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Because of the problem of local scour caused by the change of the flow structure caused by the water-resistance of the column bridge pier, the theoretical analysis, and indoor water tank test were used to study the effect of installing a new anti-scouring device in front of the bridge pier on the local scour reduction effect; the influence of the main design parameters such as the height of the protective device, the angle of the protective device and the distance from the protective device to the bridge pier on the local scour of the bridge pier was selected, and the optimal parameter design combination was selected. The test results show that: under the same water flow conditions, the maximum scour depth reduction rate of the measuring point under the protection of the protective device is 48.4% to 74.2% compared with the unprotected scour; the reduction rate of the bridge pier is relative to the relative height of the device and the device equivalent. The angle and the distance between the device and the bridge pier are related, and the shock reduction rate decreases with the increase of the flow intensity. In the test range, the ratio of the device height to the water depth is 2/3, the device angle is 60 °, and the distance from the bridge pier is 3. When the diameter of the pier is doubled, the effect of reducing the impact on the pier is the best.

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

Agroforestry has large potential for carbon (C) sequestration while providing many economical, social, and ecological benefits via its diversified products. Airborne lidar is considered as the most accurate technology for mapping aboveground biomass (AGB) over landscape levels. However, little research in the past has been done to study AGB of agroforestry systems using airborne lidar data. Focusing on an agroforestry system in the Brazilian Amazon, this study first predicted plot-level AGB using fixed-effects regression models that assumed the regression coefficients to be constants. The model prediction errors were then analyzed from the perspectives of tree DBH (diameter at breast height)—height relationships and plot-level wood density, which suggested the need for stratifying agroforestry fields to improve plot-level AGB modeling. We separated teak plantations from other agroforestry types and predicted AGB using mixed-effects models that can incorporate the variation of AGB-height relationship across agroforestry types. We found that, at the plot scale, mixed-effects models led to better model prediction performance (based on leave-one-out cross-validation) than the fixed-effects models, with the coefficient of determination (R2) increasing from 0.38 to 0.64. At the landscape level, the difference between AGB densities from the two types of models was ~10% on average and up to ~30% at the pixel level. This study suggested the importance of stratification based on tree AGB allometry and the utility of mixed-effects models in modeling and mapping AGB of agroforestry systems.