What is he best known for?
The fields of study he is best known for:
- Remote sensing
- Ecosystem
- Machine learning
His primary areas of investigation include Remote sensing, Canopy, Water content, Normalized Difference Vegetation Index and Mediterranean climate.
David Riaño studies Lidar which is a part of Remote sensing.
His studies deal with areas such as Bulk density and Crown as well as Lidar.
As a part of the same scientific study, he usually deals with the Canopy, concentrating on Leaf area index and frequently concerns with Atmospheric radiative transfer codes, Tree canopy and Quercus pyrenaica.
David Riaño focuses mostly in the field of Water content, narrowing it down to topics relating to Soil science and, in certain cases, Liana, Vineyard, Water stress and Hyperspectral imaging.
His Normalized Difference Vegetation Index research focuses on Shrub and how it relates to Chaparral, Atmospheric correction and Endmember.
His most cited work include:
- Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003) (368 citations)
- Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling (246 citations)
- Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling (246 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Remote sensing, Vegetation, Canopy, Water content and Lidar.
His Remote sensing research incorporates themes from Atmospheric radiative transfer codes, Satellite, Leaf area index and Normalized Difference Vegetation Index.
His work investigates the relationship between Vegetation and topics such as Mediterranean climate that intersect with problems in Satellite imagery.
His work on Tree canopy as part of general Canopy research is frequently linked to Laser scanning and Mean squared error, bridging the gap between disciplines.
In his study, which falls under the umbrella issue of Water content, Carbon sink, Taiga, Fuel moisture content and Boreal is strongly linked to Soil science.
David Riaño has researched Lidar in several fields, including Elevation, Atmospheric sciences, Hydrology, Understory and Crown.
He most often published in these fields:
- Remote sensing (117.56%)
- Vegetation (50.38%)
- Canopy (47.33%)
What were the highlights of his more recent work (between 2018-2021)?
- Remote sensing (117.56%)
- Vegetation (50.38%)
- Lidar (31.30%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Remote sensing, Vegetation, Lidar, Globe and Ecophysiology.
Specifically, his work in Remote sensing is concerned with the study of Hyperspectral imaging.
He interconnects Spatial ecology, Pixel and Land cover in the investigation of issues within Hyperspectral imaging.
His Vegetation research integrates issues from Atmospheric sciences, Eddy covariance, Energy balance and Earth observation.
His Lidar research includes elements of Ignition system and Multispectral image.
His Multispectral image research includes themes of Atmospheric radiative transfer codes and Soil color.
Between 2018 and 2021, his most popular works were:
- Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications (15 citations)
- Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications (15 citations)
- Spectral mapping methods applied to LiDAR data: Application to fuel type mapping (13 citations)
In his most recent research, the most cited papers focused on:
- Ecosystem
- Remote sensing
- Machine learning
His primary scientific interests are in Vegetation, Lidar, Remote sensing, Endmember and Spatial ecology.
The various areas that David Riaño examines in his Vegetation study include Water stress, Environmental change, Forest ecology and Calibration and validation.
His work carried out in the field of Lidar brings together such families of science as Algorithm and Spectral matching.
His research in the fields of Multispectral image and Hyperspectral imaging overlaps with other disciplines such as Spectral mapping.
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