This paper investigates the reliability of free and open-source algorithms used in the geographical object-based image classification (GEOBIA) of very high resolution (VHR) imagery surveyed by unmanned aerial vehicles (UAVs). UAV surveys were carried out in a cork oak woodland located in central Portugal at two different periods of the year (spring and summer). Segmentation and classification algorithms were implemented in the Orfeo ToolBox (OTB) configured in the QGIS environment for the GEOBIA process. Image segmentation was carried out using the Large-Scale Mean-Shift (LSMS) algorithm, while classification was performed by the means of two supervised classifiers, random forest (RF) and support vector machines (SVM), both of which are based on a machine learning approach. The original, informative content of the surveyed imagery, consisting of three radiometric bands (red, green, and NIR), was combined to obtain the normalized difference vegetation index (NDVI) and the digital surface model (DSM). The adopted methodology resulted in a classification with higher accuracy that is suitable for a structurally complex Mediterranean forest ecosystem such as cork oak woodlands, which are characterized by the presence of shrubs and herbs in the understory as well as tree shadows. To improve segmentation, which significantly affects the subsequent classification phase, several tests were performed using different values of the range radius and minimum region size parameters. Moreover, the consistent selection of training polygons proved to be critical to improving the results of both the RF and SVM classifiers. For both spring and summer imagery, the validation of the obtained results shows a very high accuracy level for both the SVM and RF classifiers, with kappa coefficient values ranging from 0.928 to 0.973 for RF and from 0.847 to 0.935 for SVM. Furthermore, the land cover class with the highest accuracy for both classifiers and for both flights was cork oak, which occupies the largest part of the study area. This study shows the reliability of fixed-wing UAV imagery for forest monitoring. The study also evidences the importance of planning UAV flights at solar noon to significantly reduce the shadows of trees in the obtained imagery, which is critical for classifying open forest ecosystems such as cork oak woodlands.

Daily variations in photoprotective mechanisms were studied in sun and shade leaves of 40-year-old cork oak (Quercus suber L.) trees during early summer in Portugal. Although trees were not severely water stressed because predawn leaf water potentials remained high, photosynthesis and stomatal conductance decreased at midday. The midday depression in gas exchange was not reversed by short-term exposure to “optimal” conditions of temperature, light and vapor pressure deficit. Chlorophyll a fluorescence, maximum photochemical yield of photosystem II and the quantum yield of noncyclic electron transport showed midday depressions, but recovered by the evening. Both short-term changes in the components of the xanthophyll cycle (reversible de-epoxidation of violaxanthin during the day) as well as long-term changes (higher xanthophyll content in sun compared with shade leaves) were detected and may play a role in the dissipation of excess energy at midday. Because the activities of enzymes of the antioxidant system, superoxide dismutase and ascorbate peroxidase, were high enough to cope with the increase in oxygen reactive species likely to arise under the stressful conditions of midday, we conclude that these enzymes may provide an additional mechanism for energy dissipation.

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring.

This paper reviews the currently available optical sensors, their limitations and opportunities for deployment at Eddy Covariance (EC) sites in Europe. This review is based on the results obtained from an online survey designed and disseminated by the Co-cooperation in Science and Technology (COST) Action ESO903—“Spectral Sampling Tools for Vegetation Biophysical Parameters and Flux Measurements in Europe” that provided a complete view on spectral sampling activities carried out within the different research teams in European countries. The results have highlighted that a wide variety of optical sensors are in use at flux sites across Europe, and responses further demonstrated that users were not always fully aware of the key issues underpinning repeatability and the reproducibility of their spectral measurements. The key findings of this survey point towards the need for greater awareness of the need for standardisation and development of a common protocol of optical sampling at the European EC sites.

Resolving the spatial and temporal dynamics of gross primary productivity (GPP) of terrestrial ecosystems across different scales remains a challenge. Remote sensing is regarded as the solution to upscale point observations conducted at the ecosystem level, using the eddy covariance (EC) technique, to the landscape and global levels. In addition to traditional vegetation indices, the photochemical reflectance index (PRI) and the emission of solar-induced chlorophyll fluorescence (SIF), now measurable from space, provide a new range of opportunities to monitor the global carbon cycle using remote sensing. However, the scale mismatch between EC observations and the much coarser satellite-derived data complicate the integration of the two sources of data. The solution is to establish a network of in situ spectral measurements that can act as a bridge between EC measurements and remote-sensing data. In situ spectral measurements have already been conducted for many years at EC sites, but using variable instrumentation, setups, and measurement standards. In Europe in particular, in situ spectral measurements remain highly heterogeneous. The goal of EUROSPEC Cost Action ES0930 was to promote the development of common measuring protocols and new instruments towards establishing best practices and standardization of these measurements. In this review we describe the background and main tradeoffs of in situ spectral measurements, review the main results of EUROSPEC Cost Action, and discuss the future challenges and opportunities of in situ spectral measurements for improved estimation of local and global estimates of GPP over terrestrial ecosystems.

Respiration of stems and branches of trees (R_S) has typically been estimated by measuring radial CO₂ efflux from woody tissue (E_A) and rates of efflux are often scaled temporally using a temperature relationship (Q₁₀). High concentrations of CO₂ in xylem sap ([$CO2*$]) have been shown to affect E_A, and the transport of CO₂ in the xylem stream has been suggested as a mechanism to explain field observations of temperature-independent fluctuations in E_A. Sap velocity and temperature were manipulated in detached branch segments of sycamore (Platanus occidentalis L.) under controlled conditions to quantify these effects. Within individual branches of similar size, E_A and [$CO2*$] were greater at low sap velocity, while the amount of respired CO₂ transported in sap (transport flux, F_T) was greater at high sap velocity. E_A was linearly correlated with [$CO2*$]. In branches of three diameter classes (1, 2, and 3 cm), volume-based E_A, F_T, and R_S did not differ, but surface-area based CO₂ fluxes increased with diameter class. Regardless of diameter, E_A accounted for only 30% of respired CO₂ at high sap velocity, while at low sap velocity, E_A accounted for 71% of respired CO₂. E_A, F_T, and R_S measured at 5, 20, and 35 °C at the same sap velocity showed a typical exponential response to temperature. However, at the lowest temperature, E_A accounted for only 18% of the CO₂ released from respiring cells compared with 44% at the highest temperature, perhaps due to the effect of temperature on the solubility of CO₂ in water. These results directly demonstrate the transport of respired CO₂ in the xylem stream and may help to explain inconsistencies in stem and branch respiration measurements made in situ.