Mirar desde el espacio o mirar hacia otro lado : tendencias en teledetección y su situación en la geografía española

Realizamos una revisión general sobre el estado de desarrollo de la teledetección, destacando algunas líneas de avance tecnológico reciente que abren nuevas vías de aplicación e investigación. Mención especial en estos avances merece la puesta en órbita de nuevos sensores (lidar, hiperespectral, radar interferométrico), el creciente acceso a la información y el desarrollo de servicios de información basados en las imágenes. Los geógrafos podemos impulsar més este desarrollo en nuestro país, enriqueciendo la formación en esta línea y los proyectos de investigación aplicada, de tal manera que esta técnica forme parte del núcleo temático de la geografía.En aquest article, fem una revisió general sobre el desenvolupament de la teledetecció, destacant algunes línies de avenç tecnològic recent que obren noves vies d'aplicació i investigació. Una menció especial mereix la posada en òrbita de nous sensors (lidar, hiperespectral, radar interferomètric), el creixent accés a la informació i el desenvolupament de serveis d'informació basats en les imatges. Els geògrafs podem contribuir a impulsar més aquest desenvolupament en el nostre país, enriquint la formació en aquesta línia i els projectes d'investigació aplicada, de tal manera que aquesta tècnica entri a formar part del nucli temàtic de la geografia.Dans cet article nous faisons une révision générale sur le développement de la télédétection, mettant en évidence quelques lignes de progrès technologique récent que ouvrent de nouvelles voies d'application et recherche. Une mention spéciale mérite l'auberge en orbita de nouveaux capteurs (lidar, hyperspectral, radar interferomètric), le croissant accès à l'information et le développement de services d'information basés sur les images. Nous les géographes pouvons contribuer à pousser plus ce développement dans notre pays, en enrichissant la formation dans cette ligne et les projets de recherche appliquée, de telle manière que cette technique fait partie du noyau thématique de la géographie.In this paper, we present a review on the current developments in satellite remote sensing, emphasizing some of the new technological advances. The main ones are related to the launched of satellites with new sensor technologies (lidar, hiperespectral, interferometric radar), the improving in accessibility to data and information derived from satellite images, and the development of value-added services based on satellite images. Geographers should make a further effort to use these technologies, improving the university education and the applied research in this field. Therefore, satellite remote sensing can be part of the «core» of geographical teaching and research, since it is essentially a spatial technique

Aportaciones de la Teledetección espacial a la cartografía de ocupación del suelo.

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La incidencia de los incendios forestales en España

Este trabajo forma parte del número monográfico de la revista "Serie Geográfica" dedicado a Incendios forestales (No. 7, 1997-1998).[EN] Forest fíre incidence is a severe environmental problem in Spain. This paper analyses the problem through the study of the available fíre statistics. Most relevant factors affecting fíre incidence in Spain are also reviewed. The main factors of fíre occurrence are analysed as well as the situation of Spain in the European context. Special emphasis is provided on large fíres (above 500 hectares), which are the most destructive from both an environmental and economical point of view.[ES] La incidencia de incendios forestales en nuestro país constituye un problema ambiental de primera magnitud. En el presente articulo se realiza un análisis del fenómeno a través del estudio de las estadísticas más recientes. Se evalúan igualmente los principales factores de incidencia y la situación de España en el contexto europeo. Se hace especial hincapié en los grandes incendios, que son los más catastróficos, tanto desde el punto de vista ambiental como económico.Peer reviewe

Teledetección, S.I.G. y Cambio Global

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Análisis visual de imágenes «thematic mapper» para el estudio urbano. El caso de la ciudad de Madrid.

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Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2 = 0.84), quadratic mean diameter (R2 = 0.82), canopy height (R2 = 0.79), canopy base height (R2 = 0.78) and canopy fuel load (R2 = 0.79). The lowest performing models included basal area (R2 = 0.76), stand volume (R2 = 0.73), canopy bulk density (R2 = 0.67) and stand density index (R2 = 0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.This paper was developed as a result of two mobility grants funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering (TEE Project) and the Generalitat Valenciana (BEST/2012/235). The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE. In addition, the authors thank the Panther Creek Remote Sensing and Research cooperative program for the data provided for this research, Jim Flewelling (Seattle Biometrics) and George McFadden (Bureau of Land Management) for their help in data availability and preparation.Hermosilla Gómez, T.; Ruiz Fernández, LÁ.; Kazakova, AN.; Coops, N.; Moskal, LM. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire. 23(2):224-233. https://doi.org/10.1071/WF13086S224233232Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/tac.1974.1100705Andersen, H.-E., McGaughey, R. J., & Reutebuch, S. E. (2005). Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment, 94(4), 441-449. doi:10.1016/j.rse.2004.10.013Arroyo, L. A., Pascual, C., & Manzanera, J. A. (2008). Fire models and methods to map fuel types: The role of remote sensing. Forest Ecology and Management, 256(6), 1239-1252. doi:10.1016/j.foreco.2008.06.048Ashworth, A., Evans, D. L., Cooke, W. H., Londo, A., Collins, C., & Neuenschwander, A. (2010). Predicting Southeastern Forest Canopy Heights and Fire Fuel Models using GLAS Data. Photogrammetric Engineering & Remote Sensing, 76(8), 915-922. doi:10.14358/pers.76.8.915Buddenbaum, H., Seeling, S., & Hill, J. (2013). Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands. International Journal of Remote Sensing, 34(13), 4511-4524. doi:10.1080/01431161.2013.776721Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29(2), 147-159. doi:10.1016/0034-4257(89)90023-0CHUVIECO, E., & SALAS, J. (1996). Mapping the spatial distribution of forest fire danger using GIS. International journal of geographical information systems, 10(3), 333-345. doi:10.1080/02693799608902082Chuvieco, E., Riaño, D., Aguado, I., & Cocero, D. (2002). Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: Applications in fire danger assessment. International Journal of Remote Sensing, 23(11), 2145-2162. doi:10.1080/01431160110069818Chuvieco, E., Cocero, D., Riaño, D., Martin, P., Martı́nez-Vega, J., de la Riva, J., & Pérez, F. (2004). Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92(3), 322-331. doi:10.1016/j.rse.2004.01.019Cruz, M. G., Alexander, M. E., & Wakimoto, R. H. (2003). Assessing canopy fuel stratum characteristics in crown fire prone fuel types of western North America. International Journal of Wildland Fire, 12(1), 39. doi:10.1071/wf02024Drake, J. B., Dubayah, R. O., Clark, D. B., Knox, R. G., Blair, J. B., Hofton, M. A., … Prince, S. (2002). Estimation of tropical forest structural characteristics using large-footprint lidar. Remote Sensing of Environment, 79(2-3), 305-319. doi:10.1016/s0034-4257(01)00281-4Erdody, T. L., & Moskal, L. M. (2010). Fusion of LiDAR and imagery for estimating forest canopy fuels. Remote Sensing of Environment, 114(4), 725-737. doi:10.1016/j.rse.2009.11.002Falkowski, M. J., Gessler, P. E., Morgan, P., Hudak, A. T., & Smith, A. M. S. (2005). Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling. Forest Ecology and Management, 217(2-3), 129-146. doi:10.1016/j.foreco.2005.06.013Flannigan, M. ., Stocks, B. ., & Wotton, B. . (2000). Climate change and forest fires. Science of The Total Environment, 262(3), 221-229. doi:10.1016/s0048-9697(00)00524-6García, M., Popescu, S., Riaño, D., Zhao, K., Neuenschwander, A., Agca, M., & Chuvieco, E. (2012). Characterization of canopy fuels using ICESat/GLAS data. Remote Sensing of Environment, 123, 81-89. doi:10.1016/j.rse.2012.03.018González-Olabarria, J.-R., Rodríguez, F., Fernández-Landa, A., & Mola-Yudego, B. (2012). Mapping fire risk in the Model Forest of Urbión (Spain) based on airborne LiDAR measurements. Forest Ecology and Management, 282, 149-156. doi:10.1016/j.foreco.2012.06.056Hall, S. A., Burke, I. C., Box, D. O., Kaufmann, M. R., & Stoker, J. M. (2005). Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management, 208(1-3), 189-209. doi:10.1016/j.foreco.2004.12.001Harding, D. J. (2005). ICESat waveform measurements of within-footprint topographic relief and vegetation vertical structure. Geophysical Research Letters, 32(21). doi:10.1029/2005gl023471Heinzel, J., & Koch, B. (2011). Exploring full-waveform LiDAR parameters for tree species classification. International Journal of Applied Earth Observation and Geoinformation, 13(1), 152-160. doi:10.1016/j.jag.2010.09.010Höfle, B., Hollaus, M., & Hagenauer, J. (2012). Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 134-147. doi:10.1016/j.isprsjprs.2011.12.003HYDE, P., DUBAYAH, R., PETERSON, B., BLAIR, J., HOFTON, M., HUNSAKER, C., … WALKER, W. (2005). Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems. Remote Sensing of Environment, 96(3-4), 427-437. doi:10.1016/j.rse.2005.03.005Keane, R. E., Burgan, R., & van Wagtendonk, J. (2001). Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling. International Journal of Wildland Fire, 10(4), 301. doi:10.1071/wf01028Kim, Y., Yang, Z., Cohen, W. B., Pflugmacher, D., Lauver, C. L., & Vankat, J. L. (2009). Distinguishing between live and dead standing tree biomass on the North Rim of Grand Canyon National Park, USA using small-footprint lidar data. Remote Sensing of Environment, 113(11), 2499-2510. doi:10.1016/j.rse.2009.07.010Koetz, B., Morsdorf, F., Sun, G., Ranson, K. J., Itten, K., & Allgower, B. (2006). Inversion of a Lidar Waveform Model for Forest Biophysical Parameter Estimation. IEEE Geoscience and Remote Sensing Letters, 3(1), 49-53. doi:10.1109/lgrs.2005.856706Lefsky, M. A., Cohen, W. B., Acker, S. A., Parker, G. G., Spies, T. A., & Harding, D. (1999). Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests. Remote Sensing of Environment, 70(3), 339-361. doi:10.1016/s0034-4257(99)00052-8Listopad, C. M. C. S., Drake, J. B., Masters, R. E., & Weishampel, J. F. (2011). Portable and Airborne Small Footprint LiDAR: Forest Canopy Structure Estimation of Fire Managed Plots. Remote Sensing, 3(7), 1284-1307. doi:10.3390/rs3071284Mallet, C., & Bretar, F. (2009). Full-waveform topographic lidar: State-of-the-art. ISPRS Journal of Photogrammetry and Remote Sensing, 64(1), 1-16. doi:10.1016/j.isprsjprs.2008.09.007Morsdorf, F., Meier, E., Kötz, B., Itten, K. I., Dobbertin, M., & Allgöwer, B. (2004). LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sensing of Environment, 92(3), 353-362. doi:10.1016/j.rse.2004.05.013Neuenschwander, A. L. (2009). Landcover classification of small-footprint, full-waveform lidar data. Journal of Applied Remote Sensing, 3(1), 033544. doi:10.1117/1.3229944Reich, R. M., Lundquist, J. E., & Bravo, V. A. (2004). Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA. International Journal of Wildland Fire, 13(1), 119. doi:10.1071/wf02049Reitberger, J., Krzystek, P., & Stilla, U. (2008). Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing, 29(5), 1407-1431. doi:10.1080/01431160701736448Riaño, D., Chuvieco, E., Salas, J., Palacios-Orueta, A., & Bastarrika, A. (2002). Generation of fuel type maps from Landsat TM images and ancillary data in Mediterranean ecosystems. Canadian Journal of Forest Research, 32(8), 1301-1315. doi:10.1139/x02-052Riaño, D. (2003). Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling. Remote Sensing of Environment, 86(2), 177-186. doi:10.1016/s0034-4257(03)00098-1Riaño, D., Chuvieco, E., Condés, S., González-Matesanz, J., & Ustin, S. L. (2004). Generation of crown bulk density for Pinus sylvestris L. from lidar. Remote Sensing of Environment, 92(3), 345-352. doi:10.1016/j.rse.2003.12.014Riaño, D., Chuvieco, E., Ustin, S. L., Salas, J., Rodríguez-Pérez, J. R., Ribeiro, L. M., … Fernández, H. (2007). Estimation of shrub height for fuel-type mapping combining airborne LiDAR and simultaneous color infrared ortho imaging. International Journal of Wildland Fire, 16(3), 341. doi:10.1071/wf06003SKOWRONSKI, N., CLARK, K., NELSON, R., HOM, J., & PATTERSON, M. (2007). Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey. Remote Sensing of Environment, 108(2), 123-129. doi:10.1016/j.rse.2006.09.032Skowronski, N. S., Clark, K. L., Duveneck, M., & Hom, J. (2011). Three-dimensional canopy fuel loading predicted using upward and downward sensing LiDAR systems. Remote Sensing of Environment, 115(2), 703-714. doi:10.1016/j.rse.2010.10.012Van Leeuwen, M., & Nieuwenhuis, M. (2010). Retrieval of forest structural parameters using LiDAR remote sensing. European Journal of Forest Research, 129(4), 749-770. doi:10.1007/s10342-010-0381-4Vaughn, N. R., Moskal, L. M., & Turnblom, E. C. (2012). Tree Species Detection Accuracies Using Discrete Point Lidar and Airborne Waveform Lidar. Remote Sensing, 4(2), 377-403. doi:10.3390/rs4020377Wagner, W., Hollaus, M., Briese, C., & Ducic, V. (2008). 3D vegetation mapping using small‐footprint full‐waveform airborne laser scanners. International Journal of Remote Sensing, 29(5), 1433-1452. doi:10.1080/01431160701736398Wilson, B. A., Ow, C. F. Y., Heathcott, M., Milne, D., McCaffrey, T. M., Ghitter, G., & Franklin, S. E. (1994). Landsat MSS Classification of Fire Fuel Types in Wood Buffalo National Park, Northern Canada. Global Ecology and Biogeography Letters, 4(2), 33. doi:10.2307/2997751Zhao, K., Popescu, S., Meng, X., Pang, Y., & Agca, M. (2011). Characterizing forest canopy structure with lidar composite metrics and machine learning. Remote Sensing of Environment, 115(8), 1978-1996. doi:10.1016/j.rse.2011.04.00

Global burned area mapping from Sentinel-3 Synergy and VIIRS active fires

After more than two decades of successful provision of global burned area data the MODIS mission is near to its end. Therefore, using alternative images to generate moderate resolution burned area maps becomes critical to guarantee temporal continuity of these products. This paper presents the development of a hybrid algorithm based on Copernicus Sentinel-3 (S3) Synergy (SYN) data and Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fires for global detection of burned areas. Using the synergistic and co-located measurements of OLCI and SLSTR instruments on board S3A and S3B, the SYN product offers global, near-daily surface reflectance data at 300 m for both sensors. Our algorithm relied on SYN shortwave infrared (SWIR) bands to compute a multi-temporal separability index that enhanced the burn signal. Active fires from the VIIRS sensor were used to generate spatio-temporal clusters for determining local detection thresholds. Active fires were filtered from those thresholds to obtain the seeds from which a contextual growing was applied to extract burned patches. The algorithm was processed globally for 2019 data to generate a new burned area product, named FireCCIS310. Based on a stratified random sampling, error estimates showed an important reduction of omission errors versus other global burned area products while keeping the commission errors at a similar level (Oe = 41.2% ± 3.0%, Ce = 19.2% ± 1.7%). The new FireCCIS310 dataset included 4.99 million km2 for the year 2019, which implied around 1 million more than the precursor FireCCI51 product, based on MODIS 250 m reflectance values. Temporal reporting accuracy was improved as well, detecting 53% of the burned pixels within a 0–1 day difference. Besides, the new product was much less affected by the border effects than FireCCI51, as a result of an improved active fire filtering process. The FireCCIS310 product is accessible through the CCI Open Data Portal (https://climate.esa.int/es/odp/#/dashboard, last accessed on July 2022).This research has been supported by the ESA Climate Change Initiative - Fire ECV (contract no. 4000126706/19/I-NB), and the Spanish Ministry of Science, Innovation, and Universities through a FPU doctoral fellowship (FPU17/02438)

Método basado en teledetección para estimar la emisión de gases efecto invernadero por quema de biomasa

La quema de biomasa es una fuente importante de gases efecto invernadero en países en vías de desarrollo. En Colombia, el cambio de uso del suelo, la silvicultura y el sector agropecuario superan el 50% de las emisiones totales de efecto invernadero.El fuego se utiliza con frecuencia como un mecanismo para cambiar el uso del suelo. Los Llanos orientales y la Amazonía colombiana están sometidos todos los años a la quema de biomasa, especialmente entre enero y marzo. Los estudios en la distribución espacial y temporal de las emisiones son importantes de cara a los informes en el ámbito nacional. Este artículo de revisión describe el método para hacer estas estimaciones utilizando teledetección y algunos de los resultados disponibles para Colombia.ABSTRACTBiomass burning is a major source of greenhouse gas emissions in developing countries. In Colombia, land use change, forestry, and agriculture are responsible for more than 50% of the total greenhouse gas emissions. Fire is commonly used as a mechanism for land use change. In Colombia the Llanos Orientales and the Amazonia are subject to biomass burning every year during the dry season, specially from January to March. Studies of the spatial and temporal distribution of emissions are required for emissions report at a national level. The goal of this state of the art article is to describe a method to estimate emissions with a remote sensing approach and to present some of the variables already measured in Colombia.Key words: emissions, remote sensing, biomass, burned area.