Land Cover Classification Using a Uav-Borne Spectrometer

Abstract

ABSTRACT:Small fixed wing and rotor-copter unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification and precision agriculture applications. Various sensors operating in the non-visible spectrum such as multispectral, hyperspectral and thermal sensors can be used as payloads. This work presents a preliminary study on the use of unmanned aerial vehicle equipped with a compact spectrometer for land cover type characterization. When calibrated, the measured spectra by the UAV spectrometer can be processed and compared reference data to generate georeferenced reflection spectra enabling the identification, classification and characterization of land cover elements. For this case study we used a DJI Flamewheel F550 hexacopter and the FLAME-NIR spectrometer for hyperspectral measurements. The calibration of the spectrometer is described as well the approach to determine its spatial footprint. The spectrometer spectral exposure labeled ground point can be used to determine the land cover classification. Preliminary results of a case-study are presented.

UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)

“…Nowadays, the ecology community is relying on newer technologies to complete its goals of detailed land cover analysis: (UAVs). Recently, a growing number of studies have focused on using UAVs for land cover classification [8,16,[38][39][40][41][42][43]. However, most image classification methods rely on pixel-based techniques that have limitations when it comes to high-resolution satellite data and UAV imagery [16,43,44].…”

“…Other researchers highlighted the importance of UAV multispectral camera and platform capabilities to obtain more accurate results [39]. Natesan et al (2017) used the lightweight UAV spectrometer spectral exposure labeled ground point to determine land cover classification [41]. Kalantar et al (2017) presented a method that integrates the fuzzy unordered rule induction algorithm (FURIA) into OBIA to achieve accurate land cover extraction from UAV images [43].…”