Assessing UAV-Acquired RGB, Multispectral, and Hyperspectral Imagery for Crop Residue Cover Mapping Using a Fully Connected Neural Network

Cross-sensor urban land-cover characterization using hyperspectral and multispectral satellite imagery

This study establishes a cross-sensor urban remote sensing baseline for future urban digital-intelligence applications by integrating separately classified and validated multispectral (Landsat, Sentinel-2) and hyperspectral (PRISMA) imagery using a common sampling and evaluation framework. Eight land-cover c...

Detection of crop residue cover from high-resolution satellite imagery using random forest classification

Spectral-Consistency-Aware Evaluation of Deep Super-Resolution Methods for UAV Five-Band Multispectral Crop Imagery

Unmanned aerial vehicle (UAV)-based multispectral imaging enables flexible, non-destructive crop monitoring. Although UAV imagery offers much higher spatial resolution than satellite platforms, its effective spatial detail at typical operational flight altitudes can still be insufficient for plant-level interpretation...

Lightweight Near-Infrared Spectral Reconstruction from Red UAV Imagery Using Artificial Intelligence for Low-Cost Remote Sensing

The results indicate that the model captures part of the red–NIR relationship under the evaluated acquisition conditions; however, the moderate coefficient of determination suggests that the reconstructed values are an approximation rather than a replacement for measured NIR observations.

UAV LiDAR-Assisted Multi-Source Remote Sensing Estimation and Spatiotemporal Analysis of Forest Carbon Storage in Fuzhou City

Accurate estimation of regional forest carbon storage is essential for assessing forest carbon sink capacity and supporting climate change mitigation. This study developed a UAV-LiDAR-assisted multi-source remote sensing approach to estimate forest AGB and carbon storage in Fuzhou City. UAV-LiDAR data acquired close to...

Object-based image analysis for fine-grained classification and dynamic change monitoring of grassland and shrubland in karst regions using UAV RGB imagery

Fine-grained identification of grassland and shrubland in Karst regions remains a challenge for traditional satellite remote sensing due to fragmented landscapes and high spectral similarities between vegetation types. This study evaluates a cost-effective and deployable vegetation monitoring solution using high-resolu...