Monitoring global biodiversity from space through remotely sensing geospatial patterns has
high potential to add to our knowledge acquired by field observation. Although a framework …

With the goal of advancing remote sensing in biodiversity science, Spectranomics
represents an emerging approach, and a suite of quantitative methods, intended to link plant …

Learning from imbalanced datasets is a frequent but challenging task for standard
classification algorithms. Although there are different strategies to address this problem …

Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential
for many environmental and social applications. The increase in availability of RS data has …

Crop-type identification is very important in agricultural regions. Most researchers in this
area have focused on exploring the ability of synthetic-aperture radar (SAR) sensors to …

Reliable fire severity map** is a vital resource for fire scientists and land management
agencies globally. Satellite derived pre-and post-fire differenced severity indices (∆ FSI) …

In this study, we automate tree species classification and map** using field-based training
data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural …

The classification of tree species can significantly benefit from high spatial and spectral
information acquired by unmanned aerial vehicles (UAVs) associated with advanced …

Learning from imbalanced datasets is challenging for standard algorithms, as they are
designed to work with balanced class distributions. Although there are different strategies to …

The use of remote sensing data for tree species classification in tropical forests is still a
challenging task, due to their high floristic and spectral diversity. In this sense, novel sensors …