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 …

Since the 1960s, remote sensing (as an innovative, comprehensive, and interdisciplinary
academic area) has been adopted in a wide range of disciplines related to Earth …

An important success and use of Deep Learning in recent years has been in the field of
image processing. Research on Deep Learning has shown that these architectures …

Spatially continuous estimates of forest aboveground biomass (AGB) are essential to
supporting the sustainable management of forest ecosystems and providing invaluable …

Estimation of forest structural variables is essential to provide relevant insights for public and
private stakeholders in forestry and environmental sectors. Airborne light detection and …

The assessment of carbon storage in natural forests is paramount in the ongoing efforts
against climate change. While traditional field-based methods for quantifying carbon storage …

The lidar is susceptible to the dark current of the detector and the background light during
the measuring process, which results in a significant amount of noise in the lidar return …

Remote sensing technologies have been extensively used in forest management in
predicting stand parameters. The goal of this study is to use Landsat 8 and Sentinel-2 …

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 …

Inspired by the importance of Planetary Boundary Layer Heights (PBLH) for inferring Air
Quality assessments and the disappointing performance of current weather forecasts of …