Paper Info
Title
Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes
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 (D-g) and Lorey's mean height (H-g) 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/m(2)) 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 H-g, 11.7% (3.0 cm)-20.6% (5.3 cm) for D-g, 14.8% (4.0 m(2)/ha)-25.8% (6.9 m(2)/ha) for G, 15.9% (43.0 m(3)/ha)-31.2% (84.2 m(3)/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 H-g and D-g, 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 H-g, 20.6% to 19.2% for D-g, 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.
Year
DOI
Venue
2015
10.3390/rs71215809
REMOTE SENSING
Keywords
Field
DocType
forest inventory,3D technique,point cloud,airborne laser scanning,photogrammetry,radargrammetry,InSAR
Photogrammetry,Satellite,Interferometric synthetic aperture radar,Synthetic aperture radar,Forest inventory,Remote sensing,Basal area,Digital elevation model,Point cloud,Geology
Journal
Volume
Issue
Citations 
7
12
18
PageRank 
References 
Authors
1.65
7
16
Name
Order
Citations
PageRank
Xiaowei Yu127831.85
Juha Hyyppa237745.75
Mika Karjalainen3499.88
kimmo nurminen4192.37
Kirsi Karila5333.68
Mikko Vastaranta629834.91
Ville Kankare7659.21
Harri Kaartinen860863.10
Markus Holopainen935740.95
Eija Honkavaara1022027.92
Antero Kukko1148347.44
Anttoni Jaakkola1234430.30
Xinlian Liang1319323.72
Yunsheng Wang14545.41
Hannu Hyyppa1520223.16
Masato Katoh16314.25