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 30 Citations 3,214 91 World Ranking 7150 National Ranking 91

Overview

What is he best known for?

The fields of study he is best known for:

  • Ecology
  • Statistics
  • Ecosystem

Kim Calders mainly focuses on Remote sensing, Lidar, Point cloud, Tree and Mean squared error. The study incorporates disciplines such as Canopy, Phenology, Vegetation, Scale and Laser scanning in addition to Remote sensing. His Vegetation research includes elements of Forest management and Sustainable development.

His Lidar study combines topics from a wide range of disciplines, such as Forest inventory, Atmospheric sciences, Allometry and Understory. His Point cloud study integrates concerns from other disciplines, such as Tree structure, Ground truth and Plot. His studies in Tree integrate themes in fields like Range and Concordance correlation coefficient.

His most cited work include:

  • Nondestructive estimates of above‐ground biomass using terrestrial laser scanning (285 citations)
  • SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds (118 citations)
  • Terrestrial Laser Scanning for Plot-Scale Forest Measurement (115 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Lidar, Remote sensing, Tree, Ecology and Tree structure. His Lidar research includes themes of Point cloud, Canopy, Atmospheric sciences, Mean squared error and Vegetation. Temperate forest is closely connected to Laser scanning in his research, which is encompassed under the umbrella topic of Point cloud.

His work carried out in the field of Remote sensing brings together such families of science as Terrestrial laser scanning, Scale, Leaf area index and Plot. His studies deal with areas such as Biomass, Radiative transfer and Propagation of uncertainty as well as Tree. His Tree structure research incorporates themes from Agroforestry and Thinning.

He most often published in these fields:

  • Lidar (49.11%)
  • Remote sensing (45.54%)
  • Tree (22.32%)

What were the highlights of his more recent work (between 2019-2021)?

  • Biomass (16.96%)
  • Ecology (29.46%)
  • Tree (22.32%)

In recent papers he was focusing on the following fields of study:

Kim Calders focuses on Biomass, Ecology, Tree, Forest management and Tree allometry. Kim Calders interconnects Vegetation, Forestry, Allometry and Alder in the investigation of issues within Biomass. His work investigates the relationship between Vegetation and topics such as Temperate forest that intersect with problems in Canopy.

His work in the fields of Tree, such as Tree structure, intersects with other areas such as Observational error. His work in Forest ecology addresses issues such as Sensor fusion, which are connected to fields such as Remote sensing. Kim Calders focuses mostly in the field of Remote sensing, narrowing it down to topics relating to Hemispherical photography and, in certain cases, Lidar.

Between 2019 and 2021, his most popular works were:

  • Improved Supervised Learning-Based Approach for Leaf and Wood Classification From LiDAR Point Clouds of Forests (14 citations)
  • Terrestrial laser scanning in forest ecology: Expanding the horizon (13 citations)
  • 3D imaging insights into forests and coral reefs (10 citations)

In his most recent research, the most cited papers focused on:

  • Ecology
  • Statistics
  • Ecosystem

His primary areas of study are Tree, Ecology, Coral reef, Ecology and Climate change. In the subject of general Tree, his work in Tree structure is often linked to Function, thereby combining diverse domains of study. His research integrates issues of Allometry, Biomass, Tropical savanna climate, Vegetation and Crown in his study of Tree structure.

Ecosystem and Threatened species are the core of his Ecology study. His Tree allometry research is multidisciplinary, incorporating elements of Calibration, Statistics, Multivariate statistics and Linear regression. The various areas that Kim Calders examines in his Physical geography study include Land cover, Temperate forest, Biodiversity and Canopy.

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

Nondestructive estimates of above‐ground biomass using terrestrial laser scanning

Kim Calders;Glenn Newnham;Andrew Burt;Simon Murphy.
Methods in Ecology and Evolution (2015)

374 Citations

Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning

Sébastien Bauwens;Harm Bartholomeus;Kim Calders;Philippe Lejeune.
Forests (2016)

170 Citations

SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds

Jan Hackenberg;Heinrich Spiecker;Kim Calders;Mathias Disney.
Forests (2015)

153 Citations

Terrestrial Laser Scanning for Plot-Scale Forest Measurement

Glenn J. Newnham;John D. Armston;Kim Calders;Kim Calders;Mathias I. Disney.
Current Forestry Reports , 1 (4) pp. 239-251. (2015) (2015)

153 Citations

Data acquisition considerations for Terrestrial Laser Scanning of forest plots

Phil Wilkes;Alvaro Lau;Mathias Disney;Kim Calders;Kim Calders.
Remote Sensing of Environment (2017)

149 Citations

Massive-Scale Tree Modelling from Tls Data

P. Raumonen;E. Casella;K. Calders;S. Murphy.
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2015)

109 Citations

Estimation of above-ground biomass of large tropical trees with Terrestrial LiDAR

Jose Gonzalez de Tanago;Jose Gonzalez de Tanago;Alvaro Lau;Alvaro Lau;Harm Bartholomeus;Martin Herold.
Methods in Ecology and Evolution (2017)

105 Citations

Implications of sensor configuration and topography on vertical plant profiles derived from terrestrial LiDAR

Kim Calders;John Armston;Glenn Newnham;Martin Herold.
Agricultural and Forest Meteorology (2014)

99 Citations

Weighing trees with lasers: advances, challenges and opportunities.

M. I. Disney;M. Boni Vicari;A. Burt;K. Calders.
Interface Focus (2018)

96 Citations

Extracting individual trees from lidar point clouds using treeseg

Andrew Burt;Mathias Disney;Mathias Disney;Kim Calders.
Methods in Ecology and Evolution (2018)

75 Citations

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