Performance of singular spectrum analysis in separating seasonal and fast physiological dynamics of solar-induced chlorophyll fluorescence and PRI optical signals

High temporal resolution measurements of solar-induced chlorophyll fluorescence (F) and the Photochemical Reflectance Index (PRI) encode vegetation functioning. However, these signals are modulated by time-dependent processes. We tested the applicability of the Singular Spectrum Analysis (SSA) for disentangling fast components (physiology-driven) and slow components (controlled by structural and biochemical properties) from PRI, far-red F (F-760), and far-red apparent fluorescence yield (Fy*(760)). The proof of concept was developed on spectral and flux time series simulated with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. This allowed the evaluation of SSA decomposition against variables that are independent of physiology or are modified by it. Slow SSA-decomposed components of PRI and Fy*(760) showed high correlations with the reference variables (R-2 = 0.97 and 0.96, respectively). Fast SSA-decomposed components of PRI and Fy*(760) were better related to the physiological reference variables than the original signals during periods when leaf area index (LAI) was above 1 m(2) m(-2). The method was also successfully applied to predict light-use efficiency (LUE) from the fast SSA-decomposed components of PRI (R-2 = 0.70) and Fy*(760) (R-2 = 0.68) when discarding data modeled with LAI R-in < 250 W m(-2). The method was then tested on data acquired in a Mediterranean grassland. In this case, the fast SSA-decomposed component of apparent LUE* showed a stronger correlation with the fast SSA-decomposed component of Fy*(760) (R-2 = 0.42) than with original Fy*(760) (R-2 = 0.01). SSA-based approach is a promising tool for decoupling physiological information from measurements acquired with automated proximal sensing systems.Peer reviewe

Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

This is the final version. Available from MDPI via the DOI in this record. Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.European Space Agency (ESA)European Union’s Horizon 202

Науково-практичний коментар Закону України «Про запобігання корупції» [станом на 1 лип. 2018 р.]

Науково-практичний коментар Закону України «Про запобігання корупції»: станом на 1 лип. 2018 р. / [А. В. Андрєєв, І. Л. Антипова, С. В. Банах та ін.], за заг. ред. Журавльова Д. В. – Київ: Видав. дім «Професіонал», 2018. – 512 с.Коментар розрахований на осіб, уповноважених на виконання функцій держави або місцевого самоврядування та прирівняних до них осіб, які є суб’єктами, на яких поширюється дія Закону України «Про запобігання корупції», на уповноважених осіб, відповідальних за реалізацію антикорупційних програм, суддів, прокурорів, слідчих, детективів, адвокатів, а також на студентів вищих учбових закладів, які готують фахівців в галузі права

Prospects for the development of LMS MAI and Microsoft Teams platforms after the end of quarantine

Distance learning has had a huge impact on the educational process of universities and schools. The main platform through which the e-learning process at Moscow Aviation Institute (National Research University) (MAI) is carried out is LMS MAI Moodle. It should be noted that on the basis of this platform, a website with courses lms.mai.ru was created. The second in popularity and frequency of use is Microsoft Teams platform (MT). It is worth noting that thanks to the platforms for distance learning, the educational process was not only not disrupted or stopped, but was also supplemented with such advantages as autonomy and flexibility in acquiring knowledge. In connection with the coronavirus pandemic, on March 17, 2020, MAI completely switched to distance learning. At the moment, the epidemiological situation in Russia is improving and universities are again conducting face-to-face classroom studies. But what was happening with two platforms mentioned above after the end of the quarantine? Students and teachers of MAI say that the use of platforms has decreased, but classes are still held in this format. We discussed the prospects for further use of MT and LMS MAI in this article

Annelids of the eastern Australian abyss collected by the 2017 RV ‘Investigator’ voyage

https://zookeys.pensoft.net/article/57921/list/8