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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Airborne Laser Scanning Outperforms the Alternative 3D Techniques in Capturing Variation in Tree Height and Forest Density in Southern Boreal Forests. / Vastaranta, Mikko; Yrttimaa, Tuomas; Saarinen, Ninni; Yu, Xiaowei; Karjalainen, Mika; Nurminen, Kimmo; Karila, Kirsi; Kankare, Ville; Luoma, Ville; Pyörälä, Jiri; Junttila, Samuli; Tanhuanpää, Topi; Kaartinen, Harri; Kukko, Antero; Honkavaara, Eija; Jaakkola, Anttoni; Liang, Xinlian; Wang, Yunsheng; Vaaja, Matti; Hyyppä, Hannu; Katoh, Masato; Wulder, Michael A.; Holopainen, Markus; Hyyppa, Juha.

In: BALTIC FORESTRY, Vol. 24, No. 2, 11.2018, p. 268-277.

Research output: Contribution to journalArticleScientificpeer-review

Harvard

Vastaranta, M, Yrttimaa, T, Saarinen, N, Yu, X, Karjalainen, M, Nurminen, K, Karila, K, Kankare, V, Luoma, V, Pyörälä, J, Junttila, S, Tanhuanpää, T, Kaartinen, H, Kukko, A, Honkavaara, E, Jaakkola, A, Liang, X, Wang, Y, Vaaja, M, Hyyppä, H, Katoh, M, Wulder, MA, Holopainen, M & Hyyppa, 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.

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Vastaranta, Mikko ; Yrttimaa, Tuomas ; Saarinen, Ninni ; Yu, Xiaowei ; Karjalainen, Mika ; Nurminen, Kimmo ; Karila, Kirsi ; Kankare, Ville ; Luoma, Ville ; Pyörälä, Jiri ; Junttila, Samuli ; Tanhuanpää, Topi ; Kaartinen, Harri ; Kukko, Antero ; Honkavaara, Eija ; Jaakkola, Anttoni ; Liang, Xinlian ; Wang, Yunsheng ; Vaaja, Matti ; Hyyppä, Hannu ; Katoh, Masato ; Wulder, Michael A. ; Holopainen, Markus ; Hyyppa, Juha. / Airborne Laser Scanning Outperforms the Alternative 3D Techniques in Capturing Variation in Tree Height and Forest Density in Southern Boreal Forests. In: BALTIC FORESTRY. 2018 ; Vol. 24, No. 2. pp. 268-277.

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@article{65d4675b9eee431b896e36bdec17eb7b,
title = "Airborne Laser Scanning Outperforms the Alternative 3D Techniques in Capturing Variation in Tree Height and Forest Density in Southern Boreal Forests",
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.",
keywords = "airborne laser scanning, lidar, photogrammetry, radargrammetry, interferometry, tree, mapping, INVENTORY ATTRIBUTES, POINT CLOUDS, LEVEL, PREDICTION, MODELS, SAR",
author = "Mikko Vastaranta and Tuomas Yrttimaa and Ninni Saarinen and Xiaowei Yu and Mika Karjalainen and Kimmo Nurminen and Kirsi Karila and Ville Kankare and Ville Luoma and Jiri Py{\"o}r{\"a}l{\"a} and Samuli Junttila and Topi Tanhuanp{\"a}{\"a} and Harri Kaartinen and Antero Kukko and Eija Honkavaara and Anttoni Jaakkola and Xinlian Liang and Yunsheng Wang and Matti Vaaja and Hannu Hyypp{\"a} and Masato Katoh and Wulder, {Michael A.} and Markus Holopainen and Juha Hyyppa",
year = "2018",
month = "11",
language = "English",
volume = "24",
pages = "268--277",
journal = "BALTIC FORESTRY",
issn = "1392-1355",
number = "2",

}

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TY - JOUR

T1 - Airborne Laser Scanning Outperforms the Alternative 3D Techniques in Capturing Variation in Tree Height and Forest Density in Southern Boreal Forests

AU - Vastaranta, Mikko

AU - Yrttimaa, Tuomas

AU - Saarinen, Ninni

AU - Yu, Xiaowei

AU - Karjalainen, Mika

AU - Nurminen, Kimmo

AU - Karila, Kirsi

AU - Kankare, Ville

AU - Luoma, Ville

AU - Pyörälä, Jiri

AU - Junttila, Samuli

AU - Tanhuanpää, Topi

AU - Kaartinen, Harri

AU - Kukko, Antero

AU - Honkavaara, Eija

AU - Jaakkola, Anttoni

AU - Liang, Xinlian

AU - Wang, Yunsheng

AU - Vaaja, Matti

AU - Hyyppä, Hannu

AU - Katoh, Masato

AU - Wulder, Michael A.

AU - Holopainen, Markus

AU - Hyyppa, Juha

PY - 2018/11

Y1 - 2018/11

N2 - 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.

AB - 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.

KW - airborne laser scanning

KW - lidar

KW - photogrammetry

KW - radargrammetry

KW - interferometry

KW - tree

KW - mapping

KW - INVENTORY ATTRIBUTES

KW - POINT CLOUDS

KW - LEVEL

KW - PREDICTION

KW - MODELS

KW - SAR

M3 - Article

VL - 24

SP - 268

EP - 277

JO - BALTIC FORESTRY

JF - BALTIC FORESTRY

SN - 1392-1355

IS - 2

ER -

ID: 32386345