Airborne Laser Scanning Outperforms the Alternative 3D Techniques in Capturing Variation in Tree Height and Forest Density in Southern Boreal Forests

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Vastaranta , M , Yrttimaa , T , Saarinen , N , Yu , X , Karjalainen , M , Nurminen , K , Karila , K , Kankare , V , Luoma , V , Pyörälä , J , Junttila , S , Tanhuanpaa , T , Kaartinen , H , Kukko , A , Honkavaara , E , Jaakkola , A , Liang , X , Wang , Y , Vaaja , M , Hyyppä , H , Katoh , M , Wulder , M A , Holopainen , M & Hyyppä , J 2018 , ' Airborne Laser Scanning Outperforms the Alternative 3D Techniques in Capturing Variation in Tree Height and Forest Density in Southern Boreal Forests ' , Baltic Forestry , vol. 24 , no. 2 , pp. 268-277 . < https://www.balticforestry.mi.lt/bf/index.php?option=com_content&view=article&catid=14&id=586 >

Title: Airborne Laser Scanning Outperforms the Alternative 3D Techniques in Capturing Variation in Tree Height and Forest Density in Southern Boreal Forests
Author: Vastaranta, Mikko; Yrttimaa, Tuomas; Saarinen, Ninni; Yu, Xiaowei; Karjalainen, Mika; Nurminen, Kimmo; Karila, Kirsi; Kankare, Ville; Luoma, Ville; Pyörälä, Jiri; Junttila, Samuli; Tanhuanpaa, Topi; Kaartinen, Harri; Kukko, Antero; Honkavaara, Eija; Jaakkola, Anttoni; Liang, Xinlian; Wang, Yunsheng; Vaaja, Matti; Hyyppä, Hannu; Katoh, Masato; Wulder, Michael A.; Holopainen, Markus; Hyyppä, Juha
Contributor organization: Department of Forest Sciences
Laboratory of Forest Resources Management and Geo-information Science
Forest Health Group
Faculty of Agriculture and Forestry
Forest Ecology and Management
Date: 2018
Language: eng
Number of pages: 10
Belongs to series: Baltic Forestry
ISSN: 1392-1355
URI: http://hdl.handle.net/10138/307427
Abstract: The objective of this study is to better understand the relationship between forest structure and point cloud features generated from certain airborne and space borne sensors. Point cloud features derived from airborne laser scanning (ALS), aerial imagery (AI), WorldView-2 imagery (WV2), TerraSAR-X, and Tandem-X (TDX) data were classified as features characterizing forest height and density as well as variation in tree height. Correlations between these features and field-measured attributes describing forest height, density and tree height variation were investigated at plot scale. From the field-measured attributes, basal area (G) and the number of trees per unit area (N) were used as forest density indicators whereas maximum tree height (H-max) and standard deviation in tree height (H-std) were used as indicators for forest height and tree height variation, respectively. In the analyses, field observations from 91 sample plots (32 m x 32 m) located in southern Finland were used. Even though ALS was found to be the most accurate data source in characterizing forest structure, AI, WV2, and TDX were also capable of characterizing forest height at plot scale with correlation coefficients stronger than 0.85. However, ALS was the only data source capable of providing separate features for characterizing also the variation in tree height and forest density. Features related to forest height, generated from the other data sources besides ALS, also provided strongest correlation with the forest density attributes and variation in tree height, in addition to H-max. Due to these more diverse characterization capabilities, forest structural attributes can be predicted more accurately by using ALS, also in the areas where the relation between the attributes of interest is not solely dependent on forest height, compared to the other investigated 3D remote sensing data sources.
Subject: airborne laser scanning
lidar
photogrammetry
radargrammetry
interferometry
tree
mapping
INVENTORY ATTRIBUTES
POINT CLOUDS
LEVEL
PREDICTION
MODELS
SAR
4112 Forestry
Peer reviewed: Yes
Usage restriction: openAccess
Self-archived version: publishedVersion


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