Integrating geographical information systems, remote sensing, and machine learning techniques to monitor urban expansion: an application to Luanda, Angola

References

• Abdikan, S., F.B. Sanli, M. Ustuner, and F. Calò 2016. Land Cover Mapping Using Sentinel-1 SAR Data. Paper presented at the proceedings of the ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic July 12-19. XLI-B7: 757–761. doi:10.5194/isprs-archives-XLI-B7-757-2016.
   Google Scholar
• Abdollahnejad, A., D. Panagiotidis, and L. Bílek. 2019. “An Integrated GIS and Remote Sensing Approach for Monitoring Harvested Areas from Very High-resolution, Low-cost Satellite Images.” Remote Sensing 11 (21): 2539. doi:10.3390/rs11212539.
   Web of Science ®Google Scholar
• Achmad, A., S. Hasyim, B. Dahlan, and D.N. Aulia. 2015. “Modeling of Urban Growth in Tsunami-Prone City Using Logistic Regression: Analysis of Banda Aceh, Indonesia.” Applied Geography 62: 237–246.
   Web of Science ®Google Scholar
• Adam, H.E., E. Clsapovics, and M.E. Elhaja, 2016. “A Comparison of Pixel-based and Object-based Approaches for Land Use Land Cover Classification in Semi-arid Areas, Sudan.” Paper presented at the proceedings of the IOP Conference Series: Earth and Environmental Science, Kuala Lumpur, Malaysia, April 13–14. 37: 012061. doi:10.1088/1755-1315/37/1/012061.
   Google Scholar
• Akinwande, M.O., H.G. Dikko, and A. Samson. 2015. “Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis.” Open Journal of Statistics 5 (7): 754–767. doi:10.4236/ojs.2015.5.
   Google Scholar
• Allison, P. 2012. “When Can You Safely Ignore Multicollinearity?” Statistical Horizons. Accessed 18 Jun 2019. https://statisticalhorizons.com/multicollinearity.
   Google Scholar
• Alsharif, A.A.A., and B. Pradhan. 2014. “Urban Sprawl Analysis of Tripoli Metropolitan City (Libya) Using Remote Sensing Data and Multivariate Logistic Regression Model.” Journal of the Indian Society of Remote Sensing 42 (1): 149–163. doi:10.1007/s12524-013-0299-7.
   Web of Science ®Google Scholar
• Amani, M., A. Ghorbanian, S.A. Ahmadi, M. Kakooei, A. Moghimi, S.M. Mirmazloumi, S.H.A. Moghaddam, et al. 2020. “Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:5326–5350. doi:10.1109/JSTARS.2020.3021052.
   Web of Science ®Google Scholar
• Arino, O., J.J. Ramos Perez, V. Kalogirou, S. Bontemps, P. Defourny, and E. Van Bogaert. 2012. Global Land Cover Map for 2009 (Globcover 2009). © European Space Agency (ESA) & Université catholique de Louvain (UCL), Bremerhaven: PANGAEA. doi:10.1594/PANGAEA.787668.
   Google Scholar
• Atak, B.K., N. Erdogan, E. Ersoy, and E. Nurlu. 2014. “Analysing the Spatial Urban Growth Pattern by Using Logistic Regression in Didim District.” Journal of Environmental Protection and Ecology 15 (4): 1866–1876.
   Web of Science ®Google Scholar
• Angel, S., A.M. Blei, J. Parent, P. Lamson-Hall, N.G. Sánchez, D.L. Civco, R.Q. Lei, K. Thom. 2016. Atlas of Urban Expansion Volume 1: Areas and Densities. New York: New York University, Nairobi: UN-Habitat, and Cambridge, MA: Lincoln Institute of Land Policy. 1: 236-237. 978-0-9981758-0-5. ISBN: 9781.9781558442436.558442436.
   Google Scholar
• Awad, M., and R. Khanna. 2015. Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. Berlin: Springer. doi:10.1007/978-1-4302-5990-9.
   Google Scholar
• Buchhorn, M., M. Lesiv, N.E. Tsendbazar, M. Herold, L. Bertels, and B. Smets. 2020. “Copernicus Global Land Cover Layers-collection 2.” Remote Sensing 12 (6): 1044. doi:10.3390/rs12061044.
   Web of Science ®Google Scholar
• Cao, H., H. Zhang, C. Wang, and B. Zhang. 2018. “Operational Built-up Areas Extraction for Cities in China Using Sentinel-1 SAR Data.” Remote Sensing 10 (6): 874. doi:10.3390/rs10060874.
   Web of Science ®Google Scholar
• Carranza-García, M., J. García-Gutiérrez, and J.C. Riquelme. 2019. “A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks.” Remote Sensing 11 (3): 274. doi:10.3390/rs11030274.
   Web of Science ®Google Scholar
• Cavur, M., S. Kemec, L. Nabdel, and H. Sebnem Duzgun 2015. “An Evaluation of Land Use Land Cover (LULC) Classification for Urban Applications with Quickbird and WorldView2 Data.” Paper presented at the proceedings of the 2015 Joint Urban Remote Sensing Event (JURSE), Lausanne, Switzerland, March 30–April 1, pp 1–4. doi: 10.1109/JURSE.2015.7120486.
   Google Scholar
• Chen, Y., Y. Zhou, Y. Ge, R. An, and Y. Chen. 2018. “Enhancing Land Cover Mapping through Integration of Pixel-based and Object-based Classifications from Remotely Sensed Imagery.” Remote Sensing 10 (2): 77. doi:10.3390/rs10010077.
   Google Scholar
• Cheţan, M.A., A. Dornik, and P. Urdea. 2017. “Comparison of Object and Pixel-based Land Cover Classification through Three Supervised Methods.” ZFV - Zeitschrift Fur Geodasie, Geoinformation Und Landmanagement [ZFV - Magazine for Geodesy, Geoinformation and Land Management] 142 (5): 265–270. doi:10.12902/zfv-0165-2017.
   Google Scholar
• Choodarathnakara, A.L., A. Kumar, and G. Patil. 2012. “Mixed Pixels: A Challenge in Remote Sensing Data Classification for Improving Performance.” International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 1 (9): 261–271.
   Google Scholar
• Coelho, P.R.S., F.T.O. Ker, A.D. Araújo, R.J.P.S. Guimarães, D.A. Negrão Corrêa, R.L. Caldeira, and S.M. Geiger. 2021. “Identification of Risk Areas for Intestinal Schistosomiasis, Based on Malacological and Environmental Data and on Reported Human Cases.” Frontiers in Medicine 8: 1288. doi:10.3389/fmed.2021.642348.
   Web of Science ®Google Scholar
• Corbane, C., J.F. Faure, N. Baghdadi, N. Villeneuve, and M. Petit. 2008. “Rapid Urban Mapping Using SAR/Optical Imagery Synergy.” Sensors 8 (11): 7125–7143. doi:10.3390/s8117125.
   PubMed Web of Science ®Google Scholar
• Corredor, X. 2018. AcATaMa Qgis Plugin (Version 18.11.21.Q3). Accessed 15 Jun 2019. https://smbyc.bitbucket.io/qgisplugins/acatama.
   Google Scholar
• Dangermond, J., and M.F. Goodchild. 2020. “Building Geospatial Infrastructure.” Geo-spatial Information Science 23 (1): 1–9. doi:10.1080/10095020.2019.1698274.
   Web of Science ®Google Scholar
• ESA (European Space Agency) 2017. Climate Change Initiative Land Cover Project - S2 Prototype Land Cover 20 M Map of Africa 2016. Accessed 12 Jul 2019. http://2016africalandcover20m.esrin.esa.int.
   Google Scholar
• ESA (European Space Agency) N.d.b. Esa Copernicus. Accessed 16 February 2019. https://scihub.copernicus.eu/dhus/#/home.
   Google Scholar
• Eyoh, A., and D. Nihinlola. 2012. “Modelling and Predicting Future Urban Expansion of Lagos, Nigeria from Remote Sensing Data Using Logistic Regression and GIS.” International Journal of Applied Science and Technology 2 (5): 1–9. https://www.ijastnet.com/journal/index/261.
   Google Scholar
• Fan, J., S. Upadhye, and A. Worster. 2006. “Understanding Receiver Operating Characteristic (ROC) Curves.” Canadian Journal of Emerging Medicine 8 (1): 19–20. doi:10.1017/S1481803500013336.
   Google Scholar
• Forum-angonet, (n.d.) Data Panel: Urban Forum on Angonet [Painel de Dados: Fórum Digital Urbano de Angola]. Accessed 15 Sep 2018. https://forum.angonet.org/painel-de-dados.
   Google Scholar
• 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. doi:10.1016/S0034-4257(96)00067-3.
   Web of Science ®Google Scholar
• Gill, T., and S. Phinn. 2008. “Estimates of Bare Ground and Vegetation Cover from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Short-wave-infrared Reflectance Imagery.” Journal of Applied Remote Sensing 2 (1): 023511. doi:10.1117/1.2907748.
   Google Scholar
• Gong, P., B. Chen, X. Li, H. Liu, J. Wang, Y. Bai, J. Chen, et al. 2020. “Mapping Essential Urban Land Use Categories in China (Euluc-china): Preliminary Results for 2018.” Science Bulletin 65 (3): 182–187. doi:10.1016/J.SCIB.2019.12.007.
   PubMed Web of Science ®Google Scholar
• GPL (Government of the Province of Luanda [Governo da Província de Luanda]). 2014. Provincial Development Plan. [Plano de Desenvolvimento Provincial] 2013/2017. Luanda: GPL.
   Google Scholar
• Guobin, Z., S. Krakover, and D. Blumberg. 2003. “Urban Open Space Study Based on Remote Sensing and Geographic Information Systems.” Geo-spatial Information Science 6 (3): 76–78. doi:10.1007/bf02826899.
   Google Scholar
• Harris Geospatial Solutions n.d.a. “Spectral Indices.” Accessed 3 Nov 2017. http://www.harrisgeospatial.com/docs/SpectralIndices.html.
   Google Scholar
• Harris Geospatial Solutions n.d.b. “K-Means.” Accessed 13 Nov 2019. https://www.harrisgeospatial.com/docs/KMeansClassification.html.
   Google Scholar
• Hede, A.N.H., K. Kashiwaya, K. Koike, and S. Sakurai. 2015. “A New Vegetation Index for Detecting Vegetation Anomalies Due to Mineral Deposits with Application to A Tropical Forest Area.” Remote Sensing of Environment 171: 83–97. doi:10.1016/j.rse.2015.10.006.
   Web of Science ®Google Scholar
• Hu, B., M. Palta, and J. Shao. 2006. “PSEUDO-R 2 in Logistic Regression Model.” Statistica Sinica 16: 847–860. http://www3.stat.sinica.edu.tw/statistica/J16N3/J16N39/J16N39.html.
   Web of Science ®Google Scholar
• Huang, X., D. Wen, J. Li, and R. Qin. 2017. “Multi-level Monitoring of Subtle Urban Changes for the Megacities of China Using High-resolution Multi-view Satellite Imagery.” Remote Sensing of Environment 196: 56–75. doi:10.1016/J.RSE.2017.05.001.
   Web of Science ®Google Scholar
• Huang, X., T. Hu, J. Li, Q. Wang, and J.A. Benediktsson. 2018. “Mapping Urban Areas in China Using Multisource Data with a Novel Ensemble SVM Method.” Paper Presented at the Proceedings of the IEEE Transactions on Geoscience and Remote Sensing 56 (8): 4258–4273. doi:10.1109/TGRS.2018.2805829.
   Google Scholar
• Huang, X., J. Huang, D. Wen, and J. Li. 2021. “An Updated MODIS Global Urban Extent Product (MGUP) from 2001 to 2018 Based on an Automated Mapping Approach.” International Journal of Applied Earth Observation and Geoinformation 95: 102255. doi:10.1016/J.JAG.2020.102255.
   Web of Science ®Google Scholar
• Huete, A.R. 1988. “A Soil-adjusted Vegetation Index (SAVI).” Remote Sensing of Environment 25 (3): 295–309. doi:10.1016/0034-4257(88)90106-X.
   Web of Science ®Google Scholar
• Hurd, J. 2015. “Atlas of Global Expansion 2015 Edition: Cities Classification Procedures Manual.” New York University Libraries 1. http://hdl.handle.net/2451/38181.
   Google Scholar
• INE (National Institute of Statistics [Instituto Nacional de Estatística]). 2016a. Census 2014: Definitive Results of the General Population and Housing Census of Angola 2014 [Census 2014: Resultados Definitivos Do Recenseamento Geral da População E da Habitação de. Angola 2014.], Luanda: INE.
   Google Scholar
• INE (National Institute of Statistics [Instituto Nacional de Estatística]). 2016b. Projection of the Population of the Province of Luanda 2014–2050. ( Projeção da população da província de Luanda 2014–2050), Luanda: INE.
   Google Scholar
• Info-Angola n.d. Amendment law of the Political-Administrative Division of the Provinces of Luanda and Bengo [Lei de Alteração da Divisão Político-Administrativa das Províncias de Luanda e Bengo]. Accessed 12 October 2017. https://www.info-angola.com/attachments/article/3442/Lei%20n_29-11,de_1_de_Setembro.pdf.
   Google Scholar
• IPGUL (Luanda Urban Planning and Management Institute [Instituto de Planeamento e Gestão Urbana de Luanda]). 2015. “Luanda Metropolitan Master Plan: Vision and Strategy. Administrative Boundaries [Plano Director Geral Metropolitano de Luanda: Visão E Estratégia.” Limites Administrativos] 1: 100–127.
   Google Scholar
• IPGUL (Luanda Urban Planning and Management Institute [Instituto de Planeamento e Gestão Urbana de Luanda]) n.d. PDGML Framework [Enquadramento do PDGML]. Accessed 9 Jun 2018. http://ipgul.net/images/PDGML.pdf.
   Google Scholar
• JAXA (Japan Aerospace Exploration Agency) n.d. ALOS Global Digital Surface Model “ALOS World 3D - 30m” (AW3D30). Accessed 12 May 2018. https://www.eorc.jaxa.jp/ALOS/en/aw3d30/index.htm.
   Google Scholar
• Jenkins, P., P. Robson, and A. Cain. 2002. “Luanda.” Cities 19 (2): 139–150. doi:10.1016/S0264-2751(02)00010-0.
   Web of Science ®Google Scholar
• Junting, Y., L. Xiaosong, W. Bo, W. Junjun, S. Bin, Y. Changzhen, and G. Zhihai. 2021. “High Spatial Resolution Topsoil Organic Matter Content Mapping across Desertified Land in Northern China.” Frontiers in Environmental Science 9: 341. doi:10.3389/fenvs.2021.668912.
   Web of Science ®Google Scholar
• Karra, K., C. Kontgis, Z. Statman-Weil, J.C. Mazzariello, M. Mathis, and S.P. Brumby 2021.“Global Land Use/land Cover with Sentinel-2 and Deep Learning.” Paper presented at the proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium, July 11–16. 4704–4707. doi:10.1109/IGARSS47720.2021.9553499.
   Google Scholar
• Laipelt, L., R. Henrique Bloedow Kayser, A. Santos Fleischmann, A. Ruhoff, W. Bastiaanssen, T.A. Erickson, and F. Melton. 2021. “Long-term Monitoring of Evapotranspiration Using the SEBAL Algorithm and Google Earth Engine Cloud Computing.” ISPRS Journal of Photogrammetry and Remote Sensing 178: 81–96. doi:10.1016/J.ISPRSJP.
   Web of Science ®Google Scholar
• Lee, D. 2013. “A Comparison of Choice-based Landscape Preference Models between British and Korean Visitors to National Parks.” Life Science Journal 10 (2): 2028–2036. doi:10.7537/marslsj100213.286.
   Google Scholar
• Lennert, M., T. Grippa, J. Radoux, C. Bassine, B. Beaumont, B. Defourny, and E. Wolff 2019. “Object-based VS Pixel-based Mapping of Fire Scars Using Multi-scale Satellite Data.” Paper presented at the proceedings of the ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, XLII-4/W14, Bucharest, Romania, August 26–30. 151–157. doi:10.5194/isprs-archives-XLII-4-W14-151-2019.
   Google Scholar
• Lesschen, J.P., P.H. Verburg, and S.J. Staal 2005. Statistical Methods for Analysing the Spatial Dimension of Changes in Land Use and Farming Systems, Report Series, 7. Nairobi, Kenya: The International Livestock Research Institute and LUCC Focus 3 Office.
   Google Scholar
• Li, E., P. Du, A. Samat, J. Xia, and M. Che. 2015. “An Automatic Approach for Urban Land-cover Classification from Landsat-8 OLI Data.” International Journal of Remote Sensing 36 (24): 5983–6007. doi:10.1080/01431161.2015.11.
   Web of Science ®Google Scholar
• Li, S. 2017. “Building A Logistic Regression in Python, Step by Step. Towards Data Science.” Accessed 19 April 2019.https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8.
   Google Scholar
• Liau, Y.T. 2016. ““Object‐based Segmentation to Identify Water Features from Landsat 8 and SAR Images: A Preliminary Study.” National Water Center Innovators Program Summer Institute Report. doi:10.4211/technical.2016.
   Google Scholar
• MacLachlan, A., Roberts, G., Biggs, E., and Boruff, B. 2017. “Subpixel land-cover classification for improved urban area estimates using landsat.” International Journal of Remote Sensing, 38 (20): 5763–5792. doi:10.1080/01431161.2017.134.
   Web of Science ®Google Scholar
• Madasa, A., I.R. Orimoloye, and O.O. Ololade. 2021. “Application of Geospatial Indices for Mapping Land Cover/use Change Detection in a Mining Area.” Journal of African Earth Sciences 175: 104108. doi:10.1016/j.jafrearsci.2021.104108.
   Web of Science ®Google Scholar
• Mallinis, G., M. Plenioub, and N. Koutsiasb. 2010, “Object-based VS Pixel-based Mapping of Fire Scars Using Multi-scale Satellite Data.” Paper Presented at the Proceedings of the ISPRS Archives: Geographic Object-Based Image Analysis, edited by E.A. Addink and F.M.B. Van Coillie. Ghent, Ghent, Belgium: June 29– July 02. XXXVIII 4/C7
   Google Scholar
• Mao, Y., D.L. Harris, Z. Xie, and S. Phinn 2021. “Efficient Measurement of Large-scale Decadal Shoreline Change with Increased Accuracy in Tide-dominated Coastal Environments with Google Earth Engine.” ISPRS Journal of Photogrammetry and Remote Sensing 181: 385–399. doi:10.1016/J.ISPRSJPRS.2021.09.021.
   Web of Science ®Google Scholar
• Mapcruzin, n.d. “Download Free Angola Country, City, Region, Boundaries GIS Shapefile Map Layers.” Accessed 6 Sep. 2017. https://mapcruzin.com/free-angola-country-city-place-gis-shapefiles.htm.
   Google Scholar
• Matsui, Y., K. Ogaki, T. Yamasaki, and K. Aizawa 2017. “PQk-means: Billion-scale Clustering for Product-quantized Codes.” Paper presented at the proceedings of the ACM International Conference on Multimedia, Mountain View, California, USA, October 23–27. pp 1725–1733. https://arxiv.org/abs/1709.03708v1
   Google Scholar
• Maxwell, A.E., T.E. Warner, and F. Fang. 2018. “Implementation of Machine-learning Classification in Remote Sensing: An Applied Review.” International Journal of Remote Sensing 39 (9): 2784–2817. doi:10.1080/01431161.2.
   Web of Science ®Google Scholar
• McFeeters, S.K. 1996. “The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features.” International Journal of Remote Sensing 17 (7): 1425–1432. doi:10.1080/01431169608948714.
   Web of Science ®Google Scholar
• McHugh, M.L. 2012. “Interrater Reliability: The Kappa Statistic.” Zagreb: Biochemia Medica 22 (3): 276–282. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3900052.
   PubMed Web of Science ®Google Scholar
• Mendelsohn, J., S. Mendelsohn, and N. Nakanyete 2010. “An Expanded Assessment of Risks from Flooding in Luanda.” Accessed 3 July 2019. https://www.raison.com.na/sites/default/files/Angola/20-202010/report-on-flood-risk-in-Luanda.pdf.
   Google Scholar
• Merchant, J.W., and S. Narumalani. 2008. “Integrating Remote Sensing and Geographic Information Systems.” In The SAGE Handbook of Remote Sensing, 257–268. 55. City Road, London: SAGE Publications . doi:10.4135/9780857021052.n18.
   Google Scholar
• Midekisa, A., F. Holl, D.J. Savory, R. Andrade-Pacheco, P.W. Gething, A. Bennett, H.J.W. Sturrock, and G.J.-P. Schumann. 2017. “Mapping Land Cover Change over Continental Africa Using Landsat and Google Earth Engine Cloud Computing.” Plos one 12 (9): e0184926. doi:10.1371/journal.pone.0184926.
   PubMed Web of Science ®Google Scholar
• Mobility in chain 2015. Luanda Metropolitan Master Plan [Plano Director Geral Metropolitano de Luanda]. Accessed 19 Aug 2018. http://www.michain.com/en/works/plano-director-geral-metropolitano-de-luanda.
   Google Scholar
• Muller, A.C., and S. Guido. 2017. Introduction to Machine Learning with Python: A Guide for Data Scientists. 1005 Graveinstein Highway North. Sebastopol: O’Reilly Media.
   Google Scholar
• Musa, S.I., M. Hashim, and M.N. Reba. 2016. “A Review of Geospatial-based Urban Growth Models and Modelling Initiatives.” Geocarto International 32 (8): 813–833. doi:10.1080/10106049.2016.1213891.
   Web of Science ®Google Scholar
• Mwakapuja, F., E. Liwa, and J. Kashaigili. 2013. “Usage of Indices for Extraction of Built-up Areas and Vegetation Features from Landsat TM Image: A Case of Dar Es Salaam and Kisarawe Peri-Urban Areas, Tanzania.” International Journal of Agriculture and Forestry 3 (7): 273–283. doi:10.5923/j.ijaf.20130307.04.
   Google Scholar
• NASA (National Aeronautics and Space Administration) 2017. “Advanced webinar: Land Cover Classification with Satellite Imagery; Exercise 2: Improving Your Supervised Classification.” Accessed 3 May 2019.https://arset.gsfc.nasa.gov/land/webinars/advanced-land-classification.
   Google Scholar
• Olofsson, P., G.M. Foody, S.V. Stehman, and C.E. Woodcock. 2013. “Making Better Use of Accuracy Data in Land Change Studies: Estimating Accuracy and Area and Quantifying Uncertainty Using Stratified Estimation.” Remote Sensing of Environment 129: 122–131. doi:10.1016/j.rse.2012.10.031.
   Web of Science ®Google Scholar
• Orimoloye, I.R., and O.O. Ololade. 2020. “Spatial Evaluation of Land-use Dynamics in Gold Mining Area Using Remote Sensing and GIS Technology.” International Journal of Environmental Science and Technology 17 (11): 4465–4480. doi:10.1007/s13762-020-02789-8.
   Web of Science ®Google Scholar
• Patel, N., and R. Mukherjee. 2015. “Extraction of Impervious Features from Spectral Indices Using Artificial Neural Network.” Arabian Journal of Geosciences 8 (6): 3729–3741. doi:10.1007/s12517-014-1492-x.
   Web of Science ®Google Scholar
• Rasul, A., H. Balzter, G. Ibrahim, H. Hameed, J. Wheeler, B. Adamu, S. Ibrahim, and P. Najmaddin. 2018. “Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates.” Land 7 (3): 81. doi:10.3390/land7030081.
   Web of Science ®Google Scholar
• Shao, Z., N.S. Sumari, A. Portnov, F. Ujoh, W. Musakwa, and P.J. Mandela. 2021. “Urban Sprawl and Its Impact on Sustainable Urban Development: A Combination of Remote Sensing and Social Media Data.” Geo-spatial Information Science 24 (2): 241–255. doi:10.1080/10095020.2020.1787800.
   Web of Science ®Google Scholar
• Shishir, S., and S. Tsuyuzaki. 2018. “Hierarchical Classification of Land Use Types Using Multiple Vegetation Indices to Measure the Effects of Urbanization.” Environmental Monitoring and Assessment 190 (6): 342. doi:10.1007/s10661-018-6714-3.
   PubMed Web of Science ®Google Scholar
• Simwanda, M., and Y. Murayama. 2017. “Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia.” ISPRS International Journal of Geo-Information 6 (4): 102. doi:10.3390/ijgi6040102.
   Web of Science ®Google Scholar
• Sinha, S., A. Santra, and S.S. Mitra 2018. “A Method for Built-Up Area Extraction Using Dual Polarimetric Alos Palsar.” Paper presented at the proceedings of the ISPRS Symposium Geospatial technology – Pixel to People, IV-5, 455–458, Dehradun, India, November 20–23. doi:10.5194/isprs-annals-IV-5-455-2018.
   Google Scholar
• Smith, M., and A. Hoffmann. 2000. “Parcel-based Approaches to the Classification of Fine Spatial Resolution Imagery: Example Methodologies Using HRSC-A Data.”Paper Presented at the Proceedings of the 19th ISPRS Congress, edited by Gabor Remetey-Fülöpp, Jan Clevers, Klaas Jan Beek, Amsterdan, The Netherlands: 1418–1422. 33-B7. July 16 – 23. https://www.isprs.org/proceedings/XXXIII/congress/part7
   Google Scholar
• Smith, T., and C. McKenna 2013. “A Comparison of Logistic Regression Pseudo R 2 Indices.” Accessed 19 Apr 2019.http://www.glmj.org/archives/articles/Smith_v39n2.pdf.
   Google Scholar
• Tamiminia, H., B. Salehi, M. Mahdianpari, L. Quackenbush, S. Adeli, and B. Brisco. 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. doi:10.1016/J.ISPRSJPRS.2020.04.001.
   Web of Science ®Google Scholar
• Tassi, A., D. Gigante, G. Modica, L. Di Martino, and M. Vizzari. 2021. “Pixel-vs. Object-based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park.” Remote Sensing 13 (12): 2299. doi:10.3390/rs13122299.
   Web of Science ®Google Scholar
• Tavares, E. 2017. “Variance Inflation Factor (VIF) Explained.” Github. Accessed 20 May 2019. https://etav.github.io/python/vif_factor_python.html.
   Google Scholar
• Tavares, P.A., N.E.S. Beltrão, U.S. Guimarães, and A.C. Teodoro. 2019. “Integration of Sentinel-1 and Sentinel-2 for Classification and LULC Mapping in the Urban Area of Belém, Eastern Brazilian Amazon.” Sensors 19 (5): 1140. doi:10.3390/s19051140.
   PubMed Web of Science ®Google Scholar
• Traore, A., and T. Watanabe. 2017. “Modeling Determinants of Urban Growth in Conakry, Guinea: A Spatial Logistic Approach.” Urban Science 1 (2): 12. doi:10.3390/urbansci1020012.
   Google Scholar
• Trinder, J., and Q. Liu. 2020. “Assessing Environmental Impacts of Urban Growth Using Remote Sensing.” Geo-spatial Information Science 23 (1): 20–39. doi:10.1080/10095020.2019.1710438.
   Web of Science ®Google Scholar
• UCLA (University of California, Los Angeles) 2011. “FAQ: What are pseudo R-squareds?” Accessed 6 Jun 2019.https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-pseudo-r-squareds.
   Google Scholar
• USGS (United States Geological Survey) n.d. EarthExplorer – Home. Accessed 25 Feb 2018.https://earthexplorer.usgs.gov.
   Google Scholar
• Viegas, S. 2012. “Urbanization in Luanda: Geopolitical Framework and Socio-territorial Analysis.” Paper presented at the proceedings of the 15th International Planning History Society Conference, São Paulo: FAU-USP/IPHS, Brasil, July 15–18. http://www.usp.br/fau/iphs/abstracts-and-papers.html.
   Google Scholar
• Vincent, R.K. 1997. Fundamentals of Geological and Environmental Remote Sensing. Upper Saddle River, New Jersey: Prentice Hall.
   Google Scholar
• Vopham, T., J.E. Hart, F. Laden, and Y.Y. Chiang. 2018. “Emerging Trends in Geospatial Artificial Intelligence (Geoai): Potential Applications for Environmental Epidemiology.” Environmental Health 17 (1): 40. doi:10.1186/s12940-018-0386-x.
   PubMed Web of Science ®Google Scholar
• Weih, R.C., and N.D. Riggan. 2010. “Object-based Classification VS. Pixel-based Classification: COMPARITIVE IMPORTANCE OF MULTI-RESOLUTION IMAGERY.” In Paper Presented at the Proceedings of the ISPRS Archives: Geographic Object-Based Image Analysis (GEOBIA), XXXVIII–4/C7, edited by E.A. Addink and F.M.B. Van Coillie. Belgium: Ghent. Jun – Jul 29 02. https://www.isprs.org/proceedings/XXXVIII/4-C7
   Google Scholar
• Williams, R. 2015. “Scalar Measures of Fit: Pseudo R2 and Information Measures (AIC & BIC).” Univ. Notre Dame. Accessed 12 January 2020. https://www3.nd.edu/~rwilliam/stats3/L05.pdf.
   Google Scholar
• Xie, C., B. Huang, C. Claramunt, and C. Chandramouli 2005. “Spatial Logistic Regression and GIS to Model Rural-urban Land Conversion.” Paper presented at the proceedings of the PROCESSUS Second International Colloquium on the Behavioural Foundations of Integrated Land-Use and Transportation Models: Frameworks, Models and Applications, Toronto, June 12–15.
   Google Scholar
• Xu, H. 2007. “Extraction of Urban Built-up Land Features from Landsat Imagery Using a Thematic-oriented Index Combination Technique.” Photogrammetric Engineering and Remote Sensing 73 (12): 1381–1391. doi:10.14358/PERS.73.12.1381.
   Web of Science ®Google Scholar
• Xu, H. 2008. “A New Index for Delineating Built-up Land Features in Satellite Imagery.”. International Journal of Remote Sensing 29 (14): 4276–4279. doi:10.1080/01431160802039957.
   Web of Science ®Google Scholar
• Yang, J., and X. Huang. 2021. “The 30 m Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019.” Earth System Science Data 13 (8): 3907–3925. doi:10.5194/essd-13-3907-2021.
   Web of Science ®Google Scholar
• Yang, X., and D. Shi . 2016. “An Assessment of Algorithmic Parameters Affecting Image Classification Accuracy by Random Forests.” Photogrammetric Engineering & Remote Sensing 82 (6): 407–417. doi:10.14358/PERS.82.6.407.
   Web of Science ®Google Scholar