The detection of pest infestation is an important aspect of forest management. In the case of the oak splendour beetle (Agrilus biguttatus) infestation, the affected oaks (Quercus sp.) show high levels of defoliation and altered canopy reflection signature. These critical features can be identified in high-resolution colour infrared (CIR) images of the tree crown and branches level captured by Unmanned Aerial Systems (UAS). In this study, we used a small UAS equipped with a compact digital camera which has been calibrated and modified to record not only the visual but also the near infrared reflection (NIR) of possibly infested oaks. The flight campaigns were realized in August 2013, covering two study sites which are located in a rural area in western Germany. Both locations represent small-scale, privately managed commercial forests in which oaks are economically valuable species. Our workflow includes the CIR/NIR image acquisition, mosaicking, georeferencing and pixel-based image enhancement followed by object-based image classification techniques. A modified Normalized Difference Vegetation Index (NDVImod) derived classification was used to distinguish between five vegetation health classes, i.e., infested, healthy or dead branches, other vegetation and canopy gaps. We achieved an overall Kappa Index of Agreement (KIA) of 0.81 and 0.77 for each study site, respectively. This approach offers a low-cost alternative to private forest owners who pursue a sustainable management strategy.

South Patagonian peat bogs are little studied sources of methane (CH₄). Since CH₄ fluxes can vary greatly on a small scale of meters, high-quality maps are needed to accurately quantify CH₄ fluxes from bogs. We used high-resolution color infrared (CIR) images captured by an Unmanned Aerial System (UAS) to investigate potential uncertainties in total ecosystem CH₄ fluxes introduced by the classification of the surface area. An object-based approach was used to classify vegetation both on species and microform level. We achieved an overall Kappa Index of Agreement (KIA) of 0.90 for the species- and 0.83 for the microform-level classification, respectively. CH₄ fluxes were determined by closed chamber measurements on four predominant microforms of the studied bog. Both classification approaches were employed to up-scale CH₄ closed chamber measurements in a total area of around 1.8 hectares. Including proportions of the surface area where no chamber measurements were conducted, we estimated a potential uncertainty in ecosystem CH₄ fluxes introduced by the classification of the surface area. This potential uncertainty ranged from 14.2 mg·m⁻²·day⁻¹ to 26.8 mg·m⁻²·day⁻¹. Our results show that a simple classification with only few classes potentially leads to pronounced bias in total ecosystem CH₄ fluxes when plot-scale fluxes are up-scaled.

The Tibetan Plateau (TP) is the largest alpine plateau on Earth and plays an important role in global climate dynamics. On the TP, climate change is happening particularly fast, with an increase in air temperature twice the global average. The particular sensitivity of this high mountain environment allows observation and tracking of abiotic and biotic feedback mechanisms. Closed lake systems, such as Nam Co on the central TP, represent important natural laboratories for tracking past and recent climatic changes, as well as geobiological processes and interactions within their respective catchments. This review gives an interdisciplinary overview of past and modern environmental changes using Nam Co as a case study. In the catchment area, ongoing rise in air temperature forces glaciers to melt, contributing to a rise in lake level and changes in water chemistry. Some studies base their conclusions on inconsistent glacier inventories, but an ever-increasing deglaciation and thus higher water availability have persisted over the last few decades. Increasing water availability causes translocation of sediments, nutrients and dissolved organic matter to the lake, as well as higher carbon emissions to the atmosphere. The intensity of grazing has an additional and significant effect on CO₂ fluxes, with moderate grazing enhancing belowground allocation of carbon while adversely affecting the C sink potential through reduction of above-surface and subsurface biomass at higher grazing intensities. Furthermore, increasing pressure from human activities and livestock grazing are enhancing grassland degradation processes, thus shaping biodiversity patterns in the lake and catchment. The environmental signal provided by taxon-specific analysis (e.g., diatoms and ostracods) in Nam Co revealed profound climatic fluctuations between warmer–cooler and wetter–drier periods since the late Pleistocene and an increasing input of freshwater and nutrients from the catchment in recent years. Based on the reviewed literature, we outline perspectives to further understand the effects of global warming on geodiversity and biodiversity and their interplay at Nam Co, which acts as a case study for potentially TP-level or even worldwide processes that are currently shaping high mountain areas.

The Tibetan alpine steppe ecosystem covers an area of roughly 800 000 km² and contains up to 3.3 % soil organic carbon in the uppermost 30 cm, summing up to 1.93 Pg C for the Tibet Autonomous Region only (472 037 km²). With temperatures rising 2 to 3 times faster than the global average, these carbon stocks are at risk of loss due to enhanced soil respiration. The remote location and the harsh environmental conditions on the Tibetan Plateau (TP) make it challenging to derive accurate data on the ecosystem–atmosphere exchange of carbon dioxide (CO₂) and water vapor (H₂O). Here, we provide the first multiyear data set of CO₂ and H₂O fluxes from the central Tibetan alpine steppe ecosystem, measured in situ using the eddy covariance technique. The calculated fluxes were rigorously quality checked and carefully corrected for a drift in concentration measurements. The gas analyzer self-heating effect during cold conditions was evaluated using the standard correction procedure and newly revised formulations (Burba et al., 2008; Frank and Massman, 2020). A wind field analysis was conducted to identify influences of adjacent buildings on the turbulence regime and to exclude the disturbed fluxes from subsequent computations. The presented CO₂ fluxes were additionally gap filled using a standardized approach. The very low net carbon uptake across the 15-year data set highlights the special vulnerability of the Tibetan alpine steppe ecosystem to become a source of CO₂ due to global warming. The data are freely available at https://doi.org/10.5281/zenodo.3733202 (Nieberding et al., 2020 a) and https://doi.org/10.11888/Meteoro.tpdc.270333 (Nieberding et al., 2020 b) and may help us to better understand the role of the Tibetan alpine steppe in the global carbon–climate feedback.

The international journal of Aerosol and Air Quality Research (AAQR) covers all aspects of aerosol science and technology, atmospheric science and air quality related issues.

Soil moisture profile sensors (SMPSs) have a high potential for climate-smart agriculture due to their easy handling and ability to perform simultaneous measurements at different depths. To date, an accurate and easy-to-use method for the evaluation of long SMPSs is not available. In this study, we developed laboratory and field experiments to evaluate three different SMPSs (SoilVUE10, Drill&Drop, and SMT500) in terms of measurement accuracy, sensor-to-sensor variability, and temperature stability. The laboratory experiment features a temperature-controlled lysimeter to evaluate intra-sensor variability and temperature stability of SMPSs. The field experiment features a water level-controlled sandbox and reference TDR measurements to evaluate the soil water measurement accuracy of the SMPS. In both experiments, a well-characterized fine sand was used as measurement medium to ensure homogeneous dielectric properties in the measurement domain of the sensors. The laboratory experiments with the lysimeter showed that the Drill&Drop sensor has the highest temperature sensitivity with a decrease of 0.014 m³ m⁻³ per 10 °C, but at the same time showed the lowest intra- and inter-sensor variability. The field experiment with the sandbox showed that all three SMPSs have a similar performance (average RMSE ≈ 0.023 m³ m⁻³) with higher uncertainties at intermediate soil moisture contents. The presented combination of laboratory and field tests were found to be well suited to evaluate the performance of SMPSs and will be used to test additional SMPSs in the future.