Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities

  • Cristina Gómez INIA. Forest Research Centre. Department of Forest Dynamics and Management. Ctra. La Coruña km 7.5 28040 Madrid, Spain. Department of Geography and Environment, School of Geoscience, University of Aberdeen, Aberdeen AB24 3UE, Scotland, UK.
  • Pablo Alejandro Quasar Science Resources, Ctra. La Coruña km 22.3, Las Rozas, 28232 Madrid.
  • Txomin Hermosilla Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, British Columbia, V8Z 1M5, Canada. Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC, V6T 1Z4.
  • Fernando Montes INIA. Forest Research Centre. Department of Forest Dynamics and Management. Ctra. La Coruña km 7.5 28040 Madrid.
  • Cristina Pascual Sustainable Environmental Management Group (SILVANET), Department of Forest and Environmental Engineering and Management. Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid.
  • Luis Angel Ruiz Geo-Environmental Cartography and Remote Sensing Group, Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia.
  • Flor Álvarez-Taboada Geomatics and Cartographic Engineering Group (GEOINCA), Department of Cartographic Engineering, Geodesy and Photogrammetry, Universidad de León, Campus de Ponferrada, Avda. Astorga s/n, 24401 Ponferrada, León.
  • Mihai Tanase Department of Geology, Geography and Environment, University of Alcala, C. Colegios 2, Alcala de Henares 28801, Spain. National Institute for Research and Development in Forestry, Bd. Eroilor 128, Ilfov.
  • Ruben Valbuena University of Cambridge, Department of Plant Sciences, Forest Ecology and Conservation, Downing Street, CB2 3EA Cambridge, UK. University of Eastern Finland, Faculty of Forest Sciences, PO Box 111, Joensuu, Finland. Bangor University, School of Natural Sciences, LL57 2DGA Bangor.


Forest ecosystems provide a host of services and societal benefits, including carbon storage, habitat for fauna, recreation, and provision of wood or non-wood products. In a context of complex demands on forest resources, identifying priorities for biodiversity and carbon budgets require accurate tools with sufficient temporal frequency. Moreover, understanding long term forest dynamics is necessary for sustainable planning and management. Remote sensing (RS) is a powerful means for analysis, synthesis and report, providing insights and contributing to inform decisions upon forest ecosystems. In this communication we review current applications of RS techniques in Spanish forests, examining possible trends, needs, and opportunities offered by RS in a forestry context. Currently, wall-to-wall optical and LiDAR data are extensively used for a wide range of applications—many times in combination—whilst radar or hyperspectral data are rarely used in the analysis of Spanish forests. Unmanned Aerial Vehicles (UAVs) carrying visible and infrared sensors are gaining ground in acquisition of data locally and at small scale, particularly for health assessments. Forest fire identification and characterization are prevalent applications at the landscape scale, whereas structural assessments are the most widespread analyses carried out at limited extents. Unparalleled opportunities are offered by the availability of diverse RS data like those provided by the European Copernicus programme and recent satellite LiDAR launches, processing capacity, and synergies with other ancillary sources to produce information of our forests. Overall, we live in times of unprecedented opportunities for monitoring forest ecosystems with a growing support from RS technologies.


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Adnan S, Maltamo M, Coomes DA, Valbuena R, 2017. Effects of Plot Size, Stand Density and Scan Density on the Relationship between Airborne Laser Scanning Metrics and the Gini Coefficient of Tree Size Inequality. Can J Forest Res 47 (12): 1590-1602.

Adnan S, Maltamo M, Coomes DA, García-Abril A, Malhi Y, Manzanera JA, Butt N, Morecroft M, Valbuena R, 2018. A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions. Forest Ecol Manag 433: 111-121.

Alberdi I, Vallejo R, Álvarez-González JG, Condés S, González-Ferreiro E, Guerrero S, Hernández L, Martínez-Jáuregui M, Montes F, Oliveira N, et al., 2017. The multi-objective Spanish National Forest Inventory. Forest Syst 26 (2): e04S.

Alonso-Benito A, Arroyo LA, Arbelo M, Hernández-Leal P, 2016. Fusion of WorldView-2 and LiDAR data to map fuel types in the Canary Islands. Remote Sens-Basel 8 (8): 669.

Álvarez-Martínez JM, Jiménez-Alfaro B, Barquín J, Ondiviela B, Recio M, Silió-Calzada A, Juanes JA, 2017. Modelling the area of occupancy of habitat types with remote sensing. Methods Ecol Evol 9: 580-593.

Álvarez-Taboada MF, Cimadevila HL, Rodríguez Pérez JR, Picos Martín J, 2004. Workflow to improve the forest management of Eucalyptus globulus stands affected by Gonipterus scutellatus in Galicia, Spain using remote sensing and GIS, Proc. SPIE 5574, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IV, (22 October 2004).

Álvarez-Taboada MF, 2006. Remote sensing and Geoinformation systems applied to the forest management of Eucalyptus globulus Labill. Stands damaged by Gonipterus scutellatus Gyllendall in Galicia. Doctoral Thesis. Universidade de Vigo. 319 pp.

Álvarez-Taboada MF, Lorenzo Cimadevila H, Wulder M, 2007a. Monitorización del estado sanitario de las masas de Eucapyptus globulus en Galicia empleando modelos de proceso, SIG y teledetección. Proc 2º simposio iberoamericano de Eucalipto Globulus in Vigo (Spain). October 17-20, CIDEU 4 vol II, pp 41-47.

Álvarez-Taboada MF, Rodríguez-Pérez JR, Castedo-Dorado F, Vega-Nieva D, 2007b. An operational protocol for post-fire evaluation at landscape scale in an object-oriented environment. Proceedings of the 6th International Workshop of the EARSeL Special Interest Group on Forest Fires JRC 8072. pp 202 – 207 (2007). 6th International Workshop of the EARSeL Special Interest Group on Forest Fires. Tesalonica, Grecia.

Álvarez-Taboada, F, Sanz-Ablanedo, E, Rodríguez Pérez, JR, Castedo-Dorado F, Lombardero MJ, 2014. Multi-sensor and multi-scale system for monitoring forest health in Pinus radiata stands defoliated by Lymantria dispar in NW Spain. Proceedings of the ForestSAT Open Conference System,

Aragonés D, Rodríguez-Galiano V, Caparros-Santiago JA, Navarro-Cerrillo RM, 2017. El uso de la fenología de la superficie terrestre para discriminar entre especies de pinos mediterráneos. Nuevas plataformas y sensores de teledetección, XVII Congreso de la Asociación Española de Teledetección (Eds. Ruiz LA, Estornell J, Erena M), Murcia (Spain), October 3-7, pp: 219-222.

Arellano S, Vega JA, Rodríguez y Silva F, Fernández C, Vega-Nieva D, Álvarez-González JG, Ruiz-González AD, 2017. Validación de los índices de teledetección dNBR y RdNBR para determinar la severidad del fuego en el incendio forestal de Oia-O Rosal (Pontevedra) en 2013. Revista de Teledetección 49: 49-61.

Arellano‐Pérez S, Castedo‐Dorado F, López‐Sánchez C, González‐Ferreiro E, Yang Z, Díaz‐Varela R, Ruiz‐González A, 2018. Potential of Sentinel‐2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard. Remote Sens-Basel 10(10): 1645.

Arias-Rodil M, Diéguez-Aranda U, Álvarez-González JG, Pérez-Cruzado C, Castedo-Dorado F, González-Ferreiro E, 2018. Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data. Ann For Sci 75 (2): 1-12.

Arozarena A, Villa G, Hermosilla J, Papí F, Valcárcel N, Peces JJ, Doménech E, García C, Tejeiro JA, 2006. El Plan Nacional de Observación del Territorio en España: situación actual y próximos pasos. Mapping Interactivo 111: 16-22.

Arroyo LA, Healey SP, Cohen WB, Cocero D, Manzanera JA, 2006. Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region. J Geophys Res 111: G04S04.

Askne JIH, Santoro M, Smith G, Fransson JES, 2003. Multitemporal Repeat-Pass SAR Interferometry of Boreal Forests. IEEE Trans Geosci Rem Sens 41: 1540-1550.

Aulló-Maestro I, Gómez C, Cuevas R, Rubio A, Montes F, 2017. Dinámica forestal de Pinus sylvestris L. y Quercus pyrenaica Willd. en el bosque de Hoyocasero (Ávila) mediante serie temporal Landsat (1984-2016) y métodos geoestadísticos. Nuevas plataformas y sensores de teledetección, XVII Congreso de la Asociación Española de Teledetección (Eds. Ruiz LA, Estornell J, Erena M), Murcia (Spain), October 3-7, pp: 143-146.

Axelsson C, Skidmore AK, Schlerf M, Fauzi A, Verhoef W, 2012. Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression. Int J Remote Sens 34: 1724-1743.

Banskota A, Kayastha N, Falkowski MJ, Wulder MA, Froese RE, White JC, 2014. Forest Monitoring Using Landsat Time Series Data: A Review. Can J Remote Sens 40: 362-384.

Bastarrika A, Alvarado M, Artano K, Martínez MP, Mesanza A, Torre L, Ramo R, Chuvieco E, 2014. BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data. Remote Sens-Basel 6: 12360-12380.

Belenguer-Plomer MA, Tanase MA, Fernández-Carrillo A, Chuvieco E, 2018. Insights into burned areas detection from Sentinel-1 data and locally adaptive algorithms, Proc. SPIE 10790, Earth Resources and Environmental Remote Sensing/GIS Applications IX, 107901S (9 October 2018).

Belward AS, Skøien JO, 2015. Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS J Photogramm103: 115-128.

Bengoa JL, De Blanco V, Nafria DA, 2017. Clasificación semiautomática de cubiertas naturales arboladas en Castilla y León. 7º Congreso Forestal Español. 26-30 de junio de 2017. Plasencia, Cáceres, España.

Blanco-Martínez J, Rodríguez F, Martínez S, Martínez AA, García JB, Fernández JJ, Roldán A, Diez FJ, Lizarralde I, Cabrera M, 2017. Generación de un inventario forestal regional y una cartografía de modelos de combustible para Castilla-La Mancha. 7 Congreso Forestal Español, 26-30 de junio, Plasencia, Spain.

Blázquez-Casado Á, González-Olabarria JR, Martín-Alcón S, Just A, Cabré M, Coll Ll, 2015. Assessing post-storm forest dynamics in the Pyrenees using high-resolution LiDAR data and aerial photographs. J Mt Sci 12 (4): 841.

Bonal R, Vargas-Osuna E, Mena JD, Aparicio JM, Santoro M, Martín A, 2018. Looking for variable molecular markers in the chestnut gall wasp Dryocosmus kuriphilus: First comparison across genes Scientific Reports. 8. 10.1038/s41598-018-23754-z.

Borràs J, Delegido J, Pezzola A, Pereira M, Morassi G, Camps-Valls G, 2017. Clasificación de usos del suelo a partir de imágenes Sentinel-2. Revista de Teledetección 48: 55-66.

Botella-Martínez MA, Fernández-Manso A, 2017. Estudio de la severidad post-incendio en la Comunidad Valenciana comparando los índices dNBR, RdNBR y RBR a partir de imágenes Landsat 8. Revista de Teledetección 49: 33-47.

Bottalico F, Chirici G, Giannini R, Mele S, Mura M, Puxeddu M, McRoberts RE, Valbuena R, Travaglini D, 2017. Modeling Mediterranean Forest Structure Using Airborne Laser Scanning Data. Int J Appl Earth Obs 57: 145-153.

Bradley BA, 2014. Remote Detection of Invasive Plants: A Review of Spectral, Textural and Phenological Approaches. Biol Invasions 16: 1411-1425.

Cabello J, Alcaraz-Segura D, Reyes A, Lourenço P, Requena JM, Bonache J, Castillo P, Valencia S, Naya J, Ramírez L, Serrada J, 2016. Sistema para el seguimiento del funcionamiento de ecosistemas en la Red de Parques Nacionales de España mediante teledetección. Revista de Teledetección 46: 119-131.

Camarero JJ, Bigler C, Linares JC, Gil-Pelegrín E, 2011. Synergistic effects of past historical logging and drought on the decline of Pyrenean silver fir forests. For Ecol Manage 262: 759–769.

Cardil A, Vepakomma U, Brotons Ll, 2017. Assessing processionary moth defoliation using unmanned aerial systems. Forests 8: 402.

Carter G, 1993. Responses of leaf spectral reflectance to plant stress. Am J Bot 80: 231-243.

Cartus O, Santoro, M., Kellndorfer J, 2012. Mapping forest aboveground biomass in the Northeastern United States with ALOS PALSAR dual-polarization L-band. Remote Sens Environ 124: 466-478.

Castedo-Dorado F, Lago-Parra G, Lombardero MJ, Liebhold AM, Álvarez-Taboada F, 2016. European gypsy moth (Lymantria dispar dispar L.) completes development and defoliates exotic radiata pine plantations in Spain. New Zeal J For Sci 46: 18.

Chrysafis I, Mallinis G, Gitas I, Tsakiri-Strati M, 2017. Estimating Mediterranean forest parameters using multi seasonal Landsat 8 OLI imagery and an ensemble learning method. Remote Sens Environ 199: 154-166.

Chuvieco 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. Int J Remote Sens 23 (11): 2145-2162.

Chuvieco E, Aguado I, Cocero D, Riaño D, 2003. Design of an empirical index to estimate fuel moisture content from NOAA–AVHRR analysis in forest fire danger studies. Int J Remote Sens 24(8): 1621-1637.

Chuvieco E, Cocero D, Riaño D, Martínez P, Martínez-Vega J, De la Riva J, Pérez F, 2004a. Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sens Environ 92: 322-331.

Chuvieco E, Cocero D, Aguado I, Palacios-Orueta A, Prado E, 2004b. Improving burning efficiency estimates through satellite assessment of fuel moisture content. J Geoph Res-Atmos 109: D14S07.

Chuvieco E, De Santis A, Riaño D, Halligan K, 2007. Simulation approaches for burn severity estimation using remotely sensed images. Fire Eco 3 (1): 129-150.

Cicuéndez V, Litago J, Huesca M, Rodríguez-Rastrero M, Recuero L, Merino de Miguel S, Palacios-Orueta A, 2015. Assessment of the gross primary production dynamics of a Mediterranean holm oak forest by remote sensing time series analysis. Agroforest Syst 89 (3): 491-510.

Cifuentes JM, Fernández-Manso A, Sanz-Ablanedo E, 2017. Utilización de vehículo aéreo no tripulado (VANT) en el estudio de los niveles de severidad por chancro del castaño en el NO de España. In: Nuevas plataformas y sensores de teledetección. Nuevas plataformas y sensores de teledetección, XVII Congreso de la Asociación Española de Teledetección (Eds. Ruiz LA, Estornell J, Erena M), Murcia (Spain), October 3-7, pp: 477-480.

Clark ML, Roberts DA, Clark DB, 2005. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sens Environ 96: 375-398.

Claverie M, Ju J, Masek JG, Dungan JL, Vermote EF, Roger J-C, Skakun SV, Justice C, 2018. The harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sens Environ 219: 145-161.

Cloude SR, Papathanassiou KP, 1998. Polarimetric SAR Interferometry. IEEE Trans Geosci Rem Sens 36: 1551-1565.

Cohen WB, Goward SN, 2004. Landsat’s role in ecological applications of remote sensing. Biosciences 54 (6): 535-545.[0535:LRIEAO]2.0.CO;2

Cohen WB, Yang Z, Stehman SV, Schroeder TA, Bell DM, Masek JG, Huang Ch, Meighs GW, 2016. Forest disturbance across the conterminous United States from 1985-2012: the emerging dominance of forest decline. Forest Ecol Manag 360: 242-252.

Cohen WB, Yang Z, Healey SP, Kennedy RE, Gorelick N, 2018. A LandTrendr multispectral ensemble for forest disturbance detection. Remote Sens Environ 205: 131-140.

Condés S, Fernández-Landa A, Rodríguez F, 2013. Influencia del inventario de campo en el error de muestreo obtenido en un inventario con tecnología LiDAR. 6° Congreso Forestal Español. SECF

Crespo-Peremarch P, Tompalski P, Coops NC, Ruiz LA, 2018. Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data. Remote Sens Environ 217: 400-413.

Cubbage F, Harou P, Sills E, 2007. Policy instruments to enhance multi-functional forest management. Forest Policy Econ 9: 833-851.

Datt B, McVicar TR, Van Niel TG, Jupp DLB, Pearlman JS, 2003. PreprocessingEO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE T Geosci Remote 41: 1246-1259.

De Santis A, Chuvieco E, 2007. Burn severity estimation from remotely sensed data: performance of simulation versus empirical models. Remote Sens Environ 108: 422-435.

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.

Debouk H, Riera-Tatche R, Vega-García C, 2013. Assessing Post-Fire Regeneration in a Mediterranean Mixed Forest Using LiDAR Data and Artificial Neural Networks. 2013. Photogramm Eng Rem S 79 (12): 1121-1130.

Diaz-Balteiro L, Romero C, 2008. Making forestry decisions with multiple criteria: A review and an assessment. Forest Ecol Manag 255: 3222-3241.

Díaz-Delgado R, Pons X, 1999. Empleo de imágenes de teledetección para el análisis de los niveles de severidad causados por el fuego. Revista de Teledetección 12: 63-68.

Dobson MC, Ulaby T, Le Toan T, Beaudoin A, Kasischke ES, 1992. Dependence of radar backscatter on coniferous forest biomass. IEEE Trans Geosci Rem Sens 30: 412-415.

Domingo D, Lamelas-Gracia MT, Montealegre-Gracia AL, de la Riva-Fernández J, 2017. Comparison of regression models to estimate biomass losses and CO2 emissions using low-density airborne laser scanning data in a burnt Aleppo pine forest. Eur J Remote Sens 50 (1): 384-396.

Domingo D, Lamelas M, Montealegre A, de la Riva J, 2018. Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data. Forests 9 (4): 158.

Duncanson LI, Neimann KO, Wulder MA, 2010. Integration of GLAS and Landsat TM data for aboveground biomass estimation. Can J Remote Sens 36 (2): 129-141.

Estornell J, Ruiz LA, Velázquez-Martí B, Fernández-Sarría A, 2011a. Estimation of shrub biomass by airborne LiDAR data in small forest stands. Forest Ecol Manag 262: 1697-1703.

Estornell J, Ruiz LA, Velázquez-Marti B, 2011b. Study of shrub cover and height using LiDAR data in a Mediterranean area. For Sci 57 (3): 171-179.

Estornell J, Ruiz LA, Velázquez-Martí B, Hermosilla T, 2012. Estimation of biomass and volumen of shrub vegetation using LiDAR and spectral data in a Mediterranean environment. Biomass Bioenerg 46: 710-721.

EC (European Comission), 2017.

Fassnacht FE, Latifi H, Stereńczak K, Modzelewska A, Lefsky M, Waser LT, Straub C, Ghosh A, 2016. Review of studies on tree species classification from remotely sensed data. Remote Sens Environ 186: 64-87.

Fauzi A, Skidmore AK, Gils H, Schlerf M, Heitkönig I, 2013. Shrimp pond effluent dominates foliar nitrogen in disturbed mangroves as mapped using hyperspectral imagery. Marine Poll Bull 76: 42-51.

Fernández-García V, Santamarta M, Fernández-Manso A, Quintano C, Marcos E, Calvo L, 2018. Burn severity metrics in fire-prone pine ecosystems along a climatic gradient using Landsat imagery, Remote Sens Environ 206: 205-217.

Fernández-Guisuraga JM, Sanz-Ablanedo E, Suárez-Seoane S, Calvo L, 2018. Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges. Sensors 18: 586.

Fernández-Landa A, Tomé JL, Sandoval VJ, Vallejo R, 2017. Integrando datos LiDAR, información satelital y parcelas del Inventario Forestal Nacional Español en la predicción de variables de inventario. 7º Congreso Forestal Español. 26-30 de junio de 2017. Plasencia, Cáceres, España.

Fernández-Landa A, Fernández-Moya J, Tomé JL, Algeet-Abarquero N, Guillén-Climent ML, Vallejo R, Sandoval V, Marchamalo M, 2018. High resolution forest inventory of pure and mixed stands at regional level combining National Forest Inventory field plots, Landsat, and low density LiDAR. Int J Remote Sens 39 (14):4830-4844.

Fernández-Manso O, Fernández-Manso A, Quintano C, 2014. Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images. Int J of Appl Earth Obs 31: 45-56.

Fernández-Manso A, Quintano C, Roberts DA, 2016a. Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems. Remote Sens Environ 184: 112-123.

Fernández-Manso A, Fernández-Manso O, Quintano C, 2016b. SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. Int J of Appl Earth Obs 50: 170-175.

García M, Riaño D, Chuvieco E, Danson FM, 2010. Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data. Remote Sens. Environ. 114: 816–830.

García M, Riaño D, Chuvieco E, Salas J, Danson FM, 2011. Multispectral and LiDAR data fusión for fuel type mapping using Support Vector Machine and decision rules. Remote Sens Environ 115: 1369-1379.

García M, Popescu S, Riaño D, Zhao K, Neuenschwander A, Agca M, Chuvieco E, 2012. Characterization of canopy fuels using ICESat/GLAS data, Remote Sens Environ 123: 81-89.

García-Álvarez D, Camacho Olmedo MT, 2017. Changes in the methodology used in the production of the Spanish CORINE: uncertainty analysis of the new maps. Int J of Appl Earth Obs 63: 55-67.

García-Gutiérrez J, González-Ferreiro E, Riquelme-Santos JC, Miranda D, Diéguez-Aranda U, Navarro-Cerrillo RM, 2014. Evolutionary feature selection to estimate forest stand variables using LiDAR, Int J of Appl Earth Obs 26: 119-131.

Garestier F, Dubois-Fernández PC, Papathanassiou KP, 2008. Pine Forest Height Inversion Using Single-Pass X-Band PolInSAR Data. IEEE Trans Geosci Rem Sens 46: 59-68.

Gastón A, Ciudad C, Mateo-Sánchez MC, García-Viñas JI, López-Leiva C, Fernández-Landa A, Marchamalo M, Cuevas J, de la Fuente B, Fortin M-J, Saura S, 2017. Species' habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data? Int J of Appl Earth Obs 55: 1-8.

Gómez C, Wulder MA, Montes F, Delgado JA, 2011. Forest structural diversity characterization in Mediterranean pines of Central Spain with QuickBird-2 imagery and canonical correlation analysis. Can J Remote Sens 37 (6): 628-642.

Gómez C, Wulder MA, White JC, Montes F, Delgado JA, 2012a. Characterizing 25 years of change in the area, distribution, and carbon stock of Mediterranean pines in Central Spain. Int J Remote Sens 33 (17): 5546-5573.

Gómez C, Wulder JA, Montes F, Delgado JA, 2012b. Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART). Remote Sens-Basel 4 (1): 135-159.

Gómez C, White JC, Wulder MA, Alejandro P, 2014. Historical forest biomass dynamics modelled with Landsat spectral trajectories. ISPRS J Photogramm93: 14-28.

Gómez C, Aulló-Maestro I, Montes F, 2016a. Dominant tree species dynamics informed by 30 years of Landsat time series in mountain areas of Northern Spain. ForestSAT 2016, Santiago (Chile), November, 15-18.

Gómez C, White JC, Wulder MA, 2016b. Optical remotely sensed time series data for land cover classification: A review. ISPRS Int J Remote Sens 116: 55-72.

Gómez C, Green D, 2017. Small unmanned airborne systems to support oil and gas pipeline monitoring and mapping. Arab J Geosci 10 (9): 202.

Gómez C, Hermosilla T, Martínez-Fernández J, Montes F, Aulló-Maestro I, White JC, Wulder MA, Coops NC, Vázquez A, 2017. Annual cartography of fire (1985-2015) in forest areas of the NW Spain mapped with time series of Landsat data and Composite2Change. Nuevas plataformas y sensores de teledetección, XVII Congreso de la Asociación Española de Teledetección (Eds. Ruiz LA, Estornell J, Erena M), Murcia (Spain), October 3-7, pp: 169-172.

Gómez C, Aulló-Maestro I, Alejandro P, Montes F, 2018. Presence of European beech in its Spanish southernmost limit characterized with Landsat intra-annual time series. AIT2018 IX Conference of the Italian Society of Remote Sensing. Florence (Italy), July 4-6.

Gonçalves-Seco L, González-Ferreiro E, Diéguez-Aranda U, Fraga-Bugallo B, Crecente R, Miranda D, 2011. Assessomg the attributes of high-density Eucalyptus globulus stands using airborne laser scanner data. Int J Remote Sens 32(24): 9821-9841.

González-Alonso F, Merino-de-Miguel S, Roldán-Zamarron A, García Gigorro S, Cuevas JM, 2006. Forest biomass estimation through NDVI composites. The role of remote sensed data to assess Spanish forests as carbon sinks. Int J Remote Sens, 27: 5409-5415.

González-Ferreiro E, Diéguez-Aranda U, Miranda D, 2012. Estimation of stand variables in Pinus radiata D. Don plantations using different LiDAR pulse densities. Forestry 85: 281-292.

González-Ferreiro E, Miranda D, Barreiro-Fernández L, Buján S, García-Gutiérrez J, Diéguez-Aranda U, 2013a. Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities. Forest Syst 22: 510-525.

González-Ferreiro E, Diéguez-Aranda U, Barreiro-Fernández L, Buján S, Barbosa M, Suárez JC, Bye IJ, Miranda D, 2013b. A mixed pixel- and region-based approach for using airborne laser scanning data for individual tree crown delineation in Pinus radiata D. Don plantations. Int J Remote Sens 34(21): 7671-7690.

González‐Ferreiro E, Diéguez‐Aranda U, Crecente‐Campo F, Barreiro‐Fernández L, Miranda D, Castedo‐Dorado F, 2014. Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low‐density LiDAR data. I J Wild Fire 23(3): 350‐362.

González-Ferreiro F, Arellano-Pérez S, Castedo-Dorado F, Hevia A, Vega JA, Vega-Nieva D, Álvarez-González JG, Ruiz-González AD, 2017. Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airborne laser scanning data. PLoS ONE 12(4): e0176114.

González-Olabarría JR, Rodríguez F, Fernández-Landa A, Mola-Yudego B, 2012. Mapping fire risk in the model forest of Urbión (Spain) based on airborne LiDAR measurements. For Ecol Manag 282: 149-156.

Gonzalo J, López D, Domínguez D, García A, Escapa A, 2017. On the capabilities and limitations of high altitude pseudo-satellites. Prog Aero Sci 98: 34-56.

Guerra-Hernández J, Görgens EB, García-Gutiérrez J, Carlos L, Rodríguez E, Tomé M, González-Ferreiro E, 2016. Comparison of ALS based models for estimating aboveground biomass in three types of Mediterranean forest. Eur. J. Remote Sens 49: 185–204.

Guerra-Hernández J, González-Ferreiro E, Monleón VJ, Faias SP, Tomé M, Díaz-Varela RA, 2017. Use of Multi-Temporal UAV-Derived Imagery for Estimating Individual Tree Growth in Pinus pinea Stands. Forests 8: 300.

Hall RJ, Castilla G, White JC, Cooke BJ, Skakun RS, 2016. Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective. Can Entomol 148: 1-61.

Hajnsek I, Pottier E, Cloude SR, 2003. Inversion of Surface Parameters From Polarimetric SAR. IEEE Trans Geosci Rem Sens 41: 727-744.

Henderson FM, Lewis AJ, 1998. Principles and applications of imaging radar. Manual of remote sensing: Third edition, Volume 2. United States. 896 pp.

Hermosilla T, Díaz-Manso JM, Ruiz LA, Recio JA, Fernández-Sarría A, Ferradáns-Nogueira P, 2012. Analysis of parcel-based image classification methods for monitoring the activities of the Land Bank of Galicia (Spain). Appl Geomat 4(4): 245-255.

Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, 2015. An Integrated Landsat Time Series Protocol for Change Detection and Generation of Annual Gap-Free Surface Reflectance Composites. Remote Sens Environ 158: 220-234.

Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, Campbell LB, 2016. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. Int J Digit Earth 9 (11): 1035-1054.

Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, 2017. Updating Landsat time series of surface-reflectance composites and forest change products with new observations. Int J Appl Earth Obs 63: 104-111.

Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, 2018. Disturbance-informed annual land cover classification maps of Canada’s forested ecosystems for a 29-year Landsat time series. Can J Remote Sens 44 (1): 67-87.

Hernández L, Martínez-Fernández J, Cañellas I, Vázquez de la Cueva A, 2014. Assessing spatio-temporal rates, patterns and determinants of biological invasions in forest ecosystems. The case of Acacia species in NW Spain, Forest Ecol Manag 329: 206-213.

Hernández-Clemente R, North PRJ, Hornero A, Zarco-Tejada PJ, 2017. Assessing the effects of forest health on sun-induced chlorophyll fluorescence using the FluorFLIGHT 3-D radiative transfer model to account for forest structure. Remote Sens Environ 193: 165-179.

Hernando A, Velázquez J, Valbuena R, Legrand M, García-Abril A, 2017. Influence of the Resolution of Forest Cover Maps in Evaluating Fragmentation and Connectivity to Assess Habitat Conservation Status. Ecol Indic 79: 295-302.

Hernando A, Puerto L, Mola-Yudego B, Manzanera J, García-Abril A, Maltamo M, Valbuena R, 2019. Estimation of forest biomass components through airborne LiDAR and multispectral sensors. iForest

Hevia A, Álvarez‐González JG, Ruiz Fernández E, Prendes C, Ruiz González AD, Majada J, González‐Ferreiro E, 2016. Modelling canopy fuel and forest stand variables and characterizing the influence of thinning in the stand structure using airborne LiDAR. Revista Teledetección 45: 41-55.

Hilker T, Wulder MA, Coops NC, 2008. Update of forest inventory data with LiDAR and high spatial resolution satellite imagery. Can J Remote Sens 34: 5-12.

Huang H, Roy DO, Boschetti L, Zhang HK, Yan L, Kumar SS, Gómez-Dans J, Li J, 2016. Separability analysis of Sentinel-2A multi-spectral instrument (MSI) data for burned area discrimination. Remote Sens-Basel 8: 973.

Huesca M, Merino-de-Miguel S, González-Alonso F, 2013a. An intercomparison of satellite burned area maps derived from MODIS, MERIS, SPOT-VEGETATION and ATSR images. An application to the August 2006 Galicia (Spain) forest fires. For Syst 22 (2): 222-231.

Huesca M, Merino-de-Miguel S, González-Alonso F, Martínez S, Cuevas JM, Calle A, 2013b. Using AHS hyper-spectral images to study forest vegetation recovery after a fire. Int J Remote Sens 34 (11): 4025-4048.

Hyyppä J, Inkinen M, 1999. Detecting and estimating attributes for single tree using laser scanner. Phot J Fin 16: 27-42.

INE 2017. España en cifras.">

Jensen J, 2005. Introductory digital image processing: a remote sensing perspective, 3rd ed. Pearson Education, Inc. 526 pp.

Joshi N, Mitchard ETA, Brolly M, Schumacher J, Fernández-Landa A, Johannsen VK, Marchamalo M, Fensholt R, 2017. Understanding 'saturation' of radar signals over forests. Sci Rep-UK 7 (1): 3505.

Karnkowski W, Sahajdak A, 2010. Occurrence of the pinewood nematode in Portugal and Spain - Threat for pine forests in Europe. Prog Plant Prot 50: 1260-1264.

Keane RE, Burgan R, Wagtendonk JV, 2001. Mapping wildland fuels for fire management across multiple scales: Integrating remote sensing, GIS, and biophysical modeling, Int J Wildand Fire 10: 301-319.

Kennedy RE, Yang Z, Cohen WB, 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-temporal segmentation algorithms. Remote Sens Environ 114: 2897-2910.

Kennedy RE, Yang Z, Braaten J, Copass C, Antonova N, Jordan C, Nelson P, 2015. Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA. Remote Sens Environ 166: 271-285.

Key CH, Benson NC, 1999. Measuring and remote sensing of burn severity. In L. F. Neuenschwander and K. C. Ryan (Eds.), Proceedings Joint Fire Science Conference and Workshop, Vol. II. (pp. 284). Moscow, ID: University of Idaho and International Association of Wildland Fire.

Kissinger G, Herold M, De Sy V, 2012. Drivers of Deforestation and Forest Degradation: A Synthesis Report for REDD+ Policymakers. Lexeme Consulting, Vancouver Canada, 46 pp.

Le Toan T, Beaudoin A, Guyon D, 1992. Relating forest biomass to SAR data. IEEE Trans Geosci Rem Sens 30: 403-411.

Le Toan T, Quegan S, Davidson MWJ, Balzter H, Paillou P, Papathanassiou K, Plummer S, Rocca F, Saatchi S, Shugart H, Ulander LMH, 2011. The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ 115: 2850-2860.

Leberl F, Irschara A, Pock T, Meixner P, Gruber M, Scholz S, Wiechert A, 2010. Point clouds: LiDAR versus three dimensional vision. Photogramm Eng Rem Sens76: 1123-1134.

Lefsky MA, Cohen WB, Acker SA, Parker GG, Spies TA, Harding D, 1999. LiDAR remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests. Remote Sens Environ 70: 339-361.

Lefsky MA, 2010. A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System. Geophys Res Lett 37 L15401.

Li J, Roy DP, 2017. A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sens-Basel 9: 902.

Lisein J, Pierrot-Deseilligny M, Bonnet, S, Lejeune P, 2013. A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery. Forests 4: 922-944.

Lohberger S, Stängel M, Atwood EC, Siegert F, 2018. Spatial evaluation of Indonesia’s 2015 fire-affected area and estimated carbon emissions using Sentinel-1. Glob Chang Biol 24 (2): 644-654.

López-Sánchez JM, Ballester-Berman JD, 2009. Potentials of Polarimetric SAR Interferometry for Agriculture Monitoring. Radio Sci 44: RS2010.

Malak DA, Pausas JG, Pardo-Pascual JE, Ruiz LA, 2015. Fire recurrence and the dynamics of the Enhanced Vegetation Index in a Mediterranean ecosystem. Int J App Geosp Res 6 (2): 18-35.

Manfreda S, McCabe MF, Miller PE, Lucas R, Pajuelo Madrigal V, Mallinis G, Ben Dor E, Helman D, Estes L, Ciraolo G, et al., 2018. On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sens-Basel 10: 641.

Manzanera JA, García-Abril A, Pascual C, Tejera R, Martín-Fernández S, Tokola T, Valbuena R, 2016. Fusion of airborne LiDAR and multispectral sensors reveals synergic capabilities in forest structure characterization. GISci Rem Sens 53(6): 723-738.

MAPAMA, 2011. Anuario de estadística forestal. Ministerio de Agricultura, Alimentación y Medio Ambiente, Spain, 103 pp

Marino E, Ranz P, Tomé JL, Noriega MA, Esteban J, Madrigal J, 2016. Generation of high-resolution fuel models from discrete airborne laser scanner and Landsat-8 OLI: a low-cost and highly updated methodology for large areas. Remote Sens Environ 187: 267-280.

Marino E, Tomé JL, Madrigal J, Guijarro M, Hernando C, 2017a. Efecto de la densidad de pulsos LiDAR en la caracterización estructural de combustibles en masas de pinar. 7º Congreso Forestal Español. 26-30 de junio de 2017. Plasencia, Cáceres, España.

Marino E, Ranz P, Tomé JL, 2017b. Evolución post-incendio de la estructura de la vegetación en el PN de Garajonay a partir de datos LiDAR. 7º Congreso Forestal Español. 26-30 de junio de 2017. Plasencia, Cáceres, España.

Marino E, Montes F, Tomé JL, Navarro JA, Hernando C, 2018. Vertical forest structure analysis for wildfire prevention: comparing airborne laser scanning data and stereoscopic hemispherical images. Int J Appl Earth 73: 438-449.

Martín-Alcón S, Coll Ll, de Cáceres M, Guitart L, Cabré M, Just A, González-Olabarría JR, 2015. Combining aerial LiDAR and multi-spectral imagery to assess post-fire regeneration types in a Mediterranean forest. Can J For Res 45 (7): 856-866.

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.

Martínez-Fernández J, Ruiz-Benito P, Bonet-Jornet A, Gómez C, 2019. Methodological variations in the production of CORINE Land Cover and consequences for long-term land cover change studies. The case of Spain. Int J Remote Sens

Matasci G, Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, Zald HSJ, 2018. Large-area mapping of Canadian boreal forest cover, height, biomass and other structural attributes using Landsat composites and LiDAR plots. Remote Sens Environ 209: 90-106.

Mauro F, Valbuena R, Manzanera JA, García-Abril A, 2011. Influence of Global Navigation Satellite System errors in positioning inventory plots for tree-height distribution studies. Can J For Res 41 (1): 11-23.

Mauro F, Molina I, García-Abril A, Valbuena R, Ayuga-Téllez E, 2016. Remote sensing estimates and measures of uncertainty for forest variables at different aggregation levels. Environmetrics 27 (4): 225-238.

Mauro F, Monleón VJ, Temesgen H, Ford KR, 2017a. Analysis of area level and unit level models for small area estimation in forest inventories assisted with LiDAR auxiliary information. PloS ONE 12 (12), e0189401

Mauro F, Monleón VJ, Temesgen H, Ruiz LA, 2017b. Analysis of spatial correlation in predictive models of forest variables that use LiDAR auxiliary information. Can J For Res 47 (6): 788-799.

Melicharová L, Vizoso-Arribe O, 2012. Situation of sweet chestnut (Castanea sativa Mill.) in Spain, Galicia: A review. Scientia Agr Boh 2012: 78-84.

Méndez E, Valés JJ, Pino I, Granado L, Montoya G, Prieto R, Carpintero IR, Giménez de Azcárate F, Cáceres F, Moreira JM, et al., 2016. Determination of forest biomass using remote sensing techniques with radar images. Pilot study in area of the province of Huelva. REDIAM. Revista de Teledetección 45: 71-86.

Merino de Miguel S, Huesca M, González-Alonso F, 2010. MODIS reflectance and active fire data for burn mapping and assessment at regional level. Ecol Model 221: 67-74.

Moessner KE, 1953. Photo interpretation in forest inventories. Photogramm Eng, June 1953: 496-507.

Montealegre AL, Lamelas MT, de la Riva J, García-Martín A, Escribano F, 2016. Use of low point density ALS data to estimate stand-level structural variables in Mediterranean Aleppo pine forest. Forestry 0: 1-10.

Montealegre AL, Lamelas MT, Tanase MA, de la Riva J, 2017a. Estimación de la severidad en incendios forestales a partir de datos LiDAR-PNOA y valores de Composite Burn Index. Revista de Teledetección 49: 1-16.

Montealegre AL, Lamelas-Gracia MT, García-Martín A, de la Riva-Fernández J, Escribano-Bernal F, 2017b. Using low density discrete Airborne Laser Scanning data to assess the potential carbon dioxide emission in case of a fire event in a Mediterranean pine forest. GISci Rem Sens 54 (5): 721-740.

Montero G, Ruiz-Peinado R, Muñoz M, 2005. Producción de biomasa y fijación de CO2 por los bosques espa-oles. Monografías Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Serie Forestal, Madrid, Spain.

Montero G, Serrada R, 2013. La situación de los bosques y el sector forestal en España-ISFE 2013. Sociedad Española de Ciencias Forestales. Lourizán (Pontevedra), Spain. 257 pp.

Moreira A, Krieger G, Hajnsek I, Papathanassiou K, Younis M, Lopez-Dekker P, Huber S, Villano M, Pardini M, Eineder M, et al., 2015. Tandem-L/ALOSNext: A Highly Innovative Bistatic SAR Mission for Global Observation of Dynamic Processes on the Earth's Surface. IEEE Geosc Remote Sens Mag 3 (2): 8-23.

Næsset E, 2002. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens Environ 80: 88-99.

Navarro JA, Fernández-Landa A, Tomé JL, Guillén-Climent ML, Ojeda JC, 2018. Testing the quality of forest variable estimation using dense image matching: a comparison with airborne laser scanning in a Mediterranean pine forest. Int J Rem Sens 39 (14): 4744-4760.

Navarro-Cerrillo RM, González-Ferreiro E, García-Gutiérrez J, Ceacero Ruiz CJ, Hernández-Clemente R, 2017. Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain. J For Sci 63: 88-97.

Oeser J, Pflugmacher D, Senf C, Heurich M, Hostert P, 2017. Using intra-annual Landsat time series for attributing forest disturbance agents in Central Europe. Forests 8: 251.

Olesk A, Praks J, Antropov O, Zalite K, Arumäe T, Voormansik K, 2016. Interferometric SAR coherence models for characterization of hemiboreal forests using TanDEM-X data. Remote Sens-Basel 8: 700.

Packalén P, Maltamo M, 2006. Predicting the plot volume by tree species using airborne laser scanning and aerial photographs. Forest Sci 52: 611−622.

Packalén P, Suvanto A, Maltamo M, 2009. A two stage method to estimate species-specific growing stock. Photogramm Eng Rem Sens 75: 1451−1460.

Pajares G, 2015. Overview and current status of remote sensing applications based on Unmanned Aerial Vehicles (UAVs). Photogramm Eng Rem Sens 81 (4): 281-329.

Parra A, Chuvieco E, 2005. Assessing burn severity using Hyperion data. In J Riva, Pérez-Cabello F, Chuvieco E (Eds.) Proceedings of the 5th international workshop on remote sensing and GIS applications to forest fire management: fire effects assessment (pp 239-244) Paris. Universidad d Zaragoza, GOFC-GOLD, EARSeL.

Pascual A, Pukkala T, Rodríguez F, de-Miguel S, 2016. Using Spatial Optimization to Create Dynamic Harvest Blocks from LiDAR-Based Small Interpretation Units. Forests 7(10): 220.

Pascual A, Pukkala T, de-Miguel S, 2018a. Effects of plot positioning errors on the optimality of harvest prescriptions in spatial forest planning based on ALS data. Forests 9(7): 371.

Pascual A, Pukkala T, de-Miguel S, Pesonen A, Packalen P, 2018b. Influence of timber harvesting costs on the layout of cuttings and economic return in forest planning based on dynamic treatment units. For Syst 27:1.

Pascual C, García-Abril A, García-Montero LG, Martín-Fernández S, Cohen WB, 2008. Object-based semi-automatic approach for forest structure characterization using LIDAR data in heterogeneous Pinus sylvestris stands. Forest Ecol Manag 255: 3677-3685.

Pascual C, García-Abril A, Cohen WB, Martín-Fernández S, 2010. Relationship between LiDAR-derived forest canopy height and Landsat images. Int J Remote Sens 31 (5): 1261-1280.

Pascual C, García-Montero LG, Arroyo LA, García-Abril A, 2013. Increasing the use of expert opinion in forest characterisation approaches based on LiDAR data. Annals of Forest Science 70: 87-99.

Pasquarella VJ, Holden CE, Kaufman L, Woodcock CE, 2016. From imagery to ecology: leveraging time series of all available Landsat observations to map and monitor ecosystem state and dynamics. Rem Sens Ecol Conserv 2 (3): 151-170.

Pasquarella VJ, Bradley BA, Woodcock CE, 2017. Near-Real-Time Monitoring of Insect Defoliation Using Landsat Time Series. Forests 8 (8): 275.

Pulliainen JT, Heiska K, Hyyappa J, Hallikainen MT, 1994. Backscattering properties of boreal forests at the C- and X-Bands. IEEE Trans Geosci Rem Sens, 32, 1041-1050.

Qi W, Dubayah RO, 2016. Combining Tandem-X InSAR and simulated GEDI LiDAR observations for forest structure mapping. Remote Sens Environ 187: 253-266.

Quintano C, Fernández-Manso A, Fernández-Manos O, Shimabukuro YE, 2006. Mapping burned areas in Mediterranean countries using spectral mixture analysis from a uni-temporal perspective. Int J Remote Sens 27(4): 645-662.

Quintano C, Fernández-Manso A, Calvo L, Marcos E, Valbuena L, 2015. Land Surface temperature as potential indicator of burn severity in forest Mediterranean ecosystems. Int J Appl Earth 36: 1-12.

Quintano C, Fernández-Manso A, Fernández-Manso O, 2018. Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity. Int J Appl Earth Obs Geoinformation 64: 221-225.

Radeloff VC, Mildenoff DJ, Boyce MS, 1999. Detecting Jack Pine budworm defoliation using spectral mixture analysis: separating effects from determinants. Remote Sens Environ 69: 156-169.

Regos A, Ninyerola M, Moré G, Pons X, 2015. Linking land cover dynamics with driving forces in mountain landscape of the Northwestern Iberian Peninsula. Int J Appl Earth 38: 1-14.

Riaño 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. Can J Forest Res 32: 1301-1315.

Riaño D, Chuvieco E, Condés S, González-Matesanz J, Ustin SL, 2004. Generation of crown bulk density for Pinus sylvestris L. from LiDAR. Remote Sens Environ 92: 345-352.

Rignot EJ, Way J, Williams C, Viereck L, 1994. Radar Estimates of Aboveground Biomass in Boreal Forests of Interior Alaska. IEEE Trans Geosci Rem Sens 32: 1117-1124.

Robles A, Rodríguez-Garrido MA, Álvarez-Taboada MF, 2016. Characterization of wildland-urban interfaces using LiDAR data to estimate the risk of wildfire damage. Revista de Teledetección. (Special Issue): 57-69.

Rock BN, Vogelmann JE, Williams DL, Vogehnann AF, Hoshizaki T, 1986. Remote detection of forest damage. Bioscience 36: 439–445.

Rubio A, Gavilán RG, Montes F, Gutiérrez-Girón A, Díaz-Pines E, Mezquida ET, 2011. Biodiversity measures applied to stand-level management: Can they really be useful? Ecol Indic 11: 545-556.

Ruiz LA, Recio JA, Fenández-Sarría A, 2005. Clasificación de entornos forestales mediterráneos mediante técnicas de análisis de texturas. Cuadernos de la Sociedad Española de Ciencias Forestales (SECF) 19: 187-192.

Ruiz LA, Hermosilla T, Mauro F, Godino M, 2014. Analysis of the influence of plot size and LiDAR density on forest structure attribute estimates. Forests 5 (5): 936-957.

Ruiz LA, Recio JA, Crespo-Peremarch P, Sapena M, 2018. An object-based approach for mapping forest structural types based on low density LiDAR and multispectral imagery. Geocarto Int 33: 443-457.

Ruiz-Gallardo JR, Castaño S, Calera A, 2004. Application of remote sensing and GIS to locate priority intervention areas after wildland fires in Mediterranean systems: a case study from south-eastern Spain. Int J Wildland Fire 13: 241-252.

Ruiz-Peinado R, Del Rio M, Montero G, 2011. New models for estimating the carbon sink capacity of Spanish softwood species. For Syst 20: 176-188.

Rullán-Silva CD, Olthoff AE, Delgado JA, Pajares-Alonso JA, 2013. Remote monitoring of forest insect defoliation. A review. Forest Syst 22 (3): 377-391.

Rullán-Silva C, Olthoff AE, Pando V, Pajares JA, Delgado JA, 2015. Remote monitoring of defoliation by the beech leaf-mining weevil Rhynchaenus fagi in northern Spain. Forest Ecol Manag 347: 200-208.

Sandberg G, Ulander LMH, Fransson JES, Holmgren J, Toan TL, 2011. L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest. Remote Sens Environ 115: 2874-2886.

Sangüesa-Barreda G, Camarero JJ, García-Martín A, Rodolfo Hernández R, de la Riva J, 2014. Remote-sensing and tree-ring based characterization of forest defoliation and growth loss due to the Mediterranean pine processionary moth. Forest Ecol Manag 320: 171-181.

Sangüesa-Barreda G, Camarero JJ, Oliva J, Montes F, Gazol A, 2015. Past logging, drought and pathogens interact and contribute to forest dieback. Agric For Meteorol 208: 85-94.

Sankey T, Donager J, McVay J, Sankey JB, 2017. UAV LiDAR and hyperspectral fusion for forest monitoring in the southwestern USA. Remote Sens Environ 195: 30-43.

Schlerf M, Atzberger C, Hill J, Buddenbaum H, Werner W, Schuler G, 2010. Retrieval of chlorophyll and nitrogen in Norway spruce (Picea abies L. Karst.) using imaging spectroscopy. Int J Appl Earth 12: 17-26.

Schutz BE, Zwally HJ, Shuman CA, Hancock D, DiMarzio JP, 2005. Overview of the ICESat Mission, Geophys Res Lett 32: L21S01.

Sevillano-Marco E, Fernández-Manso A, Quintano C, Poulain M, 2013. CCD CBERS and ASTER data in dasometric characterization of Pinus radiata D. Don (North-Western Spain). Cerne, Lavras 19(1): 103-110.

Shimada M, Itoh T, Motooka T, Watanabe M, Shiraishi T, Thapa R, Lucas R, 2014. New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sens Environ 155: 13-31.

Sherald J, 2007. Bacterial Leaf Scorch of Landscape Trees: What We Know and What We Do Not Know. Arb Urb Forestry 33.

Simonson W, Allen H, Coomes D, 2018. Effect of Tree Phenology on LiDAR Measurement of Mediterranean Forest Structure. Remote Sens 10: 659

Silveira EM, de Mello JM, Acerbi FW, dos Reis AA, Withey KD, Ruiz LA, 2018. Characterizing landscape spatial heterogeneity using semivariogram parameters derived from NDVI images. CERNE, 23 (4): 413-422.

Smith MW, Carrivick J, Quincey D, 2016. Structure from Motion Photogrammetry in Physical Geography. Prog Phys Geog 40 (2): 247-275.

Tanase M, Santoro M, Wegmüller U, de la Riva J, Pérez-Cabello F, 2010b. Properties of X-, C- and L-band repeat-pass interferometric SAR coherence in Mediterranean pine forests affected by fires, Remote Sens Environ 114: 2182-2194.

Tanase MA, 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.

Tanase MA, 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.

Tanase MA, Panciera R, Lowell K, Tian S, García-Martín A, Walker JP, 2014a. Sensitivity of L-band radar backscatter to forest biomass in semi-arid environments: a comparative analysis of parametric and non-parametric models. IEEE Trans Geosci Rem Sens: 52, 1-15.

Tanase MA, Panciera R, Lowell K, Aponte C, Hacker JM, Walker JP, 2014b. Forest biomass estimation at high spatial resolution: Radar vs. LiDAR sensors. IEEE Trans Geosci Rem Sens Lett 11 (3): 711-715.

Tanase MA, Santoro M, Aponte C, De la Riva J, 2014c. Polarimetric Properties of Burned Forest Areas at C- and L-Band. IEEE Trans Geosci Rem Sens 7 (1): 267-276.

Tanase MA, Kennedy R, Aponte C, 2015a. Fire severity from space: a comparison of active and passive sensors and their synergy for different forest types. Int J Wildland Fire 24(8): 1062-1075.

Tanase MA, Kennedy R, Aponte C, 2015b. Radar Burn Ratio for fire severity estimation at canopy level: an example for temperate forests. Remote Sens Environ 170: 14-31.

Tanase MA, Panciera R, Lowell K, Aponte C, 2015c. Monitoring live fuel moisture in semi-arid environments using L-band radar data. Int J Wildland Fire 24: 560-572.

Tanase MA, Aponte C, Mermoz S, Bouvet A, Le Toan T, Heurich M, 2018. Detection of windthrows and insect outbreaks by L-band SAR: A case study in the Bavarian Forest National Park. Remote Sens Environ 209: 700-711.


Valbuena-Rabadán M, Santamaría-Pe-a J, Sanz-Adán F, 2016. Estimation of diameter and height of individual trees for Pinus sylvestris L. based on the individualising of crowns using airborne LiDAR and the National Forest Inventory data. Forest Systems 25(1): e046

All references

How to Cite
GómezC., AlejandroP., HermosillaT., MontesF., PascualC., RuizL. A., Álvarez-TaboadaF., TanaseM., & ValbuenaR. (2019). Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities. Forest Systems, 28(1), eR001.