[
Andersson E., Haase D., Scheuer S., Wellmann T. 2020. Neighbourhood character affects the spatial extent and magnitude of the functional footprint of urban green infrastructure. Landscape Ecology, 35, 7: 1605–1618.10.1007/s10980-020-01039-z
]Search in Google Scholar
[
Arboit M.E., Maglione D.S. 2018. Multi-temporal and multi-spatial analysis of the normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI) in forested urban centers and irrigated oases, with dry climates. Boletín de Estudios geográficos, 109 [in Spanish].
]Search in Google Scholar
[
Arias M.E., Celemin J. 2021. Spatial distribution of street trees in the Center of the city of Santiago del Estero (Argentina). Revista da Casa da Geografia de Sobral, 23: 434 – 454 [in Spanish].10.35701/rcgs.v23.811
]Search in Google Scholar
[
Bensús Talavera V. 2018. (Un)planned densification of a metropolis. The case of the Metropolitan Area of Lima 2000–2014. Revista INVI, 33(92): 9–51 [in Spanish].10.4067/S0718-83582018000100009
]Search in Google Scholar
[
Berkowitz A.R., Nilon C.H., Hollweg K.S. 2003. The importance of understanding urban ecosystems: Themes. [in:] A.R. Berkowitz, C.H. Nilon, K.S. Hollweg (eds.) Understanding urban ecosystems - A new frontier for science and education. Sringer-Verlag, New York: 15–17.10.1007/b97613
]Search in Google Scholar
[
Berndtsson R., Becker P., Persson A., Aspegren H., Haghighatafshar S., Jönsson K., ... Tussupova K. 2019. Drivers of changing urban flood risk: A framework for action. Journal of Environmental Management, 240: 47–56.10.1016/j.jenvman.2019.03.094
]Search in Google Scholar
[
Cellucci C., Sivo M.D. 2021. Green Densification Strategies in Inner City for Psycho-Physical-Social Wellbeing. [in:] T. Ahram, R. Taiar, F. Groff (eds) Human Interaction, Emerging Technologies and Future Applications IV. IHIETAI 2021. Advances in Intelligent Systems and Computing, vol 1378, Springer, Cham: 350–358.10.1007/978-3-030-74009-2_45
]Search in Google Scholar
[
Coppola E. 2012. Densification vs Urban Sprawl. Tema-Journal of Land Use Mobility And Environment, 5, 1: 131–143.
]Search in Google Scholar
[
Darchen S., Poitras C. 2018. Accommodating densification and social sustainability in the inner city: Case study of Griffintown, Montreal. [in:] G. Searle (ed.) Compulsory Property Acquisition for Urban Densification. Routledge, London: 67–80.10.1201/9781315144085-6
]Search in Google Scholar
[
De Carvalho R.M., Szlafsztein C.F. 2018. Urban vegetation loss and ecosystem services: The influence on climate regulation and noise and air pollution. Environmental Pollution, 245: 844–852.10.1016/j.envpol.2018.10.114
]Search in Google Scholar
[
De la Barrera F., Henríquez C. 2017. Vegetation cover change in growing urban agglomerations in Chile. Ecological Indicators, 81: 265–273.10.1016/j.ecolind.2017.05.067
]Search in Google Scholar
[
del Río M.V. 2017. Impact of intensive residential densification on the segmentation of the urban fabric of Santiago: a quantitative approach. Revista, 180(40) [in Spanish].
]Search in Google Scholar
[
Do J., Ahn S., Kang J. 2021. Urbanization effect of mega sporting events using sentinel-2 satellite images: The case of the Pyeongchang olympics. Sustainable Cities and Society, 74: 103158.10.1016/j.scs.2021.103158
]Search in Google Scholar
[
Eggimann S., Wagner M., Ho Y.N., Züger M., Schneider U., Orehounig K. 2021. Geospatial simulation of urban neighbourhood densification potentials. Sustainable Cities and Society, 72: 103068.10.1016/j.scs.2021.103068
]Search in Google Scholar
[
Emilsson T., Ode Sang Å. 2017. Impacts of climate change on urban areas and nature-based solutions for adaptation. [in:] N. Kabisch, H. Korn, J. Stadler, A. Bonn (eds) Nature-Based Solutions to Climate Change Adaptation in Urban Areas. Theory and Practice of Urban Sustainability Transitions. Springer, Cham 15–27.10.1007/978-3-319-56091-5_2
]Search in Google Scholar
[
Ferrelli F., Bustos M.L., Huamantinco Cisneros M.A., Piccolo M.C. 2015. Use of satellite images for the study of the thermal distribution in different land covers of the city of Bahía Blanca. Revista de Teledetección, 44: 31–42 [in Spanish].10.4995/raet.2015.4018
]Search in Google Scholar
[
Ferrelli F., Cisneros M.A.H., Delgado A.L., Piccolo M.C. 2018. Spatial and temporal analysis of the LST-NDVI relationship for the study of land cover changes and their contribution to urban planning in Monte Hermoso, Argentina. Documents d’Anàlisi Geogràfica, 64, 1: 25–47.10.5565/rev/dag.355
]Search in Google Scholar
[
Ferro J.S. 2001. Expansion or Densification? Reflections on the Bogotá Case. Bitácora Urbano-Territorial, 5, 1: 21–35 [in Spanish].
]Search in Google Scholar
[
Firozjaei M.K., Sedighi A., Kiavarz M., Qureshi S., Haase D., Alavipanah S.K. 2019. Automated built-up extraction index: A new technique for mapping surface built-up areas using LANDSAT 8 OLI imagery. Remote Sensing, 11, 17: 1966.10.3390/rs11171966
]Search in Google Scholar
[
Fontenelle M.R., Lorente S., Gonçalves Bastos L.E. 2015. The impact of urbanization on air flow pattern: the case of Rio de Janeiro. International Journal of Green Energy, 12: 908–916.10.1080/15435075.2014.892005
]Search in Google Scholar
[
Gaw L.Y.F., Richards D.R. 2021. Development of spontaneous vegetation on reclaimed land in Singapore measured by NDVI. Plos one, 16, 1: e0245220.10.1371/journal.pone.0245220
]Search in Google Scholar
[
Grover A., Singh R.B. 2015. Analysis of urban heat island (UHI) in relation to normalized difference vegetation index (NDVI): A comparative study of Delhi and Mumbai. Environments, 2, 2: 125–138.10.3390/environments2020125
]Search in Google Scholar
[
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: 213–221.10.1080/01431161.2010.481681
]Search in Google Scholar
[
Hidayati I.N., Kusumawardani K.P., Ayudyanti A.G., Prabaswara R.R. 2021. Urban Biophysical Quality Modelling Based on Remote Sensing Data in Semarang, Indonesia. Geography, Environment, Sustainability, 14, 3: 14–23.10.24057/2071-9388-2020-173
]Search in Google Scholar
[
Huang S.L., Wang S.H., Budd W.W. 2009. Sprawl in Taipei’s peri-urban zone: Responses to spatial planning and implications for adapting global environmental change. Landscape and Urban Planning, 90: 20–32.10.1016/j.landurbplan.2008.10.010
]Search in Google Scholar
[
INDEC. 1991 Census. Argentina.
]Search in Google Scholar
[
INDEC. 2001 Census. REDATAM Database, Argentina.
]Search in Google Scholar
[
INDEC. 2010 Census. REDATAM Database, Argentina.
]Search in Google Scholar
[
Jianya G., Haigang S., Guorui M., Qiming Z.A. 2008. Review of Multi-Temporal Remote Sensing Data Change Detection Algorithms. The International Archives of the Photo-grammetry. Remote Sensing and Spatial Information Sciences, 37: 757–762.
]Search in Google Scholar
[
Kawamura M., Jayamana S., Tsujiko Y. 1996. Relation between Social and Environmental Conditions in Colombo Sri Lanka and the Urban Index Estimated by Satellite Remote Sensing Data. International Archieve of Photo-grammetry and Remote Sensing, 31 (B7): 321–326.
]Search in Google Scholar
[
Kumari B., Tayyab M., Ahmed I.A., Baig M.R.I., Khan M.F., Rahman A. 2020. Longitudinal study of land surface temperature (LST) using mono-and split-window algorithms and its relationship with ND VI and NDBI over selected metro cities of India. Arabian Journal of Geosciences, 13(19): 1–19.10.1007/s12517-020-06068-1
]Search in Google Scholar
[
Lemonsu A., Viguié V., Daniel M., Masson V. 2015. Vulnerability to heat waves: impact of urban expansion scenarios on urban heat island and heat stress in Paris (France). Urban Climate, 14: 586–605.10.1016/j.uclim.2015.10.007
]Search in Google Scholar
[
Li J., Roy D.P. 2017. A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9,9: 902.10.3390/rs9090902
]Search in Google Scholar
[
Li X., Sunikka-Blank M. 2021. Urban densification and social capital: neighbourhood restructuring in Jinan, China. Buildings and Cities, 2, 1: 244–263.10.5334/bc.70
]Search in Google Scholar
[
Libertun de Duren N., Guerrero Compeán R. 2016. Growing resources for growing cities: Density and the cost of municipal public services in Latin America. Urban Studies, 53, 14: 3082–3107.10.1177/0042098015601579
]Search in Google Scholar
[
Mattos C. 2016. Lógica financiera, geografía de la financiarización y crecimiento urbano mercantilizado. [in:] F. Link, J. Noyola y A. Orellana (eds.), Urbanización planetaria y la reconstrucción de la ciudad. RIL Editores, Santiago de Chile, 29–55 [in Spanish].
]Search in Google Scholar
[
Merlotto A., Piccolo M.C., Bértola G.R. 2012. Urban growth and land use/cover changes in the cities of Necochea and Quequén, Buenos Aires, Argentina. Revista de Geografía Norte Grande, 53: 159–176 [in Spanish].10.4067/S0718-34022012000300010
]Search in Google Scholar
[
Mugiraneza T., Nascetti A., Ban Y. 2020. Continuous monitoring of urban land cover change trajectories with landsat time series and landtrendr-google earth engine cloud computing. Remote Sensing, 12, 18: 2883.10.3390/rs12182883
]Search in Google Scholar
[
Næss P., Saglie I.L., Richardson T. 2020. Urban sustainability: is densification sufficient? European Planning Studies, 28, 1: 146–165.10.1080/09654313.2019.1604633
]Search in Google Scholar
[
Nolè G., Lasaponara R., Murgante B. 2013. Applying spatial autocorrelation techniques to multi-temporal satellite data for measuring urban sprawl. International Journal of Environmental Protection, 3, 7: 11.
]Search in Google Scholar
[
Paolini L., Aráoz E., Gioia A., Powell P.A. 2016. Vegetation productivity trends in response to urban dynamics. Urban Forestry & Urban Greening, 17: 211–216.10.1016/j.ufug.2016.04.005
]Search in Google Scholar
[
Pauleit S., Ennos R., Golding Y. 2005. Modeling the environmental impacts of urban land use and land cover change–a study in Merseyside, UK. Landscape and Urban Planning, 71, 2–4: 295–310.10.1016/S0169-2046(04)00083-0
]Search in Google Scholar
[
Richards D.R., Belcher R.N. 2019. Global changes in urban vegetation cover. Remote Sensing, 12 (1): 23.10.3390/rs12010023
]Search in Google Scholar
[
Roger E., Palacio M., Coria O., Díaz R. 2016. Notes on the urban flora cultivated in the city of Santiago del Estero, Argentina. Multequina, 25, 1: 29–41 [in Spanish].
]Search in Google Scholar
[
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. International Journal of Advanced Remote Sensing and GIS, 5, 8: 1886–1895.10.23953/cloud.ijarsg.67
]Search in Google Scholar
[
Skrede J., Berg S.K. 2019. Cultural heritage and sustainable development: the case of urban densification. The Historic Environment: Policy & Practice, 10, 1: 83–102. 10.1080/17567505.2019.1558027
]Search in Google Scholar
[
Soto-Estrada E. 2019. Estimation of the urban heat island in Medellin, Colombia. Revista Internacional de Contaminación Ambiental, 35, 2: 421–434.10.20937/RICA.2019.35.02.13
]Search in Google Scholar
[
Tillie N., Borsboom-van Beurden J., Doepel D., Aarts M. 2018. Exploring a stakeholder based urban densification and greening agenda for Rotterdam inner city—accelerating the transition to a liveable low carbon city. Sustainability, 10(6), 1927.10.3390/su10061927
]Search in Google Scholar
[
Treija S., Bratuškins U., Koroļova A. 2018. Urban Densification of Large Housing Estates in the Context of Privatisation of Public Open Space: the Case of Imanta, Riga. Architecture & Urban Planning, 14, 1: 105 – 110.10.2478/aup-2018-0014
]Search in Google Scholar
[
Trombetti M., Riaño D., Rubio M.A., Cheng Y.B., Ustin S.L. 2008. Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA. Remote Sensing of Environment, 112 (1): 203–-215.10.1016/j.rse.2007.04.013
]Search in Google Scholar
[
UN - Habitat (United Nations Human Settlements Programme). 2012. Leveragig Density: Urban Patterns for a Green Economy. UN Habitat, Nairobi.
]Search in Google Scholar
[
Wang S.H., Huang S.L., Huang P.J. 2018. Can spatial planning really mitigate carbon dioxide emissions in urban areas? A case study in Taipei, Taiwan. Landscape and Urban Planning, 169: 22–36.10.1016/j.landurbplan.2017.08.001
]Search in Google Scholar
[
Vargas-Bolaños C., Orozco-Montoya R., Vargas-Hernández A., Aguilar-Arias J. 2020. Methodology for determining the growth of the urban sprawl in the capitals of the Central American region (1975–1995–2014). Revista Geográfica de América Central, 64: 41–74 [in Spanish].
]Search in Google Scholar
[
Vega J.J.P., Zárate-Gómez R., Vela R.J.M., Brañas M.M., Rios J.E.B. 2019. (Predicción de la pérdida de la cobertura vegetal por aumento de áreas urbanas en Iquitos, Perú). Ciencia Amazónica (Iquitos), 7, 1: 37–50 [in Spanish].10.22386/ca.v7i1.263
]Search in Google Scholar
[
Xi Y., Thinh N.X., Li C. 2019. Preliminary comparative assessment of various spectral indices for built-up land derived from Landsat-8 OLI and Sentinel-2A MSI imageries. European Journal of Remote Sensing, 52, 1: 240–252.10.1080/22797254.2019.1584737
]Search in Google Scholar
[
Xie Q., Sun Q. 2021. Monitoring the Spatial Variation of Aerosol Optical Depth and Its Correlation with Land Use/Land Cover in Wuhan, China: A Perspective of Urban Planning. International Journal of Environmental Research and Public Health, 18, 3: 1132.10.3390/ijerph18031132
]Search in Google Scholar
[
Xu H. 2008. A New Index for Delineating Built-Up Land Features in Satellite Imagery. International Journal of Remote Sensing, 29: 4269–4276.10.1080/01431160802039957
]Search in Google Scholar
[
Yépez Rincón F.D., Lozano García D.F. 2014. Mapping of urban trees with aerial. Revista Mexicana de Ciencias Forestales, 5, 26: 58–75 [in Spanish].10.29298/rmcf.v5i26.290
]Search in Google Scholar
[
Zhao H.M., Chen X.L. 2005. Use of Normalized Difference Bareness Index in Quickly Mapping Bare Areas from TM/ETM+. Geoscience and Remote Sensing Symposium, 3(25–29): 1666–1668.
]Search in Google Scholar
[
Zurqani H.A., Post C.J., Mikhailova E.A., Allen J.S. 2019. Mapping urbanization trends in a forested landscape using Google Earth Engine. Remote Sensing in Earth Systems Sciences, 2, 4: 173–182.10.1007/s41976-019-00020-y
]Search in Google Scholar
and 