Teledetección aplicada al análisis de los impactos ecosistémicos generados por los incendios del año 2020 en la provincia de Córdoba, Argentina, e identificación de áreas prioritarias de restauración

Autores/as

  • Hebert Castillo Ordenamiento Territorial, Conservación Internacional, Perú https://orcid.org/0000-0002-7421-8438
  • Pablo Baldassini Departamento de Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, Universidad de Buenos Aires, Argentina. LART-IFEVA, Universidad de Buenos Aires, CONICET, Facultad de Agronomia, Buenos Aires, Argentina. Instituto Nacional de Investigación Agropecuaria, INIA La Estanzuela, Colonia, Uruguay., Argentina https://orcid.org/0000-0002-9741-604X

DOI:

https://doi.org/10.14198/INGEO.23754

Palabras clave:

funcionamiento ecosistémico, IVN, NBR, severidad del incendio, análisis multicriterio, áreas prioritarias para restauración, Córdoba

Resumen

El fuego es uno de los eventos destructivos más frecuentes y el principal causante de alteraciones en el funcionamiento de los ecosistemas. Los objetivos de este trabajo fueron: 1) identificar el área afectada por incendios ocurridos en la provincia de Córdoba, Argentina, entre agosto y septiembre de 2020, 2) analizar su impacto sobre cuatro variables ecosistémicas: la productividad primaria neta aérea (PPNA), la evapotranspiración (ET), el albedo y la temperatura superficial (LST), y 3) identificar áreas prioritarias para su restauración mediante un análisis multicriterio. Se usaron imágenes Sentinel-2 y productos MODIS utilizando la plataforma Google Earth Engine. El fuego afectó 109.307 hectáreas, de las cuales el 40% presentaron una severidad moderada a alta. La ET fue la variable más afectada, disminuyendo hasta un 24,8% respecto a la situación promedio prefuego. La LST y la PPNA mostraron un impacto moderado, registrando cambios extremos de 10,2% y 10,6%, respectivamente. Se identificaron cuatro áreas prioritarias para restauración. Las áreas con baja prioridad representaron el 50% del área total quemada, mientras que las áreas con alta prioridad representaron solo el 7%. Este trabajo permitió generar información de gran importancia para la implementación de políticas públicas destinadas a garantizar la sostenibilidad de estos ecosistemas.

Financiación

Universidad de Buenos Aires, CONICET, Conservación Internacional

Descargas

Los datos de descargas todavía no están disponibles.

Citas

Alcaraz-Segura, D., Cabello, J., Paruelo, J. M., & Delibes, M. (2009). Use of descriptors of ecosystems functioning for monitoring a national park network: a remote sensing approach. Environmental Management, (43), 38-48. https://doi.org/10.1007/s00267-008-9154-y

Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop evapotranspiration-guidelines for computing crop water requirements. FAO irrigation and drainage paper no. 56. FAO, Rome. http://www.avwatermaster.org/filingdocs/195/70653/172618e_5xAGWAx8.pdf

Argañaraz, J. P., Pizarro, G. G., Zak, M., & Bellis, L. M. (2015a). Fire regime, climate, and vegetation in the Sierras de Córdoba, Argentina. Fire Ecology, (11), 55-73. https://doi.org/10.4996/fireecology.1101055

Argañaraz, J. P., Pizarro, G. G., Zak, M., Landi, M. A., & Bellis, L. M. (2015b). Human and biophysical drivers of fires in Semiarid Chaco mountains of Central Argentina. Science of the Total Environment, (550), 1-12. http://dx.doi.org/10.1016/j.scitotenv.2015.02.081

Amani, M., Member, Ghorbanian, A., Ahmadi, S. A., Kakooei, A. M., Mirmazloumi, S. M, Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (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), 1-25. https://doi.org/10.1109/JSTARS.2020.3021052

Axel, A. C. (2018). Burned area mapping of an escape fire into tropical dry forest in western Madagascar using milti-season Landsat OLI data. Remote Sensing, (10), 1-17. https://doi.org/10.3390/rs10030371

Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., & Grammalidis, N. (2020). A review on early forest fire detection systems using optical remote sensing. Sensors, (20), 6442. https://doi.org/10.3390/s20226442.

Barnes, W., Xiong, X., Guenther, B., & Salomonson, V. (2003). Develpment, characterization, and performance of the EOS MODIS sensors. Proceedings of SPIE, (5151), 337-345. https://doi.org/10.1117/12.504818

Beringer, J., Hutley, L. B., Tapper, N. J., Coutts, A., Kerley, A., & Grady, A. P. O. (2003). Fire impacts on surface heat, moisture, and carbon fluxes from tropical savanna in northern Australia. International Journal of Wildland Fire, (12), 333-340. https://doi.org/10.1071/WF03023

Buchhorn, M., Lesiv, M., Tsendbazar, N. E., Herold, M., Bertels, L., & Smets, B. (2020). Copernicus global land cover layers – Collection 2. Remote Sensing, (12), 1-14. https://doi.org/10.3390/rs12061044

Cabello, J., Fernández, N., Alcaraz-Segura, D., Oyonarte, C., Piñeiro, G., Altesor, A., Delibes, M., & Paruelo, J. M. (2012). The ecosystem functioning dimension in conservation: insights from remote sensing. Biodiversity and Conservation, (21), 3287-3305. https://doi.org/10.1007/s10531-012-0370-7

Cabido, M., Breimer, R., & Vega, G. (1987). Plant communities and associeted soil tipes in a High Plateau of a Cordoba mountains, central Argentina. Mountain Research and Development, (7), 25-42. https://www.jstor.org/stable/3673322

Cabido, M., Zeballos, S. R., Zak, M., Carranza, M. L., Giorgis, M. A., Cantero, J. J., & Acosta, A. T. R. (2018). Native woody vegetation in central Argentina: Classification of Chaco and Espinal forests. Appl Veg Sci., (21), 298-311. https://doi.org/10.1111/avsc.12369

Carranza, M. L., Hoyos, L., Frate, L., Acosta, A. T. R., & Cabido, M. (2015). Measuring forest fragmentation using multitemporal forest cover maps: Forest loss and spatial pattern analysis in the Gran Chaco central Argentina. Landscape and Urban Planning, (142), 238-247. http://dx.doi.org/10.1016/j.landurbplan.2015.08.006

Casey, K., Polashenki, C. M., Chen, J., & Tedesco, M. (2017). Impact of MODIS sensor calibration updates on Greenland ice sheet surface reflectance and albedo trends. The Cryosphere Discussions, (38), 1-24. https://doi.org/10.5194/tc-2017-38

Charlson, R. J., Ackerman, A. S., Bender, F. A-M., Anderson, T. L., & Liu, Z. (2007). On the climate forcing consequences of the albedo continuum between cloudy and clear air. Tellus, (59), 715-727. https://doi.org/10.1111/j.1600-0889.2007.00297.x

Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M., & Olivia, P. (2020). Satellite remote sensing contributions to wildland fire science and managment. Current Forestry Reports, (6), 81-96. https://doi.org/10.1007/s40725-020-00116-5

Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J-C., Skakun, S. V., & Justice, C. (2018). The harmonize Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, (219), 145-161. https://doi.org/10.1016/j.rse.2018.09.002

Courault, D., Seguin, B., & Olioso, A. (2005). Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modelling approaches. Irrigation and Drainage Systems, (19), 223-249. https://doi.org/10.1007/s10795-005-5186-0

Costa, M. B., De Menezes, L. F. T., & Nascimento, M. T. (2017). Post-fire regeneration in seasonally dry tropical forest fragments in southeastern Brasil. Anais da Academia Brasileira de Ciências, (89), 2687-2695. http://dx.doi.org/10.1590/0001-3765201720160728

De Andrade, M. D., Delgado, R. C., da Costa de Menezes, S. J. M., Rodrigues, R. A., Teodoro, P. E., Junior, C. A. J., & Pereira, M. G. (2021). Evaluation of the MOD11A2 product for canopy temperature monitoring in the Brazilian Atlantic Forest. Environmental Monitoring Assessment, 193(45), 1-20. https://doi.org/10.1007/s10661-020-08788-z

De Santis, A., & Chuvieco, E. (2007). Burn Severity estimation from remotely sensed data: performance of simulation versus empirical models. Remote Sensing of Environment, (108), 422-435. https://doi.org/10.1016/j.rse.2006.11.022

Di Bella, C. M., Paruelo, J. M., Becerra, J. E., Bacour, C., & Baret, F. (2004). Effect of senescent leaves on NDVI-based estimates of fAPAR: Experimental and modelling evidences. International Journal of Remote Sensing, 25(33), 5415-55427. http://dx.doi.org/10.1080/01431160412331269724

Di Bella, C. M., Jobbágy, E. G., Paruelo, J. M., & Pinnok, S. (2006). Continental fire density patterns in South America. Global Ecology and Biogeography, (15), 192-199. https://doi.org/10.1111/j.1466-822X.2006.00225.x

Di Bella, C. M., Fischer, M. A., & Jobbágy, E. G. (2011). Fire patterns in north-eastern Argentina: influences of climate and land use/cover. International Journal of Remote Sensing, (32), 4961-4971. http://dx.doi.org/10.1080/01431161.2010.494167

Didan, K., & Munoz, A. B. (2015). MODIS Vegetation Index User’s Guide (MODIS13 Series). Vegetation Index and Phenology Lab, 3.10, 1-33. Enlace: https://vip.arizona.edu/documents/MODIS/MODIS_VI_UsersGuide_June_2015_C6.pdf

Doxsey-Whitefield, E., MacManus, K., Adamo, S. B., Pistolesi, J. S., Olena, B., & Baptista, S. R. (2015). Taking advantage of the improved availability of census data: a first look at the gridded population of the world, versión 4. Papers in Applied Geography, (1), 226-234. http://dx.doi.org/10.1080/23754931.2015.1014272

Epting, J., Verbyla, D., & Sorbel, B. (2005). Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM+. Remote Sensing of Environment, (96), 328-339. https://doi.org/10.1016/j.rse.2005.03.002

Escuin, S., Navarro, R., & Fernández, P. (2008). Fire severity assessment by using NBR (Normalize Burn Ratio) and NDVI (Normalize Difference Vegetation Index) derived from LANDSAT TM/ETM images. International Jounal of Remote Sensing, (29), 1053-1073. http://dx.doi.org/10.1080/01431160701281072

Esmail, B. A., & Geneletti, D. (2018). Multi-criteria decision analysis for nature conservation: A review of 20 years of applications. Methods in Ecology and Evolution, (9), 42-53. https://doi.org/10.1111/2041-210X.12899

Espelta, J. M., Retana, J., & Habrouk, A. (2003). An economic and ecological multi-criteria evaluation of reforestation methods to recover burned Pinus nigra forests in NE Spain. Forest Ecology and Management, (180), 185-198. https://doi.org/10.1016/S0378-1127(02)00599-6

Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D, Shaffer, S., Shimada, J., Umland, J., Werner, M., M., Oskin, M., Burbank, D., & Alsdorf, D. (2007). The shuttle radar topography mission. Reviews of Geophysics (45), 1-33. https://doi.org/10.1029/2005RG000183

Fernández-Buces, N., Siebe, C., Cram, S., & Palacio, J. L. (2006). Mapping soil salinity using a combined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico. Journal of Arid Environments, (65), 644-667. https://doi.org/10.1016/j.jaridenv.2005.08.005

Gale, M. G., Cary, G. J., Van Dijk, A. I. J. M., & Yebra, M. (2021). Forest fire fuel through the lens of remote sensing: Review of approaches, challenges and future directions in the remote sensing of biotic determinants of fire behavior. Remote Sensing of Environment, (255), 112282. https://doi.org/10.1016/j.rse.2020.112282

Gorelick, N., Hancher, M., Dixonb, M., Ilyushchenko, S., Thaub, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 1-10. http://dx.doi.org/10.1016/j.rse.2017.06.031

Gould K. A., Fredericksen, T. S., Morales, F., Kennard, D., Putz, F. E., Mostacedo, B., & Toledo, M. (2002). Post-fire tree regeneration in lowland Bolivia: implication for fire management. Forest Ecology and Management, (165), 225-234. https://doi.org/10.1016/S0378-1127(01)00620-X.

Grandis, C. G., Brandi, C. G., Picciani, A. L., & Finola, A. 2014. Análisis de la amenaza antrópica como componente del riesgo ambiental: estudio de las variaciones sufridas en la vegetación en un área de las Sierras de Comechingones afectada por incendios forestales, Achiras, prov. de Córdoba. Revista de Investigación de la Facultad de Ciencias Humanas, (9), 125-145. http://hdl.handle.net/11336/34484

Groves, C. R., Jensen, D. B., Valutis, L. L., Redford, K. H., Shaffer, M. L., Scott, J. M., Baumgartner, J. V., Higgins, J. V., Beck, M. W., & Anderson, M. G. (2002). Planning for biodiversity conservation: Putting conservation science into practice. BioScience, (52), 499-512. https://doi.org/10.1641/0006-3568(2002)052[0499:PFBCPC]2.0.CO;2

Hall, R. J., Freeburn, J. T., de Groot, W. J., Pritchard, J. M., Lynham, T. J., & Landry, R. (2008). Remote sensing of burn severity: experience from western Canada boreal fires. International Journal of Wildland Fire, (17), 476-489. https://doi.org/10.1071/WF08013

Hesslerová, P., Pokorný, J., Brom, J., & Rejˇsková–Procházková, A. (2013). Daily dynamics of radiation surface temperature of different land cover types in a temperate cultural landscape: Consequences for the local climate. Ecological Engineering, (54), 145-154. http://dx.doi.org/10.1016/j.ecoleng.2013.01.036

Houspanossian, J., Giménez, R., Jobbágy, E., & Nosetto, M. 2017. Surface albedo raise in the South American Chaco: combined effects of deforestation and agricultural changes. Agricultural and Forest Meteorology, (232), 118-127. https://doi.org/10.1016/j.agrformet.2016.08.015

Houspanossian, J., Nosetto, M., & Jobbágy, E. (2013). Radiation Budget changes with dry forest clearing in temperate Argentina. Global Change Biology, (19), 1211-1222. https://doi.org/10.1111/gcb.12121

Hughes, L. (2000). Biological consequences of global warming: is the signal already apparent? Trends in Ecology and Evolution, (15), 1-6. https://doi.org/10.1016/S0169-5347(99)01764-4

Hutchison, K. D. (2003). Applications of MODIS satellite data and products for monitoring air quality in the state of Texas. Atmospheric Environment, (37), 2403-2412. https://doi.org/10.1016/S1352-2310(03)00128-6

Jaimes, N. B. P., Sendra, J. B., Delgado, M. G., Plata, R. F., Némiga, X. A., & Solís, L. R. M. (2012). Determination of optimal zones for forest plantations in the State of Mexico using multi-criteria spatial analysis and GIS. Journal of Geographic Information System, (4), 204-218. http://dx.doi.org/10.4236/jgis.2012.43025

Jaureguiberry, P., Argañaraz, J. P., & Giorgis, M. A. (2021). Incendios en la provincial de Córdoba: La urgencia de un aborgaje integral. Revista de Comunicación de las Ciencias de la Tierra (5), 2618-2122. https://www.researchgate.net/publication/353558283_Incendios_en_la_Provincia_de_Cordoba_la_urgencia_de_un_abordaje_integral

Justice, C. O., Townshend, J. R. G., Vermote, E. F., Masuoka, E., Wolfe, R. E., Saleous, N., Roy, D. P., & Morisette, J. T. (2002). An overview of MODIS Land data processing and product status. Remote Sensing of Environment (83), 3-15. https://doi.org/10.1016/S0034-4257(02)00084-6

Key, C. H., & Benson, N. (1999). Measuring and remote sensing of burn severity: the CBI and NBR. Proceedings Joint Fire Science Conference and Workshop, (2), 1. https://www.researchgate.net/publication/241687936

Kokaly, F. R., Barnaby, W. R., Sandra, L. H., & King, T. V. V. (2007). Characterization of post-fire Surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing. Remote Sensing of Environment, (106), 305-325. https://doi.org/10.1016/j.rse.2006.08.006

Krishnan, P., Meyers, T. P., Scott, R. L., Kennedy, L., & Heuer, M. (2012). Energy exchange and evapotranspiration over two temperate semi-arid grasslands in North America. Agricultural and Forest Meteorology, (153), 31-44. https://doi.org/10.1016/j.agrformet.2011.09.017

Lentile, L. B., Holden, Z. A., Smith, A. M. S., Falkowski, M. J., Hudak, A. T., Morgan, P., Lewis, S. A., Gessler, P. E., & Benson, N. C. (2006). Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, (15), 319-345. https://doi.org/10.1071/WF05097

Li, M., Liew, S. C., & Kwoh. (2004). Automated production of cloud-free and cloud-shadow-free image mosaics from cloudy satellite imagery. Centre of Remote Imaging, Sensing and Processing, National University of Singapore, 1-5. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.184.3612&rep=rep1&type=pdf

Li, Z. L., Tang, R., Wang, Z., Bi, Y., Zhou, C., Tang, B., Yan, G., & Zhang, X. (2009). A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors (9), 3801-3853. https://doi.org/10.3390/s90503801

Lo Seen Chong, D., Mougin, E., & Gastellu-Etchegorry, J. P. (1993). Relating the global vegetation index to net primary productivity and actual evapotranspiration over Africa. International Journal of Remote Sensing, 14(8), 1517-1546. http://dx.doi.org/10.1080/01431169308953984

Lopes, T. R., Moura, L. B., Nasimento, J. G., Junior, L. S. F., Zolin, C. A., Duarte, S. N., Folegatti, M. & V., Santos, O. N. A. (2020). Priority areas for forest restoration aiming at the maintenance of water resources in a basin int the Cerrado/Amazon ecotone, Brasil. Journal of South American Earth Sciences, (101), 102630. https://doi.org/10.1016/j.jsames.2020.102630

Lopresti, M. F., Di Bella, C. M., & Degioanni, A. J. (2015). Relationship between MODIS-NDVI data and wheat yield: a case study in Northern Buenos Aires province, Argentina. Information Procesing in Agriculture S2214-3173(15)00027-X. https://doi.org/10.1016/j.inpa.2015.06.001

Ma, Q., Bales, R. C., Rungee, J., Conklin, M. H., Collins, B. M., & Goulden, M. L. (2020). Wildfire controls on evapotranspiration in California’s Sierra Nevada. Journal of Hydrology, (590), 125364. https://doi.org/10.1016/j.jhydrol.2020.125364

MAPBIOMAS. (2022). MapBiomas general “Handbook”: Algorithm Theoretical Basis Document (ATDB). Cellection 6, vsion 1.0 https://mapbiomas-br-site.s3.amazonaws.com/Metodologia/ATBD_Collection_6_v1_January_2022.pdf

Mari, N. A., Ahumada, M., & Pons, D. (2021). Incendios en la provincial de Córdoba: año 2020. Proyecto: Prevención y evaluación de la emergencia y Desastre Agropecuario – componente 1.6.2.3.PE.I064. https://repositorio.inta.gob.ar/handle/20.500.12123/9591

Mateos, A. C., Amarillo, A. C., Busso, T., & Carreras, H. A. (2019). Influence of meteorological variables and forest fires events on air quality in an urban area (Córdoba, Argentina). Archives of Environmental Contamination and Toxicology, (77), 171-179. https://doi.org/10.1007/s00244-019-00618-9

Mendoza, G. A., & Martins, H. (2006). Multi-criteria decision analysis in natural resource management: A critical review of methods and new modelling paradigms. Forest Ecology and Management, (230), 1-22. https://doi.org/10.1016/j.foreco.2006.03.023

McNaughton, S., Oesterheld, M., Frank, D. A., & Williams, K. J. (1989). Ecosystem-level patterns of primary productivity and herbivory in terrestrial habitats. Nature (341), 142–144. https://doi.org/10.1038/341142a0

Miller, J. D., & Thode, A. E. (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, (109), 66-80. https://doi.org/10.1016/j.rse.2006.12.006

Mitri, G. H., & Gitas, I. Z. (2013). Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery. International Journal of Applied Eath Observation and Geoinformation, (20), 60-66. https://doi.org/10.1016/j.jag.2011.09.001

Mu, Q., Heinsch, F. A., Zhao, M., & Running, S. W. (2013). Development of a global evapotranspiration based on MODIS and global meteorology data. Remote Sensing of Environment, (111), 519-536. https://doi.org/10.1016/j.rse.2007.04.015

Nolan, R. H., Lane, P. N. J., Benyon, R. G., Bradstock, R. A., & Mitchell, P. J. (2014). Changes in evapotranspiration following wildfire in resprouting eucalypt forests. Ecohydroly, (7), 1363-1377. https://doi.org/10.1002/eco.1463

Nosetto, M. D, Jobbágy, E. G., Brizuela, A. B., & Jackson, R. B. (2012). The hydrologic consequences of land cover change in central Argentina. Agriculture, Ecosystems and Environment, (154), 2-11. https://doi.org/10.1016/j.agee.2011.01.008

Nosetto, M. D, Toledo, E. L., Magliano, P. N., Figuerola, P., Blanco, L. J., & Jobbágy, E. G. (2020). Constrasting CO2 and water vapour fluxes in dry forest and pasture sites of Central Argentina. Ecohydrology, (13), 1-15. https://doi.org/10.1002/eco.2244

Noy-Meir, I., Mascó, M., Giorgis, M. A., Gurvich, D. E., Perazzolo, D., & Ruiz, G. (2012). Estructura y diversidad de dos fragmentos del bosque de Espinal en Córdoba, un ecosistema amenazado. Bol. Soc. Argent. Bot., 47(1-2), 119-133.

Orsi, F., & Geneletti, D. (2010). Identifying priority areas for forest Landscape Restoration in Chiapas (Mexico): An operational approach combining ecological and socioeconomic criteria. Landscape and Urban Planing, (94), 20-30. https://doi.org/10.1016/j.landurbplan.2009.07.014

Paruelo, J. M. (2008). La caracterización funcional de ecosistemas mediante sensores remotos. Ecosistemas, 17(3), 4-22. https://www.revistaecosistemas.net/index.php/ecosistemas/article/view/83

Pausas, J. G., & Keeley, J. E. (2014). Evolutionary ecology of resprouting and seeding in fire-prone ecosystems. New Phytologist, (204), 55-65. https://doi.org/10.1111/nph.12921

Pérez-Luque, A. J., Pérez-Pérez, R., Bonet-García, F. J., & Magaña, P. J. (2014). An ontological system based on MODIS images to assess ecosystem functioning of Natura 2000 habitats: A case study for Quercus pyrenaica forests. International Journal of Applied Earth Observation and Geoinformation, (37), 142-151. https://doi.org/10.1016/j.jag.2014.09.003

Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J-M., Turker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. TRENDS in Ecology and Evolution, (20), 503-510. https://doi:10.1016/j.tree.2005.05.011

Poon, P. K., & Kinoshita, A. M. (2018a). Spatial and temporal evapotranspiration trends after wildfire in semi-arid landscapes. Journal of Hydrology, (559), 71-83. https://doi.org/10.1016/j.jhydrol.2018.02.023

Poon, P. K., & Kinoshita, A. M. (2018b). Estimating evapotranspiration in a post-fire environment using remote sensing and machine learning. Remote Sensing, 10(1728), 1-15. https://doi:10.3390/rs10111728

Remelgado, R., Leutner, B., Safi, K., Sonnenschein, R., Kuebert, C., & Wegmann, M. (2018). Linking animal movement and remote sensing-mapping resource suitability from a remote sensing perspective. Remote Sensing in Ecology and Conservation, 4(3), 211-224. https://doi.org/10.1002/rse2.70

Roche, J. W., Ma, Q., Rungee, J., & Bales, R. C. (2020). Evapotranspiration mapping for forest management in California’s Sierra Nevada. Frontiers in Forest and Global Change, (3), 1-14. https://doi.org/10.3389/ffgc.2020.00069

Rodriguez, J. M., Estrabou, C., Fenoglio, R., Robbiati, F., Salas, M. C., & Quiroga, G. (2009). Recuperación post-fuego de la comunidad de líquenes epífitos en la provincia de Córdoba, Argentina. Acta bot. bras., (23), 854-859. https://doi.org/10.1109/LGRS.2005.858485

Roy, D. P., Boschetti, L., & Trigg S. N. (2006). Remote sensing of fire severity: assessing the performance of normalized burn ratio. IEEE Geoscience and Remote Sensing Letters, (3), 112-116. https://doi.org/10.1109/LGRS.2005.858485

Running, S. W., Mu, Q., Zhao, M., & Moreno, A. (2017). User’s guide MODIS Global Terrestrial Evapotranspiration (ET) Product (NASA MOD13A2/A3). NASA Earth Observing System MODIS Land Algorithm, (1.5), 1-34. https://landweb.modaps.eosdis.nasa.gov/QA_WWW/forPage/user_guide/MOD16UsersGuide2016V1.52017May23.pdf

Sun, C., Beirne, C., Burgar, J. M., Howey, T., Fisher, J., & Burton, C. (2021). Simultaneously monitoring of vegetation dynamics and wildlife activity with camara traps to assess habitat change. Remote Sensing in Ecology and Conservation, 7(4), 666-684. https://doi.org/10.1002/rse2.222

Schwaiger, H. P., & Bird, D. N. (2010). Integration of albedo effects caused by land use change into the climate balance: Should we still account in greenhouse gas units? Forest Ecology and Management, (260), 278-286. https://doi.org/10.1016/j.foreco.2009.12.002

Sun, Z., Wang, Q., Batkhishig, O., & Ouyang, Z. (2016). Relationship between evapotranspiration and land surface temperature under energy-and water limited conditions in dry and cold climates. Advances in Meteorology http://dx.doi.org/10.1155/2016/1835487

Szilagyi, J., Rundquist, D. C., & Gosselin, D. C. (1998). NDVI relationship to monthly evaporation. Geophysical Research Letters, (25), 1753-1756. https://doi.org/10.1029/98GL01176

Szpakowski, D. M., & Jensen, J. L. R. (2019). A review of the applications of remote sensing in fire ecology. Remote Sensing, (11), 2638. https://doi.org/10.3390/rs11222638

Tian, Y., Dickinson, R. E., Zhou, L., Myneni, R. B., Friedl, M., Schaaf, C. B., Carroll, M., & Gao, F. (2004). Land boundary conditions from MODIS data and consequences for the albedo of a climate model. Geophysical Research Letters, (31), L05504. https://doi.org/10.1029/2003GL019104

Torres, R. C., Giorgis, M. A., Trillo, C., Volkmann, L., Demaio, P., Heredia, J., & Renison, D. (2013). Post-fire recovery occurs overwhelmingly by resprouting in the Serrano forest of Central Argentina. Austral Ecology, (39), 346-354. https://doi.org/10.1111/aec.12084

Uribe, D., Geneletti, D., del Castillo, R. F., & Orsi, F. (2014). Integrating stakeholder preferences and GIS-based multicriteria análisis to identify forest landscape restoration priorities. Sustainability, (6), 935-951. https://doi.org/10.3390/su6020935

Valente, R. A., Petean, F. C. S., & Vettorazzi, C. A. (2017). Multicriteria decision análisis for prioritizing areas for forest restoration. CERNE (23), 53-60. https://doi.org/10.1590/01047760201723012258

Valente, R. A., de Mello, K., Metedieri, J. F., & Américo, C. (2021). A multicriteria evaluation approach to set forest restoration priorities base don wáter ecosystem services. Journal of Environmental Management, (285), 112049. https://doi.org/10.1016/j.jenvman.2021.112049

Veraverbeke, S., Lhermitte, S., Verstraeten, W. W., & Goossens, R. (2011). Evaluation of pre/post-fire differenced spectral indices for assessing burn severity in a Mediterranean environment with Landsat Thematic Mapper. International Journal of Remote Sensing, 32(12), 3521-3537. https://doi.org/10.1080/01431161003752430

Veraverbeke, S., Verstraeten, W. W., Lhermitte, Stefaan., Van De Kerchove, R., & Goosens, R. (2012). Assessment of post-fire changes in land surface temperature and surface albedo, and their relation with fire-burn severity using multitemporal MODIS imagery. International Journal of Wildland fire, (21), 243-256. http://dx.doi.org/10.1071/WF10075

Verzino, G., Joseau, J., Dorado, M., Gellert, E., Reartes, S. R., & Nóbile, R. (2005). Impacto de los incendios sobre la diversidad vegetal, Sierras de Córdoba, Argentina. Ecología Aplicada (4), 25-34. https://doi.org/10.21704/rea.v4i1-2.294

Wan, Z. (2013). Collection-6 MODIS land surface temperature products user’s guide. ERI, University of California, Santa Bárbara. https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf

Wang, Z., Schaaf, C. B., Sun, Q., Shuai, Y., & Román, M. (2018). Capturin rapid land Surface dynamics with collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products. Remote Sensing of Environment, (207), 50-64. https://doi.org/10.1016/j.rse.2018.02.001

Xiong, X., Chiang, K., Sun, J., Barnes, W. L., Guenther, B., & Salomonson V. V. (2009). NASA EOS Terra and Aqua MODIS on-orbit performance. Advances in Space Research, (43), 413-422. https://doi.org/doi:10.1016/j.asr.2008.04.008

Yakimov, N., & Ponomarev, E. (2020). Dynamics of post-fire effects in larch forests of Central Siberia based on satellite data. E3S Web of Conferences (149), 03008. https://doi.org/10.1051/e3sconf/202014903008

Zhang, K., Kimball, J., & Running, S. W. (2016). A review of remote sensing based actual evapotranspiration estimation. WIREs Water, (3), 834-853. https://doi.org/10.1002/wat2.1168

Descargas

Estadísticas

Estadísticas en RUA

Publicado

19-07-2023

Cómo citar

Castillo, H., & Baldassini, P. (2023). Teledetección aplicada al análisis de los impactos ecosistémicos generados por los incendios del año 2020 en la provincia de Córdoba, Argentina, e identificación de áreas prioritarias de restauración. Investigaciones Geográficas, (80), 81–105. https://doi.org/10.14198/INGEO.23754

Número

Sección

Artículos