Conventional measurement methods for above-ground biomass (AGB) are time-consuming,
inaccurate, and labor-intensive. Unmanned aerial systems (UASs) have emerged as a …

Remote sensors, onboard orbital platforms, aircraft, or unmanned aerial vehicles (UAVs)
have emerged as a promising technology to enhance our understanding of changes in …

Precision agriculture has revolutionized crop management and agricultural production, with
LiDAR technology attracting significant interest among various technological advancements …

This study provided a comprehensive evaluation of eight machine learning regression
algorithms for forest aboveground biomass (AGB) estimation from satellite data based on …

Multimodal data fusion, eg, hyperspectral image (HSI) and light detection and ranging
(LiDAR) data fusion, plays an important role in object recognition and classification tasks …

Forest aboveground biomass (AGB) estimation is crucial for carbon cycle studies and
climate change mitigation actions. However, because of limitations in timely and reliable …

Accurately quantifying the aboveground biomass (AGB) of forests is crucial for
understanding global change-related issues such as the carbon cycle and climate change …

Canopy gaps are openings in the forest canopy resulting from branch fall and tree mortality
events. The geographical distribution of large canopy gaps may reflect underlying variation …

Several methods have been proposed to delineate management zones in agricultural fields,
which can guide interventions of the farmers to increase crop yield. In this study, we propose …

Accurate monitoring of forest aboveground biomass (AGB) is vital for sustainable forest
management. Generally, the AGB is estimated by combining satellite images and field …