Arif, C., & Setiawan, B. I. (2019). Automated near-real-time mapping and monitoring of rice extent,
cropping patterns, and growth stages in Southeast Asia using sentinel-1 time series on a google earth engine platform. Remote Sensing, 11(14), 1666.
https://doi.org/10.3390/rs11141666
Rustiadi, E., Pravitasari, A. E., Setiawan, Y., Mulya, S. P., Pribadi, D. O., & Tsutsumida, N. (2019). Impact of continuous Jakarta megacity urban expansion on the formation of the Jakarta-Bandung conurbation over the rice farm regions. Cities, 111, 103000. https://doi.org/10.1016/j.cities.2020.103000
Rustiadi, E., Pribadi, D. O., Pravitasari, A. E., Indraprahasta, G. S., & Iman, L. S. (2015). Jabodetabek megacity: from city development toward urban complex management system. in R. B. Singh (ed.), Urban Development Challenges, Risks and Resilience in Asian Mega Cities. 421-445). Tokyo, Jepang: Springer Nature Switzerland AG.
Sadek, M., & Li, X. (2019). Low-cost solution for assessment of urban flash flood impacts using Sentinel-2 satellite images and fuzzy analytic hierarchy
process: a case study of Ras Ghareb City, Egypt. Advances in Civil Engineering, 2019(23), 1 - 15. https://doi.org/10.1155/2019/2561215
Sakieh, Y., & Salmanmahiny, A. (2016). Performance assessment of geospatial simulation models of land-use change—a landscape metric-based approach.
Environmental Monitoring and Assessment, 188(3), 1 - 16.
Saputra, M. H., & Lee, H. S. (2019). Prediction of land use and land cover changes for north sumatra, indonesia, using an artificial-neural-network-based
cellular automaton. Sustainability, 11(11), 3024. https://doi.org/10.3390/su11113024
Sidhu, N., Pebesma, E., & Câmara, G. (2018). Using google earth engine to detect land cover change: Singapore as a use case. European Journal of Remote
Sensing, 51(1), 486-500. https://doi.org/10.1080/22797254.2018.1451782
Surya, B., Ahmad, D. N. A., Sakti, H. H., & Sahban, H. (2020). Land use change, spatial interaction, and sustainable development in the metropolitan urban
areas, South Sulawesi Province, Indonesia. Land, 9(3), 95. https://doi.org/10.3390/land9030095
Tarigan, A. K., Sagala, S., Samsura, D. A. A., Fiisabiilillah, D. F., Simarmata, H. A., & Nababan, M. (2016). Bandung City, Indonesia. Cities, (50), 100-110.
https://doi.org/10.1016/j.cities.2015.09.005
Xu, H. (2008) ‘A New Index for Delineating Built-Up Land Features in Satellite Imagery’, International Journal of Remote Sensing.
https://doi.org/10.1080/01431160802039957
Yu, W., Zang, S., Wu, C., Liu, W., & Na, X. (2011). Analyzing and modeling land use land cover change (LUCC) in The Daqing City, China. Applied Geography, 31(2), 600 - 608. https://doi.org/10.1016/j.apgeog.2010.11.019