What is he best known for?
The fields of study he is best known for:
- Statistics
- Regression analysis
- Artificial intelligence
His primary scientific interests are in Remote sensing, Laser scanning, Forest inventory, Canopy and Lidar.
His study on Remote sensing also encompasses disciplines like
- Standard deviation which intersects with area such as Ground truth,
- Tree canopy together with Arithmetic mean.
His Laser scanning research is multidisciplinary, incorporating elements of Basal area, Picea abies, Leaf area index, Laser data and Tree species.
His study in Forest inventory is interdisciplinary in nature, drawing from both Mean squared error, Statistics, Photogrammetry and Hyperspectral imaging.
He has researched Canopy in several fields, including Hydrology, Regression analysis, Young forest and Sample.
His Lidar study integrates concerns from other disciplines, such as Change detection, Aerial survey, Inference, Survey sampling and Estimator.
His most cited work include:
- Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data (1048 citations)
- Determination of mean tree height of forest stands using airborne laser scanner data (501 citations)
- Practical large-scale forest stand inventory using a small-footprint airborne scanning laser (455 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Remote sensing, Laser scanning, Forest inventory, Statistics and Lidar.
His Remote sensing research includes themes of Terrain, Standard deviation, Tree, Vegetation and Taiga.
The Tree study combines topics in areas such as Crown and Hyperspectral imaging, Artificial intelligence.
His Laser scanning study incorporates themes from Point cloud, Forestry, Basal area, Canopy and Regression analysis.
His research in Canopy intersects with topics in Hydrology and Atmospheric sciences.
Erik Næsset has included themes like Photogrammetry, Regression and Plot in his Forest inventory study.
He most often published in these fields:
- Remote sensing (46.59%)
- Laser scanning (33.71%)
- Forest inventory (28.03%)
What were the highlights of his more recent work (between 2016-2021)?
- Remote sensing (46.59%)
- Laser scanning (33.71%)
- Statistics (25.76%)
In recent papers he was focusing on the following fields of study:
Erik Næsset focuses on Remote sensing, Laser scanning, Statistics, Forest inventory and Lidar.
His Remote sensing research incorporates elements of Tree and Terrain.
His studies in Laser scanning integrate themes in fields like Volume and Basal area.
His research investigates the connection between Statistics and topics such as Forest management that intersect with issues in Site index and Scots pine.
His biological study spans a wide range of topics, including GNSS applications and Linear model.
The study incorporates disciplines such as Sampling, Tree species and Aboveground biomass in addition to Lidar.
Between 2016 and 2021, his most popular works were:
- Combining UAV and Sentinel-2 auxiliary data for forest growing stock volume estimation through hierarchical model-based inference (64 citations)
- Remote sensing and forest inventories in Nordic countries – roadmap for the future (50 citations)
- Assessing 3D point clouds from aerial photographs for species-specific forest inventories (49 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Regression analysis
His primary scientific interests are in Remote sensing, Forest inventory, Statistics, Laser scanning and Photogrammetry.
His studies in Hyperspectral imaging and Lidar are all subfields of Remote sensing research.
His Forest inventory study combines topics from a wide range of disciplines, such as Remote sensing and Standard error.
His study in the field of Mean squared error is also linked to topics like Overfitting.
Erik Næsset interconnects Calibration and Basal area in the investigation of issues within Laser scanning.
His study looks at the intersection of Photogrammetry and topics like Forest management with Sample, Emerging technologies, Reducing emissions from deforestation and forest degradation, Hydrology and Standard deviation.
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