A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory

Nilsson, Mats; Nordkvist, Karin; Jonzén, Jonas; Lindgren, Nils; Axensten, Peder; Wallerman, Jörgen; Egberth, Mikael; Larsson, Svante; Nilsson, Liselott; Eriksson, Johan; Olsson, Håkan

doi:10.1016/j.rse.2016.10.022

Cited by 183 publications

(152 citation statements)

References 13 publications

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“…Hence, to gain an even more complete and fair evaluation, summer acquisitions should be evaluated, or perhaps only acquisitions with frozen ground conditions. The accuracy of these AGB/VOL maps (21%-25% RMSE) is higher than all earlier Swedish satellite-based nation-wide products, which generally have reported VOL accuracies of about 30%-80% RMSE at the stand level, and the accuracy is slightly lower than ALS-based maps (17%-22% RMSE) [3,[9][10][11]. It seems natural to think that the greatly better accuracy in TanDEM-X-and ALS-based maps is due to the ability of acquiring 3D data, as the other maps were based on 2D data.…”

Section: Discussioncontrasting

confidence: 67%

“…A large number of forest variables was measured or estimated on each plot, including basal-area weighted mean tree height, basal-area weighted mean stem diameter, basal area, stand age, AGB and VOL. The plots were positioned with GPS receivers (Garmin GPSMAP 64), which have a horizontal positional accuracy of approximately 5 m, and most permanent plots had also been assigned updated coordinates using a high precision GPS receiver with a positional accuracy of 1 m [3].…”

Section: Field Datamentioning

confidence: 99%

“…For forest-related purposes, field inventories paired with airborne laser scanning (ALS) data have made forest maps of several vital variables, e.g., tree height, basal area, above-ground biomass (AGB) and stem volume (VOL), of yet unprecedented accuracy available [1][2][3]. AGB and VOL are strongly correlated, although AGB also includes the branches and is more commonly used in climate modelling, while the industry and forest owners tend to use VOL, as this is a metric of the merchantable forest products [4].…”

Section: Introductionmentioning

confidence: 99%

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Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data

et al. 2017

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This paper report experiences from the processing and mosaicking of 518 TanDEM-X image pairs covering the entirety of Sweden, with two single map products of above-ground biomass (AGB) and forest stem volume (VOL), both with 10 m resolution. The main objective was to explore the possibilities and overcome the challenges related to forest mapping extending a large number of adjacent satellite scenes. Hence, numerous examples are presented to illustrate challenges and possible solutions. To derive the forest maps, the observables backscatter, interferometric phase height and interferometric coherence, obtained from TanDEM-X, were evaluated using empirical robust linear regression models with reference data extracted from 2288 national forest inventory plots with a 10 m radius. The interferometric phase height was the single most important observable, to predict AGB and VOL. The mosaics were evaluated on different datasets with field-inventoried stands across Sweden. The root mean square error (RMSE) was about 21%-25% (27-30 tons/ha and 52-65 m 3 /ha) at the stand level. It was noted that the most influencing factors on the observables in this study were local temperature and geolocation errors that were challenging to robustly compensate against. Because of this variability at the scene-level, determinations of AGB and VOL for single stands are recommended to be used with care, as an equivalent accuracy is difficult to achieve for all different scenes, with varying acquisition conditions. Still, for the evaluated stands, the mosaics were of sufficient accuracy to be used for forest management at the stand level.

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Section: Discussioncontrasting

confidence: 67%

Section: Field Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data

et al. 2017

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“…ALS has been used to produce nationwide estimations of forest variables, such as height, volume, stem diameter and basal area, with high accuracy [1,2]. Estimations are usually made by using regression analysis of height distribution and density features of the ALS-derived point cloud.…”

Section: Introductionmentioning

confidence: 99%

Exploring Multispectral ALS Data for Tree Species Classification

2018

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Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm, 1064 nm and 532 nm) are best suited for tree species classification. Remote sensing data were gathered over hemi-boreal forest in southern Sweden (58 • 27 18.35 N, 13 • 39 08.03 E) on 21 July 2016. The field data consisted of 179 solitary trees from nine genera and ten species. Two new methods for feature extraction were tested and compared to features of height and intensity distributions. The features that were most important for tree species classification were intensity distribution features. Features from the upper part of the upper and outer parts of the crown were better for classification purposes than others. The best classification model was created using distribution features of both intensity and height in multispectral data, with a leave-one-out cross-validated accuracy of 76.5%. As a comparison, only structural features resulted in an highest accuracy of 43.0%. Picea abies and Pinus sylvestris had high user's and producer's accuracies and were not confused with any deciduous species. Tilia cordata was the deciduous species with a large sample that was most frequently confused with many other deciduous species. The results, although based on a small and special data set, suggest that multispectral ALS is a technology with great potential for tree species classification.

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“…A relationship between scanning density and 5 maximum tree height was shown in Boudreault et al (2015). For the site considered here, the scanning density was between 0.25 and 1 points per m 2 , leading to an underestimation of the tree height of up to 2 m. Nilsson et al (2017) created maps of the mean tree height from Airborne Laser Scans using the the 95 percentile of points located above 1.5m above ground, leading to a somewhat lower tree height. Compared to the uncertainties involved in the determination of roughness for the case when no tree height information is available, we consider this uncertainty to be of little importance.…”

Section: Estimating Elevation and Forest Heightmentioning

confidence: 99%

From lidar scans to roughness maps for wind resource modeling in forested areas

Floors¹,

Enevoldsen²,

Davis³

et al. 2018

Preprint

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Abstract. Applying erroneous roughness lengths can have a large impact on the estimated performance of wind turbines, particularly in forested areas. In this study, a new method called the Objective Roughness Approach (ORA), which converts tree height maps created using airborne lidar scans to roughness maps suitable for wind modeling, is evaluated via crosspredictions between different anemometers at a complex forested site with seven tall meteorological masts using the Wind Atlas Analysis and Application program (WAsP). The cross-predictions were made using ORA maps created at four spatial 5 resolutions and from four freely available roughness maps based on land-use classifications. The validation showed that the use of ORA maps resulted in a closer agreement with observational data for all investigated resolutions compared to the land-use maps. Further, when using the ORA maps, the risk of making large errors (> 25 %) in predicted power density was reduced by 40-50 % compared to satellite based products with the same resolution. The results could be further improved for highresolution ORA maps by adding the displacement height. The improvement when using the ORA maps came down to two 10 factors, first they had a higher roughness length for forests, which was confirmed to by increasing the forest roughness value of the land-use based maps to the value of the ORA map, and second, due to the higher resolution of the ORA data, since the ORA maps with the highest resolution had the largest reduction in mean absolute errors.

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A nationwide forest attribute map of Sweden predicted using airborne laser scanning data and field data from the National Forest Inventory

Cited by 183 publications

References 13 publications

Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data

Experiences from Large-Scale Forest Mapping of Sweden Using TanDEM-X Data

Exploring Multispectral ALS Data for Tree Species Classification

From lidar scans to roughness maps for wind resource modeling in forested areas

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