Analysis of the influence of urban areas on natural environments using Geographic Information Technologies in the Greater Resistencia Metropolitan Area (Argentina)
Keywords:
Natural Landscapes, Geographic Information Technologies, Statistical Analysis, Spectral Indices, Distance EffectsAbstract
Over the last few decades, urban expansion has become as one of the primary drivers of natural environment degradation, and its impacts have raised concerns among organizations of various sectors as well as governments at different administrative levels. The aim of this study is to assess the influence of distance from urban areas on spectral indices used as proxies for physiological traits, vegetation disturbance, moisture conditions, and soil optical properties. To achieve this, spectral indices were derived and processed from Sentinel-2 satellite imagery using the Google Earth Engine platform in combination with desktop Geographic Information System (GIS) software, including ArcGIS Pro and QGIS. Descriptive and correlational statistical analyses were conducted to characterise the general features of the landscapes, while differences among distance classes were assessed using Analysis of Variance (ANOVA). Furthermore, graphical analyses were generated to illustrate the behaviour of the spectral indices along the distance gradient. The results demonstrate that the structural and functional characteristics of natural landscapes are significantly influenced by their proximity to urban areas, thereby raising new questions regarding effective conversation strategie.
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Abberton, M., Richard, C., y Batello, C. (Eds.). (2010). Grassland carbon sequestration: Management, policy and economics. Recuperado de https://www.fao.org/4/i1880e/i1880e.pdf
Alberto, J. A. (2006). El Chaco Oriental y sus fisonomías vegetales [The Eastern Chaco and its vegetable physiognomies]. Geográfica Digital, 3(5), 1–14. https://doi.org/10.30972/geo.352823
Alencar, A., Shimbo, J. Z., Lenti, F., Balzani Marques, C., Zimbres, B., Rosa, M.,Arruda, V., Castro, I., Ribeiro, J. P. F. M., Varela, V., Alencar, I., Piontekowski,V., Ribeiro, V., Bustamante, M. C., Sano, E. E., & Barroso, M. (2020). Mapping three decades of changes in the Brazilian savanna native vegetation using Landsat data processed in the Google Earth Engine platform. Remote Sensing, 12(6), 1-22.https://doi.org/10.3390/rs12060924
Almohamad, H., y Alshwesh, I. O. (2023). Evaluation of index-based methods for impervious surface mapping from Landsat-8 to cities in dry climates: A case study of Buraydah City, KSA. Sustainability, 15, 9704. https://doi.org/10.3390/su15129704
Barnes, E. M., Clarke, T. R., Richards, S. E., Colaizzi, P. D., Haberland, J., Kostrzewski, M., Waller, P., Choi, C., Riley, E., Thompson, T., Lascano, R. J., Li, H., y Moran, M. S. (2000). Coincident detection of crop water stress, nitrogen status and canopy density using ground-based multispectral data. Trabajo presentado en 5th International Conference on Precision Agriculture. Minneapolis, MN, United States.
Bannari, A., Morin, D., Bonn, F., y Huete, A. R. (1995). A review of vegetation indices. Remote Sensing Reviews, 13(1–2), 95–120. https://doi.org/10.1080/02757259509532298
Bond, W. J., y Parr, C. L. (2010). Beyond the forest edge: Ecology, diversity and conservation of the grassy biomes. Biological Conservation, 143(10), 2395–2404. https://doi.org/10.1016/j.biocon.2009.12.012
Campo-Bescós, M. A., Muñoz-Carpena, R., Southworth, J., Zhu, L., Waylen, P. R., y Bunting, E. (2013). Combined spatial and temporal effects of environmental controls on long-term monthly NDVI in the Southern Africa savanna. Remote Sensing, 5(12), 6513–6538. https://doi.org/10.3390/rs5126513
Cardozo, O. D. y Bondar, C.E. (2019). Estados de situación de coberturas y usos de la tierra en Latinoamérica. En G. Buzai (Comp). Teoría y Métodos de la Geografía Cuantitativa. Libro 2: por una Geografía Empírica (pp. 185-196). Luján, Argentina: Universidad Nacional de Luján.
Chuvieco, E. (2008). Teledetección ambiental: La observación de la Tierra desde el espacio. Barcelona: Ariel.
Daughtry, C. S. T., Walthall, C. L., Kim, M. S., De Colstoun, E. B., Y McMurtrey III, J. E. (2000). Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment, 74(2), 229–239. https://doi.org/10.1016/S0034-4257(00)00113-9
Da Silva, C. J., Insaurralde, J. A., y Cardozo, O. D. (2014). Cartografía de coberturas del suelo mediante sensores remotos, de la ciudad de Resistencia, Argentina (2013). Revista Geográfica Digital, 11(21). http://hum.unne.edu.ar/revistas/geoweb/Geo21/archivos/dasilva14.pdf
Da Silva, C. J., López, P. E., y Arias, F. C. (2022). Las inundaciones en las áreas urbanas: el caso del Área Metropolitana del Gran Resistencia. Revista de Geografía Norte Grande, (82), 333–354. https://www.redalyc.org/articulo.oa?id=30081515017
Da Silva, C. J. (2022). Zonificación del transporte en Resistencia (Chaco, Argentina) a partir de sistemas de información geográfica (Tesis doctoral). http://repositorio.unne.edu.ar/handle/123456789/51163
Delaney, B., Tansey, K., y Whelan, M. (2025). Satellite remote sensing techniques and limitations for identifying bare soil. Remote Sensing, 17, 630. https://doi.org/10.3390/rs17040630
Deng, Y., Wu, C., Li, M., y Chen, R. (2015). RNDSI: A ratio normalized difference soil index for remote sensing of urban/suburban environments. International Journal of Applied Earth Observation and Geoinformation, 39, 40–48. https://doi.org/10.1016/j.jag.2015.02.010
Escadafal, R., Belghith, A., Y Ben-Moussa, H. (1994). Indices spectraux pour la télédétection de la dégradation des milieux naturels en Tunisie aride. En Actes du 6ème Symposium International sur les Mesures Physiques et Signatures en Télédétection (pp. 253–259). Val d’Isère, Francia.
European Space Agency y Copernicus Data Space Ecosystem. (2025). Sentinel-2 | Copernicus Data Space Ecosystem. Copernicus Data Space Ecosystem. https://dataspace.copernicus.eu/data-collections/copernicus-sen-tinel-data/sentinel-2
Furlaneto Sampaio, A. C., Hoffmann, P. M., Gurski, E. M., Bizarro, O. R., Velazco, S. J. E., Bergmann Carlucci, M., Borgo, M., Monteiro Ferreira, M., Camargo Angelo, A., y Blum, C. T. (2025). Filling knowledge gaps: A methodological approach for the rapid investigation of species with restricted populations. Perspectives in Ecology and Conservation, 23(2), 130–140. https://doi.org/10.1016/j.pecon.2025.04.003
García, R., González, A., y Benavidez-Silva, C. (2023). Análisis espacio-temporal del Índice de Vegetación de Diferencia Normalizado (NDVI) en el ecosistema bosque semideciduo piemontano del Catamayo Alamor. CEDAMAZ, 13(2), 140-147. https://doi.org/10.54753/cedamaz.v13i2.2053
Gcayi, S. R., Adelabu, S. A., Nduku, L., y Chirima, J. G. (2024). A bibliometric analysis for remote sensing applications in bush encroachment mapping of grassland and savanna ecosystems. Applied Geomatics, 16, 881–896. https://doi.org/10.1007/s12518-024-00589-0
Ginzburg, R., Adámoli, J., Herrera, P., y Torrella, S. (2005). Los humedales del Chaco: Clasificación, inventario y mapeo a escala regional. Miscelánea, 14, 121–138. http://www.insugeo.org.ar/publicaciones/docs/misc-14-11.pdf
Gutiérrez-Castorena, E. V., Silva-Núñez, J. A., Gaytán-Martínez, F. D., Encinia-Uribe, V. V., Ramírez-Gómez, G. A., y Olivares-Sáenz, E. (2025). Evaluation of statistical models of NDVI and agronomic variables in a protected agriculture system. Horticulturae, 11(2), 131 https://doi.org/10.3390/horticulturae11020131
Helfenstein, I. S., Schneider, F. D., Schaepman, M. E., y Morsdorf, F. (2022). Assessing biodiversity from space: Impact of spatial and spectral resolution on trait-based functional diversity. Remote Sensing of Environment, 275, 113024. https://doi.org/10.1016/j.rse.2022.113024
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309.
Jan, S. L., y Shieh, G. (2014). Sample size determinations for Welch’s test in one-way heteroscedastic ANOVA. British Journal of Mathematical and Statistical Psychology, 67(1), 72–93. https://doi.org/10.1111/bmsp.12006
Key, C. H., y Benson, N. C. (2006). Landscape assessment: Ground measure of severity, the Composite Burn Index; and remote sensing of severity, the Normalized Burn Ratio (Gen. Tech. Rep. RMRS-GTR-164-CD: LA1–LA51). Ogden: USDA Forest Service, Rocky Mountain Research Station.
Lehmann, C. E. R., Anderson, T. M., Sankaran, M., Higgins, S. I., Archibald, S., Hoffmann, W. A., Hanan, N. P., Williams, R. J., Fensham, R. J., Felfili, J., Hutley, L. B., Ratnam, J., San Jose, J., Montes, R., Franklin, D., Russell-Smith, J., Ryan, C. M., Durigan, G., Hiernaux, P., Haidar, R., Bowman, D. M. J. S., y Bond, W. J. (2014). Savanna vegetation-fire-climate relationships differ among continents. Science, 343(6170), 548–552. https://doi.org/10.1126/science.1247355
Lewis, K., de V. Barros, F., Cure, M. B., et al. (2022). Mapping native and non-native vegetation in the Brazilian Cerrado using freely available satellite products. Scientific Reports, 12, 1588. https://doi.org/10.1038/s41598-022-05332-6
Li, G., Fang, C., Li, Y., Wang, Z., Sun, S., He, S., Qi, W., Bao, C., Ma, H., Fan, Y., Feng, Y., y Liu, X. (2022). Global impacts of future urban expansion on terrestrial vertebrate diversity. Nature Communications, 13, 1628. https://doi.org/10.1038/s41467-022-29324-2
López, P. E. (2025). Cambios en las coberturas de tierra y sus impactos en la temperatura del departamento Comandante Fernández (Chaco, Argentina). Huellas, 29(2), 153–161. https://doi.org/10.19137/huellas-2025-2919
Mc Donald, R. I., Forman, R. T. T., Kareiva, P., Neugarten, R., Salzer, D., y Fisher, J. (2009). Urban effects, distance, and protected areas in an urbanizing world. Landscape and Urban Planning, 93(1), 63–75. https://doi.org/10.1016/j.landurbplan.2009.06.002
Montgomery, D. C. (2012). Design and analysis of experiments (8th ed.). Wiley.
Morello, J., y Adámoli, J. (1974). Fitogeografía del Chaco: Consideraciones sobre su vegetación y clasificación. Buenos Aires: INTA.
Morcillo-Pallarés, P., Rivera-Caicedo, J. P., Belda, S., De Grave, C., Burriel, H., Moreno, J., y Verrelst, J. (2019). Quantifying the robustness of vegetation indices through global sensitivity analysis of homogeneous and forest leaf-canopy radiative transfer models. Remote Sensing, 11(20), 2418. https://doi.org/10.3390/rs11202418
Organización de las Naciones Unidas [ONU]. (2023). Biodiversidad. Recuperado el 10 de octubre, de 2025 de https://www.un.org/sustainabledevelopment/es/biodiversity/
Pillar, V. D., y Overbeck, G. E. (2025). Nature conservation policies are biased toward forests and neglect grassy ecosystems worldwide. Science, 388(6747), eadx7441. https://doi.org/10.1126/science.adx7441
Pontius, J., Schaberg, P. G., y Hanavan, R. P. (2020). Remote sensing for early, detailed, and accurate detection of forest disturbance and decline for protection of biodiversity. En J. Cavender-Bares, J. A. Gamon, & P. A. Townsend (Eds.), Remote sensing of plant biodiversity (pp. 121–154). Cham: Springer. https://doi.org/10.1007/978-3-030-33157-3_6
Ramírez, L., y Pértile, V. (2013). Cambio de uso de suelo y tendencias de la expansión urbana entre 1990 y 2030 en Juan José Castelli y Villa Ángela, Chaco, Argentina. Geografía y Sistemas de Información Geográfica (GEOSIG), 5, 194–216. https://docs.wixstatic.com/ugd/79758e_86dffc357db74c17b170acd58bc-1d69d.pdf
Ren, Q., He, C., Huang, Q., Zhang, D., Shi, P., y Lu, W. (2023). Impacts of global urban expansion on natural habitats undermine the 2050 vision for biodiversity. Resources, Conservation and Recycling, 190, 106834. https://doi.org/10.1016/j.resconrec.2022.106834
Rikimaru, A., Roy, P. S., & Miyatake, S. (2002). Tropical forest cover density mapping (Bare Soil Index reference). Tropical Ecology, 43(1), 39–47.
Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. Proceedings of the Third Earth Resources Technology Satellite-1 Symposium, 1, 309–317.
Servicio Meteorológico Nacional. (2025). Valores climatológicos medios 1991–2020 (Resistencia Aero) [Gráfico]. https://www.smn.gob.ar/estadisticas
Seto, K. C., Güneralp, B., y Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences of the United States of America, 109(40), 16083–16088. https://doi.org/10.1073/pnas.1211658109
Simkin, R. D., Seto, K. C., McDonald, R. I., y Jetz, W. (2022). Biodiversity impacts and conservation implications of urban land expansion projected to 2050. Proceedings of the National Academy of Sciences of the United States of America, 119(12), e2117297119. https://doi.org/10.1073/pnas.2117297119
Silveira, E. M. D. O., Mello, J. M. D., Acerbi, F. W., Reis, A. A. D., Withey, K. D., y Ruiz, L. A. (2017). Characterizing landscape spatial heterogeneity using semivariogram parameters derived from NDVI images. Cerne, 23(4), 413-422. https://doi.org/10.1590/01047760201723042370
Strahler, A. N., y Strahler, A. H. (2005). Geografía física (3.ª ed., reimpresión). Barcelona: Omega.
Tobler, W. R. (1972). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46(2), 234–240. https://doi.org/10.2307/143141
Tran, T. V., Reef, R., y Zhu, X. (2022). A review of spectral indices for mangrove remote sensing. Remote Sensing, 14, 4868. https://doi.org/10.3390/rs14194868
United Cities and Local Governments. (2018). Intermediary cities. Recuperado el 8 de octubre, de 2025 de https://www.uclg.org/en/agenda/intermediary-cities.
U.S. Geological Survey. (2025). Landsat missions. Recuperado el 10 de agosto de 2025, de https://www.usgs.gov/landsat-missions
Verrelst, J., Schaepman, M. E., Koetz, B., y Kneubühler, M. (2008). Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data.Remote Sensing of Environment, 112(5), 2341–2353. https://doi.org/10.1016/j.rse.2007.11.001
Wachiye, S., Pellikka, P., Rinne, J., Heiskanen, J., Abwanda, S., y Merbold, L. (2022). Effects of livestock and wildlife grazing intensity on soil carbon dioxide flux in the savanna grassland of Kenya. Agriculture, Ecosystems & Environment, 325, 107713. https://doi.org/10.1016/j.agee.2022.107713
Xu, H. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27(14), 3025–3033. https://doi.org/10.1080/01431160600589179
Zoungrana, B. J.-B., y Dimobé, K. (2023). NDVI-derived vegetation trends and driving factors in West African Sudanian Savanna. American Journal of Plant Sciences, 14, 1130–1145. https://doi.org/10.4236/ajps.2023.1410077
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