Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR

Forest biomass is an important descriptor for studying carbon storage, carbon cycles, and global change science. The full-waveform spaceborne Light Detection And Ranging (LiDAR) Geoscience Laser Altimeter System (GLAS) provides great possibilities for large-scale and long-term biomass estimation. To the best of our knowledge, most of the existing research has utilized average tree height (or height metrics) within a GLAS footprint as the key parameter for biomass estimation. However, the vertical distribution of tree height is usually not as homogeneous as we would expect within such a large footprint of more than 2000 m2, which would limit the biomass estimation accuracy vastly. Therefore, we aim to develop a novel canopy height layering biomass estimation model (CHL-BEM) with GLAS data in this study. First, all the trees with similar height were regarded as one canopy layer within each GLAS footprint. Second, the canopy height and canopy cover of each layer were derived from GLAS waveform parameters. These parameters were extracted using a waveform decomposition algorithm (refined Levenberg–Marquardt—RLM), which assumed that each decomposed vegetation signal corresponded to a particular canopy height layer. Third, the biomass estimation model (CHL-BEM) was established by using the canopy height and canopy cover of each height layer. Finally, the CHL-BEM was compared with two typical biomass estimation models of GLAS in the study site located in Ejina, China, where the dominant species was Populus euphratica. The results showed that the CHL-BEM presented good agreement with the field measurement biomass (R2 = 0.741, RMSE = 0.487, %RMSE = 24.192) and achieved a significantly higher accuracy than the other two models. As a whole, we expect our method to advance all the full-waveform LiDAR development and applications, e.g., the newly launched Global Ecosystem Dynamics Investigation (GEDI)

Irrigation-facilitated low-density polyethylene microplastic vertical transport along soil profile : An empirical model developed by column experiment

The emerging issue of microplastic pollution of agricultural soils derives from the intensive utilization of plastic mulching film. Although surface runoff may transport microplastic off-site, infiltration may also facilitate microplastic transport from surface soil to deeper depths. Microplastic comprises a relatively new category of soil contaminants, whose transport in the soil has not yet been widely studied. In this study, we investigated microplastic transport from contaminated surface soil (50 g kg-1) driven by irrigation, from permanent wilting point to saturation, and developed an empirical model to characterize the resulting accumulation of microplastic along soil profile. A soil column experiment was conducted under various treatments: the control, 1, 2 and 4 runs of irrigation. Soil samples were collected from inside and outside of soil cracks (if present) in each soil layer (0–2 cm (source layer), 2–5 cm, 5–10 cm, 10–20 cm, 20–30 cm, 30–40 cm, 40–50 cm). The results showed that with increasing irrigation runs, microplastic in the source soil layer decreased, while microplastic contents in deeper soil depths increased significantly (p 0.92). Further research is needed to develop an physical-based model in order to assess microplastic migration risks driven by irrigation and other agricultural management practices