Monitoring forest health typically relies on remote sensing tools such as light detection and ranging (LiDAR), radar, and ...

Unmanned aerial vehicles (UAVs), commonly known as drones, have revolutionized spatial data collection in recent years by offering flexible, low-cost platforms for aerial imagery and remote sensing.

A research team shows that phenomic prediction, which integrates full multispectral and thermal information rather than ...

By training statistical and machine-learning models to predict expert visual scores, the study demonstrates that phenomics can match or outperform ...

The results show that the peak annual average non-photosynthetic vegetation coverage accounts for about 37 percent of China's total land area and is increasing significantly at a rate of 0.14 percent ...

Abstract: Hyperspectral remote sensing images (HSIs) capture detailed spectral characteristics of features, while multispectral remote sensing images (MSIs) provide clear spatial distribution. Fusing ...

Harmful algal blooms (HABs) pose a growing threat to aquatic ecosystems, human health, and economies worldwide. Climate change and nutrient enrichment have driven an increase in both the size and ...

Abstract: This article introduces an uncrewed aerial vehicle (UAV)-based multispectral model, YOLO-SPAD, for rapid, nondestructive estimation of the relative chlorophyll content of tea leaves. This is ...

A team of CU Boulder researchers has introduced a quantum sensing technique that could lead to improvements in how we monitor infrastructure, detect changes in the environment and conduct geophysical ...

Accurate classification of wetland vegetation is essential for biodiversity conservation and carbon cycle monitoring. This study developed an ...

Excessive nitrogen application and low nitrogen use efficiency have been major issues in China’s agricultural development, posing significant challenges for field management. Nitrogen is a critical ...