اسلمی، فرنوش؛ قربانی، اردوان؛ سبحانی، بهروز و پناهنده، محسن (1394). مقایسه روش های شبکه عصبی مصنوعی، ماشین بردار پشتیبان و شیگرا در استخراج کاربری و پوشش اراضی از تصاویر لندست 8، سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 6(3)، صص. 14-1.
جهانبخشی، فرشید و اختصاصی، محمدرضا (1397). ارزیابی عملکرد سه روش طبقه بندی تصویر (جنگل تصادفی، ماشین بردار پشتیبان و بیشترین شباهت) در تهیه نقشه کاربری اراضی، علوم آب و خاک، 22(4)، صص. 247- 235.
شنانی، مائده و زارعی، حیدر (1395). مقایسه الگوریتم های طبقه بندی شبکه عصبی مصنوعی، ماشین بردار پشتیبان و حداکثر احتمال در استخراج نقشه کاربری اراضی حوزه آبخیز ابوالعباس، علوم و مهندسی آبخیزداری، 10(33) ، صص. 84- 73.
روستایی، شهرام؛ مختاری، داوود؛ ولیزاده، خلیل و خدایی، لیلا (1398). مقایسه روش پیکسل پایه(بیشترین شباهت) و شی گرا (ماشین بردار پشتیبان) در طبقه بندی کاربری اراضی (منطقه اهر و ورزقان)، پژوهش های ژئومورفولوژی کمی، 8(1)، صص. 129- 118.
عبدلی، مهسا و حقیقی، مریم (1399). مقایسه روش های طبقه بندی ماشین بردار پشتیبان و شبکه عصبی مصنوعی در تهیه نقشه کاربری اراضی، پژوهش و فناوری محیط زیست، 5(8)، صص. 60- 47.
نجفی، احمد؛ عزیزی، سارا و مختاری، محمد حسی ن (1396). کاربرد ماشین بردار پشتیبان در طبقه بندی کاربری اراضی حوزه چشمه کیله چالکرود، پژوهشنامه مدیریت حوزه آبخیز، 8(15) ، صص. 92- 101.
Alphan, H., & Derse, M.A. (2013). Change detection in Southern Turkey using normalized difference vegetation index (NDVI). J. Environ. Eng. Landscape Manage, 21(1), 12–18.
Andriani, N., Dinar, D.A.P., Azhar, K.A., & Eddy, I. (2018). Interpretation of land use and land cover at lowland area using NDVI and NDBI. Ecol. Environ. Conserv, 24(2), 651–657.
As-syakur, A.R., Adnyana, I.W.S., Arthana, I.W., Nuarsa, I.W. (2012). Enhanced built- UP and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote Sensing, 4(10), 2957–2970.
Awange, J.L., Aluoch, J., Ogallo, L.A., Omulo, M., & Omondi, P. (2007). Frequency and severity of drought in the Lake Victoria region (Kenya) and its effects on food security. Climate Research,33(2), 135–142.
Ayele, G. T., Tebeje, A. K., Demissie, S. S., Belete, M. A., Jemberrie, M. A., Teshome, W. M. & Teshale, E. Z. (2018). Time series land cover mapping and change detection analysis using geographic information system and remote sensing, Northern Ethiopia. Air, Soil and Water Research, 11, 1178622117751603.
Ballanti, L., Byrd, K.B., Woo, I., & Ellings, C. (2017). Remote sensing for wetland mapping and historical change detection at the Nisqually River Delta. Sustainability, 9(11), 1919-1935.
Bhatti, S.S., & Tripathi, N.K. (2014). Built-up area extraction using Landsat 8 OLI imagery. GIScience & remote sensing 51(4), pp. 445–467.
Bijeesh, T. V., & Narasimhamurthy, K. N. (2019), March. A Comparative Study of Spectral Indices for Surface Water Delineation Using Landsat 8 Images. In 2019 IEEE International Conference on Data Science and Communication (IconDSC), 1-5.
Breiman, Leo. (2001). Random Forests. Machine Learning. 45, pp. 5-32.
Chu, H.J., Lin, Y.P., Huang, Y.L., & Wang, Y.C. (2009). Detecting the land-cover changes induced by large-physical disturbances using landscape metrics, spatial sampling, simulation, and spatial analysis. Sensors, 9(9), 6670–6700.
Demir, B., Bovolo, F., Bruzzone, L. (2012). Updating land-cover maps by classification of image time series: A novel change-detection-driven transfer learning approach. IEEE Trans. Geosci. Remote Sens, 51(1), 300–312.
Dezso, Z., Bartholy, J., Pongracz, R., & Barcza, Z. (2005). Analysis of land-use/land-cover change in the Carpathian region based on remote sensing techniques. Phys. Chem. Earth, 30, 109–115.
Dópido, I., Villa, A., Plaza, A., & Gamba, P. (2012). A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens, 5(2), 421– 435.
Denil, M., Matheson, D., & Freitas, N. (2014). Narrowing the Gap: Random Forests in Theory and in Practice. Proceedings of the 31st International Conference on Machine Learning, Beijing, China. JMLR: W and P. Vol.32. 9 pages.
Eid, A.N.M., Olatubara, C.O., Ewemoje, T.A., Farouk, H., & El-Hennawy, M.T. (2020). Coastal wetland vegetation features and digital Change Detection Mapping based on remotely sensed imagery: El-Burullus Lake, Egypt. Int. Soil Water Conserv. Res, 8(1), 66–79.
Gong, Y., Duan, B., Fang, S., Zhu, R., Wu, X., Ma, Y., & Peng, Y. (2018). Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis, Plant Methods, 14(1), 1–14.
He, C., Shi, P., Xie, D., & Zhao, Y. (2010). Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sensing Letters, 1(4), pp. 213-221.
Hesping, M. (2020). Remote sensing-based land cover classification and change detection using Sentinel-2 data and Random Forest: A case study of Rusinga Island, Kenya (Master’s thesis. Linköpings universitet, Linköping, Sweden.
Hidayati, I.N., Suharyadi, R., & Danoedoro, P. (2018). Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index. In Forum Geografi, 32(1), 96–108.
Hussain, M., Chen, D., Cheng, A., Wei, H., & Stanley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS J. Photogramm. Remote Sens. 80, 91–106.
Hussain, S., Mubeen, M., Ahmad, A., Akram, W., Hammad, H. M., Ali, M., ... & Nasim, W. (2020). Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan. Environmental Science and Pollution Research, 27, 39676-39692.
IPCC. (2019). IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhous gas fluxes in Terrestrial Ecosystems. Summary for Policymakers. Retrieved January, 13, 2020, from
https://www.ipcc.ch/srccl/.
Lary, D.J., Alavi, A.H., Gandomi, A.H., & Walker, A.L. (2016). Machine learning in geosciences and remote sensing. Geosci. Front. 7(1), 3–10.
Liu, Y., & Jiang, Y. (2021). Urban growth sustainability of Islamabad, Pakistan, over the last 3 decades: a perspective based on object-based backdating change detection. GeoJournal, 86, 2035-2055.
Liu, L., & Zhang, Y. (2011). Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing, 3(7), 1535–1552.
Louppe, G. (2014). Understanding Random Forests, From Theory to Practice. University of Liège. Faculty of Applied Sciences. Department of Electrical Engineering and Computer Science. 223 pages.
Lu, D., Mausel, P., Brondizio, E., &Moran, E., (2004). Change detection techniques. Int. J. Remote Sensing, 25(12), pp. 2365–2401.
Maxwell, A.E., Warner, T.A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. Int. J. Remote Sensing, 39(9), 2784–2817.
Mas, J.F., & Flores, J.J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. Int. J. Remote Sensing, 29(3), pp. 617–663.
Naburi, N.D., Edward, M.M., & Obiri, J.F. (2018). Determinants of Watershed Governance and Food Security among Households in the Lower Sio River Watershed, Busia County, Kenya. nternational Journal of Agriculture, Environment and Bioresearch, 3(05), 30–55.
Nath, B. (2014). Quantitative assessment of forest cover change of a part of Bandarban Hill tracts using NDVI techniques, J. Geosci. Geomatics, 2(1), 21–27.
Odada, E.O., Ochola, W.O., & Olago, D.O. (2009). Drivers of ecosystem change and their impacts on human well-being in Lake Victoria basin. Afr. J. Ecol. 47, 46–54.
Ogashawara, I., & Bastos, V.D.S.B. (2012). A quantitative approach for analyzing the relationship between urban heat islands and land cover. Remote Sensing, 4(11), 3596–3618.
Oommen, T., Misra, D., Twarakavi, N.K., Prakash, A., Sahoo, B., & Bandopadhyay, S. (2008). An objective analysis of support vector machine-based classification for remote sensing, Math. Geosci, 40(4), 409–424.
Panuju, D.R., Paull, D.J., & Griffin, A.L. (2020). Change Detection Techniques Based on Multispectral Images for Investigating Land Cover Dynamics, Remote Sensing, 12(11), pp. 1781-1797.
Pattanayak, S.P., & Diwakar, S.K. (2018). Seasonal Comparative Study of NDVI, NDBI, and NDWI of Hyderabad City (Telangana) Based on LISS-III Image Using Remote Sensing and DIP. Khoj: An International Peer Reviewed, J. Geogr, 5(1), 78-92.
Plaza, A., Du, Q., Bioucas-Dias, J.M., Jia, X., & Kruse, F.A. (2011). Foreword to the special issue on spectral unmixing of remotely sensed data. IEEE Trans, Geosci. Remote Sens, 49(11), 4103–4110.
Polykretis, C., Grillakis, M.G., & Alexakis, D.D. (2020). Exploring the impact of various spectral indices on land cover change detection using change vector analysis: A case study of Crete Island, Greece. Remote Sens. 12(2), 319-334.
Pravalie, R., Sîrodoev, I., Peptenatu, D. (2014). Detecting climate change effects on forest ecosystems in Southwestern Romania using Landsat TM NDVI data. J. Geog. Sci. 24(5), 815–832.
Rogan, J., Chen, D. (2004). Remote sensing technology for mapping and monitoring land-cover and land-use change. Prog. Plann. 61, 301–325.
Rokni, K., Ahmad, A., Selamat, A., Hazini, S. (2014). Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sensing, 6(5), 4173–4189.
Sahebjalal, E., Dashtekian, K. (2013). Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods, Afr. J. Agric. Res, 8(37), 4614–4622.
Salmon, B.P., Kleynhans, W., Van den Bergh, F., Olivier, J.C., Grobler, T.L., & Wessels, K.J. (2013). Land cover change detection using the internal covariance matrix of the extended Kalman filter over multiple spectral bands. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing, 6(3), 1079–1085.
Senkal, O. (2010). Modeling of solar radiation using remote sensing and artificial neural network in Turkey. Energy, 35(12), 4795–4801.
Singha, S., Bellerby, T.J., & Trieschmann, O. (2013). Satellite oil spill detection using artificial neural networks. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sensing. 6(6), 2355–2363.
Sinha, P., Verma, N.K., & Ayele, E. (2016). Urban built-up area extraction and change detection of Adama municipal area using time-series Landsat images, Int. J. Adv. Remote Sens. GIS, 5(8), 1886–1895.
Simwanda, M., & Murayama, Y. (2018). Spatiotemporal patterns of urban land-use change in the rapidly growing city of Lusaka, Zambia: Implications for sustainable urban development. Sustain. Cities Soc. 39, pp. 262–274.
Uddin, S., Al Ghadban, A.N., Al Dousari, A., Al Murad, M., & Al Shamroukh, D. (2010). A remote sensing classification for land-cover changes and micro-climate in Kuwait, Int. J. Sustain. Dev. Planning, 5(4), pp. 367–377.
Uddin, M.J., & Mondal, C. (2020). Effect of earth covering and water body on land surface temperature (LST). J. Civ. Eng. Sci. Technol, 11(1), 45–56.
UNEP. (2016). GEO-6 Regional Assessment for Africa. Retrieved March 4, 2019.
Willis, K.S. (2015). Remote sensing change detection for ecological monitoring in United States protected areas. Biol. Conserv. 182, 233–242.
Wu, C., & Murray, A.T. (2003). Estimating impervious surface distribution by spectral mixture analysis. Remote Sens. Environ. 84 (4), 493–505.
Wu, C. (2009). Quantifying high-resolution impervious surfaces using spectral mixture analysis. Int. J. Remote Sens. 30(11), 2915–2932.
Xu, H. (2008). A New Index for Delineating Built-Up Land Features in Satellite Imagery. Int. J. Remote Sens. 29, 4269–4276.
Xu, H. (2006). Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27(14), 3025–3033.
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Remote Sens. 24(3), 583–594.
Zhong, Y., Wang, X., Zhao, L., Feng, R., Zhang, L., & Xu, Y. (2016). Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery. ISPRS J. Photogramm. Remote Sensing. 119, 49–63.
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