Remote Vegetation Diagnostics in Ghana with a Hyperspectral Fluorescence Lidar

Abstract

Spectral imaging and lidar methods for the characterization of vegetation are vital for understanding plants and, in turn, food security, biodiversity, and vegetation health in a changing climate. While novel hyperspectral imaging, canopy lidar,and solar-induced fluorescence provide details on species, state, structure, and plant physiology, such data come from different instruments. Thus, post-processing and data fusion struggles with synchronization, spatial overlap, and resolution issues. Especially in the tropical regions of sub-Saharan Africa, these complex, expensive, and bulky instruments remain inaccessible. Here, in Ghana, we have built a low-cost, lightweight, and realistic instrument for simultaneously acquiring hyperspectral data of vegetation fluorescence and canopy structure with perfect spatial overlap. In this paper, we demonstrate the application of the hyperspectral fluorescence lidar for diagnostics and species specificity of locally significant crops. We demonstrate simultaneous range and fluorescence measurements of forest canopy, conducted in full sunlight. Our results indicate that the upper side of the leaves shows a more substantial deviation for stressed plants, whilethe lower side shows greater contrast for plant species. This new and simple tool provides a combined method for hyperspectral classification and assessment of the physiological state while also reporting the vegetation height over ground and its diversity.