Remote Sensing of Vegetation from Uav Platforms Using Lightweight Multispectral and Thermal Imaging Sensors

Current high spatial resolution satellite sensors lack the spectral resolution required for many quantitative remote sensing applications and, given the limited spectral resolution, they only allow the calculation of a limited number of vegetation indices and remote sensing products. Additionally if short revisit time is required for management applications, the cost of high resolution imagery becomes a limiting factor whereas sensors with shorter revisit time lack the necessary spatial resolution which is critical, particularly for heterogeneous covers. Combining high spatial resolution and quick turnaround times is essential to generate useful remote sensing products for vegetation monitoring applied to agriculture and the environment. Alternatives based on manned airborne platforms provide high spatial resolution and potentially short revisit time, but their use is limited by their high operational complexity and costs. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) represent an option to fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. However miniaturized electro-optical sensors onboard UAVs require radiometric and geometric calibrations in order to allow further extraction of quantitative results and accurate georeferencing. This paper describes how to generate quantitative remote sensing products using rotating-wing and fixedwing UAVs equipped with commercial off-the-shell (COTS) thermal and narrowband multispectral imaging sensors. The paper also focuses on the radiometric calibration, atmospheric correction and photogrammetric methods required to obtain accurate remote sensing products that are useful for vegetation monitoring. During summer of 2007 and 2008, UAV platforms were flown over agricultural fields, obtaining thermal imagery in the 7.5–13-μm region (40 cm spatial resolution) and narrow-band multispectral imagery in the 400–800-nm spectral region (20 cm spatial resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN connected to photogrammetric methods to solve the thermal path length. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with PROSPECT, SAILH and FLIGHT models. Based on these parameters, image products of leaf area index, chlorophyll content (Cab), and water stress detection (based on the photochemical reflectance index and on canopy temperature) were produced and successfully validated. GPS/INS data from the autonomous navigation system were used into the aerotriangulation, to georeference the collected imagery, requiring only a minimum number of ground control points. This paper demonstrates that results obtained with a low-cost UAV system for vegetation monitoring applications yield comparable estimations, if not better, than those obtained with more traditional manned airborne sensors. * Corresponding author.