Tree-crown and height are primary tree measurements in forest inventory. Convolutional
neural networks (CNNs) are a class of neural networks, which can be used in forest …

Detecting and mapping individual trees accurately and automatically from remote sensing
images is of great significance for precision forest management. Many algorithms, including …

Mapping tree crown is critical for estimating the functional and spatial distribution of
ecosystem services. However, accurate and up-to-date urban crown mapping remains a …

The increasing use of unmanned aerial vehicles (UAV) and high spatial resolution imagery
from associated sensors necessitates the continued advancement of efficient means of …

High-resolution UAV imagery paired with a convolutional neural network approach offers
significant advantages in accurately measuring forestry ecosystems. Despite numerous …

Abstract Purpose of Review Crown detection and measurement at the individual tree level
provide detailed information for accurate forest management. To efficiently acquire such …

The automatic detection of tree crowns and estimation of crown areas from remotely sensed
information offer a quick approach for grasping the dynamics of forest ecosystems and are of …

Deriving individual tree crown (ITC) information from light detection and ranging (LiDAR)
data is of great significance to forest resource assessment and smart management. After …

Tropical forests concentrate the largest diversity of species on the planet and play a key role
in maintaining environmental processes. Due to the importance of those forests, there is …

Mangrove forests are vulnerable ecosystems that require broad-scale monitoring. Various
solutions based on satellite imagery have emerged for this purpose but still suffer from the …