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See detailForest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning
Bauwens, Sébastien ULg; Bartholomeus, Harm; Calders, Kim et al

in Forests (2016), 7(6), 127

The application of static terrestrial laser scanning (TLS) in forest inventories is becoming more effective. Nevertheless, the occlusion effect is still limiting the processing efficiency to extract ... [more ▼]

The application of static terrestrial laser scanning (TLS) in forest inventories is becoming more effective. Nevertheless, the occlusion effect is still limiting the processing efficiency to extract forest attributes. The use of a mobile laser scanner (MLS) would reduce this occlusion. In this study, we assessed and compared a hand-held mobile laser scanner (HMLS) with two TLS approaches (single scan: SS, and multi scan: MS) for the estimation of several forest parameters in a wide range of forest types and structures. We found that SS is competitive to extract the ground surface of forest plots, while MS gives the best result to describe the upper part of the canopy. The whole cross-section at 1.3 m height is scanned for 91% of the trees (DBH > 10 cm) with the HMLS leading to the best results for DBH estimates (bias of 0.08 cm and RMSE of 1.11 cm), compared to no fully-scanned trees for SS and 42% fully-scanned trees for MS. Irregularities, such as bark roughness and non-circular cross-section may explain the negative bias encountered for all of the scanning approaches. The success of using MLS in forests will allow for 3D structure acquisition on a larger scale and in a time-efficient manner. [less ▲]

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See detailForest inventory with Terrestrial LiDAR: what about Hand-Held Mobile LiDAR?
Bauwens, Sébastien ULg; Bartholomeus, Harm; Piboule, Alexandre et al

Conference (2014, November 05)

For a decade, studies of the application of static Terrestrial Laser Scanner (TLS) in plotwise forest inventories are giving more and more effective results. In spite of the improvements occurring in ... [more ▼]

For a decade, studies of the application of static Terrestrial Laser Scanner (TLS) in plotwise forest inventories are giving more and more effective results. In spite of the improvements occurring in processing scan data to extract forest attributes, the occlusion effect is still limiting the processing efficiency. A multi-scan approach is recommended to reduce this effect. However, such approach needs pre-scan preparations (setting up the plot, targets positioning), it requires data registration and it comes at a higher data collection cost. In this study we explore the potential of a Hand-held mobile LiDAR System (HMLS) as new LiDAR tool to scan forest plots. HMLS data are compared to static TLS data (single and multi-scan) in terms of data acquisition, registration time and quality of automatic DBH extraction. The low weight, small size of the instrument and no targets requirements reduce the time of pre-scan preparations to the time needed for single scan which is 6 times less than scanning a plot with 5 scans. The registration time depends of the time spent to scan the plot and it is of the same magnitude than single scan. The resulting point cloud of the HMLS is noisier than TLS point clouds. Nevertheless, error on DBH estimations is similar to scanning a plot with a TLS positioned at 5 locations. RMSE is higher than multi-scan and close to single scan for trees detected by the both LiDAR technologies. This first study exhibits the high potential of HMLS by its simple use, which needs only one operator while presenting similar results in automatic DBH extraction than static TLS. Technology and registering method improvements of this type of mobile LiDAR will reduce the noise of the point cloud, which might reduce the DBH RMSE. [less ▲]

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See detailSoil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy
Bartholomeus, Harm; Kooistra, Lammert; Stevens, Antoine et al

in International Journal of Applied Earth Observation and Geoinformation (2011), 13(1), 81-88

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping ... [more ▼]

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the influence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34 g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation. [less ▲]

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