Data processing for forestry applications is challenged by the increasing availability of multi-
source and multi-temporal data. The advancements of Deep Learning (DL) algorithms have …

The northern forest-tundra ecotone is one of the fastest warming regions of the globe.
Models of vegetation change generally predict a northward advance of boreal forests and …

Sustainable monitoring and determining the biophysical characteristics of crops is of global
importance due to the increase in demand for food. In this context, remote sensing data …

The canopy height model (CHM) is a representation of the height of the top of vegetation
from the surrounding ground level. It is crucial for the extraction of various forest …

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma
characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An …

Key message Despite showing a cost-effective potential for quantifying vertical forest
structure, the GEDI and ICESat-2 satellite LiDAR missions fall short of the data accuracy …

The availability of canopy height information in the Ice, Cloud, and Land Elevation Satellite-
2's (ICESat-2's) land and vegetation product, or ATL08, presents opportunities for …

Featured Application This work applies machine learning to enhance the prediction of
deforestation related to oil palm. This research can be used for decision makers trying to …

Predictive analytics using artificial intelligence is a useful tool in cancer research. A
multilayer perceptron neural network used gene expression data to predict the lymphoma …

In the remote sensing of forests, point cloud data from airborne laser scanning contains high-
value information for predicting the volume of growing stock and the size of trees. At the …