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Emilio Chuvieco
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2020 – today
- 2023
- [c7]Pegah Mohammadpour
, Domingos Xavier Viegas
Vegetation Fuel Type Classification Using Optimised Synergy of Sentinel Data and Texture Feature. IGARSS 2023: 3098-3101 - 2022
- [j29]Magí Franquesa
Reference Data Accuracy Impacts Burned Area Product Validation: The Role of the Expert Analyst. Remote. Sens. 14(17): 4354 (2022) - 2021
- [j28]Gonzalo Otón
Development of a consistent global long-term burned area product (1982-2018) based on AVHRR-LTDR data. Int. J. Appl. Earth Obs. Geoinformation 103: 102473 (2021) - [j27]Ekhi Roteta
Landsat and Sentinel-2 Based Burned Area Mapping Tools in Google Earth Engine. Remote. Sens. 13(4): 816 (2021) - [j26]Joshua Lizundia-Loiola
Implementation of the Burned Area Component of the Copernicus Climate Change Service: From MODIS to OLCI Data. Remote. Sens. 13(21): 4295 (2021) - [j25]Ekhi Roteta
A Preliminary Global Automatic Burned-Area Algorithm at Medium Resolution in Google Earth Engine. Remote. Sens. 13(21): 4298 (2021) - [c6]Matteo Sali, Lorenzo Busetto, Mirco Boschetti, Magí Franquesa
Fire Reference Perimeters Extracted from Sentinel-2 Data for Validation of Burned Area Products in Africa Biomes. IGARSS 2021: 3749-3752 - 2020
- [j24]Joshua Lizundia-Loiola
Temporal Anomalies in Burned Area Trends: Satellite Estimations of the Amazonian 2019 Fire Crisis. Remote. Sens. 12(1): 151 (2020) - [j23]Mihai A. Tanase
Burned Area Detection and Mapping: Intercomparison of Sentinel-1 and Sentinel-2 Based Algorithms over Tropical Africa. Remote. Sens. 12(2): 334 (2020) - [j22]Gonzalo Otón
Correction: Otón, G., et al. Global Detection of Long-Term (1982-2017) Burned Area with AVHRR-LTDR Data. Remote Sensing 2019, 11, 2079. Remote. Sens. 12(14): 2324 (2020) - [c5]Miguel A. Belenguer-Plomer, Emilio Chuvieco, Mihai A. Tanase:
Optimum Sentinel-1 Pixel Spacing for Burned Area Mapping. IGARSS 2020: 4858-4861
2010 – 2019
- 2019
- [j21]Marco Turco
A comparison of remotely-sensed and inventory datasets for burned area in Mediterranean Europe. Int. J. Appl. Earth Obs. Geoinformation 82 (2019) - [j20]María Isabel Cruz-López, Lilia de Lourdes Manzo-Delgado
Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Ecoregions. Remote. Sens. 11(10): 1185 (2019) - [j19]Gonzalo Otón
Global Detection of Long-Term (1982-2017) Burned Area with AVHRR-LTDR Data. Remote. Sens. 11(18): 2079 (2019) - [j18]Miguel A. Belenguer-Plomer
Temporal Decorrelation of C-Band Backscatter Coefficient in Mediterranean Burned Areas. Remote. Sens. 11(22): 2661 (2019) - [c4]Emilio Chuvieco:
Global analysis of burned areas for climate assessment: experiences from the Fire_CCI project. IGARSS 2019: 9950-9952 - 2018
- [j17]Rubén Ramo
A data mining approach for global burned area mapping. Int. J. Appl. Earth Obs. Geoinformation 73: 39-51 (2018) - [c3]James Brennan
Uncertainty Characterisation & Validation within ESA Fire-CCI. IGARSS 2018: 1812-1815 - 2017
- [j16]Joana M. P. Nogueira, Julien Ruffault
Can We Go Beyond Burned Area in the Assessment of Global Remote Sensing Products with Fire Patch Metrics? Remote. Sens. 9(1): 7 (2017) - [j15]Rubén Ramo
Developing a Random Forest Algorithm for MODIS Global Burned Area Classification. Remote. Sens. 9(11): 1193 (2017) - 2015
- [j14]Saeedeh Eskandari
Fire danger assessment in Iran based on geospatial information. Int. J. Appl. Earth Obs. Geoinformation 42: 57-64 (2015) - 2014
- [j13]Florent Mouillot
Ten years of global burned area products from spaceborne remote sensing - A review: Analysis of user needs and recommendations for future developments. Int. J. Appl. Earth Obs. Geoinformation 26: 64-79 (2014) - [j12]Marc Padilla
Assessing the Temporal Stability of the Accuracy of a Time Series of Burned Area Products. Remote. Sens. 6(3): 2050-2068 (2014) - [j11]Aitor Bastarrika
BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data. Remote. Sens. 6(12): 12360-12380 (2014) - 2012
- [c2]Andrew V. Bradley
The ESA climate change initiative: Merging burned area estimates for the Fire Essential Climate Variable. IGARSS 2012: 7153-7156 - 2011
- [j10]Mariano García
Terrestrial laser scanning to estimate plot-level forest canopy fuel properties. Int. J. Appl. Earth Obs. Geoinformation 13(4): 636-645 (2011) - [j9]Stijn Hantson
Evaluation of different topographic correction methods for Landsat imagery. Int. J. Appl. Earth Obs. Geoinformation 13(5): 691-700 (2011) - [j8]Aitor Bastarrika
Automatic Burned Land Mapping From MODIS Time Series Images: Assessment in Mediterranean Ecosystems. IEEE Trans. Geosci. Remote. Sens. 49(9): 3401-3413 (2011)
2000 – 2009
- 2009
- [j7]Marta Yebra
Generation of a Species-Specific Look-Up Table for Fuel Moisture Content Assessment. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 2(1): 21-26 (2009) - 2008
- [j6]Ioannis Z. Gitas
Introduction to the Issue on Wildland Fires and Biomass Burning. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 1(4): 218-219 (2008) - 2005
- [j5]David Riaño
Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level. IEEE Trans. Geosci. Remote. Sens. 43(4): 819-826 (2005) - [c1]Germán Bianchini, Ana Cortés, Tomàs Margalef, Emilio Luque, Emilio Chuvieco:
Wildland Fire Propagation Danger Maps Based on Factorial Experimentation. ITEE 2005: 173-185 - 2003
- [j4]David Riaño
Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003). IEEE Trans. Geosci. Remote. Sens. 41(5): 1056-1061 (2003)
1990 – 1999
- 1997
- [j3]Emilio Chuvieco
Author's Reply to Arthur and Nalle. Int. J. Geogr. Inf. Sci. 11(4): 403 (1997) - 1996
- [j2]Emilio Chuvieco
Mapping the Spatial Distribution of Forest Fire Danger Using GIS. Int. J. Geogr. Inf. Sci. 10(3): 333-345 (1996) - 1993
- [j1]Emilio Chuvieco
Integration of Linear Programming and GIS for Land-Use Modelling. Int. J. Geogr. Inf. Sci. 7(1): 71-83 (1993)
Coauthor Index
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