ANTONIO ALFONSO
FERNANDEZ MANSO
Investigador en el periodo 1997-1999
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![Foto de University of California, Santa Barbara](/img/noimage_org.png)
University of California, Santa Barbara
Santa Bárbara, Estados UnidosPublicaciones en colaboración con investigadores/as de University of California, Santa Barbara (10)
2024
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Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models
Remote Sensing, Vol. 16, Núm. 2
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Next-gen regional fire risk mapping: Integrating hyperspectral imagery and National Forest Inventory data to identify hot-spot wildland-urban interfaces
Science of the Total Environment, Vol. 940
2020
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Can landsat-derived variables related to energy balance improve understanding of burn severity from current operational techniques?
Remote Sensing, Vol. 12, Núm. 5
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Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm
Remote Sensing of Environment, Vol. 244
2019
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Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data
ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 155, pp. 102-118
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Vegetation and soil fire damage analysis based on species distribution modeling trained with multispectral satellite data
Remote Sensing, Vol. 11, Núm. 15
2017
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Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature
Remote Sensing of Environment, Vol. 190, pp. 83-95
2016
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Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems
Remote Sensing of Environment, Vol. 184, pp. 112-123
2013
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Multiple Endmember Spectral Mixture Analysis (MESMA) to map burn severity levels from Landsat images in Mediterranean countries
Remote Sensing of Environment, Vol. 136, pp. 76-88
2012
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Evaluation of potential of multiple endmember spectral mixture analysis (MESMA) for surface coal mining affected area mapping in different world forest ecosystems
Remote Sensing of Environment, Vol. 127, pp. 181-193