Review of Remote Sensing Methods for the Detection and Evaluation of the Severity of Forest Fires
The effects that fires have on forest ecosystems are variable, depending on various factors, including the severity of the fire. Which, in turn, affects your recovery. However, evaluating fire-affected areas directly in the field involves high investment of resources that, along with time, are gener...
| Published in: | Advances in Climate Change Research |
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| Main Authors: | , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | Spanish |
| Published: |
Universidad Nacional de Colombia - Sede Bogotá - Instituto de Estudios Ambientales (IDEA)
2020
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| Subjects: | |
| Online Access: | https://revistas.unal.edu.co/index.php/gestion/article/view/93682 |
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ftuncolombiarev:oai:www.revistas.unal.edu.co:article/93682 |
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Universidad Nacional de Colombia: Portal de Revistas UN |
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ftuncolombiarev |
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Spanish |
| topic |
Fire spectral indices satellite images natural regeneration reflectance Conservation and protection Forestry Fuego índices espectrales imágenes satelitales regeneración natural reflectancia Conservación y Protección Ciencias forestales |
| spellingShingle |
Fire spectral indices satellite images natural regeneration reflectance Conservation and protection Forestry Fuego índices espectrales imágenes satelitales regeneración natural reflectancia Conservación y Protección Ciencias forestales Flores-Rodríguez, Ana Graciela Flores-Garnica, José Germán González-Eguiarte, Diego Raymundo Gallegos-Rodríguez, Agustín Zarazúa-Villaseñor, Patricia Mena-Munguía, Salvador Review of Remote Sensing Methods for the Detection and Evaluation of the Severity of Forest Fires |
| topic_facet |
Fire spectral indices satellite images natural regeneration reflectance Conservation and protection Forestry Fuego índices espectrales imágenes satelitales regeneración natural reflectancia Conservación y Protección Ciencias forestales |
| description |
The effects that fires have on forest ecosystems are variable, depending on various factors, including the severity of the fire. Which, in turn, affects your recovery. However, evaluating fire-affected areas directly in the field involves high investment of resources that, along with time, are generally limited. However, for the planning of management and restoration strategies it is necessary to have knowledge of the impact of fire. For this, remote sensors are a practical tool for the evaluation of large areas, or inaccessible areas, impacted by forest fires. Whose use is increasing, following different evaluation perspectives, such as the infrared spectrum, the detection of vegetation, ash location, etc. So to know which is the best alternative in the study of forest fires, it is necessary to know the full range of possibilities and thus be able to choose the most convenient one. Due to this, in this work a review is made of different evaluation proposals of areas impacted by forest fires through remote sensors. Which are mainly defined in a series of spectral indices, based on which, directly or indirectly, it is intended not only to locate and size forest fires, but, in some cases, to determine the level of severity. Thus, in this document the main proposals are grouped, based on their objectives for detecting impacted areas: vegetation, soil, water, burned area and radar. Los efectos que tienen los incendios en los ecosistemas forestales son variables, dependiendo de diversos factores entre los cuales se encuentra la severidad del fuego. Lo cual, a su vez, repercute en su recuperación. Sin embargo, evaluar áreas afectadas por fuego directamente en campo implica alta inversión de recursos que, junto con el tiempo, son generalmente limitados. No obstante, para la planeación de las estrategias de manejo y de restauración es necesario tener conocimiento del impacto del fuego. Para esto, los sensores remotos son una herramienta práctica para la evaluación de grandes áreas, o áreas inaccesibles, impactadas por ... |
| format |
Article in Journal/Newspaper |
| author |
Flores-Rodríguez, Ana Graciela Flores-Garnica, José Germán González-Eguiarte, Diego Raymundo Gallegos-Rodríguez, Agustín Zarazúa-Villaseñor, Patricia Mena-Munguía, Salvador |
| author_facet |
Flores-Rodríguez, Ana Graciela Flores-Garnica, José Germán González-Eguiarte, Diego Raymundo Gallegos-Rodríguez, Agustín Zarazúa-Villaseñor, Patricia Mena-Munguía, Salvador |
| author_sort |
Flores-Rodríguez, Ana Graciela |
| title |
Review of Remote Sensing Methods for the Detection and Evaluation of the Severity of Forest Fires |
| title_short |
Review of Remote Sensing Methods for the Detection and Evaluation of the Severity of Forest Fires |
| title_full |
Review of Remote Sensing Methods for the Detection and Evaluation of the Severity of Forest Fires |
| title_fullStr |
Review of Remote Sensing Methods for the Detection and Evaluation of the Severity of Forest Fires |
| title_full_unstemmed |
Review of Remote Sensing Methods for the Detection and Evaluation of the Severity of Forest Fires |
| title_sort |
review of remote sensing methods for the detection and evaluation of the severity of forest fires |
| publisher |
Universidad Nacional de Colombia - Sede Bogotá - Instituto de Estudios Ambientales (IDEA) |
| publishDate |
2020 |
| url |
https://revistas.unal.edu.co/index.php/gestion/article/view/93682 |
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ENVELOPE(-46.733,-46.733,-60.567,-60.567) |
| geographic |
Alta Inaccesibles |
| geographic_facet |
Alta Inaccesibles |
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Arctic |
| genre_facet |
Arctic |
| op_source |
Gestión y Ambiente; Vol. 23 Núm. 2 (2020); 273-283 Gestión y Ambiente; Vol. 23 No. 2 (2020); 273-283 2357-5905 0124-177X |
| op_relation |
https://revistas.unal.edu.co/index.php/gestion/article/view/93682/80679 Baret, F., Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens. Environ. 35, 161-173. DOI:10.1016/0034-4257(91)90009-U Bastarrika, A., Chuvieco, E., Martín, M., 2011a. Automatic burned land mapping from MODIS time series images: Assessment in mediterranean ecosystems. IEEE Trans. Geosci. Remote Sens. 49, 3401-3413. DOI:10.1109/TGRS.2011.2128327 Bastarrika, A., Chuvieco, E., Martín, M., 2011b. Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors. Remote Sens. Environ. 115, 1003-1012. DOI:10.1016/j.rse.2010.12.005 Bisson, M., Fornaciai, A., Coli, A., Mazzarini, F., Pareschi, M., 2008. The Vegetation Resilience After Fire (VRAF) index: Development, implementation and an illustration from central Italy. Int. J. Appl. Earth Obs. Geoinf. 10, 312-329. DOI:10.1016/j.jag.2007.12.003 Cardil, A., Mola-Yudego, B., Blázquez-Casado, Á., González-Olabarria, J., 2019. Fire and burn severity assessment: Calibration of Relative Differenced Normalized Burn Ratio (RdNBR) with field data. J. Environ. Manag. 235, 342-349. DOI:10.1016/j.jenvman.2019.01.077 Chen, Y., Lara, M., Hu, F., 2020. A robust visible near-infrared index for fire severity mapping in Arctic tundra ecosystems. ISPRS J. Photogramm. Remote Sens. 159, 101-113. DOI:10.1016/j.isprsjprs.2019.11.012 De Santis, A., Chuvieco, E., 2009. GeoCBI: A modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data. Remote Sens. Environ. 113, 554-562. DOI:10.1016/j.rse.2008.10.011 Domaç, A., Zeydanli, U., Yeşilnacar, E., Süzen, M., 2004. Integration and usage of indices, feature components and topography in vegetation classification for regional biodiversity assessment. En: 20th Congreso de ISPRS. Estambul. pp. 204-208. Edwards, A., Maier, S., Hutley, L., Williams, R., Russell-Smith, J., 2013. Spectral analysis of fire severity in north Australian tropical savannas. Remote Sens. Environ. 136, 56-65. DOI:10.1016/j.rse.2013.04.013 Fernandes, M., Aguiar, F., Martins, M., Rico, N., Ferreira, M., Correia, A., 2020. Carbon stock estimations in a mediterranean riparian forest: A case study combining field data and UAV imagery. Forests 11, 376-397. DOI:10.3390/f11040376 Fornacca, D., Guopeng, R., Xiao, W., 2018. Evaluating the best spectral indices for the detection of burn scars at several post-fire dates in a Mountainous region of Northwest Yunnan, China. Remote Sens. 10, 1196. DOI:10.3390/rs10081196 Fernández-Manso, A., Fernández-Manso, O., Quintano, C., 2016. SENTINEL-2A red-edge spectral indices suitability for discriminatingburn severity. Int. J. Appl. Earth Obs. Geoinf. 50, 170-175. DOI:10.1016/j.jag.2016.03.005 García M., Pérez-Cabello, E., 2015. Análisis de la regeneración vegetal mediante imágenes Landsat-8 y el producto MCD15A2 de MODIS: el caso del incendio de O Pindo. En: de la Riva, J., Ibarra, P., Montorio, R., Rodrigues, M. (Eds.), Análisis espacial y representación geográfica: innovación y aplicación. Asociación de Geógrafos Españoles; Universidad de Zaragoza, Zaragoza, España. pp. 621-630. Gao, B., 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257-266. DOI:10.1117/12.210877 Gitas, I., Mitri, G., Veraverbeke, S., Polychronaki, A., 2012. Advances in remote sensing of post-fire vegetation recovery monitoring-a review. En: Moisan, T., Sathyendranath, S., Bouman, H. (Eds.), Remote sensing of biomass-principles and applications. Intech. DOI:10.5772/20571 González, M., Schwendenmann, L., Jiméne, J., Schulz, R., 2008. Forest structure and woody plant species composition along a fire chronosequence in mixed pine-oak forest in the Sierra Madre Oriental, Northeast Mexico. For. Ecol. Manag. 256, 161-167. DOI:10.1016/j.foreco.2008.04.021 Harris, S., Veraverbeke, S., Hook, S., 2011. Evaluating spectral indices for assessing fire severity in Chaparral ecosystems (Southern California) using MODIS/ASTER (MASTER) airborne simulator data. Remote Sens. 3, 2403-2419. DOI:10.3390/rs3112403 Herawati, H., Gonzales-Olabarria, J., Wijaya, A., Martius, C., Purnomo, H., Andriani, R., 2015. Tools for assessing the impacts of climate variability and change on wildfire regimes in forests. Forests 6, 1476-1499. DOI:10.3390/f6051476 Hudak, A., Morgan, P., Bobbitt, M., Smith, A., Lewis, S., Lentile, L., Robichaud, P., Clark, J., McKinley, R., 2007. The relationship of multispectral satellite imagery to immediate fire effects. Fire Ecol. 3(1), 64-90. DOI:10.4996/fireecology.0301064 Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X., Ferreira L., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation índices. Remote Sens. Environ. 83, 195-213. DOI:10.1016/S0034-4257(02)00096-2 Huete, A.,1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25, 295-309. DOI:10.1016/0034-4257(88)90106-X Ichoku, C., Kahn, R., Chin, M., 2012. Satellite contributions to the quantitative characterization of biomass burning for climate modeling. Atmos. Res. 111, 1-28. DOI:10.1016/j.atmosres.2012.03.007 Ireland, G., Petropoulos, G., 2015. Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: A case study from the Montane Cordillera Ecozones of Western Canada. Appl. Geogr. 56, 232-248. DOI:10.1016/j.apgeog.2014.11.016 Kasischke, E., Turetsky, M., Ottmar, R., French, N., Hoy, E., Kane, E., 2008. Evaluation of the composite burn index for assessing fire severity in Alaskan black spruce forests. Int. J. Wildland Fire 17, 515-526. DOI:10.1071/WF08002 Kauth, R., Thomas, G., 1976. The tasselled Cap-A descripción gráfica del desarrollo espectral-temporal de cultivos agrícolas según lo visto por Landsat. LARS Symposia Paper 159. Purdue University. West Lafayette, IN. Key, C., Benson, N., 2006. Landscape Assessment (LA) sampling and analysis methods. En: Lutes, D., Keane, R., Caratti, J., Key, C., Benson, N., Sutherland, S., Gangi, L. (Eds.), FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. RMRSGTR- 164-CD. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO. pp. LA1- LA 55. López G., A., 2020. Estudio de la severidad del incendio de 2012 y regeneración de la vegetación del Bosque La Primavera, México, mediante imágenes LANDSAT 7. Rev. Cartogr. 101, 35-50. DOI:10.35424/rcarto.i101.420 Li, R., Kaufman, Y., Hao, W., Salmon, J., Gao, B., 2004. A technique for tecting burn scars using MODIS data. IEEE Trans. Geosci. Remote Sens. 42, 1300-1308. DOI:10.1109/TGRS.2004.826801 Lu, B., He, Y., Tong, A., 2016. Evaluation of spectral indices for estimating burn severity in semiarid grasslands. Int. J. Wildland Fire 25, 147-157. DOI:10.1071/WF15098 Mallinis, G., Mitsopoulos, I., Chrysafi, I., 2018. Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GISci. Remote Sens. 55, 11-18. DOI:10.1080/15481603.2017.1354803 Mallinis, G., Koutsias, N., 2012. Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data. Int. J. Remote Sens. 33, 4408-4433. DOI:10.1080/01431161.2011.648284 Martínez-Barbáchano, R., Solís-Miranda, G., 2018. Caracterización espectral y detección de flecha seca en palma africana en Puntarenas, Costa Rica. Rev. Geog. Amer. Central 61, 349-377. DOI:10.15359/rgac.61-2.13 McCarley, T., Kolden, C., Vaillant, N., Hudak, A., Smith, A., Wing, B., Kellogg, B., Kreitler, J., 2017. Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure. Remote Sens. Environ. 191, 419-432. DOI:10.1016/j.rse.2016.12.022 Miller, J., Knapp, E., Key, C., Skinner, C., Isbell, C., Creasy, R., Sherlock, J., 2009. Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens. Environ. 113, 645-656. DOI:10.1016/j.rse.2008.11.009 Oliva, P., Martín, P., Chuvieco, E., 2011. Burned area mapping with MERIS post-fire image. Int. J. Remote Sens. 32, 4175-4201. DOI:10.1080/01431161.2010.489062 Parks, S., Dillon, G., Miller, C., 2014. A new metric for quantifying burn severity: The relativized burn ratio. Remote Sens. 6, 1827-1844. DOI:10.3390/rs6031827 Quintano, C., Fernández-Manso, A., Stein, A., Bijker, W., 2011. Estimation of area burned by forest fires in Mediterranean countries: A remote sensing data mining perspective. Forest Ecol. Manag. 262, 1597-1607. DOI:10.1016/j.foreco.2011.07.010 Riaño, D., Chuvieco, E., Ustina, S., Zomer, R., Dennison, P., Roberts, D., Salas, J., 2002. Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains. Remote Sens. Environ. 79, 60-71. DOI:10.1016/S0034-4257(01)00239-5 Rondeaux, G., Steven, M., Baret, F., 1996. Optimization of soil-adjusted vegetation indices. Remote Sens. Environ. 55, 95-107. DOI:10.1016/0034-4257(95)00186-7 Soverel, N., Coops, N., Perrakis, D., Daniels, L., Gergel, S., 2011. The transferability of a dNBR-derived model to predict burn severity across 10 wildland fires in western Canada. Int. J. Wildland Fire 20, 518-531. DOI:10.1071/WF10081 Smith, A., Drake, N., Wooster, M., Hudak, A., Holden, Z., Gibbons, C., 2007. Production of Landsat ETM + reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS. Int. J. Remote Sens. 28, 2753-2775. DOI:10.1080/01431160600954704 Tanase, M., de la Riva, J., Pérez-Cabello, F., 2011a. Estimating burn severity at the regional level using optically based indices. Can. J. For. Res. 41, 863-872. DOI:10.1139/x11-011 Tanase, M., de la Riva, J., Santoro, M., Pérez-Cabello, F., Kasischke, E., 2011b. Sensitivity of SAR data to post-fire forest regrowth in Mediterranean and boreal forests. Remote Sens. Environ. 115, 2075-2085. DOI:10.1016/j.rse.2011.04.009 Tanase, M., Kennedy, R., Aponte, C., 2015a. Radar burn ratio for fire severity estimation at canopy level: An example for temperate forests. Remote Sens. Environ. 170, 14-31. DOI:10.1016/j.rse.2015.08.025 Tanase, M., Kennedy, R., Aponte C., 2015b. Fire severity estimation from space: a comparison of active and passive sensors and their synergy for different forest types. Int. J. Wildland Fire 24, 1062-1075. DOI:10.1071/WF15059 Tran, B., Tanase, M., Bemmett, L., Aponte, C., 2018. Evaluation of spectral indices for assessing fire severity in Australian temperate forests. Remote Sens. 10, 1680. DOI:10.3390/rs10111680 Tran, N., Tanase, M., Bennett, L., Aponte C., 2019. Fire-severity classification across temperate Australian forests: random forests versus spectral index thresholding. Proc. SPIE 11149. Remote Sens. Agric. Ecosyst. Hydrol. 21, 111490U. DOI:10.1117/12.2535616 Urbanski, S., Salmon, J., Nordgren, B., Hao, W., 2009. A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States. Remote Sens. Environ. 113, 2511-2526. DOI:10.1016/j.rse.2009.07.007 Veraverbeke, S., Lhermitte, S., Verstraeten, W., Goossens, R., 2010. The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: The case of the large 2007 Peloponnese wildfires in Greece. Remote Sens. Environ. 114, 2548-2563. DOI:10.1016/j.rse.2010.05.029 |
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ftuncolombiarev:oai:www.revistas.unal.edu.co:article/93682 2023-05-15T14:28:31+02:00 Review of Remote Sensing Methods for the Detection and Evaluation of the Severity of Forest Fires Revisión de métodos de sensores remotos para la detección y evaluación de la severidad de incendios forestales Flores-Rodríguez, Ana Graciela Flores-Garnica, José Germán González-Eguiarte, Diego Raymundo Gallegos-Rodríguez, Agustín Zarazúa-Villaseñor, Patricia Mena-Munguía, Salvador 2020-07-01 application/pdf https://revistas.unal.edu.co/index.php/gestion/article/view/93682 spa spa Universidad Nacional de Colombia - Sede Bogotá - Instituto de Estudios Ambientales (IDEA) https://revistas.unal.edu.co/index.php/gestion/article/view/93682/80679 Baret, F., Guyot, G., 1991. Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sens. Environ. 35, 161-173. DOI:10.1016/0034-4257(91)90009-U Bastarrika, A., Chuvieco, E., Martín, M., 2011a. Automatic burned land mapping from MODIS time series images: Assessment in mediterranean ecosystems. IEEE Trans. Geosci. Remote Sens. 49, 3401-3413. DOI:10.1109/TGRS.2011.2128327 Bastarrika, A., Chuvieco, E., Martín, M., 2011b. Mapping burned areas from Landsat TM/ETM+ data with a two-phase algorithm: Balancing omission and commission errors. Remote Sens. Environ. 115, 1003-1012. DOI:10.1016/j.rse.2010.12.005 Bisson, M., Fornaciai, A., Coli, A., Mazzarini, F., Pareschi, M., 2008. The Vegetation Resilience After Fire (VRAF) index: Development, implementation and an illustration from central Italy. Int. J. Appl. Earth Obs. Geoinf. 10, 312-329. DOI:10.1016/j.jag.2007.12.003 Cardil, A., Mola-Yudego, B., Blázquez-Casado, Á., González-Olabarria, J., 2019. Fire and burn severity assessment: Calibration of Relative Differenced Normalized Burn Ratio (RdNBR) with field data. J. Environ. Manag. 235, 342-349. DOI:10.1016/j.jenvman.2019.01.077 Chen, Y., Lara, M., Hu, F., 2020. A robust visible near-infrared index for fire severity mapping in Arctic tundra ecosystems. ISPRS J. Photogramm. Remote Sens. 159, 101-113. DOI:10.1016/j.isprsjprs.2019.11.012 De Santis, A., Chuvieco, E., 2009. GeoCBI: A modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data. Remote Sens. Environ. 113, 554-562. DOI:10.1016/j.rse.2008.10.011 Domaç, A., Zeydanli, U., Yeşilnacar, E., Süzen, M., 2004. Integration and usage of indices, feature components and topography in vegetation classification for regional biodiversity assessment. En: 20th Congreso de ISPRS. Estambul. pp. 204-208. Edwards, A., Maier, S., Hutley, L., Williams, R., Russell-Smith, J., 2013. Spectral analysis of fire severity in north Australian tropical savannas. Remote Sens. Environ. 136, 56-65. DOI:10.1016/j.rse.2013.04.013 Fernandes, M., Aguiar, F., Martins, M., Rico, N., Ferreira, M., Correia, A., 2020. Carbon stock estimations in a mediterranean riparian forest: A case study combining field data and UAV imagery. Forests 11, 376-397. DOI:10.3390/f11040376 Fornacca, D., Guopeng, R., Xiao, W., 2018. Evaluating the best spectral indices for the detection of burn scars at several post-fire dates in a Mountainous region of Northwest Yunnan, China. Remote Sens. 10, 1196. DOI:10.3390/rs10081196 Fernández-Manso, A., Fernández-Manso, O., Quintano, C., 2016. SENTINEL-2A red-edge spectral indices suitability for discriminatingburn severity. Int. J. Appl. Earth Obs. Geoinf. 50, 170-175. DOI:10.1016/j.jag.2016.03.005 García M., Pérez-Cabello, E., 2015. Análisis de la regeneración vegetal mediante imágenes Landsat-8 y el producto MCD15A2 de MODIS: el caso del incendio de O Pindo. En: de la Riva, J., Ibarra, P., Montorio, R., Rodrigues, M. (Eds.), Análisis espacial y representación geográfica: innovación y aplicación. Asociación de Geógrafos Españoles; Universidad de Zaragoza, Zaragoza, España. pp. 621-630. Gao, B., 1996. NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257-266. DOI:10.1117/12.210877 Gitas, I., Mitri, G., Veraverbeke, S., Polychronaki, A., 2012. Advances in remote sensing of post-fire vegetation recovery monitoring-a review. En: Moisan, T., Sathyendranath, S., Bouman, H. (Eds.), Remote sensing of biomass-principles and applications. Intech. DOI:10.5772/20571 González, M., Schwendenmann, L., Jiméne, J., Schulz, R., 2008. Forest structure and woody plant species composition along a fire chronosequence in mixed pine-oak forest in the Sierra Madre Oriental, Northeast Mexico. For. Ecol. Manag. 256, 161-167. DOI:10.1016/j.foreco.2008.04.021 Harris, S., Veraverbeke, S., Hook, S., 2011. Evaluating spectral indices for assessing fire severity in Chaparral ecosystems (Southern California) using MODIS/ASTER (MASTER) airborne simulator data. Remote Sens. 3, 2403-2419. DOI:10.3390/rs3112403 Herawati, H., Gonzales-Olabarria, J., Wijaya, A., Martius, C., Purnomo, H., Andriani, R., 2015. Tools for assessing the impacts of climate variability and change on wildfire regimes in forests. Forests 6, 1476-1499. DOI:10.3390/f6051476 Hudak, A., Morgan, P., Bobbitt, M., Smith, A., Lewis, S., Lentile, L., Robichaud, P., Clark, J., McKinley, R., 2007. The relationship of multispectral satellite imagery to immediate fire effects. Fire Ecol. 3(1), 64-90. DOI:10.4996/fireecology.0301064 Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X., Ferreira L., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation índices. Remote Sens. Environ. 83, 195-213. DOI:10.1016/S0034-4257(02)00096-2 Huete, A.,1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25, 295-309. DOI:10.1016/0034-4257(88)90106-X Ichoku, C., Kahn, R., Chin, M., 2012. Satellite contributions to the quantitative characterization of biomass burning for climate modeling. Atmos. Res. 111, 1-28. DOI:10.1016/j.atmosres.2012.03.007 Ireland, G., Petropoulos, G., 2015. Exploring the relationships between post-fire vegetation regeneration dynamics, topography and burn severity: A case study from the Montane Cordillera Ecozones of Western Canada. Appl. Geogr. 56, 232-248. DOI:10.1016/j.apgeog.2014.11.016 Kasischke, E., Turetsky, M., Ottmar, R., French, N., Hoy, E., Kane, E., 2008. Evaluation of the composite burn index for assessing fire severity in Alaskan black spruce forests. Int. J. Wildland Fire 17, 515-526. DOI:10.1071/WF08002 Kauth, R., Thomas, G., 1976. The tasselled Cap-A descripción gráfica del desarrollo espectral-temporal de cultivos agrícolas según lo visto por Landsat. LARS Symposia Paper 159. Purdue University. West Lafayette, IN. Key, C., Benson, N., 2006. Landscape Assessment (LA) sampling and analysis methods. En: Lutes, D., Keane, R., Caratti, J., Key, C., Benson, N., Sutherland, S., Gangi, L. (Eds.), FIREMON: Fire effects monitoring and inventory system. Gen. Tech. Rep. RMRSGTR- 164-CD. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO. pp. LA1- LA 55. López G., A., 2020. Estudio de la severidad del incendio de 2012 y regeneración de la vegetación del Bosque La Primavera, México, mediante imágenes LANDSAT 7. Rev. Cartogr. 101, 35-50. DOI:10.35424/rcarto.i101.420 Li, R., Kaufman, Y., Hao, W., Salmon, J., Gao, B., 2004. A technique for tecting burn scars using MODIS data. IEEE Trans. Geosci. Remote Sens. 42, 1300-1308. DOI:10.1109/TGRS.2004.826801 Lu, B., He, Y., Tong, A., 2016. Evaluation of spectral indices for estimating burn severity in semiarid grasslands. Int. J. Wildland Fire 25, 147-157. DOI:10.1071/WF15098 Mallinis, G., Mitsopoulos, I., Chrysafi, I., 2018. 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DOI:10.1016/j.rse.2010.05.029 https://creativecommons.org/licenses/by-nc-sa/4.0 CC-BY-NC-SA Gestión y Ambiente; Vol. 23 Núm. 2 (2020); 273-283 Gestión y Ambiente; Vol. 23 No. 2 (2020); 273-283 2357-5905 0124-177X Fire spectral indices satellite images natural regeneration reflectance Conservation and protection Forestry Fuego índices espectrales imágenes satelitales regeneración natural reflectancia Conservación y Protección Ciencias forestales info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2020 ftuncolombiarev https://doi.org/10.1016/0034-4257(91)90009-U https://doi.org/10.1109/TGRS.2011.2128327 https://doi.org/10.1016/j.rse.2010.12.005 https://doi.org/10.1016/j.jag.2007.12.003 https://doi.org/10.1016/j.jenvman.2019.01.077 https://doi.org/10.1016/j.is 2022-12-14T08:44:14Z The effects that fires have on forest ecosystems are variable, depending on various factors, including the severity of the fire. Which, in turn, affects your recovery. However, evaluating fire-affected areas directly in the field involves high investment of resources that, along with time, are generally limited. However, for the planning of management and restoration strategies it is necessary to have knowledge of the impact of fire. For this, remote sensors are a practical tool for the evaluation of large areas, or inaccessible areas, impacted by forest fires. Whose use is increasing, following different evaluation perspectives, such as the infrared spectrum, the detection of vegetation, ash location, etc. So to know which is the best alternative in the study of forest fires, it is necessary to know the full range of possibilities and thus be able to choose the most convenient one. Due to this, in this work a review is made of different evaluation proposals of areas impacted by forest fires through remote sensors. Which are mainly defined in a series of spectral indices, based on which, directly or indirectly, it is intended not only to locate and size forest fires, but, in some cases, to determine the level of severity. Thus, in this document the main proposals are grouped, based on their objectives for detecting impacted areas: vegetation, soil, water, burned area and radar. Los efectos que tienen los incendios en los ecosistemas forestales son variables, dependiendo de diversos factores entre los cuales se encuentra la severidad del fuego. Lo cual, a su vez, repercute en su recuperación. Sin embargo, evaluar áreas afectadas por fuego directamente en campo implica alta inversión de recursos que, junto con el tiempo, son generalmente limitados. No obstante, para la planeación de las estrategias de manejo y de restauración es necesario tener conocimiento del impacto del fuego. Para esto, los sensores remotos son una herramienta práctica para la evaluación de grandes áreas, o áreas inaccesibles, impactadas por ... Article in Journal/Newspaper Arctic Universidad Nacional de Colombia: Portal de Revistas UN Alta Inaccesibles ENVELOPE(-46.733,-46.733,-60.567,-60.567) Advances in Climate Change Research 12 4 539 552 |