What is he best known for?
The fields of study he is best known for:
- Ecology
- Agriculture
- Remote sensing
Harm Bartholomeus mainly focuses on Remote sensing, Lidar, Mean squared error, Laser scanning and Partial least squares regression.
His Remote sensing research is multidisciplinary, incorporating elements of Sampling, Target surface, Vegetation and Reflectivity.
His studies deal with areas such as Agroforestry, Canopy, Allometry and Digital elevation model as well as Lidar.
His research investigates the connection between Laser scanning and topics such as Point cloud that intersect with issues in Plot and Temperate forest.
His Partial least squares regression research incorporates themes from Soil water, Soil carbon, Soil test, Calibration and Spatial variability.
His work in the fields of Soil science, such as Cation-exchange capacity, overlaps with other areas such as Scale.
His most cited work include:
- A global spectral library to characterize the world’s soil (280 citations)
- Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils (187 citations)
- Spectral reflectance based indices for soil organic carbon quantification (113 citations)
What are the main themes of his work throughout his whole career to date?
Harm Bartholomeus mostly deals with Remote sensing, Lidar, Vegetation, Soil science and Hyperspectral imaging.
His study in Remote sensing is interdisciplinary in nature, drawing from both Canopy, Scale, Spectrometer and Laser scanning.
Harm Bartholomeus interconnects Tropical forest, Point cloud and Atmospheric sciences in the investigation of issues within Lidar.
He has included themes like Land use, Imaging spectroscopy, Hydrology, Topsoil and Soil salinity in his Vegetation study.
The concepts of his Soil science study are interwoven with issues in Carbon cycle and Field.
His Hyperspectral imaging research is multidisciplinary, relying on both Zenith, Spectral bands and Precision agriculture.
He most often published in these fields:
- Remote sensing (53.60%)
- Lidar (23.20%)
- Vegetation (20.00%)
What were the highlights of his more recent work (between 2016-2021)?
- Remote sensing (53.60%)
- Lidar (23.20%)
- Canopy (12.80%)
In recent papers he was focusing on the following fields of study:
Remote sensing, Lidar, Canopy, Point cloud and Vegetation are his primary areas of study.
His research in the fields of Remote sensing overlaps with other disciplines such as Section.
The study incorporates disciplines such as Biomass and Atmospheric sciences in addition to Lidar.
His research in Canopy tackles topics such as Hyperspectral imaging which are related to areas like Spectral bands, Partial least squares regression and Substrate.
His work deals with themes such as Biodiversity, Tropical savanna climate, Tree allometry and Laser scanning, which intersect with Point cloud.
Harm Bartholomeus has researched Vegetation in several fields, including Sampling, Climate change, Spatial variability and Soil salinity.
Between 2016 and 2021, his most popular works were:
- Data acquisition considerations for Terrestrial Laser Scanning of forest plots (107 citations)
- Estimation of above-ground biomass of large tropical trees with Terrestrial LiDAR (82 citations)
- Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. (76 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Agriculture
- Ecosystem
The scientist’s investigation covers issues in Remote sensing, Lidar, Point cloud, Laser scanning and Canopy.
He combines subjects such as Infrared thermometer and Reflectivity with his study of Remote sensing.
Lidar and Atmospheric sciences are commonly linked in his work.
His studies in Laser scanning integrate themes in fields like Temperate forest and Diameter at breast height.
His Temperate forest study combines topics from a wide range of disciplines, such as Sampling, Tree allometry and Plot.
His biological study deals with issues like Digital elevation model, which deal with fields such as Ground truth, Vegetation, Normalized Difference Vegetation Index and Hydrology.
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.
