Laboratory for
Climatology and Remote Sensing

Abstract:

Hyperspectral remote sensing is a promising tool for a variety of applications including ecology, geology, analytical chemistry and medical research. This article presents the new hsdar package for R statistical software, which performs a variety of analysis steps taken during a typical hyperspectral remote sensing approach. The package introduces a new class for efficiently storing large hyperspectral data sets such as hyperspectral cubes within R. The package includes several important hyperspectral analysis tools such as continuum removal, normalized ratio indices and integrates two widely used radiation transfer models. In addition, the package provides methods to directly use the functionality of the caret package for machine learning tasks. Two case studies demonstrate the package’s range of functionality: First, plant leaf chlorophyll content is estimated and second, cancer in the human larynx is detected from hyperspectral data.

Abstract:

Tropical montane forests, particularly Andean rainforest, are important ecosystems for regional carbon and water cycles as well as for biological diversity and speciation. Owing to their remoteness, however, ecological key-processes are less understood as in the tropical lowlands. Remote sensing allows modeling of variables related to spatial patterns of carbon stocks and fluxes (e.g., biomass) and ecosystem functioning (e.g., functional leaf traits). However, at a landscape scale most studies conducted so far are based on airborne remote sensing data which is often available only locally and for one time-point. In contrast, multispectral satellites at moderate spectral and spatial resolutions are able to provide spatially continuous and repeated observations. Here, we investigated the effectiveness of Landsat-8 imagery in modeling tropical montane forest biomass, its productivity and selected canopy traits. Topographical, spectral and textural metrics were derived as predictors. To train and validate the models, in-situ data was sampled in 54 permanent plots in forests of southern Ecuador distributed within three study sites at 1000?m, 2000?m and 3000?m a.s.l. We used partial least squares regressions to model and map all response variables. Along the whole elevation gradient biomass and productivity models explained 31%, 43%, 69% and 63% of variance in aboveground biomass, annual wood production, fine litter production and aboveground net primary production, respectively. Regression models of canopy traits measured as community weighted means explained 62%, 78%, 65% and 65% of variance in leaf toughness, specific leaf area, foliar N concentration, and foliar P concentration, respectively. Models at single study sites hardly explained variation in aboveground biomass and the annual wood production indicating that these measures are mainly determined by the change of forest types along with elevation. In contrast, the models of fine litter production and canopy traits explained between 8%–85% in variation depending on the study site. We found spectral metrics, in particular a vegetation index using the red and the green band to provide complementary information to topographical metrics. The model performances for estimating leaf toughness, biochemical canopy traits and related fine litter production all improved when adding spectral information. Our findings therefore revealed that differences in fine litter production and canopy traits in our study area are driven by local changes in vegetation edaphically induced by topography. We conclude that Landsat-derived metrics are useful in modeling fine litter production and biochemical canopy traits, in a topographically and ecologically complex tropical montane forest.

Abstract:

Fast Fog and Low Stratus Detection (FFLSD) is a processing tool enabling detection of fog and low stratus (FLS) on very large time-series of Meteosat Second Generation data. We combine approaches for FLS detection by day and night into one homogeneous and efficient processing tool. The main unification improvements are: (1) consistent spatial tiling instead of various pixel clustering approaches, (2) uniform generation of sun and viewing angle dependent thresholds for individual tiles, (3) flexible study areas instead of area dependence, (4) parallel raster processing using the Open Computing Language (OpenCL), (5) and efficient algorithms for complex processing steps like histogram generation and analysis. As a result, users are enabled to generate products like long-term FLS climatologies with reasonable resources and short processing times. FFLSD is available as open source and allows reuse and extensions for other tasks.

Abstract:

Fog plays a major role in many ecological aspects and it influences human life in various ways. In this study, a temporally highly resolved and spatially explicit anal- ysis of variations in fog occurrence was conducted for Europe and links to general weather conditions were investigated. To this end, a high-resolution fog product based on Meteosat Second Generation data was developed. Characteristic fog distri- butions were identified by applying a Self Organizing Map approach to the dataset. It was found that the resulting fog patterns are primarily determined by terrain char- acteristics. Simultaneous occurrences between these patterns and the predominant general weather situations were analyzed. The results show that the general weather situations can be categorized into three main groups, each responsible for the forma- tion of a different group of fog patterns. Additionally, distinct regional differences could be identified in the diurnal and annual fog frequency cycles and the derived region-specific frequency variations were used to draw conclusions about the fog types prevailing in these regions.

Abstract:

The meteorological measurements in Brno, Czech Republic, is among the world's oldest measurements, operating since 1799. Like many others, station was initially installed in the city center, relocated several times, and currently operates at an airport outside the city. These geographical changes potentially bias the temperature record due to different station surroundings and varying degrees of urban heat island effects. Here, we assess the influence of land cover on spatial temperature variations in Brno, capitol of Moravia and the second largest city of the Czech Republic. We therefore use a unique dataset of half-hourly resolved measurements from 11 stations spanning a period of more than 3.5 years and apply this information to reduce relocation biases in the long-term temperature record from 1799 to the present. Regression analysis reveals a significant warming influence from nearby buildings and a cooling influence from vegetation, explaining up to 80{\%} of the spatial variability within our network. The influence is strongest during the warm season and for land cover changes between 300 and 500 m around stations. Relying on historical maps and recent satellite data, it was possible to capture the building densities surrounding the past locations of the meteorological station. Using the previously established land cover--temperature relation, the anthropogenic warming for each measurement site could be quantified and hence eliminated from the temperature record accordingly, thereby increasing the long-term warming trend.

Abstract:

With 450,000?km2Kobresia (syn. Carex) pygmaea dominated pastures in the eastern Tibetan highlands are the world's largest pastoral alpine ecosystem forming a durable turf cover at 3000–6000?m?a.s.l. Kobresia's resilience and competitiveness is based on dwarf habit, predominantly below-ground allocation of photo assimilates, mixture of seed production and clonal growth, and high genetic diversity. Kobresia growth is co-limited by livestock-mediated nutrient withdrawal and, in the drier parts of the plateau, low rainfall during the short and cold growing season. Overstocking has caused pasture degradation and soil deterioration over most parts of the Tibetan highlands and is the basis for this man-made ecosystem. Natural autocyclic processes of turf destruction and soil erosion are initiated through polygonal turf cover cracking, and accelerated by soil-dwelling endemic small mammals in the absence of predators. The major consequences of vegetation cover deterioration include the release of large amounts of C, earlier diurnal formation of clouds, and decreased surface temperatures. These effects decrease the recovery potential of Kobresia pastures and make them more vulnerable to anthropogenic pressure and climate change. Traditional migratory rangeland management was sustainable over millennia, and possibly still offers the best strategy to conserve and possibly increase C stocks in the Kobresia turf.

Abstract:

Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Dierent time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with dierent features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant dierence was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the AndesMountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.

Abstract:

The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two dierent sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and dierent vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two dierent methods. The firstmethod used the RGB images and applied the structure formotion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha 1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized dierence vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation eect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

Abstract:

Information about clouds is important for observing and predicting weather and climate as well as for generating and distributing solar power. Most existing approaches extract cloud information from satellite data by classifying individual pixels instead of using closely integrated spatial information, ignoring the fact that clouds are highly dynamic, spatially continuous entities. This paper proposes a novel cloud classification method based on deep learning. Relying on a Convolutional Neural Network (CNN) architecture for image segmentation, the presented Cloud Segmentation CNN (CS-CNN), classifies all pixels of a scene simultaneously rather than individually. We show that CS-CNN can successfully process multispectral satellite data to classify continuous phenomena such as highly dynamic clouds. The proposed approach produces excellent results on Meteosat Second Generation (MSG) satellite data in terms of quality, robustness, and runtime compared to other machine learning methods such as random forests. In particular, comparing CS-CNN with the CLAAS-2 cloud mask derived from MSG data shows high accuracy (0.94) and Heidke Skill Score (0.90) values. In contrast to a random forest, CS-CNN produces robust results and is insensitive to challenges created by coast lines and bright (sand) surface areas. Using GPU acceleration, CS-CNN requires only 25 ms of computation time for classification of images of Europe with 508  508 pixels.

Abstract:

Atmospheric carbon (CO2) exchange, evapotranspiration (ET) processes, and their interactions with climatic drivers across tropical alpine grasslands are poorly understood. This lack of understanding is particularly evident for the páramo, the highest vegetated frontier in the northern Andes, the main source of water for inter-Andean cities, and a large carbon storage area. Studies of CO2 and ET fluxes via the standard Eddy Covariance (EC) technique have never been applied to this region, limiting the understanding of diurnal / nocturnal exchanges and budget estimations. In this paper, we report the first EC analysis conducted on the Andean páramo (3765?m a.s.l.); this analysis measured CO2, ET, and micrometeorological variables over two years (2016–2018) to understand their interactions with climatic / biophysical controls. The páramo was found to be a source of CO2 and exhibited a net positive exchange (mean = +99?±?30 gC m?2 per year). The light-responses of net CO2 exchange and the primary productivity were correlated and model-parameterized. Evapotranspiration was 635?±?9?mm per year (51% of the annual rainfall total), and we obtained crop coefficients for the dominant vegetation (Tussock grass) based on reference-ET models FAO56 and ASCE-ERWI (0.90 and 0.78, respectively). We also compared our results to those from other high-altitude (alpine) and high-latitude grasslands (tundra). Finally, we demonstrate that our measurement period is representative of the páramo’s longer-term climate dynamics. Our investigation contributes to the body of knowledge on the land surface-atmosphere processes of the tropical Andes and supports decision-making about ecosystem services management and the preservation of this vulnerable biome.

Abstract:

In tropical agriculture, the vigorously growing Bracken fern causes severe problems by invading pastures and out-competing the common pasture grasses. Due to infestation by that weed, pastures are abandoned after a few years, and as a fatal consequence, the biodi- versity-rich tropical forest is progressively cleared for new grazing areas. Here we present a broad physiological comparison of the two plant species that are the main competitors on the pastures in the tropical Ecuadorian Andes, the planted forage grass Setaria sphacelata and the weed Bracken (Pteridium arachnoideum).With increasing elevation, the competitive power of Bracken increases as shown by satellite data of the study region. Using data obtained from field measurements, the annual biomass production of both plant species, as a measure of their competitive strength, was modeled over an elevational gradient from 1800 to 2800 m. The model shows that with increasing elevation, biomass production of the two species shifts in favor of Bracken which, above 1800 m, is capable of outgrowing the grass. In greenhouse experiments, the effects on plant growth of the presumed key vari- ables of the elevational gradient, temperature and UV radiation, were separately analyzed. Low temperature, as well as UV irradiation, inhibited carbon uptake of the C4-grass more than that of the C3-plant Bracken. The less temperature-sensitive photosynthesis of Bracken and its effective protection from UV radiation contribute to the success of the weed on the highland pastures. In field samples of Bracken but not of Setaria, the content of flavo- noids as UV-scavengers increased with the elevation. Combining modeling with measure- ments in greenhouse and field allowed to explain the invasive growth of a common weed in upland pastures. The performance of Setaria decreases with elevation due to suboptimal photosynthesis at lower temperatures and the inability to adapt its cellular UV screen

Abstract:

The increasing use of renewable energies as a source of electricity has led to a fundamental transition of the power supply system. The integration of ?uctuating weather-dependent energy sources into the grid already has amajor impact on its load ?ows. As a result, the interest in forecasting wind and solar radiation with a suf?cient accuracy over short time periods (<4 h) has grown. In this study, the short-term forecast of the effective cloud albedo based on optical ?ow estimation methods is investigated. The optical ?ow method utilized here is TV-L1 from the open source library OpenCV. This method uses a multi-scale approach to capture cloud motions on various spatial scales. After the clouds are displaced, the solar surface radiation will be calculated with SPECMAGIC NOW, which computes the global irradiation spectrally resolved from satellite imagery. Due to the high temporal and spatial resolution of satellite measurements, the effective cloud albedo and thus solar radiation can be forecasted from 5 min up to 4 h with a resolution of 0.05. The validation results of this method are very promising, and the RMSE of the 30-min, 60-min, 90-min and 120-min forecast equals 10.47%, 14.28%, 16.87% and 18.83%, respectively. The paper gives a brief description of the method for the short-term forecast of the effective cloud albedo. Subsequently, evaluation results will be presented and discussed.

Abstract:

Biological soil crusts (BSC) encompassing green algae, cyanobacteria, lichens, bryophytes, heterotrophic bacteria and microfungi are keystone species in arid environments because of their role in nitrogen- and carbon-fixation, weathering and soil stabilization, all depending on the photosynthesis of the BSC. Despite their importance, little is known about the BSCs of the Atacama Desert, although especially crustose chlorolichens account for a large proportion of biomass in the arid coastal zone, where photosynthesis is mainly limited due to low water availability. Here, we present the first hyperspectral reflectance data for the most wide-spread BSC species of the southern Atacama Desert. Combining laboratory and field measurements, we establish transfer functions that allow us to estimate net photosynthesis rates for the most common BSC species. We found that spectral differences among species are high, and differences between the background soil and the BSC at inactive stages are low. Additionally, we found that the water absorption feature at 1420 nm is a more robust indicator for photosynthetic activity than the chlorophyll absorption bands. Therefore, we conclude that common vegetation indices must be taken with care to analyze the photosynthesis of BSC with multispectral data.

Abstract:

Temperate grasslands play globally an important role, for example, for biodiversity conservation, livestock forage production, and carbon storage. The latter two are primarily controlled by biomass production, which is assumed to decrease with lower amounts and higher variability of precipitation, while increasing air temperature might either foster or suppress biomass production. Additionally, a higher atmospheric CO2 concentration ([CO2]) is supposed to increase biomass productivity either by directly stimulating photosynthesis or indirectly by inducing water savings (CO2 fertilization effect). Consequently, future biomass productivity is controlled by the partially contrasting effects of changing climatic conditions and [CO2], which to date are only marginally understood. This results in high uncertainties of future biomass production and carbon storage estimates. Consequently, this study aims at statistically estimating mid-21st century grassland aboveground biomass (AGB) based on 18 years of data (1998–2015) from a free air carbon enrichment experiment. We found that lower precipitation totals and a higher precipitation variability reduced AGB. Under drier conditions accompanied by increasing air temperature, AGB further decreased. Here AGB under elevated [CO2] was partly even lower compared to AGB under ambient [CO2], probably because elevated [CO2] reduced evaporative cooling of plants, increasing heat stress. This indicates a higher susceptibility of AGB to increased air temperature under future atmospheric [CO2]. Since climate models for Central Europe project increasing air temperature and decreasing total summer precipitation associated with an increasing variability, our results suggest that grassland summer AGB will be reduced in the future, contradicting the widely expected positive yield anomalies from increasing [CO2].