D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Environmental Sciences D-index 49 Citations 8,599 153 World Ranking 2164 National Ranking 30

Overview

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.

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.

Best Publications

Lidar sampling for large-area forest characterization: A review

Michael A. Wulder;Joanne C. White;Ross F. Nelson;Erik Næsset.
Remote Sensing of Environment (2012)

560 Citations

Laser scanning of forest resources: the nordic experience

Erik Næsset;Terje Gobakken;Johan Holmgren;Hannu Hyyppä.
Scandinavian Journal of Forest Research (2004)

548 Citations

Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser

Erik Næsset;Terje Gobakken.
Remote Sensing of Environment (2008)

415 Citations

Comparative testing of single-tree detection algorithms under different types of forest

Jari Vauhkonen;Liviu Ene;Sandeep Gupta;Johannes Heinzel.
Forestry (2012)

322 Citations

Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data

Johannes Breidenbach;Erik Næsset;Vegard Lien;Terje Gobakken.
Remote Sensing of Environment (2010)

281 Citations

Tree Species Classification in Boreal Forests With Hyperspectral Data

Michele Dalponte;Hans Ole Orka;Terje Gobakken;Damiano Gianelle.
IEEE Transactions on Geoscience and Remote Sensing (2013)

276 Citations

Inventory of Small Forest Areas Using an Unmanned Aerial System

Stefano Puliti;Hans Ole Ørka;Terje Gobakken;Erik Næsset.
Remote Sensing (2015)

271 Citations

Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data

Michele Dalponte;Hans Ole Ørka;Liviu Theodor Ene;Terje Gobakken.
Remote Sensing of Environment (2014)

228 Citations

Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data

Terje GobakkenT. Gobakken;Erik NæssetE. Næsset.
Canadian Journal of Forest Research (2008)

208 Citations

Estimating forest growth using canopy metrics derived from airborne laser scanner data

Erik Næsset;Terje Gobakken.
Remote Sensing of Environment (2005)

204 Citations

Best Scientists Citing Terje Gobakken

Juha Hyyppä

Juha Hyyppä

Finnish Geospatial Research Institute

Publications: 94

Nicholas C. Coops

Nicholas C. Coops

University of British Columbia

Publications: 92

Matti Maltamo

Matti Maltamo

University of Eastern Finland

Publications: 84

Mikko Vastaranta

Mikko Vastaranta

University of Eastern Finland

Publications: 84

Markus Holopainen

Markus Holopainen

University of Helsinki

Publications: 82

Michael A. Wulder

Michael A. Wulder

Natural Resources Canada

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Joanne C. White

Joanne C. White

Natural Resources Canada

Publications: 52

Erik Næsset

Erik Næsset

Norwegian University of Life Sciences

Publications: 47

Marco Heurich

Marco Heurich

University of Freiburg

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Hannu Hyyppä

Hannu Hyyppä

Aalto University

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Ronald E. McRoberts

Ronald E. McRoberts

University of Florence

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Xiaowei Yu

Xiaowei Yu

Finnish Geospatial Research Institute

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Andrew T. Hudak

Andrew T. Hudak

US Forest Service

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Göran Ståhl

Göran Ståhl

Swedish University of Agricultural Sciences

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Sassan Saatchi

Sassan Saatchi

California Institute of Technology

Publications: 28

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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