Next Article in Journal
Precise Positioning of BDS, BDS/GPS: Implications for Tsunami Early Warning in South China Sea
Previous Article in Journal
Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(12), 15933-15954; doi:10.3390/rs71215809

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

1
Finnish Geospatial Research Institute, National Land Survey, Geodeetinrinne 2, FI-02431 Masala, Finland
2
Department of Forest Sciences, University of Helsinki, FI-00014 Helsinki, Finland
3
Research Institute of Measuring and Modelling for the Built Environment, Aalto University, P.O.Box 15800, FI-00076 Aalto, Finland
4
Institute of Mountain Science, Shinshu University, 8304, Minamiminowa-Village, Kamiina-County, Nagano 399-4598, Japan
*
Author to whom correspondence should be addressed.
Academic Editors: Randolph H. Wynne and Prasad S. Thenkabail
Received: 21 September 2015 / Revised: 11 November 2015 / Accepted: 19 November 2015 / Published: 27 November 2015
View Full-Text   |   Download PDF [4763 KB, uploaded 14 December 2015]   |  

Abstract

It is anticipated that many of the future forest mapping applications will be based on three-dimensional (3D) point clouds. A comparison study was conducted to verify the explanatory power and information contents of several 3D remote sensing data sources on the retrieval of above ground biomass (AGB), stem volume (VOL), basal area (G), basal-area weighted mean diameter (Dg) and Lorey’s mean height (Hg) at the plot level, utilizing the following data: synthetic aperture radar (SAR) Interferometry, SAR radargrammetry, satellite-imagery having stereo viewing capability, airborne laser scanning (ALS) with various densities (0.8–6 pulses/m2) and aerial stereo imagery. Laser scanning is generally known as the primary source providing a 3D point cloud. However, photogrammetric, radargrammetric and interferometric techniques can be used to produce 3D point clouds from space- and air-borne stereo images. Such an image-based point cloud could be utilized in a similar manner as ALS providing that accurate digital terrain model is available. In this study, the performance of these data sources for providing point cloud data was evaluated with 91 sample plots that were established in Evo, southern Finland within a boreal forest zone and surveyed in 2014 for this comparison. The prediction models were built using random forests technique with features derived from each data sources as independent variables and field measurements of forest attributes as response variable. The relative root mean square errors (RMSEs) varied in the ranges of 4.6% (0.97 m)–13.4% (2.83 m) for Hg, 11.7% (3.0 cm)–20.6% (5.3 cm) for Dg, 14.8% (4.0 m2/ha)–25.8% (6.9 m2/ha) for G, 15.9% (43.0 m3/ha)–31.2% (84.2 m3/ha) for VOL and 14.3% (19.2 Mg/ha)–27.5% (37.0 Mg/ha) for AGB, respectively, depending on the data used. Results indicate that ALS data achieved the most accurate estimates for all forest inventory attributes. For image-based 3D data, high-altitude aerial images and WorldView-2 satellite optical image gave similar results for Hg and Dg, which were only slightly worse than those of ALS data. As expected, spaceborne SAR data produced the worst estimates. WorldView-2 satellite data performed well, achieving accuracy comparable to the one with ALS data for G, VOL and AGB estimation. SAR interferometry data seems to contain more information for forest inventory than SAR radargrammetry and reach a better accuracy (relative RMSE decreased from 13.4% to 9.5% for Hg, 20.6% to 19.2% for Dg, 25.8% to 20.9% for G, 31.2% to 22.0% for VOL and 27.5% to 20.7% for AGB, respectively). However, the availability of interferometry data is limited. The results confirmed the high potential of all 3D remote sensing data sources for forest inventory purposes. However, the assumption of using other than ALS data is that there exist a high quality digital terrain model, in our case it was derived from ALS. View Full-Text
Keywords: forest inventory; 3D technique; point cloud; airborne laser scanning; photogrammetry; radargrammetry; InSAR forest inventory; 3D technique; point cloud; airborne laser scanning; photogrammetry; radargrammetry; InSAR
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Yu, X.; Hyyppä, J.; Karjalainen, M.; Nurminen, K.; Karila, K.; Vastaranta, M.; Kankare, V.; Kaartinen, H.; Holopainen, M.; Honkavaara, E.; Kukko, A.; Jaakkola, A.; Liang, X.; Wang, Y.; Hyyppä, H.; Katoh, M. Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes. Remote Sens. 2015, 7, 15933-15954.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top