Aldiansyah, S., & Saputra, R. A. (2023). Comparison of machine learning algorithms for land use and land cover analysis using Google Earth engine (Case study: Wanggu watershed). International Journal of Remote Sensing and Earth Sciences (IJReSES), 19(2), 197-210. https://doi.org/10.30536/j.ijreses.2022.v19.a3803.
Ali, S.A., Parvin, F., Pham, Q.B., Khedher, K.M., Dehbozorgi, M., Rabby, Y.W., Anh, D.T. & Nguyen, D.H. (2022). An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India. Natural Hazards. 113(3), 1601–1633. https://doi.org/10.1007/s11069-022-05360-5.
Amani, M., Mahdavi, S., Afshar, M., Brisco, B., Huang, W., Mirzadeh, S.M.J., White, L., Banks, S., Montgomery, J. & Hopkinson, C. (2019). Canadian Wetland Inventory Using Google Earth Engine: The First Map and Preliminary Results,
Remote Sensing,
11(
7), 842.
https://doi.org/10.3390/rs11070842.
Amin, G., Imtiaz, I., Haroon, E., Saqib, N. U., Shahzad, M. I., & Nazeer, M. (2024). Assessment of machine learning algorithms for land cover classification in a complex mountainous landscape. Journal of Geovisualization and Spatial Analysis, 8(2), 34. https://doi.org/10.1007/s41651-024-00195-z.
Beyer, F., Jarmer, T. and Siegmann, B. (2015). Identification of agricultural crop types using multitemporal rapideye data. Photogrammetrie-Fernerkundung-Geoinformation, 1: 21-32. https://doi.org/10.1127/pfg/2015/0249.
Biswas, J., Jobaer, M.A., Haque, S.F., Islam Shozib, M.S. & Limon, Z.A. (2023). Mapping and monitoring land use land cover dynamics employing Google Earth Engine and machine learning algorithms on Chattogram, Bangladesh.
Heliyon. 9(11), e21245.
10.1016/j.heliyon.2023.e21245.
Boothroyd, R.J., Williams, R.D., Hoey, T.B., Barrett, B. & Prasojo, O.A. (2021). Applications of Google Earth Engine in fluvial geomorphology for detecting river channel change.
Wiley Interdisciplinary Reviews Water. 8(1), e21496.
https://doi.org/10.1002/wat2.1496.
Bouaziz, M., Eisold, S. & Guermazi, E. (2017). Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia. Euro-Mediterranean Journal of Environmental Integration. 2, 1–7. https://doi.org/10.1007/s41207-017-0036-7.
Breiman, L. (2017). Classification and regression trees. Routledge, 368p.
Breiman, L., (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324.
Chen, B., Xiao, X., Li, X., Pan, L., Doughty, R., Ma, J., Dong, J., Qin, Y., Zhao, B., Wu, Z., Sun, R., Lan, G., Xie, G., Clinton, N. & Giri, C. (2017). A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 104-120. https://doi.org/10.1016/j.isprsjprs.2017.07.011.
Clerici, N., Valbuena Calderon, C.A. & Posada, J.M. (2017) Fusion of sentinel-1A and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia.
Journal of Maps. 13(2), 718–726.
https://doi.org/10.1080/17445647.2017.1372316.
Cracknell, M.J. & Reading, A.M. (2014). Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information.
Computers and Geosciences. 63, 22-33.
https://doi.org/10.1016/j.cageo.2013.10.008.
Ge, G., Shi, Z., Zhu, Y., Yang, X. & Hao, Y. (2020). Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: Performance assessment of four machine learning algorithms. Global Ecology and Conservation. 22, e00971. https://doi.org/10.1016/j.gecco.2020.e00971.
Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A. & Hasanlou, M. (2020). Improved Land Cover Map of Iran Using Sentinel Imagery within Google Earth Engine and a Novel Automatic Workflow for Land Cover Classification Using Migrated Training Samples.
ISPRS Journal of Photogrammetry and Remote Sensing.
167, 276–288.
https://doi.org/10.1016/j.isprsjprs.2020.07.013.
Ghosh, S., Kumar, D. & Kumari, R. (2022). Cloud-based large-scale data retrieval, mapping, and analysis for land monitoring applications with google earth engine (GEE). Environmental Challenges. 9,100605.
https://doi.org/10.1016/j.envc.2022.100605.
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.
Grinand, C., Vieilledent, G., Razafimbelo, T., Rakotoarijaona, J. R., Nourtier, M. & Bernoux, M. (2020). Landscape-scale spatial modelling of deforestation, land degradation and regeneration using machine learning tools. Land Degradation and Development. 31(13), 1699-1712. https://doi.org/10.1002/ldr.3526.
Gumma, M.K., Thenkabail, P.S., Teluguntla, P.G., Oliphant, A., Xiong, J., Giri, C., Whitbread, A.M., Dixit, S. & Whitbread. A.M. (2020). Agricultural Cropland Extent and Areas of South Asia Derived Using Landsat Satellite 30-m Time-series Big-data Using Random Forest Machine Learning Algorithms on the Google Earth Engine Cloud.
GIScience & Remote Sensing.
57(3), 302–322.
https://doi.org/10.1080/15481603.2019.1690780.
Hartfield, K.A., Landau, K.I. & Leeuwen, W.J.D.v. (2011). Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat. Remote Sensing. 3(11), 2364-2383. https://doi.org/10.3390/rs3112364.
Hashim, F., Dibs, H. & Jaber, H.S. (2021). Applying Support Vector Machine Algorithm on Multispectral Remotely sensed satellite image for Geospatial Analysis.
Journal of Physics: Conference Series, 1963.
https://doi.org/10.1088/1742-6596/1963/1/012110.
Jain, M., Mondal, P., Galford, G.L., Fiske, G. & Fries, R.S. (2017). An approach to map winter cropped area of smallholder farms across large scales using MODIS imagery.
Remote Sensing Journal. 9(6), 566-566.
https://doi.org/10.3390/rs9060566.
Knorn, J., Rabe, A., Radeloff, C.V., Kuemmerle, T., Kozak, J. & Hostert, P., 2009. Land cover mapping of large areas using chain classification of neighboring Landsat satellite images.
Remote Sensing of Environment, 113: 957– 964.
https://doi.org/10.1016/j.rse.2009.01.010.
Lawer, E.A. (2024). An evaluation of single and multi-date Landsat image classifications using random forest algorithm in a semi-arid savanna of Ghana, West Africa,
Scientific African, 26, e02434.
https://doi.org/10.1016/j.sciaf.2024.e02434.
Li, Z., Wu, W., Liu, X., Fath, B.D., Sun, H., Liu, X., Xiao, X. & Cao, J. (2017). Land use/cover change and regional climate change in an arid grassland ecosystem of Inner Mongolia, China.
Ecological Modelling. 353, 86–94.
https://doi.org/10.1016/j.ecolmodel.2016.07.019.
Mahdianpari, M., Brisco, B., Granger, J.E., Mohammadimanesh, F., Salehi, B., Banks, S., Weng, Q., Bourgeau-Chavez, L. & Weng. Q. (2020). The Second Generation Canadian Wetland Inventory Map at 10 Meters Resolution Using Google Earth Engine.
Canadian Journal of Remote Sensing. 46(3), 360–375.
https://doi.org/10.1080/07038992.2020.1802584.
Mahmoodi, M. A., Momeni, S., & Davari, M. (2019). Application of Support Vector Machines for Land Use and Land Cover Classification from Landsat ETM Imagery.
Water and Soil, 32(6), 1179-1190.
https://doi.org/10.22067/jsw.v32i6.72967. (In Persian(
Malarvizhi, K., Kumar, S.V. & Porchelvan, P. (2016). Use of high resolution Google Earth satellite imagery in land use map preparation for urban related applications.
Procedia Technology Journal, 24, 1835-1842.
https://doi.org/10.1016/j.protcy.2016.05.231.
Modica, G., Messina, G., De Luca, G., Fiozzo, V. & Pratico, S. (2020). Monitoring the vegetation vigor in heterogeneous citrus and olive orchards. A multiscale object-based approach to extract trees’ crowns from UAV multispectral imagery. Computers and Electronics in Agriculture. 175, 105500. https://doi.org/10.1016/j.compag.2020.105500.
Moharana, S., Kambhammettu, B.V.N.P., Chintala, S., Rani, A.S. & Avtar, R. (2021). Spatial distribution of inter-and intra-crop variability using time-weighted dynamic time warping analysis from Sentinel-1 datasets.
Remote Sensing Applications: Society and Environment. 24, 100630.
https://doi.org/10.1016/j.rsase.2021.100630.
Nigar, A., Li, Y., Jat Baloch, M. Y., Alrefaei, A. F., & Almutairi, M. H. (2024). Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications. Frontiers in Environmental Science, 12, 1378443. https://doi.org/10.3389/fenvs.2024.1378443.
Oliphant, A.J., Thenkabail, P.S., Teluguntla, P., Xiong, J., Gumma, M.K., Congalton, R.G. & Yadav. K. (2019). Mapping Cropland Extent of Southeast and Northeast Asia Using Multi-year Time-series Landsat 30-m Data Using a Random Forest Classifier on the Google Earth Engine Cloud.
International Journal of Applied Earth Observation and Geoinformation. 81, 110–124.
https://doi.org/10.1016/j.jag.2018.11.014.
Oo, T. K., Arunrat, N., Sereenonchai, S., Ussawarujikulchai, A., Chareonwong, U., & Nutmagul, W. (2022). Comparing four machine learning algorithms for land cover classification in gold mining: A case study of Kyaukpahto Gold Mine, Northern Myanmar.
Sustainability, 14(17), 10754.
https://doi.org/10.3390/su141710754.
Ouma, Y., Nkwae, B., Moalafhi, D., Odirile, P., Parida, B., Anderson, G. & Qi, J. (2022). Comparison of machine learning classifiers for multitemporal and multisensor mapping of urban LULC features.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3, 681–689.
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-681-2022.
Pande, C.B., Diwate, P., Orimoloye, I.R., Sidek, L.M., Pratap Mishra, A., Moharir, K.N., Pal, S.C., Alshehri, F. and Tolche, A.D. (2024). Impact of land use/land cover changes on evapotranspiration and model accuracy using Google Earth engine and classification and regression tree modeling.
Geomatics, Natural Hazards and Risk. 15(1). 2290350.
https://doi.org/10.1080/19475705.2023.2290350.
Pandey, P.C., Koutsias, N., Petropoulos, G.P., Srivastava, P.K., & Ben Dor, E. (2021). Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art.
Geocarto International, 36(9), 957–988.
https://doi.org/10.1080/10106049.2019.1629647.
Patil, A. & Panhalkar, S. (2023). Comparative analysis of machine learning algorithms for land use and land cover classification using google earth engine platform. Journal of Geomatics. 17(2), 226-233. https://doi.org/10.58825/jog.2023.17.2.96.
Pecina, M.V., Bergamo, T.F., Ward, R.D., Joyce, C.B. & Sepp, K. (2021). A novel UAV-based approach for biomass prediction and grassland structure assessment in coastal meadows. Ecological Indicators. 122, 107227. https://doi.org/10.1016/j.ecolind.2020.107227.
Phalke, A.R., Ozdogan, M., Thenkabail, P.S., Erickson, T., Gorelick, N., Yadav, K. & Congalton, R.G. (2020). Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, random Forest, and Google earth engine.
ISPRS Journal of Photogrammetry and Remote Sensing. 167, 104–122.
https://doi.org/10.1016/j.isprsjprs.2020.06.022.
Pham, Q. B., Ali, S.A., Parvin, F., Van On, V., Sidek, L.M., Durin, B., ... & Minh, N.N. (2024). Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network.
Advances in space research, 74(1), 17-47.
https://doi.org/10.1016/j.asr.2024.03.027.
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), 2291-2291.
https://doi.org/10.3390/rs12142291.
Qu, L.A., Li, M., Chen, Z. & Zhi, J. (2021). A modified self-adaptive method for mapping annual 30-m land use/land cover using Google Earth engine: a case study of Yangtze River Delta.
Chinese Geographical Science. 31, 782–794.
https://doi.org/10.1007/s11769-021-1226-4.
Rasool, R., Fayaz, A., ul Shafiq, M., Singh, H. & Ahmed, P. (2021). Land use land cover change in Kashmir Himalaya: Linking remote sensing with an indicator based DPSIR approach.
Ecological Indicators. 125, 107447.
https://doi.org/10.1016/j.ecolind.2021.107447.
Rasul, A., Balzter, H., Ibrahim, G., Hameed, H., Wheeler, J., Adamu, B., Ibrahim, S.A. & Najmaddin, P. (2018). Applying built-up and bare-soil indices from Landsat 8 to cities in dry climates. Land Journal, 7(3): 81-81. https://doi.org/10.3390/land7030081.
Revel, C., Lonjou, V., Marcq, S., Desjardins, C., Fougnie, B., Coppolani-Delle Luche, C., Guilleminot, N., Lacamp, A., Lourme, E., Miquel, Ch., & Lenot, X. (2019). Sentinel-2A and 2B absolute calibration monitoring.
European Journal of Remote Sensing, 52(1), 122–137.
https://doi.org/10.1080/22797254.2018.1562311.
Roy, P.S., Ramachandran, R.M., Paul, O., Thakur, P.K., Ravan, Sh., Behera, M.D., Sarangi, Ch. & Kanawade, V.P. (2022). Anthropogenic Land Use and Land Cover Changes—A Review on Its Environmental Consequences and Climate Change. Journal of the Indian Society of Remote Sensing. 50, 1615–1640. https://doi.org/10.1007/s12524-022-01569-w.
Rujoiu-Mare, M.R. & Mihai, B.A. (2016). Mapping land cover using remote sensing data and GIS techniques: A case study of Prahova Subcarpathians.
Procedia Environmental Sciences. 32, 244-255.
https://doi.org/10.1016/j.proenv.2016.03.029.
Saha, S., Saha, M., Mukherjee, K., Arabameri, A., Ngo, P.T.T., Paul, G.C. (2020). Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin. India. Science of the Total Environment. 730, 139197. https://doi.org/10.1016/j.scitotenv.2020.139197.
Sarker, I.H., (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2, 160. https://doi.org/10.1007/s42979-021-00592-x.
Schulz, D., Yin, H., Tischbein, B., Verleysdonk, S., Adamou, R. & Kumar, N. (2021). Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel.
ISPRS Journal of Photogrammetry and Remote Sensing. 178, 97-111.
https://doi.org/10.1016/j.isprsjprs.2021.06.005.
Serra, P., Vera, A., Tulla, A.F. & Salvati. L. (2014). Beyond Urban–rural Dichotomy: Exploring Socioeconomic and Land-use Processes of Change in Spain (1991–2011).
Applied Geography. 55, 71–81.
https://doi.org/10.1016/j.apgeog.2014.09.005.
Shafizadeh-Moghadam, H., Khazaei, M., Alavipanah, S.K., & 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 & Remote Sensing, 58(6), 914–928.
https://doi.org/10.1080/15481603.2021.1947623.
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(7), 1135. https://doi.org/10.3390/rs12071135.
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.
https://doi.org/10.1016/j.isprsjprs.2020.04.001.
Tan, J., Zuo, J., Xie, X., Ding, M., Xu, Z. & Zhou, F. (2021). MLAs land cover mapping performance across varying geomorphology with Landsat OLI-8 and minimum human intervention. Ecological Informatics. 61, 101227. https://doi.org/10.1016/j.ecoinf.2021.101227.
Tian, S., Zhang, X., Tian, J. & Sun, Q. (2016). Random forest classification of wetland land covers from multi-sensor data in the arid region of Xinjiang. China.
Remote Sensing. 8(11), 954-954.
https://doi.org/10.3390/rs8110954.
Wang, J., Chen, J., Wen, Y., Fan, W., Liu, Q. & Tarolli, P. (2021). Monitoring the coastal wetlands dynamics in Northeast Italy from 1984 to 2016. Ecological Indicators. 129, 107906. https://doi.org/10.1016/j.ecolind.2021.107906.
Wingate, V.R., Phinn, S.R., Kuhn, N., Bloemertz, L. & Dhanjal-Adams, K.L. (2016) Mapping decadal land cover changes in the woodlands of north eastern Namibia from 1975 to 2014 using the Landsat satellite archived data. Remote Sensing. 8(8), 681-681. https://doi.org/10.3390/rs8080681.
Yimer, S. M., Bouanani, A., Kumar, N., Tischbein, B. & Borgemeister, C. (2024). Comparison of different machine-learning algorithms for land use land cover mapping in a heterogenous landscape over the Eastern Nile river basin, Ethiopia.
Advances in Space Research. 74(5), 2180-2199.
https://doi.org/10.1016/j.asr.2024.06.010.
Yin, H., Brandao, A., Buchner, J., Helmers, D., Iuliano, B.G., Kimambo, N.E., Lewinska, K.E., Razenkova, E., Rizayeva, A., Rogova, N., Spawn, S.A., Xie, Y. & Radeloff, V.C. (2020). Monitoring cropland abandonment with Landsat time series.
Remote Sensing of Environment Journal. 246, 111873.
https://doi.org/10.1016/j.rse.2020.111873.
Zhang, C., Di, L., Hao, P., Yang, Z., Lin, L., Zhao, H. & Guo, L. (2021). Rapid in-season mapping of corn and soybeans using machine-learned trusted pixels from Cropland Data Layer. International Journal of Applied Earth Observation and Geoinformation. 102, 102374. https://doi.org/10.1016/j.jag.2021.102374.
Zhang, Y., Balzter, H., Liu, B. & Chen, Y. (2017). Analyzing the impacts of urbanization and seasonal variation on land surface temperature based on subpixel fractional covers using Landsat images,
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10(4), 1344–1356. https://doi.org/
10.1109/JSTARS.2016.2608390.
Zhao, Z., Islam, F., Waseem, L.A., Tariq, A., Nawaz, M., Islam, I.U., Bibi, T., Rehman, N.U., Ahmad, W., Aslam, R.W., Raza, D., & Hatamleh, W. A. (2024). Comparison of three machine learning algorithms using google earth engine for land use land cover classification.
Rangeland ecology & management, 92, 129-137.
https://doi.org/10.1016/j.rama.2023.10.007.
Zurqani, H.A., Post, C.J., Mikhailova, E.A., Schlautman, M.A. & 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. 69, 175-185.
https://doi.org/10.1016/j.jag.2017.12.006.
نشریه کاربرد سنجش از دور و سیستم اطلاعات جغرافیایی در علوم محیطی
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