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Alba Viana-Soto
Alba Viana-Soto
PhD remote sensing - Technical University of Munich
tum.de üzerinde doğrulanmış e-posta adresine sahip
Başlık
Alıntı yapanlar
Alıntı yapanlar
Yıl
Assessment of post-fire vegetation recovery using fire severity and geographical data in the mediterranean region (Spain)
A Viana-Soto, I Aguado, S Martínez
Environments 4 (4), 90, 2017
1092017
Identifying post-fire recovery trajectories and driving factors using landsat time series in fire-prone mediterranean pine forests
A Viana-Soto, I Aguado, J Salas, M García
Remote Sensing 12 (9), 1499, 2020
642020
The role of remote sensing for the assessment and monitoring of forest health: A systematic evidence synthesis
P Torres, M Rodes-Blanco, A Viana-Soto, H Nieto, M García
Forests 12 (8), 1134, 2021
622021
Using spectral indices as early warning signals of forest dieback: The case of drought-prone Pinus pinaster forests
D Moreno-Fernández, A Viana-Soto, JJ Camarero, MA Zavala, J Tijerín, ...
Science of the Total Environment 793, 148578, 2021
442021
Assessing post-fire forest structure recovery by combining LiDAR data and Landsat time series in Mediterranean pine forests
A Viana-Soto, M García, I Aguado, J Salas
International Journal of Applied Earth Observation and Geoinformation 108 …, 2022
322022
Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach
M García, P North, A Viana-Soto, NE Stavros, J Rosette, MP Martín, ...
Remote Sensing of Environment 247, 111893, 2020
322020
Periglacial deposits as indicators of paleotemperatures. A case study in the Iberian Peninsula: The mountains of Galicia
A Viana‐Soto, A Pérez‐Alberti
Permafrost and Periglacial Processes, 2019
282019
Quantifying post-fire shifts in woody-vegetation cover composition in Mediterranean pine forests using Landsat time series and regression-based unmixing
A Viana-Soto, A Okujeni, D Pflugmacher, M García, I Aguado, P Hostert
Remote Sensing of Environment 281, 113239, 2022
262022
The interplay of the tree and stand-level processes mediate drought-induced forest dieback: evidence from complementary remote sensing and tree-ring approaches
D Moreno-Fernández, JJ Camarero, M García, ER Lines, ...
Ecosystems 25 (8), 1738-1753, 2022
192022
Unmixing-based forest recovery indicators for predicting long-term recovery success
L Mandl, A Viana-Soto, R Seidl, A Stritih, C Senf
Remote Sensing of Environment 308, 114194, 2024
82024
The role of remote sensing for the assessment and monitoring of forest health: a systematic evidence synthesis. Forests 12 (8), 1134
P Torres, M Rodes-Blanco, A Viana-Soto, H Nieto, M García
52021
Benchmarking remote sensing-based forest recovery indicators for predicting long-term recovery success
L Mandl, A Viana-Soto, A Stritih, R Seidl, C Senf
EGU24, 2024
12024
Classification of post-fire recovery trajectories using Landsat time series in the Mediterranean region: Spain
A Viana-Soto, I Aguado, J Salas, M García
Earth Resources and Environmental Remote Sensing/GIS Applications X 11156, 41-49, 2019
12019
Forest disturbance regimes and trends in continental Spain (1985–2023) using dense landsat time series
S Miguel, P Ruiz-Benito, P Rebollo, A Viana-Soto, MC Mihai, ...
Environmental Research 262, 119802, 2024
2024
The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive
A Viana-Soto, C Senf
Earth System Science Data Discussions 2024, 1-42, 2024
2024
Trends and patterns in post-disturbance forest recovery estimated from Landsat and Sentinel-2 data using regression-based spectral unmixing
L Mandl, A Viana-Soto, R Seidl, C Senf
EGU23, 2023
2023
Next generation of European forest disturbance maps based on the Landsat archive
A Viana Soto, C Senf
EGU23, 2023
2023
D2. 1 Next generation European forest disturbance map
A Viana-Soto, C Senf
2023
Combinando datos LiDAR e imágenes Landsat para la evaluación de la recuperación de la estructura post-incendio en pinares mediterráneos
A Viana-Soto, M García, I Aguado, J Salas
XIX Congreso de la Asociación Española de Teledetección. Teledetección para …, 2022
2022
Shifts in post-fire forest cover composition from Landsat fraction images using machine learning regression-based unmixing
A Viana Soto, A Okujeni, D Pflugmacher, M García, I Aguado, P Hostert
ForestSAT 2022 - Berlin, 2022
2022
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