Multispectral Reconnaissance of Operational Areas

Absztrakt

Multispectral and hyperspectral imaging offer complementary capabilities for remote reconnaissance by sampling reflected energy in several optical wavelength bands. This study evaluates their suitability for operational area monitoring, with an emphasis on lightweight, drone‑borne sensors. A theoretical overview explains how spectral signatures arise from electronic transitions and molecular vibrations, leading to characteristic vegetation and soil reflectance patterns. Common vegetation and moisture indices are introduced, noting their sensitivity to chlorophyll content, water absorption and the limitations of MSI sensors lacking short‑wave infrared bands. Remote‑sensing platforms including handheld spectrometres, satellites and unmanned aerial vehicles are compared. UAVs equipped with multispectral cameras offer a favourable balance of spatial resolution, cost and ease of deployment. The DJI Mavic 3 Multispectral is selected for field experimentation due to its integrated four‑band sensor (green, red, red‑edge, near‑infrared), sunlight sensor and RTK positioning. Other MSI cameras (MicaSense RedEdge‑MX, Parrot Sequoia+) and representative hyperspectral cameras are briefly reviewed, highlighting tradeoffs in weight, spectral resolution and cost. A case study over a military training ground uses the M3M to acquire two flights – before and after a rain event – and processes the images using WebODM, QGIS and MATLAB. Vegetation indices map plant vigour and relative soil moisture, and change detection highlights vehicle tracks and wet areas. Results demonstrate that NDRE remains linear at high biomass and that simple red-edge metrics sensitively capture moisture changes despite the absence of SWIR. While the Mavic 3M lacks a blue and SWIR band limits the calculation of some water related indices, its portability and affordability make it a valuable tool for rapid reconnaissance. The paper concludes by discussing future directions, including the integration of machine learning and additional sensors to enhance material identification and real‑time processing. The index-based analysis is explicitly interpreted as relative operational change detection rather than absolute biomass, reflectance or soil-moisture retrieval, because independent ground-reference measurements were not available during the field campaign.