Projects per year
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.
|Number of pages||10|
|Publication status||Published - Nov 2018|
|MoE publication type||A1 Journal article-refereed|
- airborne laser scanning
- INVENTORY ATTRIBUTES
- POINT CLOUDS
COMBAT: Competence-Based Growth Through Integrated Disruptive Technologies of 3D Digitalization, Robotics, Geospatial Information and Image Processing/Computing - Point Cloud Ecosystem
Gullmets, H., Nieminen, J., Ahlavuo, M., Vaaja, M. T., Laitala, A., Julin, A., Hyyppä, H., Maksimainen, M., Ståhle, P., Jaalama, K., Virtanen, J., Luhtala, L., Haggren, H., Lehtola, V., Rantanen, T., Ingman, M. & Kauhanen, H.
01/05/2015 → 31/12/2017
Project: Academy of Finland: Strategic research funding