Consecuencias del fuego en la composición de la vegetación y su influencia en la distribución del Leaf Area Index (LAI) mediante imágenes multi-resolución

Pedro Martín Ortiz; Fernando Pérez-Cabello; Cristian Iranzo Cubel; Raquel Montorio Lloveria

doi:10.18172/cig.6769

Authors

Pedro Martín Ortiz Universidad de Zaragoza https://orcid.org/0009-0008-6156-3110
Fernando Pérez-Cabello Universidad de Zaragoza https://orcid.org/0000-0003-4831-4060
Cristian Iranzo Cubel Universidad de Zaragoza https://orcid.org/0000-0002-3359-6213
Raquel Montorio Lloveria Universidad de Zaragoza https://orcid.org/0000-0001-7403-1764

DOI:

https://doi.org/10.18172/cig.6769

Keywords:

remote sensing, multispectral time series, post-fire resilience, fire severity, LAI

Abstract

In recent decades, wildfires have become one of the main disturbances affecting Mediterranean forest ecosystems. Understanding how fire-affected formations recover is crucial for assessing their resilience and effectively managing potential hydrological-forest restoration measures. This study analyzes vegetation regeneration in burned areas representative of the landscape diversity of Aragón (NE Iberian Peninsula) considering (i) the type of colonizing vegetation in relation to the pre-existing one and (ii) the impact of the colonizing vegetation type on the spatial distribution of the Leaf Area Index (LAI), which is used as a proxy for the eco-physiological functionality of the affected formations. High-spatial-resolution GeoSAT-2 images and Sentinel-2 L2A collections were used to generate maps of current vegetation distribution and multitemporal LAI composites, respectively. Contingency tables derived from diachronic comparisons of dominant vegetation type (before the fire and at present) and Random Forest (RF) predictive models were employed. The RF models also determined the importance of different natural factors in the spatial distribution of colonizing vegetation formations. The results highlighted the strong dependence between pre-fire and colonizing vegetation formations (χ² = 10.067) and the role of regenerative trajectories in the spatial distribution of LAI (p < 0.05). Greater regeneration was observed in areas dominated by species with active reproductive strategies (resprouting and serotiny). Additionally, in the Random Forest modeling (OOB = 21%), pre-existing vegetation emerged as the most determining factor (MDG = 600) in predicting current vegetation, surpassing fire severity and the regenerative trend of the Normalized Difference Vegetation Index (MDG ≈ 250), whose effects vary depending on the type of vegetation formation.

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References

Alloza, J.A., Baeza, M. J., de la Riva, J.R., Duguy, B., Echeverría, M.T., Ibarra, P., Llovet, J., Pérez-Cabello, F., Rovira, P., Vallejo, R., 2006. A model to evaluate the ecological vulnerability to forest fires in Mediterranean ecosystems. Viegas, D.X. (ed.) Actas de la 5ª Conferencia Internacional sobre Investigación de Incendios Forestales. Ámsterdam: Elsevier, 12.

Ancira-Sánchez, L., Treviño, E. J., 2015. Utilización de imágenes de satélite en el manejo forestal del noreste de México. Madera y Bosques, 21(1), 77-91.

Bassett, M., Leonard, S.W.J., Chia, E.K., Clarke, M.F., Bennett, A.F., 2017. Interacting effects of fire severity, time since fire and topography on vegetation structure after wildfire. For. Ecol. Manage. 396, 26-34. https://doi.org/10.1016/j.foreco.2017.04.006

Bodí, M.B., Cerdá A., Mataix-Solera, J., Doerr, S., 2012. Efectos de los incendios forestales en la vegetación y el suelo en la cuenca mediterránea: Revisión bibliográfica. Boletín de la Asociación de Geógrafos Españoles, 58, 35-55.

Breiman, L. 2001. Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324/METRICS

Calama, R., Manso, R., Lucas-Borja, M. E., Espelta, J. M., Piqué, M., Bravo, F., del Peso, C., Pardos, M. 2017. Natural regeneration in Iberian pines: A review of dynamic processes and proposals for management. Forest Systems, 26(2), eR02S. https://doi.org/10.5424/fs/2017262-11255

Cerdà, A., Robichaud, P.R., 2009. Fire Effects on Soils and Restoration Strategies (1st ed.). CRC Press. https://doi.org/10.1201/9781439843338

Chaves, M.E.D., Picoli, M.C.A., Sanches, I.D. 2020. Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sensing, 12(18), 3062. https://doi.org/10.3390/RS12183062

Chen, J. M., Black, T. A., 1992. Defining leaf area index for non-flat leaves. Plant, Cell and Environment, 15(4), 421-429. https://doi.org/10.1111/J.1365-3040.1992.TB00992.X

Chen, X., Vogelmann, J.E., Rollins, M., Ohlen, D., Key, C.H., Yang, L., Huang, C., Shi, H., 2011. Detecting post-fire burn severity and vegetation recovery using multitemporal remote sensing spectral indices and field-collected composite burn index data in a ponderosa pine forest. International Journal of Remote Sensing, 32(23), 7905-7927. https://doi.org/10.1080/01431161.2010.524678

Choudhury, M.A.M., Marcheggiani, E., Despini, F., Costanzini, S., Rossi, P., Galli, A., Teggi, S., 2020. Urban Tree Species Identification and Carbon Stock Mapping for Urban Green Planning and Management. Forests 2020, 11, 1226. https://doi.org/10.3390/F11111226

Chu, T., Guo, X., Takeda, K., 2017. Effects of burn severity and environmental conditions on post-fire regeneration in Siberian Larch forest. Forests 8, 1-27. https://doi.org/10.3390/f8030076

Cutler, A., Cutler, D.R., Stevens, J.R., 2012. Ensemble Machine Learning. Ensemble Machine Learning, January. https://doi.org/10.1007/978-1-4419-9326-7

Elvira, N.J., Lloret, F., Jaime, L., Margalef-Marrase, J., Pérez Navarro, M.Á., Batllori, E., 2021. Species climatic niche explains post-fire regeneration of Aleppo pine (Pinus halepensis Mill.) under compounded effects of fire and drought in east Spain. Sci. Total Environ. 798, 149308. https://doi.org/10.1016/j.scitotenv.2021.149308

Francos, M., Pereira, P., Mataix-Solera, J., Arcenegui, V., Alcañiz, M., Úbeda, X., 2018. How clear-cutting affects fire severity and soil properties in a Mediterranean ecosystem. Journal of Environmental Management, 206, 625-632. https://doi.org/10.1016/j.jenvman.2017.11.011

Galiano, L., Martínez-Vilalta, J., Eugenio, M., Granzow-de la Cerda, Í., Lloret, F., 2013. Seed- ling emergence and growth of quercus spp. following severe drought effects on a Pinus sylvestris canopy. J. Veg. Sci. 24 (3). https://doi.org/10.1111/j.1654-1103. 2012.01485.x

Géron, A. 2019. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O'Reilly Media.

Gitas, Z., Katagis, T., Toukiloglou, P., 2012. Burned area mapping and post-fire monitoring of a Mediterranean forest using NDVI time-series of low resolution imagery and the BFAST method / Mapeamento de área queimada e monitoramento pós-fogo de floresta na região Mediterrânea a partir de série tempor. Revista Ambiência, 8(4), 461-470. https://doi.org/10.5777/ambiencia.2012.04.02

Grandes dominios de paisaje (ATLAS DE ARAGÓN - ICEARAGON). (n.d.). Retrieved November 11, 2024, from https://idearagon.aragon.es/atlas/Aragon/info/paisaje/grandes-dominios-de-paisaje/grandes-dominios-de-paisaje

Griffiths, P., Kuemmerle, T., Baumann, M., Radeloff, V.C., Abrudan, I. V., Lieskovsky, J., Munteanu, C., Ostapowicz, K., Hostert, P., 2014. Forest disturbances, forest recovery, and changes in forest types across the carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sensing of Environment 151, 72-88. https://doi.org/10.1016/j.rse.2013.04.022

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031

Habrouk, A., Retana, J., Espelta, J.M., 1999. Role of heat tolerance and cone protection of seeds in the response of three pine species to wildfires. Plant Ecology, 145, 91-99. https://doi.org/10.1023/A:1009851614885

Henry, M.C., Hope A. S., 1998. Monitoring post-burn recovery of chaparral vegetation in southern California using multi-temporal satellite data. International Journal of Remote Sensing, 19, 3097-3107. https://doi.org/10.1080/014311698214208

Instituto Geográfico Nacional., 2021. Cobertura Nacional VHR 2021-22–Documento explicativo de la cobertura en España con productos GeoSAT-2.

Iranzo Cubel, C., Acosta-Ruiz, A., Montorio Llovería, R., Hoffren, R., Pérez-Cabello, F., 2023. Evaluación de la maleabilidad de los paisajes forestales afectados por el fuego en Aragón. Geografía: Cambios, Retos y Adaptación. XXVIII Congreso de La Asociación Española de Geografía. AGE y Universidad de La Rioja, 165-174. https://doi.org/10.21138/cg/2023.lc

Jiménez, A., Pérez-Cabello, F., Montorio, R., 2016. Niveles de LAI/fPAR en superficies afectadas por incendios forestales en Aragón. Análisis mediante el producto MCD15A2 DE MODIS. Pirineos, 171. http://dx.doi.org/10.3989/Pirineos.2016.171003

Juola, J., Hovi, A., Rautiainen, M., 2024. Comparison of contemporaneous Sentinel-2 and EnMAP data for vegetation index-based estimation of leaf area index and canopy closure of a boreal forest. European Journal of Remote Sensing, 57(1). https://doi.org/10.1080/22797254.2024.2432975

Karavani, A., Boer, M.M., Baudena, M., Colinas, C., Díaz-Sierra, R., Pemán, J., de Luis, M., Enríquez-de-Salamanca, Á., Resco de Dios, V., 2018. Fire-induced deforestation in drought-prone Mediterranean forests: drivers and unknowns from leaves to communities. Ecological Monographs, 88(2), 141-169. https://doi.org/10.1002/ecm.1285

Kazanis, D., Spatharis, S., Kokkoris, G.D., Dimitrakopoulos, P.G., Arianoutsou, M., 2024. Drivers of Pinus halepensis Plant Community Structure across a Post-Fire Chronosequence. Fire 7. https://doi.org/10.3390/fire7090331

Keeley, J.E., 1981. Reproductive cycles and fire regimes. General Technical Report, 231-277.

Keeley, J.E., 2012. Fire in Mediterranean climate ecosystems-A comparative overview. Israel Journal of Ecology and Evolution, 58(2-3), 123-135. https://doi.org/10.1560/IJEE.58.2-3.123

Keeley, J.E., Fotheringham, C.J., Baer-Keeley, M., 2005. Factors affecting plant diversity during post-fire recovery and succession of mediterranean-climate shrublands in California, USA. Diversity and Distributions, 11(6), 525-537. https://doi.org/10.1111/j.1366-9516.2005.00200.x

Korhonen, L., Hadi, Packalen, P., Rautiainen, M., 2017. Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sensing of Environment, 195, 259-274. https://doi.org/10.1016/J.RSE.2017.03.021

Landis, J.R., Koch, G.G., 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310

Lecina-Diaz, J., Martínez-Vilalta, J., Alvarez, A., Vayreda, J., Retana, J., 2021. Assessing the Risk of Losing Forest Ecosystem Services Due to Wildfires. Ecosystems, 24(7), 1687-1701. https://doi.org/10.1007/s10021-021-00611-1

Li, S., Fang, H., Zhang, Y., Wang, Y., 2022. Comprehensive evaluation of global CI, FVC, and LAI products and their relationships using high-resolution reference data. Science of Remote Sensing, 6, 100066. https://doi.org/10.1016/J.SRS.2022.100066

Lloret, F., Pausas, J.G., Vilá, J., 2003. Responses of Mediterranean plant species to different fire frequencies in Garraf Natural Park (Catalonia, Spain): field observations and modelling predictions. Plant Ecology, 167, 223-235. https://doi.org/10.1023/A:1023911031155

Maren, E., Gesa, C., Schwanitz, C., Heinken, T., 2023. Early natural regeneration of trees after a forest fire in a Central European Scots pine forest: Forest management, fire severity, and distance are important. Ecology and Forest Management, 539, 120999. https://doi.org/10.1016/j.foreco.2023.120999

Martín-Alcón, S., Coll, L., 2016. Unraveling the relative importance of factors driving post-fire regeneration trajectories in non-serotinous Pinus nigra forests. Forest Ecology and Management, 361, 13-22. https://doi.org/10.1016/j.foreco.2015.11.006

Martínez, S., Chuvieco, E., Aguado, I., Salas, J., 2017. Severidad y regeneración en grandes incendios forestales: análisis a partir de series temporales de imágenes Landsat. Revista de Teledetección, 49, 17-32. https://doi.org/10.4995/raet.2017.7182

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

Montorio, R., Pérez-Cabello, F, Hoffrén, R., Iranzo, C., 2024. Trends in Wildfire Burn Severity in Aragon, Spain. 13th EARSeL Workshop on Forest Fires 2024. Remote Sensing of Forest Fires: Lessons Learned and Future Challenges Under a Changing Climate. Milan, 19th-20th September 2024. www.edizioni.cnr.it ISBN 978 88 8080 643 1. 1985-2020

Moreno, M., Bertolín, C., Arlanzón, D., Ortiz, P., Ortiz, R., 2023. Climate change, large fires, and cultural landscapes in the mediterranean basin: An analysis in southern Spain. Heliyon, 9(6), 1-19. https://doi.org/10.1016/j.heliyon.2023.e16941

Moya, D., González-De Vega, S., Lozano, E., García-Orenes, F., Mataix-Solera, J., Lucas-Borja, M.E., de las Heras, J., 2019. The burn severity and plant recovery relationship affect the biological and chemical soil properties of Pinus halepensis Mill. stands in the short and mid-terms after wildfire. J. Environ. Manage. 235, 250-256. https://doi.org/10.1016/j.jenvman.2019.01.029

Muñoz-Rojas, M., Pereira, P., 2020. Editorial: Fire in the environment. Journal of Environmental Management, 253, 109703. https://doi.org/10.1016/J.JENVMAN.2019.109703

Ne’eman, G., Goubitz, S., Nathan, R., 2004. Reproductive traits of Pinus halepensis in the light of fire - A critical review. Plant Ecology, 171(1-2), 69-79. https://doi.org/10.1023/B:VEGE.0000029380.04821.99

Parra, A., Moreno, J.M., 2018. Drought differentially affects the post-fire dynamics of seeders and resprouters in a Mediterranean shrubland. Science of the Total Environment, 626, 1219-1229. https://doi.org/10.1016/J.SCITOTENV.2018.01.174

Pausas, J.G., 2012. Incendios Forestales. Una visión desde la Ecología. Madrid: CSIC. Catarata. https://doi.org/10.1016/j.rse.2013

Pausas, J.G., Bradstock, R.A., Keith, D.A., Keeley, J.E., Hoffman, W., Kenny, B., Lloret, F., Trabaud, L., 2004. Plant functional traits in relation to fire in crown-fire ecosystems. Ecology, 85(4), 1085-1100. https://doi.org/10.1890/02-4094

Pausas, J.G., Keeley, J.E., Schwilk, D.W., 2017. Flammability as an ecological and evolutionary driver. Journal of Ecology, 105(2), 289-297. https://doi.org/10.1111/1365-2745.12691

Pérez-Cabello, F., 2002. Paisajes forestales y fuego en el prepirineo occidental oscense. Un modelo regional de reconstrucción ambiental, Serie Investigación. ed. Publicaciones del Consejo de Protección de la naturaleza de Aragón.

Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.M., Tucker, C.J., Stenseth, N.C., 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution, 20(9), 503-510. https://doi.org/10.1016/j.tree.2005.05.011

Pu, J., Yan, K., Roy, S., Zhu, Z., Rautiainen, M., Knyazikhin, Y., Myneni, R.B., 2024. Sensor-independent LAI/FPAR CDR: Reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022. Earth System Science Data, 16(1), 15-34. https://doi.org/10.5194/ESSD-16-15-2024

Ramos, M., Soares, R., 2004. Análisis comparativo entre los incendios forestales en Monte Alegre, Brasil y Pinar del Río, Cuba. Floresta, 34(2), 101-110.

Retana, J., Arnan, X., Arianoutsou, M., Barbati, A., Kazanis, D., Rodrigo, A., 2012. Post-Fire Management of Non-Serotinous Pine Forests. In: F. Moreira, M. Arianoutsou, P. Corona, J. De las Heras (eds.), Post-Fire Management and Restoration of Southern European Forests. Managing Forest Ecosystems, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2208-8_7

Richards, J.A., 1999. Remote Sensing Digital Image Analysis: An Introduction. Springer-Verlag, Berlin, Germany, 240. https://doi.org/10.1007/978-3-662-03978-6

Rivas-Martínez, S., 1987. Memoria del mapa de series de vegetación de España. ICONA. Madrid.

Rogan, J., Chen, D.M., 2004. Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in Planning, 61(4), 301-325. https://doi.org/10.1016/S0305-9006(03)00066-7

Ruffault, J., Curt, T., Moron, V., Trigo, R. M., Mouillot, F., Koutsias, N., Pimont, F., Martin-StPaul, N., Barbero, R., Dupuy, J.L., Russo, A., Belhadj-Khedher, C., 2020. Increased likelihood of heat-induced large wildfires in the Mediterranean Basin. Scientific Reports, 10(1), 1-9. https://doi.org/10.1038/s41598-020-70069-z

Santana, V.M., Baeza, M.J., Maestre, F.T., 2012. Seedling establishment along post-fire succession in Mediterranean shrublands dominated by obligate seeders. Acta Oecologica, 39, 51-60. https://doi.org/10.1016/j.actao.2011.12.001

Shanmuga, R., Vani, K., 2024. Vegetation change detection and recovery assessment based on post-fire satellite imagery using deep learning. Scientific Reports, 14(1), 1-23. https://doi.org/10.1038/s41598-024-63047-2

Shvetsov, E.G., Kukavskaya, E.A., Buryak, L.V., Barrett, K., 2019. Assessment of post-fire vegetation recovery in Southern Siberia using remote sensing observations. Environmental Research Letters, 14(5), 055001. https://doi.org/10.1088/1748-9326/AB083D

Simonetti, D., Simonetti, E., Szantoi, Z., Lupi, A., Eva, H.D., 2015. First Results from the Phenology-Based Synthesis Classifier Using Landsat 8 Imagery. IEEE Geoscience and Remote Sensing Letters, 12(7), 1496-1500. https://doi.org/10.1109/LGRS.2015.2409982

Vallejo R., Serrasolses, I., Alloza, J.A, Baeza, M., 2009. Long-term restoration strategies and techniques”. In: A. Cerdá y P. Robichaud (eds.), Fire Effects on Soils and Restoration Strategies. Enfield, Science Publishers, pp. 373-398.

Vayreda, J., Martinez-Vilalta, J., Gracia, M., Canadell, J.G., Retana, J., 2016. Anthropogenic-driven rapid shifts in tree distribution lead to increased dominance of broadleaf species. Global Change Biology, 22(12), 3984-3995. https://doi.org/10.1111/gcb.13394

Vermote, E.F., Tanré, D., Deuzé, J.L., Herman, M., Morcrette, J.J., 1997. Second simulation of the satellite signal in the solar spectrum, 6s: an overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3), 675-686. https://doi.org/10.1109/36.581987

Viana-Soto, A., Aguado, I., Salas, J., García, M., 2020. Identifying Post-Fire Recovery Trajectories and Driving Factors Using Landsat Time Series in Fire-Prone Mediterranean Pine Forests. Remote Sens., 12, 1499. https://doi.org/10.3390/rs12091499

Vogelmann, J.E., Xian, G., Homer, C., Tolk, B., 2012. Monitoring gradual ecosystem change using Landsat time series analyses: Case studies in selected forest and rangeland ecosystems. Remote Sensing of Environment, 122, 92-105. https://doi.org/10.1016/J.RSE.2011.06.027

Wagtendonk, J.W., Root, R.R., Key, C.H., 2004. Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity. Remote Sensing of Environment, 92, 397-408. https://doi.org/10.1016/j.rse.2003.12.015

Wilson, R.T., 2013. Py6S: A Python interface to the 6S radiative transfer model. Computers & Geosciences, 51, 166-171. https://doi.org/10.1016/j.cageo.2012.08.002

Woodgate, W., Phinn, S., Devereux, T., Aryal, R.R., 2024. Bushfire recovery at a long-term tall eucalypt flux site through the lens of a satellite: Combining multi-scale data for structural-functional insight. Remote Sensing of Environment, 317, 114530. https://doi.org/10.1016/J.RSE.2024.114530

Wulder, A.M., Roy, D.P., Radeloff, V.C., Loveland, T.R, Anderson, M.C., Johnson, D., Healey, S.; Zhu, Z., Scambos, T.A., Pahlevan, N., Hansen, M., Gorelick, N, Crawford, C.J., Masek, J., Hermosilla, T, White, J.C., Belward, A.S., Schaaf, C., Woodcock, C.E., Huntington, J.L., Lymburner, L., Hostert, P., Gao, F., Lyapustin, A., Pekel, J., Strobl, P., Cook, B.D. 2022. Fifty years of Landsat science and impacts. Remote Sensing of Environment, 280, 2-20. https://doi.org/10.1016/j.rse.2022.113195

Xuan, G., Zhang, W., Chai, P., 2001. EM algorithms of Gaussian mixture model and hidden Markov model. Proceedings 2001 International Conference on Image Processing. Thessaloniki, Greece, vol.1, 145-148. https://doi.org/10.1109/ICIP.2001.958974