Publicaciones en colaboración con investigadores/as de Universidad de León (51)

2024

  1. FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques

    Ecological Informatics, Vol. 81

  2. Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models

    Remote Sensing, Vol. 16, Núm. 2

  3. Linking crown fire likelihood with post-fire spectral variability in Mediterranean fire-prone ecosystems

    International Journal of Wildland Fire , Vol. 33, Núm. 4

  4. 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

2023

  1. First evaluation of fire severity retrieval from PRISMA hyperspectral data

    Remote Sensing of Environment, Vol. 295

  2. Fractional vegetation cover ratio estimated from radiative transfer modeling outperforms spectral indices to assess fire severity in several Mediterranean plant communities

    Remote Sensing of Environment, Vol. 290

  3. Unmixing PRISMA hyperspectral images by Multiple Endmember Spectral Mixture Analysis (MESMA) to assess fire severity in Mediterranean forest ecosystems

    Proceedings of SPIE - The International Society for Optical Engineering

2022

  1. Comparison of Physical-Based Models to Measure Forest Resilience to Fire as a Function of Burn Severity

    Remote Sensing, Vol. 14, Núm. 20

  2. Pre-fire aboveground biomass, estimated from LiDAR, spectral and field inventory data, as a major driver of burn severity in maritime pine (Pinus pinaster) ecosystems

    Forest Ecosystems, Vol. 9

2021

  1. Forest damage monitoring in South-Western Europe based on data from Unmanned Aerial Vehicles (UAV)

    Proceedings of SPIE - The International Society for Optical Engineering

  2. Multiple endmember spectral mixture analysis (Mesma) applied to the study of habitat diversity in the fine-grained landscapes of the cantabrian mountains

    Remote Sensing, Vol. 13, Núm. 5, pp. 1-19

2020

  1. A synergetic approach to burned area mapping using maximum entropy modeling trained with hyperspectral data and VIIRS hotspots

    Remote Sensing, Vol. 12, Núm. 5

  2. Can landsat-derived variables related to energy balance improve understanding of burn severity from current operational techniques?

    Remote Sensing, Vol. 12, Núm. 5

  3. Cross-diffusion based filtering as pre-processing step for remote sensing procedures

    Advances in Engineering Software, Vol. 140

  4. Enhanced burn severity estimation using fine resolution ET and MESMA fraction images with machine learning algorithm

    Remote Sensing of Environment, Vol. 244

  5. Evaluation of fire severity in fire prone-ecosystems of Spain under two different environmental conditions

    Journal of Environmental Management, Vol. 271

  6. Evaluation of prescribed fires from unmanned aerial vehicles (UAVs) imagery and machine learning algorithms

    Remote Sensing, Vol. 12, Núm. 8

  7. Sentinel-2 red-edge spectral indexes best suited to discriminate burned from unburned areas in Mediterranean forest ecosystems

    Proceedings of SPIE - The International Society for Optical Engineering

2019

  1. Assessment of the influence of biophysical properties related to fuel conditions on fire severity using remote sensing techniques: A case study on a large fire in NW Spain

    International Journal of Wildland Fire, Vol. 28, Núm. 7, pp. 512-520

  2. 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