Non-contact and active vegetation or plant parameters extraction using hyperspectral information is a prospective research direction among the remote sensing community. Hyperspectral LiDAR (HSL) is an instrument capable of acquiring spectral and spatial information actively, which could mitigate the environmental illumination influence on the spectral information collection. However, HSL usually has limited spectral resolution and coverage, which is vital for vegetation parameter extraction. In this paper, to broaden the HSL spectral range and increase the spectral resolution, an Acousto-optical Tunable Filter based Hyperspectral LiDAR (AOTF-HSL) with 10 nm spectral resolution, consecutively covering from 500–1000 nm, was designed. The AOTF-HSL was employed and evaluated for vegetation parameters extraction. “Red Edge” parameters of four different plants with green and yellow leaves were extracted in the lab experiments for evaluating the HSL vegetation parameter extraction capacity. The experiments were composed of two parts. Firstly, the first-order derivative of the spectral reflectance was employed to extract the “Red Edge” position (REP), “Red Edge” slope (RES) and “Red Edge” area (REA) of these green and yellow leaves. The results were compared with the referenced value from a standard SVC© HR-1024 spectrometer for validation. Green leaf parameter differences between HSL and SVC results were minor, which supported that notion the HSL was practical for extracting the employed parameter as an active method. Secondly, another two different REP extraction methods, Linear Four-point Interpolation technology (LFPIT) and Linear Extrapolation technology (LET), were utilized for further evaluation of using the AOTF-HSL spectral profile to determine the REP value. The differences between the plant green leaves’ REP results extracted using the three methods were all below 10%, and the some of them were below 1%, which further demonstrated that the spectral data collected from HSL with this spectral range and resolution settings was applicable for “Red Edge” parameters extraction.

During the mining operation, it is a critical task in coal mines to significantly improve the safety by precision coal mining sorting and rock classification from different layers. It implies that a technique for rapidly and accurately classifying coal/rock in-site needs to be investigated and established, which is of significance for improving the coal mining efficiency and safety. In this letter, a 91-channel hyperspectral LiDAR (HSL) using an acousto-optic tunable filter (AOTF) as the spectroscopic device is designed, which operates based on the wide-spectrum emission laser source with a 5-nm spectral resolution to tackle this issue. The spectra of four-type coal/rock specimens collected by HSL are used to classify with three multi-label classifiers: naive Bayes (NB), logistic regression (LR), and support vector machine (SVM). Furthermore, we discuss and explore whether

Huizhou-style ancient architecture was one of the most important genres of architectural heritage in China. The architecture employed bricks, woods, and stones as raw materials, and timber frames were significant structures. Due to the drawback that the timbers were vulnerable to moisture and atmospheric agents, ancient timber buildings needed frequent protective interventions to maintain its good condition. Such interventions unavoidably disrupted the consistency between the original timber components. Besides this, the modifications brought about difficulty in correctly analysing and judging the state of existing ancient buildings, which, in current preservation practices, mainly rely on the expertise of skilled craftsmen to classify wood species and to identify the building-age of the timber components. Therefore, the industry and the research community urgently need a technique to rapidly and accurately classify wood materials and to discriminate building-age. In the paper, we designed an eye-safe 81-channel hyperspectral LiDAR (HSL) to tackle these issues. The HSL used an acousto-optic tunable filter (AOTF) as a spectral bandpass filter, offering the HSL measurements with 5 nm spectral resolution. Based on the HSL measurements, we analysed the relationship between the surface and cross-section spectral profiles of timber components from different ancient architectures built in the early Qing dynasty (~300 years), late Qing dynasty (~100 years), and nowadays, and confirmed the feasibility of using surface spectra of timber components for classification purpose. We classified building-ages and wood species with multiple Naive Bayes (NB) and support vector machine (SVM) classifiers by the surface spectra of timber components; this also unveiled the possibility of classifying gnawed timber components from its spectra for the first time. The encouraging experimental results supported that the AOTF-HSL is feasible for historic timber building preservation.