Spatially explicit information on tree species composition is important for both the forest
management and conservation sectors. In combination with machine learning algorithms …

Advances in remote sensing imagery and machine learning applications unlock the
potential for developing algorithms for species classification at the level of individual tree …

Precision agriculture (PA) is becoming an essential practice for the implementation of
sustainable agriculture that encompasses the efficient use of resources and a systematic …

Airborne laser scanning (ALS) data is increasingly being used for accurate estimations of
forest inventory attributes, such as tree height and volume. However, determining tree …

Recently, the severe intensification of atmospheric carbon has highlighted the importance of
urban tree contributions in atmospheric carbon mitigations in city areas considering …

The classification of individual tree species (ITS) is beneficial to forest management and
protection. Previous studies in ITS classification that are primarily based on airborne LiDAR …

We studied the use of self-attention mechanism networks (SAN) and convolutional neural
networks (CNNs) for forest tree species classification using unmanned aerial vehicle (UAV) …

Quality tree species information gathering is the basis for making proper decisions in forest
management. By applying new technologies and remote sensing methods, very high …

Individual tree species classification is of strategic importance for forest monitoring, analysis,
and management, which are critical for sustainable forestry development. In this regard, the …

Spatially explicit information on tree species composition of any forest provides valuable
information to forest managers as well as to nature conservationists. In this study, the …