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

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 2015,7, 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.

Assessment of boreal forest height from WorldView-2 satellite stereo images

“…HL could be estimated from WV2 data with 1.5 m and 1.4 m RMSE at respective test site, which corresponds to 8.3% and 10.0% RMSE (Table 4, Table 4]. This corresponds well with the 1.4 m RMSE reported by Yu et al (2015) for a Finnish test site. The top height, ALS p100, was estimated with slightly larger RMSE at both test sites (2.1 m and 1.7 m respectively; Figures 3(c) and 3(f)).…”

“…The highest correlation and lowest RMSE for all possible combinations was found for ALS p70 at both test sites, which in Remningstorp was best described with WV2 p60, while in Krycklan WV2 p95 was the best estimator (Table 4, Figures 3(b) and 3(e)). The RMSEs were 0.8 m (5.8%) and 1.1 m (9.5%) at the respective test sites, which are lower than traditional field sampled methods (subjectively chosen locations evaluated using a relascope, about 10% to 15% RMSE) and in line with the accuracy with which HL can be estimated from ALS data (Ståhl 1992;Yu et al 2015).…”

“…Currently, airborne laser scanning (ALS) is the most accurate airborne remote sensing technique available for estimation of many forest variables, such as HL, basal area or stem volume (Hyyppä et al 2000;Naesset et al 2004;Yu et al 2015). However, satellite based platforms can provide information over large areas at lower cost and with short repetition intervals.…”

“…So far, Yu et al (2015) is the only study known to the authors, that has used WV2 data to evaluate forest variables in boreal forest (Finland). They compared numerous remote sensing techniques to estimate AGB, stem volume, basal-area, basal area-weighted mean diameter and HL at plot-level.…”

“…Remote sensing techniques can provide comprehensive and accurate information in order to further increase the value of the field inventoried data (Tomppo et al 2008). Remote sensing data have in numerous studies been shown to complement field based measures and estimations, such as basal area-weighted tree height (also known as Lorey's mean height, HL), basal-area weighted stem diameter and mean stem volume (Hyyppä et al 2000;Naesset et al 2004;Yu et al 2015). HL weights the contribution of trees to the stand height by their basal area, which causes larger trees to have a greater impact on the mean height.…”