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

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 …

Tropical forests are a major component of the global carbon cycle and home to two‐thirds of
terrestrial species. Upper‐canopy trees store the majority of forest carbon and can be …

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 …

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 …

Broad scale remote sensing promises to build forest inventories at unprecedented scales. A
crucial step in this process is to associate sensor data into individual crowns. While dozens …

In modeling forest stand growth and yield, crown width, a measure for stand density, is
among the parameters that allows for estimating stand timber volumes. However, accurately …

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

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 …

We present a deep learning-based framework for individual tree crown delineation in aerial
and satellite images. This is an important task, eg, for forest yield or carbon stock estimation …