What is he best known for?
The fields of study he is best known for:
- Statistics
- Ecology
- Artificial intelligence
His primary areas of investigation include Remote sensing, Forest inventory, Lidar, Statistics and Canopy.
His Remote sensing study incorporates themes from Biomass, Linear model, Tree, Vegetation and Taiga.
His research integrates issues of Mean squared error, Forest resource, Hyperspectral imaging and Random forest in his study of Forest inventory.
His study in the field of Regression analysis, Sampling and Least squares also crosses realms of Hectare.
His biological study spans a wide range of topics, including Standard deviation, Physical geography, Deciduous and Laser scanning.
His studies deal with areas such as Basal area and Sample as well as Laser scanning.
His most cited work include:
- Laser scanning of forest resources: the nordic experience (404 citations)
- Lidar sampling for large-area forest characterization: A review (380 citations)
- Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser (261 citations)
What are the main themes of his work throughout his whole career to date?
Terje Gobakken focuses on Remote sensing, Forest inventory, Laser scanning, Statistics and Lidar.
He has researched Remote sensing in several fields, including Terrain, Biomass, Canopy, Tree and Vegetation.
His study explores the link between Forest inventory and topics such as Basal area that cross with problems in Percentile.
His work in Laser scanning tackles topics such as Transect which are related to areas like Ecotone and Tundra.
His Lidar research is multidisciplinary, relying on both Survey sampling, Sample, Linear regression and Interferometric synthetic aperture radar.
His Estimator research includes elements of Sampling, Standard error and Econometrics.
He most often published in these fields:
- Remote sensing (43.46%)
- Forest inventory (34.11%)
- Laser scanning (29.91%)
What were the highlights of his more recent work (between 2017-2021)?
- Remote sensing (43.46%)
- Laser scanning (29.91%)
- Forest inventory (34.11%)
In recent papers he was focusing on the following fields of study:
Terje Gobakken mainly focuses on Remote sensing, Laser scanning, Forest inventory, Lidar and Statistics.
He interconnects Tree, Terrain and Vegetation in the investigation of issues within Remote sensing.
His Laser scanning research includes themes of Site index and Forest change.
His Forest inventory research incorporates themes from Diameter at breast height and Environmental resource management.
His Lidar study combines topics in areas such as Synthetic aperture radar, Regression analysis, Convolutional neural network and Tree species.
In the subject of general Statistics, his work in Mean squared error and Standard error is often linked to Stock, thereby combining diverse domains of study.
Between 2017 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)
- A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data (37 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Ecology
- Artificial intelligence
Remote sensing, Laser scanning, Forest inventory, Remote sensing and Photogrammetry are his primary areas of study.
The concepts of his Remote sensing study are interwoven with issues in Tree and Diameter at breast height.
His Laser scanning study integrates concerns from other disciplines, such as Mean squared error, Lidar, Regression analysis and Standard error.
The Forest inventory study combines topics in areas such as Calibration, Basal area and Tree canopy.
The study incorporates disciplines such as Value, Sample, Emerging technologies and Value of information in addition to Remote sensing.
His Photogrammetry research integrates issues from Terrain and Digital elevation model.
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