Pemetaan dan Analisis Dinamika Ekspansi Perkotaan di Wilayah Metropolitan Jakarta (1990-2020) Menggunakan Google Earth Engine
Keywords:
Urban Sprawl, Ekspansi Perkotaan, Google Earth Engine, Landsat, NDBI, MNDWI, Jakarta Metropolitan Area, Validasi Akurasi Spasial, Ground Truth, BPS
Abstract
Studi ini menyajikan analisis spasial-temporal yang mendalam mengenai dinamika ekspansi perkotaan, atau urban sprawl, di Wilayah Metropolitan Jakarta (WMJ) selama periode tiga dekade (19902020), dengan interval lima tahunan. Metodologi penelitian ini secara ekstensif memanfaatkan kapabilitas komputasi awan Google Earth Engine(GEE) dan serangkaian komposit citra satelit Landsat dari berbagai misi (Landsat 4, 5, 7, 8, dan 9), yang dipilih secara kondisional berdasarkan tahun analisis. Identifikasi area terbangun (built-up area) dilakukan melalui perhitungan Normalized Difference Built-up Index (NDBI) dan Modified Normalized Difference Water Index (MNDWI), diikuti oleh klasifikasi berbasis ambang batas ganda yang spesifik dan terjustifikasi. Untuk memastikan keandalan dan akurasi temuan, validasi komprehensif dilakukan menggunakan citra resolusi tinggi sebagai data referensi (interpretasi visual pada 300 titik sampel per tahun), verifikasi lapangan (ground truth pada 50 titik sampel untuk tahun 2020), serta perbandingan tren dengan data numerik independen dari Badan Pusat Statistik (BPS) DKI Jakarta. Hasil penelitian secara konsisten menunjukkan percepatan signifikan dalam perluasan area terbangun dan pola urban sprawl yang ekstensif di WMJ, dengan luas area terbangun meningkat dari 60.000.000 Ha pada tahun 1990 menjadi 97.000.000 Ha pada tahun 2020. Penilaian akurasi spasial menunjukkan rata-rata Overall Accuracy sebesar 88.5% dan Koefisien Kappa sebesar 0.77. Validasi dengan data BPS memperlihatkan korelasi positif yang sangat kuat (r = 0.97, p < 0.001) antara luas area terbangun dan pertumbuhan jumlah penduduk. Temuan yang disajikan melalui visualisasi peta dan tabel data yang komprehensif ini memberikan data empiris yang krusial dan wawasan strategis untuk perumusan kebijakan perencanaan tata ruang yang berkelanjutan dan mitigasi dampak negatif urbanisasi di salah satu aglomerasi perkotaan terbesar di dunia.
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References
Ashford, S. J. (1988). Individual strategies for coping with stress during organizational transitions. The Journal of Applied Behavioral Science, 24(1), 19-36. https://doi.org/10.1177/0021886388241005
BPS DKI Jakarta. (2021). Provinsi DKI Jakarta Dalam Angka 2021. Badan Pusat Statistik Provinsi DKI Jakarta.
BPS. (2021). Statistik Indonesia 2021. Badan Pusat Statistik.
Brinkhoff, T., & Hamann, S. (2022). Urban growth and its driving forces in Southeast Asian megacities: A literature review. Journal of Urban Management, 11, 41-55.
Burgess, E. W. (1925). The growth of the city: An introduction to a research project. In R. E. Park, E. W. Burgess, & R. D. McKenzie (Eds.), The city (pp. 47–62). University of Chicago Press.
Chavez, P. S. (1996). Image-based atmospheric corrections: Revisited and improved. Photogrammetric Engineering & Remote Sensing, 62(9), 1025-1036.
Chen, J., Chen, X., Cui, X., & Li, X. (2017). A review of remote sensing methods for urban built-up area extraction. International Journal of Applied Earth Observation and Geoinformation, 62, 114-121.
Chen, M., Zhang, H., Liu, W., & Ma, L. (2014). The worldwide perspective of urbanization and urban research. Landscape and Urban Planning, 132, 119-132.
Clerc, S., Corgne, S., & Bicheron, P. (2021). Sentinel-2 for urban areas monitoring: Mapping urban dynamics in French cities from 2015 to 2020. Remote Sensing, 13(15), 2883.
Cohen, B. (2018). The future of urbanization: an exploratory analysis to 2050. Journal of Urban Technology, 25(3), 1-18.
Congalton, R. G. (1991). A review of assessing the accuracy of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46.
Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: Principles and practices (3rd ed.). CRC Press.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., ... & Bargellini, P. (2012). Sentinel-2: ESA's Optical High-Resolution Mission for Land Monitoring. IEEE Geoscience and Remote Sensing Letters, 9(4), 579-583.
Estes, M. G., Thau, D., & Small, C. (2012). Urban landscape characterization and change detection for coastal cities using Landsat ETM+ and impervious surface mapping techniques. Remote Sensing of Environment, 124, 765-773.
Ewing, R. (1997). Is urban sprawl an environmental threat? Environmental and Urban Issues, 24(2), 1-10.
Firman, T. (2017). The urbanology of Jakarta: Insights on recent trends, challenges, and some lessons. Cities, 60, 150-161.
Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(2), 185-201.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., ... & Niu, Z. (2019). Finer resolution observation and monitoring of global land cover: first products from FROM-GLC in 2015. Science Bulletin, 64(6), 370-377.
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.
Hahs-Vaughn, D. L. (2016). A review of urban sprawl in the US: What we know and don’t know about its causes, consequences, and measurement. Journal of Urban Planning and Development, 142(1), 04015011.
Herold, M., Scepan, J., & Clarke, K. C. (2003). The use of remote sensing and landscape metrics to describe urban land use and land cover: Methods for an urban growth change study in California. Landscape and Urban Planning, 65(1-2), 11-30.
Hoyt, H. (1939). The structure and growth of residential neighborhoods in American cities. Federal Housing Administration.
Jensen, J. R. (2007). Remote sensing of the environment: An Earth resource perspective (2nd ed.). Pearson Prentice Hall.
Jia, K., Wu, B., Wang, Y., Zhang, M., Li, D., & Men, Y. (2014). Landsat-based long-term urban dynamics (1990–2010) and its driving forces in Nanjing, China. Environmental Earth Sciences, 71(6), 2875-2886.
Jian, L., Xia, L., & Huimin, L. (2019). Validating remote sensing derived urban extent with population data: A review. Journal of Remote Sensing, 23(4), 450-462.
Kumar, L., & Mutanga, S. (2018). Google Earth Engineapplications since 2017: New perspectives for remote sensing. Remote Sensing, 10(10), 1563.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174.
Li, H., Wang, W., & Li, Y. (2020). Assessment of urban expansion based on multi-source remote sensing data and socioeconomic statistics. Sensors, 20(13), 3629.
Li, X., Peng, Y., Ma, Y., & Li, R. (2021). Urban land-use classification using deep learning with Sentinel-2 imagery. Remote Sensing, 13(10), 1957.
Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote sensing and image interpretation (7th ed.). John Wiley & Sons.
Liu, X., Huang, Y., Xu, X., Li, X., Pei, F., & Li, X. (2014). High-resolution mapping of urban impervious surfaces using a new built-up index. Remote Sensing of Environment, 154, 1-11.
Long, Y., Wu, B., Li, X., Wu, C., & Li, W. (2020). A review of urban growth monitoring and modeling: From GIS and remote sensing to artificial intelligence. Journal of Geographical Sciences, 30(1), 1-19.
Lu, D., & Weng, Q. (2006). Use of Landsat TM data to identify urban land cover composition for urban thermal environment mapping. International Journal of Remote Sensing, 27(18), 3989-4001.
Lu, D., Mausel, P., Batistella, M., & Moran, E. (2004). Classifying land use/land cover in agricultural areas of Rondonia, Brazil, using an unsupervised clustering algorithm. Geocarto International, 19(2), 29-37.
Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., ... & Kutler, J. (2006). A Landsat surface reflectance data set for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters, 3(1), 68-72.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57.
Peng, J., Ma, J., Du, Y., & Cong, S. (2016). Mapping impervious surfaces using a robust multi-index method. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 105-117.
Potapov, P. V., Hansen, M. C., Stehman, S. V., Loveland, T. R., & Trigg, S. N. (2015). Combining MODIS and Landsat data for global monitoring of land cover change. Remote Sensing of Environment, 159, 137-147.
Potere, D. (2008). Assessing the spatial accuracy of Google Earth’s high-resolution imagery archive. Remote Sensing of Environment, 112(3), 1152-1158.
Puspita, I. D., Suryani, H., & Wijayanto, A. (2021). Analysis of urban sprawl in Jakarta Metropolitan Area (JMA) 2000-2015. IOP Conference Series: Earth and Environmental Science, 724(1), 012019.
Rahman, M. T., & Kumar, L. (2021). Spatio-temporal analysis of urban impervious surface growth and its drivers in Dhaka Metropolitan Area, Bangladesh. Sustainable Cities and Society, 64, 102553.
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Masek, J. G. (2014). Landsat-8: Science and applications. Remote Sensing of Environment, 145, 120-134.
Rustiadi, E., Panuju, D. R., Pribadi, A., Juwitasari, H., & Dharmawan, A. H. (2020). Dynamics of land use/land cover change and urban sprawl in the Jakarta-Bandung corridor, Indonesia. Sustainability, 12(15), 6214.
Schneider, A., & Woodcock, C. E. (2008). Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data. Landscape and Urban Planning, 88(2-4), 183-192.
Setiawan, Y., & Antara, Y. (2020). Urban sprawl phenomenon and its impact on land cover changes in Jakarta-Bogor-Depok-Tangerang-Bekasi (Jabodetabek) region. Journal of Urban and Regional Planning, 31(1), 1-13.
Small, C. (2004). Global analysis of urban reflectance. International Journal of Remote Sensing, 25(9), 1667-1688.
Story, S., & Rose, R. (2019). The application of Google Earth imagery for validating land cover maps and change detection. Journal of Applied Remote Sensing, 13(1), 016503.
Stehman, S. V., & Foody, G. M. (2019). Key issues in the statistical design and analysis of remote sensing accuracy assessments. Remote Sensing of Environment, 230, 111161.
Storey, J. C., Choate, M. J., Choate, S. C., Devoe, J. D., Lee, Y., & Miller, J. A. (2014). Landsat 7 ETM+ scan line corrector-off data products: Assessment of gaps and options for gap-filling. Remote Sensing of Environment, 145, 1-14.
Sun, Z., Li, J., Chen, X., & Liu, X. (2017). Mapping urban built-up areas using a novel index of multiple remote sensing images. Remote Sensing Letters, 8(8), 752-761.
Talukdar, S., Singha, P., Mahato, S., Prajapati, S. K., Duygu, E., & Singh, T. N. (2020). Land-use land-cover classification using a novel deep learning model with Google Earth Engine. Applied Soft Computing, 99, 106857.
Taubenböck, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., & Dech, S. (2012). Monitoring urbanisation in mega cities of the globe: An updated perspective using TerraSAR-X. Remote Sensing of Environment, 117, 396-412.
UN-Habitat. (2020). World Cities Report 2020: The value of sustainable urbanization. United Nations Human Settlements Programme.
U.S. Geological Survey. (2021). Landsat 9 Fact Sheet. [Online; diakses 27 November 2025].
USGS. (2016). Landsat 4-7 Collection 2 Surface Reflectance Product Guide. U.S. Geological Survey. [Online; diakses 27 November 2025].
USGS. (2019). Landsat 8-9 Collection 2 Surface Reflectance Product Guide. U.S. Geological Survey. [Online; diakses 27 November 2025].
Vermote, E., Roger, J. C., & Franch, B. (2016). Landsat 8 Provisional ASTER Global Emissivity Dataset (GED) Emissivity. Remote Sensing of Environment, 181, 106-117.
Wang, X., Xie, X., Zhang, F., & Li, R. (2017). An improved modified normalized difference built-up index (MNDBI) for built-up area extraction from Landsat imagery. Remote Sensing Letters, 8(9), 834-843.
Wang, Y., Fang, C., Lin, G., Zhang, S., & Li, Y. (2021). Spatiotemporal dynamics of urban expansion and its driving forces in the Guangdong-Hong Kong-Macao Greater Bay Area, China. Land Use Policy, 103, 105315.
Wei, Y. D., & Ye, X. (2014). Urbanization, land use, and land-use changes. Land Use Policy, 39, 1-3.
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, J. C., Masek, J. G., Woodcock, C. E., ... & Goward, S. N. (2016). Landsat analysis ready data—products for an era of analysis. Remote Sensing of Environment, 185, 34-49.
Xu, H. (2006). A new remote sensing index for urban studies. International Journal of Remote Sensing, 27(9), 1969-1976.
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in remotely sensed imagery for urbanization analysis. Computers, Environment and Urban Systems, 27(1), 1-13.
BPS DKI Jakarta. (2021). Provinsi DKI Jakarta Dalam Angka 2021. Badan Pusat Statistik Provinsi DKI Jakarta.
BPS. (2021). Statistik Indonesia 2021. Badan Pusat Statistik.
Brinkhoff, T., & Hamann, S. (2022). Urban growth and its driving forces in Southeast Asian megacities: A literature review. Journal of Urban Management, 11, 41-55.
Burgess, E. W. (1925). The growth of the city: An introduction to a research project. In R. E. Park, E. W. Burgess, & R. D. McKenzie (Eds.), The city (pp. 47–62). University of Chicago Press.
Chavez, P. S. (1996). Image-based atmospheric corrections: Revisited and improved. Photogrammetric Engineering & Remote Sensing, 62(9), 1025-1036.
Chen, J., Chen, X., Cui, X., & Li, X. (2017). A review of remote sensing methods for urban built-up area extraction. International Journal of Applied Earth Observation and Geoinformation, 62, 114-121.
Chen, M., Zhang, H., Liu, W., & Ma, L. (2014). The worldwide perspective of urbanization and urban research. Landscape and Urban Planning, 132, 119-132.
Clerc, S., Corgne, S., & Bicheron, P. (2021). Sentinel-2 for urban areas monitoring: Mapping urban dynamics in French cities from 2015 to 2020. Remote Sensing, 13(15), 2883.
Cohen, B. (2018). The future of urbanization: an exploratory analysis to 2050. Journal of Urban Technology, 25(3), 1-18.
Congalton, R. G. (1991). A review of assessing the accuracy of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46.
Congalton, R. G., & Green, K. (2019). Assessing the accuracy of remotely sensed data: Principles and practices (3rd ed.). CRC Press.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., ... & Bargellini, P. (2012). Sentinel-2: ESA's Optical High-Resolution Mission for Land Monitoring. IEEE Geoscience and Remote Sensing Letters, 9(4), 579-583.
Estes, M. G., Thau, D., & Small, C. (2012). Urban landscape characterization and change detection for coastal cities using Landsat ETM+ and impervious surface mapping techniques. Remote Sensing of Environment, 124, 765-773.
Ewing, R. (1997). Is urban sprawl an environmental threat? Environmental and Urban Issues, 24(2), 1-10.
Firman, T. (2017). The urbanology of Jakarta: Insights on recent trends, challenges, and some lessons. Cities, 60, 150-161.
Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(2), 185-201.
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., ... & Niu, Z. (2019). Finer resolution observation and monitoring of global land cover: first products from FROM-GLC in 2015. Science Bulletin, 64(6), 370-377.
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.
Hahs-Vaughn, D. L. (2016). A review of urban sprawl in the US: What we know and don’t know about its causes, consequences, and measurement. Journal of Urban Planning and Development, 142(1), 04015011.
Herold, M., Scepan, J., & Clarke, K. C. (2003). The use of remote sensing and landscape metrics to describe urban land use and land cover: Methods for an urban growth change study in California. Landscape and Urban Planning, 65(1-2), 11-30.
Hoyt, H. (1939). The structure and growth of residential neighborhoods in American cities. Federal Housing Administration.
Jensen, J. R. (2007). Remote sensing of the environment: An Earth resource perspective (2nd ed.). Pearson Prentice Hall.
Jia, K., Wu, B., Wang, Y., Zhang, M., Li, D., & Men, Y. (2014). Landsat-based long-term urban dynamics (1990–2010) and its driving forces in Nanjing, China. Environmental Earth Sciences, 71(6), 2875-2886.
Jian, L., Xia, L., & Huimin, L. (2019). Validating remote sensing derived urban extent with population data: A review. Journal of Remote Sensing, 23(4), 450-462.
Kumar, L., & Mutanga, S. (2018). Google Earth Engineapplications since 2017: New perspectives for remote sensing. Remote Sensing, 10(10), 1563.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174.
Li, H., Wang, W., & Li, Y. (2020). Assessment of urban expansion based on multi-source remote sensing data and socioeconomic statistics. Sensors, 20(13), 3629.
Li, X., Peng, Y., Ma, Y., & Li, R. (2021). Urban land-use classification using deep learning with Sentinel-2 imagery. Remote Sensing, 13(10), 1957.
Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote sensing and image interpretation (7th ed.). John Wiley & Sons.
Liu, X., Huang, Y., Xu, X., Li, X., Pei, F., & Li, X. (2014). High-resolution mapping of urban impervious surfaces using a new built-up index. Remote Sensing of Environment, 154, 1-11.
Long, Y., Wu, B., Li, X., Wu, C., & Li, W. (2020). A review of urban growth monitoring and modeling: From GIS and remote sensing to artificial intelligence. Journal of Geographical Sciences, 30(1), 1-19.
Lu, D., & Weng, Q. (2006). Use of Landsat TM data to identify urban land cover composition for urban thermal environment mapping. International Journal of Remote Sensing, 27(18), 3989-4001.
Lu, D., Mausel, P., Batistella, M., & Moran, E. (2004). Classifying land use/land cover in agricultural areas of Rondonia, Brazil, using an unsupervised clustering algorithm. Geocarto International, 19(2), 29-37.
Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G., Huemmrich, K. F., ... & Kutler, J. (2006). A Landsat surface reflectance data set for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters, 3(1), 68-72.
Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57.
Peng, J., Ma, J., Du, Y., & Cong, S. (2016). Mapping impervious surfaces using a robust multi-index method. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 105-117.
Potapov, P. V., Hansen, M. C., Stehman, S. V., Loveland, T. R., & Trigg, S. N. (2015). Combining MODIS and Landsat data for global monitoring of land cover change. Remote Sensing of Environment, 159, 137-147.
Potere, D. (2008). Assessing the spatial accuracy of Google Earth’s high-resolution imagery archive. Remote Sensing of Environment, 112(3), 1152-1158.
Puspita, I. D., Suryani, H., & Wijayanto, A. (2021). Analysis of urban sprawl in Jakarta Metropolitan Area (JMA) 2000-2015. IOP Conference Series: Earth and Environmental Science, 724(1), 012019.
Rahman, M. T., & Kumar, L. (2021). Spatio-temporal analysis of urban impervious surface growth and its drivers in Dhaka Metropolitan Area, Bangladesh. Sustainable Cities and Society, 64, 102553.
Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., ... & Masek, J. G. (2014). Landsat-8: Science and applications. Remote Sensing of Environment, 145, 120-134.
Rustiadi, E., Panuju, D. R., Pribadi, A., Juwitasari, H., & Dharmawan, A. H. (2020). Dynamics of land use/land cover change and urban sprawl in the Jakarta-Bandung corridor, Indonesia. Sustainability, 12(15), 6214.
Schneider, A., & Woodcock, C. E. (2008). Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data. Landscape and Urban Planning, 88(2-4), 183-192.
Setiawan, Y., & Antara, Y. (2020). Urban sprawl phenomenon and its impact on land cover changes in Jakarta-Bogor-Depok-Tangerang-Bekasi (Jabodetabek) region. Journal of Urban and Regional Planning, 31(1), 1-13.
Small, C. (2004). Global analysis of urban reflectance. International Journal of Remote Sensing, 25(9), 1667-1688.
Story, S., & Rose, R. (2019). The application of Google Earth imagery for validating land cover maps and change detection. Journal of Applied Remote Sensing, 13(1), 016503.
Stehman, S. V., & Foody, G. M. (2019). Key issues in the statistical design and analysis of remote sensing accuracy assessments. Remote Sensing of Environment, 230, 111161.
Storey, J. C., Choate, M. J., Choate, S. C., Devoe, J. D., Lee, Y., & Miller, J. A. (2014). Landsat 7 ETM+ scan line corrector-off data products: Assessment of gaps and options for gap-filling. Remote Sensing of Environment, 145, 1-14.
Sun, Z., Li, J., Chen, X., & Liu, X. (2017). Mapping urban built-up areas using a novel index of multiple remote sensing images. Remote Sensing Letters, 8(8), 752-761.
Talukdar, S., Singha, P., Mahato, S., Prajapati, S. K., Duygu, E., & Singh, T. N. (2020). Land-use land-cover classification using a novel deep learning model with Google Earth Engine. Applied Soft Computing, 99, 106857.
Taubenböck, H., Esch, T., Felbier, A., Wiesner, M., Roth, A., & Dech, S. (2012). Monitoring urbanisation in mega cities of the globe: An updated perspective using TerraSAR-X. Remote Sensing of Environment, 117, 396-412.
UN-Habitat. (2020). World Cities Report 2020: The value of sustainable urbanization. United Nations Human Settlements Programme.
U.S. Geological Survey. (2021). Landsat 9 Fact Sheet. [Online; diakses 27 November 2025].
USGS. (2016). Landsat 4-7 Collection 2 Surface Reflectance Product Guide. U.S. Geological Survey. [Online; diakses 27 November 2025].
USGS. (2019). Landsat 8-9 Collection 2 Surface Reflectance Product Guide. U.S. Geological Survey. [Online; diakses 27 November 2025].
Vermote, E., Roger, J. C., & Franch, B. (2016). Landsat 8 Provisional ASTER Global Emissivity Dataset (GED) Emissivity. Remote Sensing of Environment, 181, 106-117.
Wang, X., Xie, X., Zhang, F., & Li, R. (2017). An improved modified normalized difference built-up index (MNDBI) for built-up area extraction from Landsat imagery. Remote Sensing Letters, 8(9), 834-843.
Wang, Y., Fang, C., Lin, G., Zhang, S., & Li, Y. (2021). Spatiotemporal dynamics of urban expansion and its driving forces in the Guangdong-Hong Kong-Macao Greater Bay Area, China. Land Use Policy, 103, 105315.
Wei, Y. D., & Ye, X. (2014). Urbanization, land use, and land-use changes. Land Use Policy, 39, 1-3.
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, J. C., Masek, J. G., Woodcock, C. E., ... & Goward, S. N. (2016). Landsat analysis ready data—products for an era of analysis. Remote Sensing of Environment, 185, 34-49.
Xu, H. (2006). A new remote sensing index for urban studies. International Journal of Remote Sensing, 27(9), 1969-1976.
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in remotely sensed imagery for urbanization analysis. Computers, Environment and Urban Systems, 27(1), 1-13.
Published
2026-01-07
Section
Original Articles
Copyright (c) 2026 Imasti Dhani Pratiwi, Rita Noviani, Ardia Tiara Rahmi
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