Analisis Geospasial Perubahan Penggunaan Lahan Sawah Di Kota Malang Menggunakan Google Earth Engine
Kata Kunci:
Analisis Geospasial, Random Forest, SVM, Change Detection, Analisis Multitemporal, Google Earth EngineAbstrak
Penelitian berjudul “ANALISIS GEOSPASIAL PERUBAHAN PENGGUNAAN LAHAN SAWAH DI KOTA MALANG MENGGUNAKAN GOOGLE EARTH ENGINE” bertujuan untuk menganalisis perubahan lahan sawah di Kota Malang selama 2018-2023 dan membandingkan performa metode Support Vector Machine (SVM) serta Random Forest (RF) dalam klasifikasi citra satelit. Data Sentinel-2 diolah menggunakan pendekatan klasifikasi terbimbing untuk menghasilkan peta tutupan lahan, yang kemudian dianalisis secara multitemporal menggunakan teknik change detection. Hasil penelitian menunjukkan bahwa metode RF memiliki performa lebih tinggi dibandingkan SVM, dengan rata-rata overall accuracy sebesar 90.2% dan kappa coefficient 0.85. Tren perubahan lahan menunjukkan penurunan terbesar pada 2018-2019 dengan Kecamatan Kedungkandang sebagai penyumbang utama, sementara jenis penggunaan lahan yang paling banyak menggantikan sawah adalah vegetasi lain (67.74%) dan pemukiman (31.38%). Laju penurunan sawah tertinggi mencapai 25.42% per tahun pada 2018-2019, sedangkan peningkatan sebesar 0.97% per tahun terjadi pada 2021-2022. Penelitian ini menyimpulkan bahwa Google Earth Engine merupakan platform yang efektif untuk analisis geospasial, dan perubahan lahan sawah di Kota Malang sebagian besar dipengaruhi oleh alih fungsi menjadi vegetasi lain dan pemukiman.
Referensi
Ahmed, R., Ahmad, S.T., Wani, G.F., Ahmed, P., Mir, A.A. and Singh, A., 2022. Analysis of landuse and landcover changes in Kashmir valley, India—A review. GeoJournal, [online] 87(5), pp.4391–4403. https://doi.org/10.1007/s10708-021-10465-8.
Alshari, E.A. and Gawali, B.W., 2021. Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings, [online] 2(1), pp.8–17. https://doi.org/10.1016/j.gltp.2021.01.002.
Angraini, F., Selpiyanti, S. and Walid, A., 2020. Dampak Alih Fungsi Lahan Sawah Menjadi Non Pertanian Mengakibatkan Ancaman Degradasi Lingkungan. JURNAL SWARNABHUMI : Jurnal Geografi dan Pembelajaran Geografi, 5(2), p.36. https://doi.org/10.31851/swarnabhumi.v5i2.4741.
Anon. 2021. Luas Lahan Sawah Menurut Kecamatan dan Jenis Pengairan di Kota Malang (Hektar (ha)). [online] BPS Kota Malang. Available at: <https://malangkota.bps.go.id/id/statistics-table/2/MTcwIzI=/luas-lahan-sawah-menurut-kecamatan-dan-jenis-pengairan-di-kota-malang.html> [Accessed 26 September 2024].
Anon. 2023. Laporan Akhir Analisis Pola Konsumsi Pangan Kota Malang – Provinsi Jawa Timur Tahun 2023. [online] Malang. Available at: <https://dispangtan.malangkota.go.id/wp-content/uploads/sites/15/2024/04/Laporan-Akhir-Analisa-Pola-Konsumsi-Pangan-Kota-Malang-2023.pdf>.
Anon. 2024. Produksi Padi di Kota Malang, Jawa Timur, dan Indonesia. [online] Badan Pusat Statistik Kota Malang. Available at: <https://malangkota.bps.go.id/id/statistics-table/2/NDkzIzI=/produksi-padi-di-kota-malang--jawa-timur--dan-indonesia.html> [Accessed 26 September 2024].
Anon. 2024. Sentinel-2 Applications. [online] Sentiwiki. Available at: <https://sentiwiki.copernicus.eu/web/s2-applications> [Accessed 30 September 2024].
Arpitha, M., Ahmed, S.A. and Harishnaika, N., 2023. Land use and land cover classification using machine learning algorithms in google earth engine. Earth Science Informatics, [online] 16(4), pp.3057–3073. https://doi.org/10.1007/s12145-023-01073-w.
Basuki, Apriyeni, B.A.R., Purnamasari, I., Rachman, H.A., Rahman, F.A. and Mubarokah, N., 2023. Pengantar Informasi Geospasial. Tahta Media Group.
Chowdhury, M.S., 2024. Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting. Environmental Challenges, [online] 14(October 2023), p.100800. https://doi.org/10.1016/j.envc.2023.100800.
Floreano, I.X. and de Moraes, L.A.F., 2021. Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil. Environmental Monitoring and Assessment, [online] 193(4), pp.1–17. https://doi.org/10.1007/s10661-021-09016-y.
Guo, Y., Jia, X. and Paull, D., 2018. Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification. IEEE Transactions on Image Processing, 27(6), pp.3036–3048. https://doi.org/10.1109/TIP.2018.2808767.
Hemati, M., Hasanlou, M., Mahdianpari, M. and Mohammadimanesh, F., 2021. A systematic review of landsat data for change detection applications: 50 years of monitoring the earth. Remote Sensing, 13(15). https://doi.org/10.3390/rs13152869.
Jin, W., Li, H., Wang, J., Zhao, L., Li, X., Fan, W. and Chen, J., 2023. Continuous remote sensing ecological index (CRSEI): A novel approach for multitemporal monitoring of eco-environmental changes on large scale. Ecological Indicators, [online] 154(July), p.110739. https://doi.org/10.1016/j.ecolind.2023.110739.
Li, J. and Wang, L., 2020. Forest Type Classification with Multitemporal Sentinel-2 Data. Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartD, pp.498–504. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00091.
Liu, X., Zhai, H., Shen, Y., Lou, B., Jiang, C., Li, T., Hussain, S.B. and Shen, G., 2020. Large-Scale Crop Mapping from Multisource Remote Sensing Images in Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.414–427. https://doi.org/10.1109/JSTARS.2019.2963539.
Meng, L., Li, Y., Shen, R., Zheng, Y., Pan, B., Yuan, W., Li, J. and Zhuo, L., 2024. Large-scale and high-resolution paddy rice intensity mapping using downscaling and phenology-based algorithms on Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, [online] 128(October 2023), p.103725. https://doi.org/10.1016/j.jag.2024.103725.
Nasiri, V., Deljouei, A., Moradi, F., Seyed Mohammad Moein, S. and Borz, and S.A., 2022. Land Use and Land Cover Mapping Using Sentinel-2 , Landsat-8 Two Composition Methods. Remote Sens., 14(9), p.1977.
Nugroho, B.A., Dhonanto, D. and Darma, S., 2024. Analisis Pola Perubahan Lahan Sawah Menggunakan Sistem Informasi Geografis (Studi Kasus: Kelurahan Makroman, Samarinda). Jurnal Tanah dan Sumberdaya Lahan, 11(2), pp.309–317. https://doi.org/10.21776/ub.jtsl.2024.011.2.2.
Pech-May, F., Aquino-Santos, R., Rios-Toledo, G. and Posadas-Durán, J.P.F., 2022. Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine. Sensors, 22(13), pp.1–19. https://doi.org/10.3390/s22134729.
Prabowo, R., Bambang, A.N. and Sudarno, 2020. Pertumbuhan Penduduk dan Alih Fungsi Lahan Pertanian. Mediagro, 16(2), pp.26–36.
Qi, N., Yang, H., Shao, G., Chen, R., Wu, B., Xu, B., Feng, H., Yang, G. and Zhao, C., 2023. Mapping tea plantations using multitemporal spectral features by harmonised Sentinel-2 and Landsat images in Yingde, China. Computers and Electronics in Agriculture, 212(July). https://doi.org/10.1016/j.compag.2023.108108.
Rakuasa, H., 2022. Analisis Spasial - Temporal Perubahan Tutupan Lahan di Kabupaten Maluku Barat Daya. GEOGRAPHIA : Jurnal Pendidikan dan Penelitian Geografi, 3(2), pp.115–122. https://doi.org/10.53682/gjppg.v3i2.5262.
Ramdani, D.F., 2017. Pengantar Geoinformatika. Malang: UB Press.
Ramdani, D.F., 2021a. GEOINTELLIGENCE-KECERDASAN BERBASIS RUANG. In: GEOINTELLIGENCE-KECERDASAN BERBASIS RUANG. Kabupaten Banyumas: CV. Pena Persada. p.38.
Ramdani, D.F., 2021b. Konsep Dasar GEE. In: Google Earth Engine Metode, Teknik, dan Aplikasi. Malang: UB Press. p.1.
Rusydi, A.N. and Masitoh, F., 2023. Teknologi Pengindraan Jauh untuk Pengelolaan Lingkungan Perairan. Malang: UB Press.
Saini, P. and Nagpal, B., 2024. Spatiotemporal Landsat-Sentinel-2 satellite imagery-based Hybrid Deep Neural network for paddy crop prediction using Google Earth engine. Advances in Space Research, [online] 73(10), pp.4988–5004. https://doi.org/10.1016/j.asr.2024.02.032.
SatuDataKotaMalang, 2024. S. [online] Satu Data Kota Malang. Available at: <https://satudata.malangkota.go.id/publik/filter?bidang=Pertanian> [Accessed 3 September 2024].
Semedi, B., Rijal, S.S., Sambah, A.B. and Isdianto, A., 2021. Pengantar Penginderaan Jauh Kelautan. Malang: UB Press.
Shafizadeh-Moghadam, H., Khazaei, M., Alavipanah, S.K. and Weng, Q., 2021. Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors. GIScience and Remote Sensing, [online] 58(6), pp.914–928. https://doi.org/10.1080/15481603.2021.1947623.
Singha, C. and Swain, K.C., 2023. Rice crop growth monitoring with sentinel 1 SAR data using machine learning models in google earth engine cloud. Remote Sensing Applications: Society and Environment, [online] 32(March 2022), p.101029. https://doi.org/10.1016/j.rsase.2023.101029.
Tariq, A., Jiango, Y., Li, Q., Gao, J., Lu, L., Soufan, W., Almutairi, K.F. and Habib-ur-Rahman, M., 2023. Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data. Heliyon, [online] 9(2), p.e13212. https://doi.org/10.1016/j.heliyon.2023.e13212.
Venkatappa, M., Sasaki, N., Han, P. and Abe, I., 2021. Impacts of droughts and floods on croplands and crop production in Southeast Asia – An application of Google Earth Engine. Science of the Total Environment, [online] 795, p.148829. https://doi.org/10.1016/j.scitotenv.2021.148829.
Wang, J., Bretz, M., Dewan, M.A.A. and Delavar, M.A., 2022. Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. Science of the Total Environment, [online] 822, p.153559. https://doi.org/10.1016/j.scitotenv.2022.153559.
Wang, Y., Sun, Y., Cao, X., Wang, Y., Zhang, W. and Cheng, X., 2023. A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, [online] 206(March), pp.311–334. https://doi.org/10.1016/j.isprsjprs.2023.11.014.
Zafar, Z., Zubair, M., Zha, Y., Fahd, S. and Ahmad Nadeem, A., 2024. Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data. Egyptian Journal of Remote Sensing and Space Science, [online] 27(2), pp.216–226. https://doi.org/10.1016/j.ejrs.2024.03.003.
Zhang, C., Zhang, H. and Tian, S., 2023. Phenology-assisted supervised paddy rice mapping with the Landsat imagery on Google Earth Engine: Experiments in Heilongjiang Province of China from 1990 to 2020. Computers and Electronics in Agriculture, [online] 212(June), p.108105. https://doi.org/10.1016/j.compag.2023.108105.
Zhao, Y., An, R., Xiong, N., Ou, D. and Jiang, C., 2021. Spatio‐temporal land‐use/land‐cover change dynamics in coastal plains in hangzhou bay area, china from 2009 to 2020 using google earth engine. Land, 10(11). https://doi.org/10.3390/land10111149.
Zhu, Q., Guo, X., Deng, W., Guan, Q., Zhong, Y., Zhang, L. and Li, D., 2022. Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, [online] 184(June 2021), pp.63–78. https://doi.org/10.1016/j.isprsjprs.2021.12.005.
Zurqani, H.A., Post, C.J., Mikhailova, E.A., Schlautman, M.A. and Sharp, J.L., 2018. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, [online] 69(September 2017), pp.175–185. https://doi.org/10.1016/j.jag.2017.12.006.
Ahmed, R., Ahmad, S.T., Wani, G.F., Ahmed, P., Mir, A.A. and Singh, A., 2022. Analysis of landuse and landcover changes in Kashmir valley, India—A review. GeoJournal, [online] 87(5), pp.4391–4403. https://doi.org/10.1007/s10708-021-10465-8.
Alshari, E.A. and Gawali, B.W., 2021. Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings, [online] 2(1), pp.8–17. https://doi.org/10.1016/j.gltp.2021.01.002.
Angraini, F., Selpiyanti, S. and Walid, A., 2020. Dampak Alih Fungsi Lahan Sawah Menjadi Non Pertanian Mengakibatkan Ancaman Degradasi Lingkungan. JURNAL SWARNABHUMI : Jurnal Geografi dan Pembelajaran Geografi, 5(2), p.36. https://doi.org/10.31851/swarnabhumi.v5i2.4741.
Anon. 2021. Luas Lahan Sawah Menurut Kecamatan dan Jenis Pengairan di Kota Malang (Hektar (ha)). [online] BPS Kota Malang. Available at: <https://malangkota.bps.go.id/id/statistics-table/2/MTcwIzI=/luas-lahan-sawah-menurut-kecamatan-dan-jenis-pengairan-di-kota-malang.html> [Accessed 26 September 2024].
Anon. 2023. Laporan Akhir Analisis Pola Konsumsi Pangan Kota Malang – Provinsi Jawa Timur Tahun 2023. [online] Malang. Available at: <https://dispangtan.malangkota.go.id/wp-content/uploads/sites/15/2024/04/Laporan-Akhir-Analisa-Pola-Konsumsi-Pangan-Kota-Malang-2023.pdf>.
Anon. 2024. Produksi Padi di Kota Malang, Jawa Timur, dan Indonesia. [online] Badan Pusat Statistik Kota Malang. Available at: <https://malangkota.bps.go.id/id/statistics-table/2/NDkzIzI=/produksi-padi-di-kota-malang--jawa-timur--dan-indonesia.html> [Accessed 26 September 2024].
Anon. 2024. Sentinel-2 Applications. [online] Sentiwiki. Available at: <https://sentiwiki.copernicus.eu/web/s2-applications> [Accessed 30 September 2024].
Arpitha, M., Ahmed, S.A. and Harishnaika, N., 2023. Land use and land cover classification using machine learning algorithms in google earth engine. Earth Science Informatics, [online] 16(4), pp.3057–3073. https://doi.org/10.1007/s12145-023-01073-w.
Basuki, Apriyeni, B.A.R., Purnamasari, I., Rachman, H.A., Rahman, F.A. and Mubarokah, N., 2023. Pengantar Informasi Geospasial. Tahta Media Group.
Chowdhury, M.S., 2024. Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting. Environmental Challenges, [online] 14(October 2023), p.100800. https://doi.org/10.1016/j.envc.2023.100800.
Floreano, I.X. and de Moraes, L.A.F., 2021. Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil. Environmental Monitoring and Assessment, [online] 193(4), pp.1–17. https://doi.org/10.1007/s10661-021-09016-y.
Guo, Y., Jia, X. and Paull, D., 2018. Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification. IEEE Transactions on Image Processing, 27(6), pp.3036–3048. https://doi.org/10.1109/TIP.2018.2808767.
Hemati, M., Hasanlou, M., Mahdianpari, M. and Mohammadimanesh, F., 2021. A systematic review of landsat data for change detection applications: 50 years of monitoring the earth. Remote Sensing, 13(15). https://doi.org/10.3390/rs13152869.
Jin, W., Li, H., Wang, J., Zhao, L., Li, X., Fan, W. and Chen, J., 2023. Continuous remote sensing ecological index (CRSEI): A novel approach for multitemporal monitoring of eco-environmental changes on large scale. Ecological Indicators, [online] 154(July), p.110739. https://doi.org/10.1016/j.ecolind.2023.110739.
Li, J. and Wang, L., 2020. Forest Type Classification with Multitemporal Sentinel-2 Data. Proceedings - IEEE Congress on Cybermatics: 2020 IEEE International Conferences on Internet of Things, iThings 2020, IEEE Green Computing and Communications, GreenCom 2020, IEEE Cyber, Physical and Social Computing, CPSCom 2020 and IEEE Smart Data, SmartD, pp.498–504. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00091.
Liu, X., Zhai, H., Shen, Y., Lou, B., Jiang, C., Li, T., Hussain, S.B. and Shen, G., 2020. Large-Scale Crop Mapping from Multisource Remote Sensing Images in Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.414–427. https://doi.org/10.1109/JSTARS.2019.2963539.
Meng, L., Li, Y., Shen, R., Zheng, Y., Pan, B., Yuan, W., Li, J. and Zhuo, L., 2024. Large-scale and high-resolution paddy rice intensity mapping using downscaling and phenology-based algorithms on Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, [online] 128(October 2023), p.103725. https://doi.org/10.1016/j.jag.2024.103725.
Nasiri, V., Deljouei, A., Moradi, F., Seyed Mohammad Moein, S. and Borz, and S.A., 2022. Land Use and Land Cover Mapping Using Sentinel-2 , Landsat-8 Two Composition Methods. Remote Sens., 14(9), p.1977.
Nugroho, B.A., Dhonanto, D. and Darma, S., 2024. Analisis Pola Perubahan Lahan Sawah Menggunakan Sistem Informasi Geografis (Studi Kasus: Kelurahan Makroman, Samarinda). Jurnal Tanah dan Sumberdaya Lahan, 11(2), pp.309–317. https://doi.org/10.21776/ub.jtsl.2024.011.2.2.
Pech-May, F., Aquino-Santos, R., Rios-Toledo, G. and Posadas-Durán, J.P.F., 2022. Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine. Sensors, 22(13), pp.1–19. https://doi.org/10.3390/s22134729.
Prabowo, R., Bambang, A.N. and Sudarno, 2020. Pertumbuhan Penduduk dan Alih Fungsi Lahan Pertanian. Mediagro, 16(2), pp.26–36.
Qi, N., Yang, H., Shao, G., Chen, R., Wu, B., Xu, B., Feng, H., Yang, G. and Zhao, C., 2023. Mapping tea plantations using multitemporal spectral features by harmonised Sentinel-2 and Landsat images in Yingde, China. Computers and Electronics in Agriculture, 212(July). https://doi.org/10.1016/j.compag.2023.108108.
Rakuasa, H., 2022. Analisis Spasial - Temporal Perubahan Tutupan Lahan di Kabupaten Maluku Barat Daya. GEOGRAPHIA : Jurnal Pendidikan dan Penelitian Geografi, 3(2), pp.115–122. https://doi.org/10.53682/gjppg.v3i2.5262.
Ramdani, D.F., 2017. Pengantar Geoinformatika. Malang: UB Press.
Ramdani, D.F., 2021a. GEOINTELLIGENCE-KECERDASAN BERBASIS RUANG. In: GEOINTELLIGENCE-KECERDASAN BERBASIS RUANG. Kabupaten Banyumas: CV. Pena Persada. p.38.
Ramdani, D.F., 2021b. Konsep Dasar GEE. In: Google Earth Engine Metode, Teknik, dan Aplikasi. Malang: UB Press. p.1.
Rusydi, A.N. and Masitoh, F., 2023. Teknologi Pengindraan Jauh untuk Pengelolaan Lingkungan Perairan. Malang: UB Press.
Saini, P. and Nagpal, B., 2024. Spatiotemporal Landsat-Sentinel-2 satellite imagery-based Hybrid Deep Neural network for paddy crop prediction using Google Earth engine. Advances in Space Research, [online] 73(10), pp.4988–5004. https://doi.org/10.1016/j.asr.2024.02.032.
SatuDataKotaMalang, 2024. S. [online] Satu Data Kota Malang. Available at: <https://satudata.malangkota.go.id/publik/filter?bidang=Pertanian> [Accessed 3 September 2024].
Semedi, B., Rijal, S.S., Sambah, A.B. and Isdianto, A., 2021. Pengantar Penginderaan Jauh Kelautan. Malang: UB Press.
Shafizadeh-Moghadam, H., Khazaei, M., Alavipanah, S.K. and Weng, Q., 2021. Google Earth Engine for large-scale land use and land cover mapping: an object-based classification approach using spectral, textural and topographical factors. GIScience and Remote Sensing, [online] 58(6), pp.914–928. https://doi.org/10.1080/15481603.2021.1947623.
Singha, C. and Swain, K.C., 2023. Rice crop growth monitoring with sentinel 1 SAR data using machine learning models in google earth engine cloud. Remote Sensing Applications: Society and Environment, [online] 32(March 2022), p.101029. https://doi.org/10.1016/j.rsase.2023.101029.
Tariq, A., Jiango, Y., Li, Q., Gao, J., Lu, L., Soufan, W., Almutairi, K.F. and Habib-ur-Rahman, M., 2023. Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data. Heliyon, [online] 9(2), p.e13212. https://doi.org/10.1016/j.heliyon.2023.e13212.
Venkatappa, M., Sasaki, N., Han, P. and Abe, I., 2021. Impacts of droughts and floods on croplands and crop production in Southeast Asia – An application of Google Earth Engine. Science of the Total Environment, [online] 795, p.148829. https://doi.org/10.1016/j.scitotenv.2021.148829.
Wang, J., Bretz, M., Dewan, M.A.A. and Delavar, M.A., 2022. Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. Science of the Total Environment, [online] 822, p.153559. https://doi.org/10.1016/j.scitotenv.2022.153559.
Wang, Y., Sun, Y., Cao, X., Wang, Y., Zhang, W. and Cheng, X., 2023. A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, [online] 206(March), pp.311–334. https://doi.org/10.1016/j.isprsjprs.2023.11.014.
Zafar, Z., Zubair, M., Zha, Y., Fahd, S. and Ahmad Nadeem, A., 2024. Performance assessment of machine learning algorithms for mapping of land use/land cover using remote sensing data. Egyptian Journal of Remote Sensing and Space Science, [online] 27(2), pp.216–226. https://doi.org/10.1016/j.ejrs.2024.03.003.
Zhang, C., Zhang, H. and Tian, S., 2023. Phenology-assisted supervised paddy rice mapping with the Landsat imagery on Google Earth Engine: Experiments in Heilongjiang Province of China from 1990 to 2020. Computers and Electronics in Agriculture, [online] 212(June), p.108105. https://doi.org/10.1016/j.compag.2023.108105.
Zhao, Y., An, R., Xiong, N., Ou, D. and Jiang, C., 2021. Spatio‐temporal land‐use/land‐cover change dynamics in coastal plains in hangzhou bay area, china from 2009 to 2020 using google earth engine. Land, 10(11). https://doi.org/10.3390/land10111149.
Zhu, Q., Guo, X., Deng, W., Guan, Q., Zhong, Y., Zhang, L. and Li, D., 2022. Land-Use/Land-Cover change detection based on a Siamese global learning framework for high spatial resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, [online] 184(June 2021), pp.63–78. https://doi.org/10.1016/j.isprsjprs.2021.12.005.
Zurqani, H.A., Post, C.J., Mikhailova, E.A., Schlautman, M.A. and Sharp, J.L., 2018. Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, [online] 69(September 2017), pp.175–185. https://doi.org/10.1016/j.jag.2017.12.006.
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