An overview of using unmanned aerial system mounted sensors to measure plant above-ground biomass

A Bazrafkan, N Delavarpour, PG Oduor, N Bandillo… - Remote Sensing, 2023 - mdpi.com
Conventional measurement methods for above-ground biomass (AGB) are time-consuming,
inaccurate, and labor-intensive. Unmanned aerial systems (UASs) have emerged as a …

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

JS Estrada, A Fuentes, P Reszka… - Frontiers in plant …, 2023 - frontiersin.org
Forests are suffering water stress due to climate change; in some parts of the globe, forests
are being exposed to the highest temperatures historically recorded. Machine learning …

Automated detection of construction work at heights and deployment of safety hooks using IMU with a barometer

H Choo, B Lee, H Kim, B Choi - Automation in Construction, 2023 - Elsevier
An automated system that identifies work at height and the fastening state of safety hooks
using wearable sensors was developed to prevent falls from height (FFH). This system …

Predicting factors affecting the intention to use a 3PL during the COVID-19 pandemic: A machine learning ensemble approach

JD German, AKS Ong, AANP Redi, KPE Robas - Heliyon, 2022 - cell.com
The COVID-19 pandemic had brought changes to individuals, especially in consumer
behavior. As the government of different countries has been implementing safety protocols …

Optimal channels and features selection based ADHD detection from EEG signal using statistical and machine learning techniques

M Maniruzzaman, MAM Hasan, N Asai, J Shin - IEEE Access, 2023 - ieeexplore.ieee.org
Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and
neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based …

[HTML][HTML] Estimation of above ground biomass in tropical heterogeneous forests in India using GEDI

I Indirabai, M Nilsson - Ecological Informatics, 2024 - Elsevier
Quantifying above ground biomass (AGB) and its spatial distribution can significantly
contribute to monitor carbon stocks as well as the carbon storage dynamics in forests. For …

A novel vegetation index approach using sentinel-2 data and random forest algorithm for estimating forest stock volume in the Helan mountains, Ningxia, China

T Ma, Y Hu, J Wang, M Beckline, D Pang, L Chen, X Ni… - Remote Sensing, 2023 - mdpi.com
Forest stock volume (FSV) is a major indicator of forest ecosystem health and it also plays an
important part in understanding the worldwide carbon cycle. A precise comprehension of the …

A comprehensive comparison of machine learning and feature selection methods for maize biomass estimation using sentinel-1 SAR, sentinel-2 vegetation indices …

C Xu, Y Ding, X Zheng, Y Wang, R Zhang, H Zhang… - Remote Sensing, 2022 - mdpi.com
Rapid and accurate estimation of maize biomass is critical for predicting crop productivity.
The launched Sentinel-1 (S-1) synthetic aperture radar (SAR) and Sentinel-2 (S-2) missions …

Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data

F Moradi, SMM Sadeghi, HB Heidarlou… - Annals of Forest …, 2022 - afrjournal.org
Implementing a scheduled and reliable estimation of forest characteristics is important for
the sustainable management of forests. This study aimed at evaluating the capability of …

[HTML][HTML] Assessment of Carbon Stock and Sequestration Dynamics in Response to Land Use and Land Cover Changes in a Tropical Landscape

D Bera, ND Chatterjee, S Dinda, S Ghosh, V Dhiman… - Land, 2024 - mdpi.com
Quantitative analysis of LULC changes and their effects on carbon stock and sequestration
is important for mitigating climate change. Therefore, this study examines carbon stock and …