2023 - Research.com Ecology and Evolution in Netherlands Leader Award
2022 - Research.com Environmental Sciences in Netherlands Leader Award
His primary scientific interests are in Remote sensing, Hyperspectral imaging, Vegetation, Ecology and Red edge. He interconnects Regression analysis, Canopy, Linear regression and Leaf area index in the investigation of issues within Remote sensing. The various areas that Andrew K. Skidmore examines in his Hyperspectral imaging study include Coefficient of determination and Rangeland.
In his work, Cartography is strongly intertwined with Remote sensing, which is a subfield of Vegetation. His Ecology study frequently involves adjacent topics like Spatial variability. His Red edge research includes themes of Absorption and Least squares.
Andrew K. Skidmore focuses on Remote sensing, Ecology, Vegetation, Hyperspectral imaging and Canopy. As a part of the same scientific study, Andrew K. Skidmore usually deals with the Remote sensing, concentrating on Normalized Difference Vegetation Index and frequently concerns with Physical geography. His studies in Habitat, Biodiversity, Ecosystem, Herbivore and Species richness are all subfields of Ecology research.
Many of his studies involve connections with topics such as Environmental resource management and Biodiversity. The study of Hyperspectral imaging is intertwined with the study of Partial least squares regression in a number of ways. The Canopy study combines topics in areas such as Atmospheric radiative transfer codes, Spectroradiometer and Atmospheric sciences.
The scientist’s investigation covers issues in Remote sensing, Vegetation, Canopy, Biodiversity and Remote sensing. His Remote sensing study combines topics in areas such as Mean squared error, Atmospheric radiative transfer codes and Leaf area index. His biological study spans a wide range of topics, including Disturbance, Temperate forest, Partial least squares regression, Forest ecology and Scale.
His Canopy research is multidisciplinary, incorporating elements of Infestation, Bark beetle, Atmospheric sciences, Phenology and Chlorophyll content. To a larger extent, Andrew K. Skidmore studies Ecology with the aim of understanding Biodiversity. In his study, Information theory, Wetland, Environmental resource management and Satellite imagery is strongly linked to Ecosystem, which falls under the umbrella field of Remote sensing.
Andrew K. Skidmore spends much of his time researching Remote sensing, Vegetation, Canopy, Spectral bands and Leaf area index. His Remote sensing research includes elements of Mean squared error and Random forest. His research in Vegetation intersects with topics in Bark beetle, Temperate forest and Infestation.
Andrew K. Skidmore has researched Canopy in several fields, including Partial least squares regression, Atmospheric sciences and Remote sensing application. As a part of the same scientific family, Andrew K. Skidmore mostly works in the field of Leaf area index, focusing on Ecosystem and, on occasion, Yield, Photosynthetically active radiation, Growing season and Satellite imagery. His work in Normalized Difference Vegetation Index addresses issues such as Word error rate, which are connected to fields such as Statistics.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Collinearity: a review of methods to deal with it and a simulation study evaluating their performance
Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI
Pieter S.A. Beck;Clement Atzberger;Kjell Arild Høgda;Bernt Johansen.
Remote Sensing of Environment (2006)
Where is positional uncertainty a problem for species distribution modelling
EFFECTS OF FIRE AND HERBIVORY ON THE STABILITY OF SAVANNA ECOSYSTEMS
Frank van Langevelde;Claudius A.D.M. van de Vijver;Claudius A.D.M. van de Vijver;Lalit Kumar;Lalit Kumar;Johan van de Koppel.
Spectral discrimination of vegetation types in a coastal wetland
Remote Sensing of Environment (2003)
Narrow band vegetation indices overcome the saturation problem in biomass estimation
Onisimo Mutanga;A. K. Skidmore.
International Journal of Remote Sensing (2004)
Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests
Forest Ecology and Management (2009)
Modelling topographic variation in solar radiation in a GIS environment
International Journal of Geographical Information Science (1997)
A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method
Remote Sensing of Environment (2006)
Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland
Roshanak Darvishzadeh;Andrew Skidmore;Martin Schlerf;Clement Atzberger.
Remote Sensing of Environment (2008)
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