Search Results (2)

Estimating aboveground biomass (AGB) is vital for sustainable forest management and helps to understand the contributions of forests to carbon storage and emission goals. In this study, the effectiveness of plot-level AGB estimation using height and crown diameter derived from UAV-LiDAR, calibration of GEDI-L4A AGB and GEDI-L2A rh98 heights, and spectral variables derived from UAV-multispectral and RGB data were assessed. These calibrated AGB and height values and UAV-derived spectral variables were used to fit AGB estimations using a random forest (RF) regression model in Fuling District, China. Using Pearson correlation analysis, we identified 10 of the most important predictor variables in the AGB prediction model, including calibrated GEDI AGB and height, Visible Atmospherically Resistant Index green (VARIg), Red Blue Ratio Index (RBRI), Difference Vegetation Index (DVI), canopy cover (CC), Atmospherically Resistant Vegetation Index (ARVI), Red-Edge Normalized Difference Vegetation Index (NDVIre), Color Index of Vegetation (CIVI), elevation, and slope. The results showed that, in general, the second model based on calibrated AGB and height, Sentinel-2 indices, slope and elevation, and spectral variables from UAV-multispectral and RGB datasets with evaluation metric (for training: R² = 0.941 Mg/ha, RMSE = 13.514 Mg/ha, MAE = 8.136 Mg/ha) performed better than the first model with AGB prediction. The result was between 23.45 Mg/ha and 301.81 Mg/ha, and the standard error was between 0.14 Mg/ha and 10.18 Mg/ha. This hybrid approach significantly improves AGB prediction accuracy and addresses uncertainties in AGB prediction modeling. The findings provide a robust framework for enhancing forest carbon stock assessment and contribute to global-scale AGB monitoring, advancing methodologies for sustainable forest management and ecological research. Full article

The overall goal of the novel Functionality, Usability, Look-and-Feel, and Evaluation (FULE) user-centered methodology for product design proposed in this paper is to develop usable and aesthetic products. Comprising several product design methods, this novel methodology we devised focuses on the product designer’s role and responsibility. Following the first three formative assessment phases that define the product’s functionality, usability, and look-and-feel, the summative evaluation phase not only assesses the product, but also provide guidelines to its implementation, marketing, and support. A case study devoted to the design of an autonomous medical device illustrates how the FULE methodology can provide the designer with tools to better select among design alternatives and contribute to reducing bias and subjective decisions. Full article