What is he best known for?
The fields of study he is best known for:
- Ecology
- Artificial intelligence
- Optics
Benoit Rivard mainly focuses on Remote sensing, Hyperspectral imaging, Tropical and subtropical dry broadleaf forests, Wavelet and Species diversity.
His Remote sensing research includes elements of Conservation biology and Carbon sink.
In general Hyperspectral imaging, his work in Endmember is often linked to Tropical trees linking many areas of study.
Benoit Rivard interconnects Tropics, Secondary forest, Normalized Difference Vegetation Index and Land use in the investigation of issues within Tropical and subtropical dry broadleaf forests.
His Wavelet study combines topics from a wide range of disciplines, such as Spectral index and Water content.
As a part of the same scientific study, Benoit Rivard usually deals with the Species diversity, concentrating on Biodiversity and frequently concerns with Ecosystem services, Environmental protection, Agroforestry and Vegetation.
His most cited work include:
- Validation of global moderate-resolution LAI products: a framework proposed within the CEOS land product validation subgroup (322 citations)
- Integration of spatial–spectral information for the improved extraction of endmembers (206 citations)
- Species composition, similarity and diversity in three successional stages of a seasonally dry tropical forest (193 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Hyperspectral imaging, Remote sensing, Mineralogy, Endmember and Wavelet.
His Hyperspectral imaging research incorporates elements of Pixel, Geologic map and Ultramafic rock.
His Remote sensing study integrates concerns from other disciplines, such as Tropical and subtropical dry broadleaf forests, Ecological succession and Vegetation.
He combines subjects such as Image resolution, Lichen, Infrared, Geochemistry and Oil sands with his study of Mineralogy.
His studies in Endmember integrate themes in fields like Algorithm and Projection.
His Wavelet research incorporates themes from Multispectral image and Water content.
He most often published in these fields:
- Hyperspectral imaging (44.26%)
- Remote sensing (43.44%)
- Mineralogy (36.89%)
What were the highlights of his more recent work (between 2016-2021)?
- Hyperspectral imaging (44.26%)
- Mineralogy (36.89%)
- Remote sensing (43.44%)
In recent papers he was focusing on the following fields of study:
Benoit Rivard focuses on Hyperspectral imaging, Mineralogy, Remote sensing, Oil sands and Liana.
His Hyperspectral imaging study deals with Wavelength intersecting with Curse of dimensionality, Feature and Spectral resolution.
The Kaolinite research Benoit Rivard does as part of his general Mineralogy study is frequently linked to other disciplines of science, such as Lapis lazuli, therefore creating a link between diverse domains of science.
As part of his studies on Remote sensing, Benoit Rivard often connects relevant subjects like Wavelet.
His Wavelet research is multidisciplinary, incorporating perspectives in Image resolution, Spectrograph, Geologic map, Residual and Scale.
The various areas that Benoit Rivard examines in his Liana study include Canopy, Atmospheric sciences, Long wave infrared, Ecosystem and Dry forest.
Between 2016 and 2021, his most popular works were:
- Comparison of lithological mapping results from airborne hyperspectral VNIR-SWIR, LWIR and combined data (32 citations)
- Classification of tree species based on longwave hyperspectral data from leaves, a case study for a tropical dry forest (26 citations)
- Mapping alteration using imagery from the Tiangong-1 hyperspectral spaceborne system: Example for the Jintanzi gold province, China (13 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Artificial intelligence
- Optics
Mineralogy, Remote sensing, Hyperspectral imaging, Longwave and Wavelet are his primary areas of study.
His Mineralogy research includes themes of Shortwave infrared, EnMAP, Remote sensing and Spectral bands.
His Remote sensing study incorporates themes from Canopy, Support vector machine, Data reduction, Random forest and Principal component analysis.
His studies deal with areas such as Gemstone, Prehnite and Thin section as well as Hyperspectral imaging.
His Longwave research incorporates Species diversity, Stellar classification, Vegetation, Noise and Liana.
The concepts of his Wavelet study are interwoven with issues in VNIR, Spectrograph, Geologic map, Residual and Scale.
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