What is he best known for?
The fields of study he is best known for:
- Statistics
- Ecology
- Artificial intelligence
Mikko Vastaranta mainly investigates Remote sensing, Forest inventory, Point cloud, Laser scanning and Tree.
His Remote sensing study combines topics in areas such as Mean squared error, Diameter at breast height and Random forest.
He has researched Forest inventory in several fields, including Descriptive statistics, Photogrammetry, Aboveground biomass, Vegetation and Lidar.
His study in Point cloud is interdisciplinary in nature, drawing from both Ecology and Algorithm.
His research in Laser scanning intersects with topics in Sample, Field and Scale.
His Tree research includes themes of Terrestrial laser scanning and Statistics, Spatial analysis, Plot.
His most cited work include:
- Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data (327 citations)
- An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning (302 citations)
- Terrestrial laser scanning in forest inventories (276 citations)
What are the main themes of his work throughout his whole career to date?
Mikko Vastaranta mainly focuses on Remote sensing, Forest inventory, Lidar, Laser scanning and Tree.
His Remote sensing study combines topics from a wide range of disciplines, such as Point cloud and Canopy, Tree canopy.
His Point cloud research is multidisciplinary, incorporating elements of Scots pine, Terrestrial laser scanning, Mean squared error, Aboveground biomass and Biomass.
His Forest inventory research focuses on subjects like Basal area, which are linked to Standard deviation.
His research in Lidar focuses on subjects like Vegetation, which are connected to Hyperspectral imaging.
His study in the field of Tree inventory also crosses realms of Benchmarking.
He most often published in these fields:
- Remote sensing (77.84%)
- Forest inventory (40.00%)
- Lidar (41.08%)
What were the highlights of his more recent work (between 2018-2021)?
- Remote sensing (77.84%)
- Scots pine (19.46%)
- Taiga (17.30%)
In recent papers he was focusing on the following fields of study:
Remote sensing, Scots pine, Taiga, Forest inventory and Point cloud are his primary areas of study.
His Remote sensing study incorporates themes from Tree health, Canopy and Laser scanning.
His Scots pine research includes elements of Forest management, Parametrization, Volume and Mean squared error.
His Forest inventory research includes themes of Tree and Basal area.
The Tree study combines topics in areas such as Sample and Statistics.
Point cloud and Pinus are frequently intertwined in his study.
Between 2018 and 2021, his most popular works were:
- Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle (14 citations)
- Characterizing Seedling Stands Using Leaf-Off and Leaf-On Photogrammetric Point Clouds and Hyperspectral Imagery Acquired from Unmanned Aerial Vehicle (14 citations)
- Examining Changes in Stem Taper and Volume Growth with Two-Date 3D Point Clouds (13 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Ecology
- Artificial intelligence
His scientific interests lie mostly in Remote sensing, Forest inventory, Canopy, Lidar and Taiga.
His studies deal with areas such as Picea abies, Climate change, Scots pine, Forestry and Laser scanning as well as Remote sensing.
His work investigates the relationship between Forest inventory and topics such as Tree that intersect with problems in Soil science.
His Lidar research is multidisciplinary, incorporating perspectives in Tree health, Vegetation and Infestation.
His Taiga research integrates issues from Point cloud, Statistics, Pairwise comparison, Sample and Sustainable forest management.
His work carried out in the field of Point cloud brings together such families of science as Coarse woody debris, Basal area, Sample, Terrestrial laser scanning and Sample plot.
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