Laboratory for
Climatology and Remote Sensing

Abstract:

Canopy habitats challenge researchers with their intrinsically difficult access. The current scarcity of climatic data from forest canopies limits our understanding of the conditions and environmental variability of these diverse and dynamic habitats. We present 307 days of climate records collected between 2019 and 2020 in the tropical rainforest canopy of the Yasuní National Park, Ecuador. We monitored climate with a 10-min temporal resolution in the middle crowns of eight canopy trees. The distance between canopy climate stations ranged from 700 m to 10 km. Apart from air temperature, relative humidity, leaf wetness, and photosynthetically active radiation (PAR), measured in each canopy climate station, global radiation, rainfall, and wind speed were measured in different subsets of them. We processed the eight data series to omit erroneous records resulting from sensor failures or lack of the solar-based power supply. In addition to the eight original data series, we present three derived data series, two aggregating canopy climate for valleys or for ridges (from four stations each), and one overall average (from the eight stations). This last derived data series contains 306 days, while the shortest of the original data series covers 22 days and the longest 296 days. In addition to the data, two open-source tools, developed in RStudio, are presented that facilitate data visualization (a dashboard) and data exploration (a filtering app) of the original and aggregated records.

Abstract:

Tropical forests are highly diverse at many spatial scales. In these forests, small-sized canopy organisms can form species-rich communities already within a few cm2. Understanding how species numbers increase when expanding the sampling along the tree and the forest is critical for evaluating the processes maintaining biodiversity. We therefore studied epiphytic bryophyte diversity in tree crowns and along trunks across spatial scales in a tropical lowland forest in Amazonian Ecuador, sampling bryophytes in 100-cm2 quadrats on 24 trees (15–22 quadrats each) using a spatially hierarchical design, analyzing alpha and beta diversity at different spatial grains and extents. At the smallest grain, tree crowns held more bryophyte species than trunks, but at the largest grain the trunks held most species (93 vs. 77), as beta diversity was higher among trunks than among crowns. However, except for trunks at the largest extent (all 24 trees), the highest beta diversity among quadrats was always found between crowns and trunks. Species turnover strongly dominated beta diversity at all spatial scales. This and the high species richness resulted in highly unpredictable species compositions, especially in trunk communities. These patterns suggest different controls of diversity in crowns than on trunks and an important role for chance processes in shaping these communities. The high beta diversity within trees, in combination with the large effort involved in climbing trees, implies that diversity sampling of small canopy organisms is most efficient using an intensive (many plots on few trees) rather than extensive (many trees across a large area) sampling.

Abstract:

There is a growing demand to train Earth Observation (EO) data users in how to access and use existing and upcoming data. A promising tool for data-related training is computational notebooks, which are interactive web applications that combine text, code and computational output. Here, we present the Learning Tool for Python (LTPy), which is a training course (based on Jupyter notebooks) on atmospheric composition data. LTPy consists of more than 70 notebooks and has taught over 1000 EO data users so far, whose feedback is overall positive. We adapted five guiding principles from different fields (mainly scientific computing and Jupyter notebook research) to make the Jupyter notebooks more educational and reusable. The Jupyter notebooks developed (i) follow the literate programming paradigm by a text/code ratio of 3, (ii) use instructional design elements to improve navigation and user experience, (iii) modularize functions to follow best practices for scientific computing, (iv) leverage the wider Jupyter ecosystem to make content accessible and (v) aim for being reproducible. We see two areas for future developments: first, to collect feedback and evaluate whether the instructional design elements proposed meet their objective; and second, to develop tools that automatize the implementation of best practices.

Abstract:

Large inaccuracies still exist in accurately predicting fog formation, dissipation, and duration. To improve these deficiencies, machine learning (ML) algorithms are increasingly used in nowcasting in addition to numerical fog forecasts because of their computational speed and their ability to learn the nonlinear interactions between the variables. Although a powerful tool, ML models require precise training and thoroughly evaluation to prevent misinterpretation of the scores. In addition, a fog dataset’s temporal order and the autocorrelation of the variables must be considered. Therefore, classification-based ML related pitfalls in fog forecasting will be demonstrated in this study by using an XGBoost fog forecasting model. By also using two baseline models that simulate guessing and persistence behavior, we have established two independent evaluation thresholds allowing for a more assessable grading of the ML model’s performance. It will be shown that, despite high validation scores, the model could still fail in operational application. If persistence behavior is simulated, commonly used scores are insufficient to measure the performance. That will be demonstrated through a separate analysis of fog formation and dissipation, because these are crucial for a good fog forecast. We also show that commonly used blockwise and leave-many-out cross-validation methods might inflate the validation scores and are therefore less suitable than a temporally ordered expanding window split. The presented approach provides an evaluation score that closely mimics not only the performance on the training and test dataset but also the operational model’s fog forecasting abilities.

Abstract:

Bioturbators shape their environment with considerable consequences for ecosystem processes. However, both the composition and the impact of bioturbator communities may change along climatic gradients. For burrowing animals, their abundance and composition depend on climatic and other abiotic components, with ants and mammals dominating in arid and semiarid areas, and earthworms in humid areas. Moreover, the activity of burrowing animals is often positively associated with vegetation cover (biotic component). These observations highlight the need to understand the relative contributions of abiotic and biotic components in bioturbation in order to predict soil-shaping processes along broad climatic gradients. In this study, we estimated the activity of animal bioturbation by counting the density of holes and the quantity of bioturbation based on the volume of soil excavated by bioturbators along a gradient ranging from arid to humid in Chile. We distinguished between invertebrates and vertebrates. Overall, hole density (no/ 100 m2) decreased from arid (raw mean and standard deviation for invertebrates: 14 ± 7.8, vertebrates: 2.8 ± 2.9) to humid (invertebrates: 2.8 ± 3.1, vertebrates: 2.2 ± 2.1) environments. However, excavated soil volume did not follow the same clear geographic trend and was 300-fold larger for vertebrates than for invertebrates. The relationship between bioturbating invertebrates and vegetation cover was consistently negative whereas for vertebrates both, positive and negative relationships were determined along the gradient. Our study demonstrates complex relationships between climate, vegetation and the contribution of bioturbating invertebrates and vertebrates, which will be reflected in their impact on ecosystem functions.

Abstract:

Pastoralism is the main land use in the humid tropical Andes of South America. Wide areas of mountain rainforest have been cleared for gaining pastureland. Due to the lack of indigenous useful grasses in the pristine forests, mainly exotic grass species have been used for establishing the pastures. In the Ecuadorian Andes, Axonopus compressus, Melinis minutiflora, Pennisetum clandestinum and Holcus lanatus are common pasture grass species. Their preference for certain microsites resulted in a mosaic of different pasture types, which reflect the differing ecological conditions on the undulating terrain of the mountain slopes. During the last decades, however, another exotic grass species, Setaria sphacelata has widely been introduced which, because of its fast growth on some of the sites could successfully suppress the formerly dominant plant species. With respect to the changing microclimate and cattle stocking rates the present study explored, whether planting Setaria is the best option for the common low-input type of pasture farming in these tropical mountains. In a study over twenty years, the development of four main pasture types, dominated by the above-mentioned grass species was investigated in areas with and without Setaria, and their topographical occurrence on the sloping terrain was analyzed. On forty-eight plots a pairwise (with or without Setaria) comparison of species composition and diversity, biomass production, forage quality and soil properties was performed. Although Setaria grows faster than the other grass species, its productivity was only higher on flat terrain. The nutritive value of the Setaria plots was at best equivalent to that of the former pastures, while species richness was consistently lower. Our results suggest the maintenance of a terrain-adapted diversification of the pastures and in particular the use of Setaria only on flat terrain.

Abstract:

Heatwaves exert disproportionately strong and sometimes irreversible impacts on forest ecosystems. These impacts remain poorly understood at the tree and species level and across large spatial scales. Here, we investigate the effects of the record-breaking 2018 European heatwave on tree growth and tree water status using a collection of high-temporal resolution dendrometer data from 21 species across 53 sites. Relative to the two preceding years, annual stem growth was not consistently reduced by the 2018 heatwave but stems experienced twice the temporary shrinkage due to depletion of water reserves. Conifer species were less capable of rehydrating overnight than broadleaves across gradients of soil and atmospheric drought, suggesting less resilience toward transient stress. In particular, Norway spruce and Scots pine experienced extensive stem dehydration. Our high-resolution dendrometer network was suitable to disentangle the effects of a severe heatwave on tree growth and desiccation at large-spatial scales in situ, and provided insights on which species may be more vulnerable to climate extremes.

Abstract:

EMO-5 is a free and open European high-resolution (5 km), sub-daily, multi-variable (precipitation, temperatures, wind speed, solar radiation, vapour pressure), multi-decadal meteorological dataset based on quality-controlled observations coming from almost 30 000 stations across Europe, and is produced in near real-time. EMO-5 (v1) covers the time period from 1990 to 2019. In this paper, we have provided insight into the source data, the applied methods, and the quality assessment of EMO-5.

Abstract:

Biodiversity and ecosystem functions are highly threatened by global change. It has been proposed that geodiversity can be used as an easy-to-measure surrogate of biodiversity to guide conservation management. However, so far, there is mixed evidence to what extent geodiversity can predict biodiversity and ecosystem functions at the regional scale relevant for conservation planning. Here, we analyse how geodiversity computed as a compound index is suited to predict the diversity of four taxa and associated ecosystem functions in a tropical mountain hotspot of biodiversity and compare the results with the predictive power of environmental conditions and resources (climate, habitat, soil). We show that combinations of these environmental variables better explain species diversity and ecosystem functions than a geodiversity index and identified climate variables as more important predictors than habitat and soil variables, although the best predictors differ between taxa and functions. We conclude that a compound geodiversity index cannot be used as a single surrogate predictor for species diversity and ecosystem functions in tropical mountain rain forest ecosystems and is thus little suited to facilitate conservation management at the regional scale. Instead, both the selection and the combination of environmental variables are essential to guide conservation efforts to safeguard biodiversity and ecosystem functions.

Abstract:

Fog forecasting still shows large inaccuracies in accurately predicting fog formation, dissipation and duration. Since a few years, Machine learning (ML) algorithms are increasingly used in addition to numerical fog forecasts because of their computational speed and ability to learn non-linear interactions between the variables. Due to their black-box nature, precise and accurate training and evaluation is vital to prevent insufficient training or meaningless scores. Three main points important for fog prediction are explained in the following. 1. Fog forecasting datasets consist of autocorrelated variables. In most cases, there is an information leakage between the training and test data sets which are used to evaluate the model performance. This information leakage can have an impact on the performance scores because the stronger the information flow, the easier it is for the model to memorize. 2. Fog forecasting datasets have a temporal order. To be able to make statements about the performance of an operational model this temporal order should already be simulated during model training and evaluation. This is because for an operational model, the training data points are always older than the data points to be predicted. Commonly used training methods neglect this fact. 3. Time series used for fog forecasting usually have a large imbalance between the frequency of the fog class and non-fog class. This imbalance can have an unfavorable interaction with the confusion matrix based meteorological scores that are widely used for evaluation. All of the aforementioned points, if not considered, can lead to an insufficient forecast without even being noticed. Therefore, the negative influence on the model score of two commonly used training methods that neglect the points named above will be shown using an XGBoost model and a logistic regression model. In comparison, a training and evaluation method was evaluated that maintains the temporal order and thus simulates the performance of an operational model. It will also be shown that common meteorological scores, since they are computed based on a confusion matrix, share a weakness when the data set is unbalanced: Persistence behavior remains undetected. The study is funded by the DFG research project “FOG-ML FOrecasting radiation foG by combining station and satellite data using Machine Learning”.

Abstract:

Land degradation is a major environmental and social issue in temperate steppes. It is commonly determined from vegetation cover using remote sensing techniques. Steppes in eastern Mongolia are subject to resource extraction activities, such as mining and oil extraction, which affect land degradation. Recent technological progress in remote sensing has facilitated the acquirement of high-resolution data by, for example, the CubeSat satellite or unmanned aerial vehicles (UAV), providing data for detailed maps of vegetation cover and plant functional groups (PFGs). Traditional methods for monitoring vegetation cover often face typical scale issues, such as the upscaling of vegetation parameters if plot-scale field measurements are integrated to satellite data. Here, we studied the spatial distribution of PFG using machine learning and a combination of field measurements, UAV imagery (spatial resolution: 2 cm), and PlanetScope multi-temporal imagery. We provide two products at two spatial resolutions: one for UAV data, which is restricted to comparatively small areas around field measurements, and one for PlanetScope, which covers large parts of northeastern Mongolia. The results showed that the overall accuracies of UAV classification were 91–95%, whereas those of PlanetScope were 78–95%. In integrating the classified UAV data to the PlaneScope data, our proposed model minimized the scale issue that often impedes classification. Importantly, our findings revealed that the ecological effects of dirt road and railroad extended up to 60–120 m into the adjacent, otherwise less degraded steppe vegetation. A comparison between burned and unburned areas in different years indicates that wildfires affect the composition of PFG in reducing the fractional cover of graminoids and forbs, and that increasing cover of bare ground leads to a distinct and patchy mosaic of different vegetation types.