New vegetation stress assessment approach via WorldView-3 imagery, validated with UAV thermal imaging

This paper presents a novel approach for vegetation stress assessment using a combination of Short-Wave Infrared (SWIR) and the Red, Green, and Blue bands of the 8-band multispectral (MS) images from the WorldView-3 satellite. The method aims to identify stressed vegetation based on changes in water content within plant leaves. Initially, the RGB image is created from the Red, Green, and Blue bands, while the SWIR image is resampled to ensure pixel-wise correspondence with the RGB image. This process allows accurate representation of RGB pixels in the SWIR domain. Furthermore, the Silhouette Coefficient method is used on the SWIR pixels to determine the optimal number of clusters (k) for the subsequent k-Means clustering step. The Silhouette Coefficient method evaluates the cohesion and separation of clusters before applying k-Means clustering on the SWIR pixels. The SWIR band’s sensitivity to leaf water content enables effective crop health assessment, as it reflects the physiological response of stomatal closure in stressed plants. The method is tested in experimental vineyards (Vitis vinifera L.), with the last two clusters used to pseudo-colour the RGB image, highlighting stress areas in yellow. Additionally, an Unmanned Aerial Vehicle (UAV) equipped with high-resolution visible-spectrum (RGB) and Thermal Infrared (TIR) imaging was deployed to capture the area shortly after the satellite image acquisition. Despite differences in resolution, satellite, and UAV images produced consistent stress detection results. The proposed method demonstrates the potential of integrating SWIR and RGB images for precision agriculture applications, offering a robust framework for assessing and monitoring crop health based on real-time satellite imagery. It contributes to the ongoing efforts in developing remote sensing techniques for efficient crop management and resource optimization.